A Review of Localization and Tracking Algorithms in Wireless Sensor Networks
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paper_title: Localization algorithms of Wireless Sensor Networks: a survey

paper_content:

In Wireless Sensor Networks (WSNs), localization is one of the most important technologies since it plays a critical role in many applications, e.g., target tracking. If the users cannot obtain the accurate location information, the related applications cannot be accomplished. The main idea in most localization methods is that some deployed nodes (landmarks) with known coordinates (e.g., GPS-equipped nodes) transmit beacons with their coordinates in order to help other nodes localize themselves. In general, the main localization algorithms are classified into two categories: range-based and range-free. In this paper, we reclassify the localization algorithms with a new perspective based on the mobility state of landmarks and unknown nodes, and present a detailed analysis of the representative localization algorithms. Moreover, we compare the existing localization algorithms and analyze the future research directions for the localization algorithms in WSNs.
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paper_title: Cognitive radio, software defined radio, and adaptiv wireless systems

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Preface. Chapter 1: Introducing Adaptive, Aware, and Cognitive Radios Bruce Fette. Chapter 2: Cognitive Networks Ryan W. Thomas, Daniel H. Friend, Luiz A. DaSilva, Allen B. MacKenzie. Chapter 3: Cognitive Radio Architecture Joseph Mitola III. Chapter 4: Software Defined Radio Architectures for Cognitive radios H. Arslan, H. celebi. Chapter 5: Value-Creation and Migration in Adaptive and Cognitive Radio Systems Keith E. Nolan, Francis J. Mullany, Eamonn Ambrose, Linda E. Doyle. Chapter 6: Codes and Games for Dynamic Spectrum Access Yiping Xing, Harikeshwar Kushwaha, K.P. Subbalakshmi, R. Chandramouli. Chapter 7: Efficiency and Coexistence Strategies for Cognitive Radio Sai Shankar N. Chapter 8: Enabling Cognitive Radio Through Sensing, Awareness, and Measurements H. Arslan, S. yarkan. Chapter 9: Spectrum Sensing for Cognitive Radio Applications H. Arslan, T. Yucek. Chapter 10: Location Information Management Systems for Cognitive Wireless Networks H. Arslan, H. Celebi. Chapter 11: OFDM for Cognitive Radio: Merits and Challenges H. Arslan, H. A. Mahmoud, T.Yucek. Chapter 12: UWB Cognitive Radio H. Arslan, M.E. Sahin. Chapter 13: Applications of Cognitive radio H. Arslan, S. Ahmed. Chapter 14: Cross-layer Adaptation and Optimization for Cognitive Radio H. Arslan, S. Yarkan. Index.
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paper_title: An Efficient Compartmental Model for Real-Time Node Tracking Over Cognitive Wireless Sensor Networks

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In this paper, an efficient compartmental model for real-time node tracking over cognitive wireless sensor networks is proposed. The compartmental model is developed in a multi-sensor fusion framework with cognitive bandwidth utilization. The multi-sensor data attenuation model using radio, acoustic, and visible light signal is first derived using a sum of exponentials model. A compartmental model that selectively combines the multi-sensor data is then developed. The selection of individual sensor data is based on the criterion of bandwidth utilization. The parameters of the compartmental model are computed using the modified Prony estimator, which results in high tracking accuracies. Additional advantages of the proposed method include lower computational complexity and asymptotic distribution of the estimator. Cramer-Rao bound and elliptical error probability analysis are also discussed to highlight the advantages of the compartmental model. Experimental results for real-time node tracking in indoor environment indicate a significant improvement in tracking performance when compared to state-of-the-art methods in literature.
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paper_title: An Efficient Compartmental Model for Real-Time Node Tracking Over Cognitive Wireless Sensor Networks

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In this paper, an efficient compartmental model for real-time node tracking over cognitive wireless sensor networks is proposed. The compartmental model is developed in a multi-sensor fusion framework with cognitive bandwidth utilization. The multi-sensor data attenuation model using radio, acoustic, and visible light signal is first derived using a sum of exponentials model. A compartmental model that selectively combines the multi-sensor data is then developed. The selection of individual sensor data is based on the criterion of bandwidth utilization. The parameters of the compartmental model are computed using the modified Prony estimator, which results in high tracking accuracies. Additional advantages of the proposed method include lower computational complexity and asymptotic distribution of the estimator. Cramer-Rao bound and elliptical error probability analysis are also discussed to highlight the advantages of the compartmental model. Experimental results for real-time node tracking in indoor environment indicate a significant improvement in tracking performance when compared to state-of-the-art methods in literature.
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paper_title: Localization of Wireless Sensor Networks in the Wild: Pursuit of Ranging Quality

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Localization is a fundamental issue of wireless sensor networks that has been extensively studied in the literature. Our real-world experience from GreenOrbs, a sensor network system deployed in a forest, shows that localization in the wild remains very challenging due to various interfering factors. In this paper, we propose CDL, a Combined and Differentiated Localization approach for localization that exploits the strength of range-free approaches and range-based approaches using received signal strength indicator (RSSI). A critical observation is that ranging quality greatly impacts the overall localization accuracy. To achieve a better ranging quality, our method CDL incorporates virtual-hop localization, local filtration, and ranging-quality aware calibration. We have implemented and evaluated CDL by extensive real-world experiments in GreenOrbs and large-scale simulations. Our experimental and simulation results demonstrate that CDL outperforms current state-of-art localization approaches with a more accurate and consistent performance. For example, the average location error using CDL in GreenOrbs system is 2.9 m, while the previous best method SISR has an average error of 4.6 m.
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paper_title: Localization system for wireless networks

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Positioning in wireless environments is crucial for the continuity of rich and mobile multimedia applications. A good position accuracy is particularly difficult to obtain in indoor or mixed indoor-outdoor scenarios. An efficient positioning system must accurately localize any mobile terminal/user in these demanding environments, being at the same time low-cost and easy to deploy. This paper proposes a real time tracking system for Wi-Fi networks based on trilateration calculated from RSSI values that are returned by the different wireless network interfaces. A fundamental characteristic of the system is the fact that it operates in passive mode, which means that there is no relationship between the positioning system and the terminal/user whose position is being calculated. The characteristics of the developed application are presented, together with the most important considerations that were taken into account during its development phase. The accuracy of the proposed system is evaluated by applying it to different scenarios and the results obtained prove that the application is able to achieve a good precision level, in spite of being a low cost solution that is very easy to deploy in practice.
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paper_title: Indoor Positioning System Using Visible Light and Accelerometer

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Indoor positioning system is a critical part in location-based services. Highly precise positioning systems can support different mobile applications in future wireless systems. Positioning systems using existing wireless networks have low deployment costs, but the position error can be up to several meters. While there are positioning systems proposed in the literature that have low position error, they require extra hardware and are therefore costly to deploy. In this paper, we propose an indoor positioning system based on visible light communications (VLC). In contrast to existing works on VLC for positioning, our system estimates the location of the receiver in three dimensions even without: 1) the knowledge of the height of the receiver from ground; and 2) requiring the alignment of the receiver’s normal with the LED’s normal. Our system has low installation cost as it uses existing lighting sources as transmitters. Light sensor and accelerometer, which can be found in most smartphones, are used at the receiver’s side. They are used to measure the received light intensity and the orientation of the smartphone. A low-complexity algorithm is then used to find out the receiver’s position. Our system does not require the knowledge of the LED transmitters’ physical parameters. Experimental results show that our system achieves average position errors of less than 0.25 m.
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paper_title: Distributed Angle Estimation for Localization in Wireless Sensor Networks

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In this paper, we design a new distributed angle estimation method for localization in wireless sensor networks (WSNs) under multipath propagation environment. We employ a two-antenna anchor that can emit two linear chirp waves simultaneously, and propose to estimate the angle of departure (AOD) of the emitted waves at each receiving node via frequency measurement of the local received signal strength indication (RSSI) signal. An improved estimation method is further proposed where multiple parallel arrays are adopted to provide the space diversity. The proposed methods rely only on radio transceivers and do not require frequency synchronization or precise time synchronization between the transceivers. More importantly, the angle is estimated at each sensor in a completely distributed manner. The performance analysis is derived and simulations are presented to corroborate the proposed studies.
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paper_title: Constrained Least Squares Algorithm for TOA-Based Mobile Location under NLOS Environments

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This paper presents a mobile station (MS) location method using constrained least-squares (CLS) estimation in the non-line-sight (NLOS) conditions. Three or more time-of-arrival (TOA) measurements of a signal traveling between a MS and base stations (BSs) are necessary for its localization. However, when some of the measurements are from NLOS paths, the location errors can be very large. We propose a method that mitigates possible large TOA error measurements caused by NLOS. This method does not depend on a particular distribution of the NLOS error. Simulation results show that the location accuracy is significantly improved over traditional algorithms, even under highly NLOS conditions. geometrical approach, the geometric relationship between the mobile device and its reference is exploited to establish the Euclidean distance between them and to identify the physical location of the device. In this paper, we propose a novel least-square (LS) approach combining with geometrical relationship. It firstly adjusts the NLOS-corrupted range measurements to approach their LOS values, and then minimizes a constrained least- squares function incorporating the known relation between the intermediate variable and the position coordinate, based on the technique of Lagrange multipliers. This algorithm does not require the distinction between NLOS and LOS BSs (6), and the knowledge of the statistics of measurement noise and NLOS errors. Our approach also has the advantage of requiring no modifications to the subscriber equipment. The location estimation can be performed at either the MS if it has the functionality or at special location units in the network. The remainder of this paper is organized as follows. The proposed algorithm is outlined in Section II and the simulation results and performance analysis are discussed in Section III. Finally, conclusions are drawn in Section IV.
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paper_title: RF Localization and tracking of mobile nodes in Wireless Sensors Networks: Architectures, Algorithms and Experiments

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In this paper we address the problem of localizing, tracking ::: and navigating mobile nodes associated to operators acting in a ?xed ::: wireless sensor network (WSN) using only RF information. We propose ::: two alternative and somehow complementary strategies: the ?rst one is ::: based on an empirical map of the Radio Signal Strength (RSS) distribu- ::: tion generated by the WSN and on the stochastic model of the behavior ::: of the mobile nodes, while the second one is based on a maximum like- ::: lihood estimator and a radio channel model for the RSS. We compare ::: the two approaches and highlight pros and cons for each of them. Fi- ::: nally, after implementing them into two real-time tracking systems, we ::: analyze their performance on an experimental testbed in an industrial ::: indoor environment.
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paper_title: A new hybrid algorithm on TDOA localization in wireless sensor network

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A hybrid algorithm for TDOA localization is proposed in this paper. It has well combined the advantages of genetic algorithm and quasi-Newton algorithm. The hybrid algorithm has sufficiently displayed the characteristics of genetic algorithm's group searching and quasi-Newton method's local strong searching. At the same time it effectively overcomes the problem of high sensitivity to initial point of quasi-Newton method and shortcoming of genetic algorithm which reduces the searching efficiency in later period. The experimental results show that if the parameters are assumed reasonably the hybrid algorithm has extremely stability, higher localization rate and localization precision than genetic algorithm and quasi-Newton algorithm.
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paper_title: Experimental analysis of RSSI-based indoor localization with IEEE 802.15.4

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This paper presents a comparison between some of the most used ranging localization methods based on the Received Signal Strength Indicator (RSSI) in low-power IEEE 802.15.4 wireless sensor networks. In particular, the Trilateration, the Min-Max and the Maximum-Likelihood algorithms have been compared using only a limited number of reference nodes. In order to perform an exhaustive comparison we carried out tests in an indoor environment: dozens of RSSI values for every estimation have been gathered and cleaned from outliers values. Our results show that it is possible to some extent to obtain positioning information from nodes equipped with IEEE 802.15.4 radio modules, given the position and the number of reference nodes.
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paper_title: Joint Node Localization and Time-Varying Clock Synchronization in Wireless Sensor Networks

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The problems of node localization and clock synchronization in wireless sensor networks are naturally tied from a statistical signal processing perspective. In this work, we consider the joint estimation of an unknown node's location and clock parameters by incorporating the effect of imperfections in node oscillators, which render a time varying nature to the clock parameters. In order to alleviate the computational complexity associated with the optimal maximum a-posteriori estimator, a simpler approach based on the Expectation-Maximization (EM) algorithm is proposed which iteratively estimates the clock parameters using a Kalman smoother in the E-step, and the location of the unknown node in the M-step. The convergence and the mean square error (MSE) performance of the proposed algorithm are evaluated using simulation studies which demonstrate the high fidelity of the proposed joint estimation approach.
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paper_title: Angle-of-arrival localization based on antenna arrays for wireless sensor networks q

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Among the large number of contributions concerning the localization techniques for wireless sensor networks (WSNs), there is still no simple, energy and cost efficient solution suitable in outdoor scenarios. In this paper, a technique based on antenna arrays and angle-of-arrival (AoA) measurements is carefully discussed. While the AoA algorithms are rarely considered for WSNs due to the large dimensions of directional antennas, some system configurations are investigated that can be easily incorporated in pocket-size wireless devices. A heuristic weighting function that enables decreasing the location errors is introduced. Also, the detailed performance analysis of the presented system is provided. The localization accuracy is validated through realistic Monte-Carlo simulations that take into account the specificity of propagation conditions in WSNs as well as the radio noise effects. Finally, trade-offs between the accuracy, localization time and the number of anchors in a network are addressed.
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paper_title: Network-based wireless location: challenges faced in developing techniques for accurate wireless location information

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Wireless location refers to the geographic coordinates of a mobile subscriber in cellular or wireless local area network (WLAN) environments. Wireless location finding has emerged as an essential public safety feature of cellular systems in response to an order issued by the Federal Communications Commission (FCC) in 1996. The FCC mandate aims to solve a serious public safety problem caused by the fact that, at present, a large proportion of all 911 calls originate from mobile phones, the location of which cannot be determined with the existing technology. However, many difficulties intrinsic to the wireless environment make meeting the FCC objective challenging. These challenges include channel fading, low signal-to-noise ratios (SNRs), multiuser interference, and multipath conditions. In addition to emergency services, there are many other applications for wireless location technology, including monitoring and tracking for security reasons, location sensitive billing, fraud protection, asset tracking, fleet management, intelligent transportation systems, mobile yellow pages, and even cellular system design and management. This article provides an overview of wireless location challenges and techniques with a special focus on network-based technologies and applications.
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paper_title: Adaptive AOA/TOA Localization Using Fuzzy Particle Filter for Mobile WSNs

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Location-awareness is crucial and becoming increasingly important to many applications in wireless sensor networks. This paper presents a network-based positioning system and outlines recent work in which we have developed an efficient principled approach to localize a mobile sensor using time of arrival (TOA) and angle of arrival (AOA) information employing multiple seeds in the line-of-sight scenario. By receiving the periodic broadcasts from the seeds, the mobile target sensors can obtain adequate observations and localize themselves automatically. Based on the distance measurements and the initial position estimate, adaptive fuzzy control scheme is applied to solve the localization adjustment problem. The simulations show that the proposed approach provides adaptive flexibility and robust improvement in position estimation.
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paper_title: Localization of WSN node based on Time of Arrival using Ultra wide band spectrum

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In this paper, the proposed methodology calculates the distance between two nodes in Wireless Sensor Network for localization purpose. The methodology is a variant of Time of Arrival (TOA) methodology. The simulations are done in Matlab using Ultra wide band spectrum and Gaussian monocycle pulses. The measured distances are compared with set distances and mean square errors are calculated. Finally, the set distance and measured distances are compared for various transmission frequencies within UWB spectrum.
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paper_title: 3-D mobile node localization using constrained volume optimization over ad-hoc sensor networks

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This paper proposes a three dimensional mobile node localization and obstacle avoidance mechanism in ad-hoc sensor networks (AHSN). The localization task is performed through constrained volume optimization. Obstacle avoidance is achieved through weighted distribution method. Constrained volume optimization is performed by minimizing the squared location error and imposing the distance and boundary conditions. Obstacle avoidance is achieved by choosing the angular direction which minimizes the cost function. The solution to the constrained optimization problems ensures that the method is robust to change in environmental conditions and NLOS issues. Additionally, the algorithm is scalable and follows a distributed approach to localization. The performance of this method is assessed by deploying nodes in both indoor and outdoor environments. Improved localization accuracy is noted when compared to conventional methods in terms of statistical location estimates and Cramer-Rao lower bound localization error analysis.
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paper_title: Energy efficient optimal node-source localization using mobile beacon in ad-hoc sensor networks

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In this paper, a single mobile beacon based method to localize nodes using principle of maximum power reception is proposed. Optimal positioning of the mobile beacon for minimum energy consumption is also discussed. In contrast to existing methods, the node localization is done with prior location of only three nodes. There is no need of synchronization, as there is only one mobile anchor and each node communicates only with the anchor node. Also, this method is not constrained by a fixed sensor geometry. The localization is done in a distributed fashion, at each sensor node. Experiments on node-source localization are conducted by deploying sensors in an ad-hoc manner in both outdoor and indoor environments. Localization results obtained herein indicate a reasonable performance improvement when compared to conventional methods.
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paper_title: Hybrid TOA/AOA-Based Mobile Localization with and without Tracking in CDMA Cellular Networks

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This paper proposes a hybrid TOA/AOA (Time of Arrival/Angle of Arrival)-based localization algorithm for Code Division Multiple Access (CDMA) networks. The algorithm extends the Taylor Series Least Square (TS-LS) method originally developed for TOA-based systems to incorporate AOA measurements. In addition, tracking algorithms utilizing velocity and acceleration measurements are investigated. Simulation results illustrate that the proposed TOA/AOA TS-LS can provide better performance than conventional schemes in localization accuracy and in reduced likelihood of encountering non-convergence problem compared with TOA TS-LS. Tracking algorithms using the Extended and Unscented Kalman Filter (EKF and UKF) can track the objects relatively well, further decreasing the positioning error. UKF is found to provide closer tracking of the trajectory than EKF, for it truly captures the statistical mean and variance of the noises.
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paper_title: A simple and efficient estimator for hyperbolic location

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An effective technique in locating a source based on intersections of hyperbolic curves defined by the time differences of arrival of a signal received at a number of sensors is proposed. The approach is noniterative and gives an explicit solution. It is an approximate realization of the maximum-likelihood estimator and is shown to attain the Cramer-Rao lower bound near the small error region. Comparisons of performance with existing techniques of beamformer, spherical-interpolation, divide and conquer, and iterative Taylor-series methods are made. The proposed technique performs significantly better than spherical-interpolation, and has a higher noise threshold than divide and conquer before performance breaks away from the Cramer-Rao lower bound. It provides an explicit solution form that is not available in the beamforming and Taylor-series methods. Computational complexity is comparable to spherical-interpolation but substantially less than the Taylor-series method. >
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paper_title: NLOS mitigation in TOA-based localization using semidefinite programming

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In this work, time-of-arrival (TOA)-based wireless sensor localization in non-line-of-sight (NLOS) environments is investigated. In such environments, the accuracy of localization techniques is significantly degraded. While previous work often assumes some knowledge of the NLOS environment, we assume that the estimator knows neither which connections are NLOS nor the distribution of the NLOS errors. It is shown that the maximum likelihood estimator using only LOS connections provides a lower bound on the estimation accuracy. Furthermore, a novel NLOS mitigation technique based on semidefinite programming (SDP) is proposed. The proposed SDP technique estimates the source location jointly with the NLOS biases. The performance of the proposed estimator is compared with the aforementioned lower bound and with previous algorithms through computer simulations. Simulation results show that the proposed SDP estimator outperforms the other algorithms substantially, especially in severe NLOS environments.
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paper_title: Indoor node localization using geometric dilution of precision in ad-hoc sensor networks

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In this paper, a new method for sensor node localization using geometric dilution of precision (GDOP) is described. The proposed methods are not constrained by fixed geometry. They can be used for robust node localization under both LOS and NLOS conditions in an ad-hoc sensor network. These methods are robust since they utilize measurements obtained from both LOS and NLOS conditions. Extensive simulations and real indoor deployments are used to evaluate the performance of the proposed node localization methods based on GDOP. The localization accuracy of these algorithms is reasonably better when compared to similar methods in literature.
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paper_title: Novel Robust Direction-of-Arrival-Based Source Localization Algorithm for Wideband Signals

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Source localization for wideband signals using acoustic sensor networks has drawn much research interest recently. The maximum-likelihood is the predominant objective for a wide variety of source localization approaches, and we have previously proposed an expectation-maximization (EM) algorithm to solve the source localization problem. In this paper, we tackle the source localization problem based on the realistic assumption that the sources are corrupted by spatially-non-white noise. We explore the respective limitations of our recently proposed algorithm, namely EM source localization algorithm, and design a new direction-of-arrival (DOA) estimation based (DEB) source localization algorithm. We also derive the Cramer-Rao lower bound (CRLB) analysis and the computational complexity study for the aforementioned source localization schemes. Through Monte Carlo simulations and our derived CRLB analysis, it is demonstrated that our proposed DEB algorithm significantly outperforms the previous EM method in terms of both source localization accuracy and computational complexity.
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paper_title: Accuracy of RSS-Based Centroid Localization Algorithms in an Indoor Environment

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In this paper, we analyze the accuracy of indoor localization measurement based on a wireless sensor network. The position estimation procedure is based on the received-signal-strength measurements collected in a real indoor environment. Two different classes of low-computational-effort algorithms based on the centroid concept are considered, i.e., the weighted centroid localization method and the relative-span exponential weighted localization method. In particular, different sources of measurement uncertainty are analyzed by means of theoretical simulations and experimental results.
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paper_title: A Survey on TOA Based Wireless Localization and NLOS Mitigation Techniques

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Localization of a wireless device using the time-of-arrivals (TOAs) from different base stations has been studied extensively in the literature. Numerous localization algorithms with different accuracies, computational complexities, a-priori knowledge requirements, and different levels of robustness against non-line-of-sight (NLOS) bias effects also have been reported. However, to our best knowledge, a detailed unified survey of different localization and NLOS mitigation algorithms is not available in the literature. This paper aims to give a comprehensive review of these different TOA-based localization algorithms and their technical challenges, and to point out possible future research directions. Firstly, fundamental lower bounds and some practical estimators that achieve close to these bounds are summarized for line-of-sight (LOS) scenarios. Then, after giving the fundamental lower bounds for NLOS systems, different NLOS mitigation techniques are classified and summarized. Simulation results are also provided in order to compare the performance of various techniques. Finally, a table that summarizes the key characteristics of the investigated techniques is provided to conclude the paper.
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paper_title: Performance comparison of localization techniques for sequential WSN discovery

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In this paper, the performance of different localization algorithms are compared in the context of the sequential Wireless Sensor Network (WSN) discovery problem. Here, all sensor nodes are at unknown locations except for a very small number of so called anchor nodes at known locations. The locations of nodes are sequentially estimated such that when the location of a given node is found, it may be used to localize others. The underlying performance of such an approach is largely dependent upon the localization technique employed. In this paper, several well-known localization techniques are presented using a united notation. These methods are time of arrival (TOA), time difference of arrival (TDOA), received signal strength (RSS), direction of arrival (DOA) and large aperture array (LAA) localization. The performance of a sequential network discovery process is then compared when using each of these localization algorithms. These algorithms are implemented in the Java-DSP software package as part of a localization toolbox. (5 pages)
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paper_title: Localization of acoustic beacons using iterative null beamforming over ad-hoc wireless sensor networks

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In this paper an iterative method to localize and separate multiple audio beacons using the principles of null beam forming is proposed. In contrast to standard methods, the source separation is done optimally by putting a null on all the other sources while obtaining an estimate of a particular source. Also, this method is not constrained by fixed sensor geometry as is the case with general beamforming methods. The wireless sensor nodes can therefore be deployed in any random geometry as required. Experiments are performed to estimate the location and also the power spectral density of the separated sources. The experimental results indicate that the method can be used in ad-hoc, flexible and low-cost wireless sensor network deployment.
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paper_title: Overview of Radiolocation in CDMA Cellular Systems

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Applications for the location of subscribers of wireless services continue to expand. Consequently, location techniques for wireless technologies are being investigated. With code-division multiple access (CDMA) being deployed by a variety of cellular and PCS providers, developing an approach for location in CDMA networks is imperative. This article discusses the applications of location technology, the methods available for its implementation in CDMA networks, and the problems that are encountered when using CDMA networks for positioning.
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paper_title: Angle-of-arrival localization based on antenna arrays for wireless sensor networks q

paper_content:

Among the large number of contributions concerning the localization techniques for wireless sensor networks (WSNs), there is still no simple, energy and cost efficient solution suitable in outdoor scenarios. In this paper, a technique based on antenna arrays and angle-of-arrival (AoA) measurements is carefully discussed. While the AoA algorithms are rarely considered for WSNs due to the large dimensions of directional antennas, some system configurations are investigated that can be easily incorporated in pocket-size wireless devices. A heuristic weighting function that enables decreasing the location errors is introduced. Also, the detailed performance analysis of the presented system is provided. The localization accuracy is validated through realistic Monte-Carlo simulations that take into account the specificity of propagation conditions in WSNs as well as the radio noise effects. Finally, trade-offs between the accuracy, localization time and the number of anchors in a network are addressed.
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paper_title: Robust and Low Complexity Source Localization in Wireless Sensor Networks Using Time Difference of Arrival Measurement

paper_content:

Wireless source localization has found a number of applications in wireless sensor networks. In this work, we investigate robust and low complexity solutions to the problem of source localization based on the time-difference of arrivals (TDOA) measurement model. By adopting a min-max approximation to the maximum likelihood source location estimation, we develop two low complexity algorithms that can be reliably and rapidly solved through semi-definite relaxation. Our approach hinges on the use of a reference sensor node which can be optimized according to the Cramer-Rao lower bound or selected heuristically. Our low complexity estimate can be used either as the final location estimation output or as the initial point for other traditional search algorithms.
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paper_title: Wireless Sensor Network Localization Techniques

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Wireless sensor network localization is an important area that attracted significant research interest. This interest is expected to grow further with the proliferation of wireless sensor network applications. This paper provides an overview of the measurement techniques in sensor network localization and the one-hop localization algorithms based on these measurements. A detailed investigation on multi-hop connectivity-based and distance-based localization algorithms are presented. A list of open research problems in the area of distance-based sensor network localization is provided with discussion on possible approaches to them.
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paper_title: Localization of irregular Wireless Sensor Networks based on multidimensional scaling

paper_content:

In many applications of Wireless Sensor Networks (WSN), it is crucial to know the location of sensor nodes. Although several methods have been proposed, most of them have poor performance in irregularly shaped networks. MDS-MAP is one of the localization methods based on multidimensional scaling (MDS) technique. It uses the connectivity information to derive the location of the nodes in the network. In presence of additional data such as estimated distances between adjacent neighbors, it can also enhance the localization precision. Since MDS-MAP uses the length of the shortest path as Euclidian distance between the nodes, it is sensitive to the shape of the network. In this paper we present MDS-MAP(I), a modified algorithm based on MDS-MAP which improves the localization task in irregular networks. The simulation results show that the algorithm is more reliable in various topologies and achieves a significant performance improvement upon existing method.
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paper_title: Improved MDS-based localization

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It is often useful to know the geographic positions of nodes in a communications network, but adding GPS receivers or other sophisticated sensors to every node can be expensive. MDS-MAP is a recent localization method based on multidimensional scaling (MDS). It uses connectivity information - who is within communications range of whom - to derive the locations of the nodes in the network, and can take advantage of additional data, such as estimated distances between neighbors or known positions for certain anchor nodes, if they are available. However, MDS-MAP is an inherently centralized algorithm and is therefore of limited utility in many applications. In this paper, we present a new variant of the MDS-MAP method, which we call MDS-MAP(P) standing for MDS-MAP using patches of relative maps, that can be executed in a distributed fashion. Using extensive simulations, we show that the new algorithm not only preserves the good performance of the original method on relatively uniform layouts, but also performs much better than the original on irregularly-shaped networks. The main idea is to build a local map at each node of the immediate vicinity and then merge these maps together to form a global map. This approach works much better for topologies in which the shortest path distance between two nodes does not correspond well to their Euclidean distance. We also discuss an optional refinement step that improves solution quality even further at the expense of additional computation.
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paper_title: MDS and Trilateration Based Localization in Wireless Sensor Network

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Localization of sensor nodes is crucial in Wireless Sensor Network because of applications like surveillance, tracking, navigation etc. Various optimization techniques for localization have been proposed in literature by different researchers. In this paper, we propose a two phase hybrid approach for localization using Multidi- mensional Scaling and trilateration, namely, MDS with refinement using trilateration. Trilateration refines the estimated locations obtained by the MDS algorithm and hence acts as a post optimizer which improves the accuracy of the estimated positions of sensor nodes. Through extensive simulations, we have shown that the proposed algorithm is more robust to noise than previous approaches and provides higher accuracy for estimating the positions of sensor nodes.
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paper_title: ALESSA: MDS - based localization algorithm for Wireless Sensor Networks

paper_content:

Self - localization in Wireless Sensor Networks (WSN) should be precise and reliable. Alternative Least-Square Scaling Algorithm (ALESSA) is a recently proposed centralized Multidimensional Scaling (MDS)-based localization algorithm, which uses an iterative approach to solve for the coordinates of discrete points. While ALESSA converges most of the time, like most iterative algorithm, it can be trapped in local minima causing large errors in the location estimates. In this paper, we propose the reseeding of the initial random estimates to improve the convergence of the algorithm. Performance of the proposed algorithm is evaluated under different network topologies with limited connectivity. We also analyzed the effects of low Signal to Noise Ratio (SNR) and the population of the nodes deployed in the network to the algorithm's localization precision. Simulation results show that at 26 dB SNR reseeding always results in convergence with the estimation errors within 5 % of the reference communication range. Analysis and test runs also verified that our algorithm provides accurate and consistent localization estimates under range-based localization with limited network connectivity, even with a low SNR. The algorithm also performs well with limited number of nodes.
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paper_title: Geolocation Techniques: Principles and Applications

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Basics of Distributed and Cooperative Radio and Non-Radio Based Geolocation provides a detailed overview of geolocation technologies. The book covers the basic principles of geolocation, including ranging techniques to localization technologies, fingerprinting and localization in wireless sensor networks. This book also examines the latest algorithms and techniques such as Kalman Filtering, Gauss-Newton Filtering and Particle Filtering.
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paper_title: Localization algorithms of Wireless Sensor Networks: a survey

paper_content:

In Wireless Sensor Networks (WSNs), localization is one of the most important technologies since it plays a critical role in many applications, e.g., target tracking. If the users cannot obtain the accurate location information, the related applications cannot be accomplished. The main idea in most localization methods is that some deployed nodes (landmarks) with known coordinates (e.g., GPS-equipped nodes) transmit beacons with their coordinates in order to help other nodes localize themselves. In general, the main localization algorithms are classified into two categories: range-based and range-free. In this paper, we reclassify the localization algorithms with a new perspective based on the mobility state of landmarks and unknown nodes, and present a detailed analysis of the representative localization algorithms. Moreover, we compare the existing localization algorithms and analyze the future research directions for the localization algorithms in WSNs.
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paper_title: Monte Carlo localization for mobile wireless sensor networks

paper_content:

Localization is crucial to many applications in wireless sensor networks. In this article, we propose a range-free anchor-based localization algorithm for mobile wireless sensor networks that builds upon the Monte Carlo localization algorithm. We concentrate on improving the localization accuracy and efficiency by making better use of the information a sensor node gathers and by drawing the necessary location samples faster. To do so, we constrain the area from which samples are drawn by building a box that covers the region where anchors' radio ranges overlap. This box is the region of the deployment area where the sensor node is localized. Simulation results show that localization accuracy is improved by a minimum of 4% and by a maximum of 73% (average 30%), for varying node speeds when considering nodes with knowledge of at least three anchors. The coverage is also strongly affected by speed and its improvement ranges from 3% to 55% (average 22%). Finally, the processing time is reduced by 93% for a similar localization accuracy.
---
paper_title: DV Based Positioning in Ad Hoc Networks

paper_content:

Many ad hoc network protocols and applications assume the knowledge of geographic location of nodes. The absolute position of each networked node is an assumed fact by most sensor networks which can then present the sensed information on a geographical map. Finding position without the aid of GPS in each node of an ad hoc network is important in cases where GPS is either not accessible, or not practical to use due to power, form factor or line of sight conditions. Position would also enable routing in sufficiently isotropic large networks, without the use of large routing tables. We are proposing APS --- a localized, distributed, hop by hop positioning algorithm, that works as an extension of both distance vector routing and GPS positioning in order to provide approximate position for all nodes in a network where only a limited fraction of nodes have self positioning capability.
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paper_title: GPS-less Low Cost Outdoor Localization For Very Small Devices

paper_content:

Instrumenting the physical world through large networks of wireless sensor nodes, particularly for applications like environmental monitoring of water and soil, requires that these nodes be very small, lightweight, untethered, and unobtrusive. The problem of localization, that is, determining where a given node is physically located in a network, is a challenging one, and yet extremely crucial for many of these applications. Practical considerations such as the small size, form factor, cost and power constraints of nodes preclude the reliance on GPS of all nodes in these networks. We review localization techniques and evaluate the effectiveness of a very simple connectivity metric method for localization in outdoor environments that makes use of the inherent RF communications capabilities of these devices. A fixed number of reference points in the network with overlapping regions of coverage transmit periodic beacon signals. Nodes use a simple connectivity metric, which is more robust to environmental vagaries, to infer proximity to a given subset of these reference points. Nodes localize themselves to the centroid of their proximate reference points. The accuracy of localization is then dependent on the separation distance between two-adjacent reference points and the transmission range of these reference points. Initial experimental results show that the accuracy for 90 percent of our data points is within one-third of the separation distance. However, future work is needed to extend the technique to more cluttered environments.
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paper_title: An improved DV-Hop localization algorithm for wireless sensor networks

paper_content:

Aiming at the positioning problem of wireless sensor network node location, an improved DV-hop positioning algorithm is proposed in this paper, together with its basic principle and realization issues. The proposed method can improve location accuracy without increasing hardware cost for sensor node. Simulation results show that it has good positioning accuracy and coverage. The influences of anchor nodes on the DV-hop algorithm are also explored in the paper.
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paper_title: Maximum likelihood multiple-source localization using acoustic energy measurements with wireless sensor networks

paper_content:

A maximum likelihood (ML) acoustic source location estimation method is presented for the application in a wireless ad hoc sensor network. This method uses acoustic signal energy measurements taken at individual sensors of an ad hoc wireless sensor network to estimate the locations of multiple acoustic sources. Compared to the existing acoustic energy based source localization methods, this proposed ML method delivers more accurate results and offers the enhanced capability of multiple source localization. A multiresolution search algorithm and an expectation-maximization (EM) like iterative algorithm are proposed to expedite the computation of source locations. The Crame/spl acute/r-Rao Bound (CRB) of the ML source location estimate has been derived. The CRB is used to analyze the impacts of sensor placement to the accuracy of location estimates for single target scenario. Extensive simulations have been conducted. It is observed that the proposed ML method consistently outperforms existing acoustic energy based source localization methods. An example applying this method to track military vehicles using real world experiment data also demonstrates the performance advantage of this proposed method over a previously proposed acoustic energy source localization method.
---
paper_title: DuRT: Dual RSSI Trend Based Localization for Wireless Sensor Networks

paper_content:

Localization is a key issue in wireless sensor networks. The geographical location of sensors is important information that is required in sensor network operations such as target detection, monitoring, and rescue. These methods are classified into two categories, namely range-based and range-free. Range-based localizations achieve high location accuracy by using specific hardware or using absolute received signal strength indicator (RSSI) values, whereas range-free approaches obtain location estimates with lower accuracy. Because of the hardware and energy constraints in sensor networks, RSSI offers a convenient method to find the position of sensor nodes. However, in the presence of channel noise, fading, and attenuation, it is not possible to estimate the actual location. In this paper, we propose an RSSI-based localization scheme that considers the trend of RSSI values obtained from beacons to estimate the position of sensor nodes. Through applying polynomial modeling on the relationship between received RSSI and distance, we are able to locate the maximum RSSI point on the anchor trajectory. Using two such trajectories, the sensor position can be determined by calculating the intersection point of perpendiculars passing through the maximum RSSI point on each trajectory. In addition, we devised schemes to improve the localization method to perform under a variety of cases such as single trajectory, unavailability of RSSI trends, and so. The advantage of our scheme is that it does not rely on absolute RSSI values and hence, can be applied in dynamic environments. In simulations, we demonstrate that the proposed localization scheme achieves higher location accuracy compared with existing localization approaches.
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paper_title: Localization for mobile sensor networks

paper_content:

Many sensor network applications require location awareness, but it is often too expensive to include a GPS receiver in a sensor network node. Hence, localization schemes for sensor networks typically use a small number of seed nodes that know their location and protocols whereby other nodes estimate their location from the messages they receive. Several such localization techniques have been proposed, but none of them consider mobile nodes and seeds. Although mobility would appear to make localization more difficult, in this paper we introduce the sequential Monte Carlo Localization method and argue that it can exploit mobility to improve the accuracy and precision of localization. Our approach does not require additional hardware on the nodes and works even when the movement of seeds and nodes is uncontrollable. We analyze the properties of our technique and report experimental results from simulations. Our scheme outperforms the best known static localization schemes under a wide range of conditions.
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paper_title: Organizing a Global Coordinate System from Local Information on an Ad Hoc Sensor Network

paper_content:

We demonstrate that it is possible to achieve accurate localization and tracking of a target in a randomly placed wireless sensor network composed of inexpensive components of limited accuracy. The crucial enabler for this is a reasonably accurate local coordinate system aligned with the global coordinates. We present an algorithm for creating such a coordinate system without the use of global control, globally accessible beacon signals, or accurate estimates of inter-sensor distances. The coordinate system is robust and automatically adapts to the failure or addition of sensors. Extensive theoretical analysis and simulation results are presented. Two key theoretical results are: there is a critical minimum average neighborhood size of 15 for good accuracy and there is a fundamental limit on the resolution of any coordinate system determined strictly from local communication. Our simulation results show that we can achieve position accuracy to within 20% of the radio range even when there is variation of up to 10% in the signal strength of the radios. The algorithm improves with finer quantizations of inter-sensor distance estimates: with 6 levels of quantization position errors better than 10% are achieved. Finally we show how the algorithm gracefully generalizes to target tracking tasks.
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paper_title: Range-free localization schemes for large scale sensor networks

paper_content:

Wireless Sensor Networks have been proposed for a multitude of location-dependent applications. For such systems, the cost and limitations of the hardware on sensing nodes prevent the use of range-based localization schemes that depend on absolute point-to-point distance estimates. Because coarse accuracy is sufficient for most sensor network applications, solutions in range-free localization are being pursued as a cost-effective alternative to more expensive range-based approaches. In this paper, we present APIT, a novel localization algorithm that is range-free. We show that our APIT scheme performs best when an irregular radio pattern and random node placement are considered, and low communication overhead is desired. We compare our work via extensive simulation, with three state-of-the-art range-free localization schemes to identify the preferable system configurations of each. In addition, we study the effect of location error on routing and tracking performance. We show that routing performance and tracking accuracy are not significantly affected by localization error when the error is less than 0.4 times the communication radio radius.
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paper_title: A new weighted centroid localization algorithm in wireless sensor networks

paper_content:

Nodes in a sensor network are often randomly distributed. To assign measurements to locations, each node has to determine its own position. Algorithms for positioning in wireless sensor networks are classified into two groups: approximate and exact. In this paper, we propose a range-based approximate positioning approach which is almost the combination of WCL and EBTB. Then, compare it with two other approximate positioning approaches (WCL with time complexity of O(n)) and EBTB with time complexity of O(n*n) and an exact positioning approach (QR Factorization with time complexity of O(n*n*n)). Finally, it will be shown that EWCL (with time complexity of O(n*n)) is the best localization algorithm with respect to the three other localization algorithms when the noise is high and its accuracy is close to the accuracy of QR when the noise is medium.
---
paper_title: Monte Carlo localization for mobile wireless sensor networks

paper_content:

Localization is crucial to many applications in wireless sensor networks. In this article, we propose a range-free anchor-based localization algorithm for mobile wireless sensor networks that builds upon the Monte Carlo localization algorithm. We concentrate on improving the localization accuracy and efficiency by making better use of the information a sensor node gathers and by drawing the necessary location samples faster. To do so, we constrain the area from which samples are drawn by building a box that covers the region where anchors' radio ranges overlap. This box is the region of the deployment area where the sensor node is localized. Simulation results show that localization accuracy is improved by a minimum of 4% and by a maximum of 73% (average 30%), for varying node speeds when considering nodes with knowledge of at least three anchors. The coverage is also strongly affected by speed and its improvement ranges from 3% to 55% (average 22%). Finally, the processing time is reduced by 93% for a similar localization accuracy.
---
paper_title: An adaptive anchor navigation algorithm for localization in MANET

paper_content:

Automatic anchor navigation for sensor node localization in mobile networks is a challenging task. Previous work on anchor navigation has mostly concentrated on static node localization. This paper proposes an adaptive anchor navigation algorithm to localize sensor nodes in a mobile ad-hoc networks (MANET). The novelty of the proposed algorithm lies in fact that an anchor chooses an optimal position at every instant. This adaptive position selection of the anchor ensures low energy consumption. On the other hand, an optimal path mechanism proposed herein constrains the anchor to traverse in a region densely populated with nodes. This ensures that the path traversed by the anchors is minimal. In order to localize the nodes, the ratio of grid benefit to distance (GBD) within the probable region of anchor is first computed. Subsequently, an optimality criterion is used to decide the next anchor location. Localization is then performed using geometric methods. Experimental results on node localization in a mobile network scenario indicate a reasonable performance improvement when compared to conventional methods.
---
paper_title: Optimal anchor guiding algorithms for maximal node localization in mobile sensor networks

paper_content:

Localization of mobile nodes is a challenging problem especially when both anchor and node are mobile. In this paper, algorithms for optimal anchor guiding, to localize maximum number of nodes, are proposed. The algorithms are based on the principle of jointly maximizing a grid benefit criterion and the number of nodes localized. The advantage of these algorithms is that, that both the anchors and nodes can be deployed randomly and can traverse the region with varying speeds. Additionally, the optimal path for the anchor is decided in such a way that the maximum number of nodes are localized. The proposed algorithms are extensively analyzed for their performance by conducting experiments on both, NI and Crossbow set-up. The results obtained from localization error analysis indicate that the algorithms discussed in this work perform reasonably better than the similar conventional algorithms available in literature. Additional analysis performed on energy consumption indicate that the proposed algorithms are energy efficient.
---
paper_title: Localization for mobile sensor networks

paper_content:

Many sensor network applications require location awareness, but it is often too expensive to include a GPS receiver in a sensor network node. Hence, localization schemes for sensor networks typically use a small number of seed nodes that know their location and protocols whereby other nodes estimate their location from the messages they receive. Several such localization techniques have been proposed, but none of them consider mobile nodes and seeds. Although mobility would appear to make localization more difficult, in this paper we introduce the sequential Monte Carlo Localization method and argue that it can exploit mobility to improve the accuracy and precision of localization. Our approach does not require additional hardware on the nodes and works even when the movement of seeds and nodes is uncontrollable. We analyze the properties of our technique and report experimental results from simulations. Our scheme outperforms the best known static localization schemes under a wide range of conditions.
---
paper_title: An improved DV-Hop localization algorithm for wireless sensor networks

paper_content:

Aiming at the positioning problem of wireless sensor network node location, an improved DV-hop positioning algorithm is proposed in this paper, together with its basic principle and realization issues. The proposed method can improve location accuracy without increasing hardware cost for sensor node. Simulation results show that it has good positioning accuracy and coverage. The influences of anchor nodes on the DV-hop algorithm are also explored in the paper.
---
paper_title: Maximum likelihood multiple-source localization using acoustic energy measurements with wireless sensor networks

paper_content:

A maximum likelihood (ML) acoustic source location estimation method is presented for the application in a wireless ad hoc sensor network. This method uses acoustic signal energy measurements taken at individual sensors of an ad hoc wireless sensor network to estimate the locations of multiple acoustic sources. Compared to the existing acoustic energy based source localization methods, this proposed ML method delivers more accurate results and offers the enhanced capability of multiple source localization. A multiresolution search algorithm and an expectation-maximization (EM) like iterative algorithm are proposed to expedite the computation of source locations. The Crame/spl acute/r-Rao Bound (CRB) of the ML source location estimate has been derived. The CRB is used to analyze the impacts of sensor placement to the accuracy of location estimates for single target scenario. Extensive simulations have been conducted. It is observed that the proposed ML method consistently outperforms existing acoustic energy based source localization methods. An example applying this method to track military vehicles using real world experiment data also demonstrates the performance advantage of this proposed method over a previously proposed acoustic energy source localization method.
---
paper_title: Multi-sensor data fusion methods for indoor localization under collinear ambiguity

paper_content:

Sensor node localization in mobile ad-hoc sensor networks is a challenging problem. Often, the anchor nodes tend to line up in a linear fashion in a mobile sensor network when nodes are deployed in an ad-hoc manner. This paper discusses novel node localization methods under the conditions of collinear ambiguity of the anchors. Additionally, the work presented herein also describes a methodology to fuse data available from multiple sensors for improved localization performance under conditions of collinear ambiguity. In this context, data is first acquired from multiple sensors sensing different modalities. The data acquired from each sensor is used to compute attenuation models for each sensor. Subsequently, a combined multi-sensor attenuation model is developed. The fusion methodology uses a joint error optimization approach on the multi-sensor data. The distance between each sensor node and anchor is itself computed using the differential power principle. These distances are used in the localization of sensor nodes under the condition of collinear ambiguity of anchors. Localization error analysis is also carried out in indoor conditions and compared with the Cramer-Rao lower bound. Experimental results on node localization using simulations and real field deployments indicate reasonable improvements in terms of localization accuracy when compared to methods likes MLAR and MGLR.
---
paper_title: Sensor node tracking using semi-supervised Hidden Markov Models

paper_content:

In this paper, a novel method for mobile sensor node tracking using semi-supervised Hidden Markov Models (HMM) is discussed. A new methodology to develop a combined attenuation model from data gathered from multiple sensors is also described. Observations emitted from the nodes are sparsely measured over the network area with beacons placed on the boundaries. HMMs are trained using observations measured at each grid point. The distances between a node passing through a specific grid point and beacons are estimated using likelihood maximization. The local location co-ordinates of the node positions are then computed by solving a constrained volume optimization problem. Quaternion rotation is used to finally obtain global coordinates of the node location. Several standard manoeuvres of mobile nodes are first simulated. Similar manoeuvres are also recorded from real field deployments. The experimental results are obtained for node localization and tracking from these experiments. Results indicate an improvement in the localization accuracy, when compared to the conventional localization methods.
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paper_title: Semi-supervised Laplacian regularized least squares algorithm for localization in wireless sensor networks

paper_content:

In this paper, we propose a new approach for localization in wireless sensor networks based on semi-supervised Laplacian regularized least squares algorithm. We consider two kinds of localization data: signal strength and pair-wise distance between nodes. When nodes are close within their physical location space, their localization data vectors should be similar. We first propose a solution using the alignment criterion to learn an appropriate kernel function in terms of the similarities between anchors, and the kernel function is used to measure the similarity between pair-wise sensor nodes in the networks. We then propose a semi-supervised learning algorithm based upon manifold regularization to obtain the locations of the non-anchors. We evaluate our algorithm under various network topology, transmission range and signal noise, and analyze its performance. We also compare our approach with several existing approaches, and demonstrate the high efficiency of our proposed algorithm in terms of location estimation error.
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paper_title: RF Localization and tracking of mobile nodes in Wireless Sensors Networks: Architectures, Algorithms and Experiments

paper_content:

In this paper we address the problem of localizing, tracking ::: and navigating mobile nodes associated to operators acting in a ?xed ::: wireless sensor network (WSN) using only RF information. We propose ::: two alternative and somehow complementary strategies: the ?rst one is ::: based on an empirical map of the Radio Signal Strength (RSS) distribu- ::: tion generated by the WSN and on the stochastic model of the behavior ::: of the mobile nodes, while the second one is based on a maximum like- ::: lihood estimator and a radio channel model for the RSS. We compare ::: the two approaches and highlight pros and cons for each of them. Fi- ::: nally, after implementing them into two real-time tracking systems, we ::: analyze their performance on an experimental testbed in an industrial ::: indoor environment.
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paper_title: Non-Line-of-Sight Identification and Mitigation Using Received Signal Strength

paper_content:

Indoor wireless systems often operate under non-line-of-sight (NLOS) conditions that can cause ranging errors for location-based applications. As such, these applications could benefit greatly from NLOS identification and mitigation techniques. These techniques have been primarily investigated for ultra-wide band (UWB) systems, but little attention has been paid to WiFi systems, which are far more prevalent in practice. In this study, we address the NLOS identification and mitigation problems using multiple received signal strength (RSS) measurements from WiFi signals. Key to our approach is exploiting several statistical features of the RSS time series, which are shown to be particularly effective. We develop and compare two algorithms based on machine learning and a third based on hypothesis testing to separate LOS/NLOS measurements. Extensive experiments in various indoor environments show that our techniques can distinguish between LOS/NLOS conditions with an accuracy of around 95%. Furthermore, the presented techniques improve distance estimation accuracy by 60% as compared to state-of-the-art NLOS mitigation techniques. Finally, improvements in distance estimation accuracy of 50% are achieved even without environment-specific training data, demonstrating the practicality of our approach to real world implementations.
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paper_title: A tutorial on hidden Markov models and selected applications in speech recognition

paper_content:

This tutorial provides an overview of the basic theory of hidden Markov models (HMMs) as originated by L.E. Baum and T. Petrie (1966) and gives practical details on methods of implementation of the theory along with a description of selected applications of the theory to distinct problems in speech recognition. Results from a number of original sources are combined to provide a single source of acquiring the background required to pursue further this area of research. The author first reviews the theory of discrete Markov chains and shows how the concept of hidden states, where the observation is a probabilistic function of the state, can be used effectively. The theory is illustrated with two simple examples, namely coin-tossing, and the classic balls-in-urns system. Three fundamental problems of HMMs are noted and several practical techniques for solving these problems are given. The various types of HMMs that have been studied, including ergodic as well as left-right models, are described. >
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paper_title: A RSS-EKF localization method using HMM-based LOS/NLOS channel identification

paper_content:

Knowing channel sight condition is important as it has a great impact on localization performance. In this paper, a RSS-based localization algorithm, which jointly takes into consideration the effect of channel sight conditions, is investigated. In our approach, the channel sight conditions experience by a moving target to all sensors is modeled as a hidden Markov model (HMM), with the quantized measured RSSs as its observation. The parameters of HMM are obtained by an off-line training assuming that the LOS/NLOS can be identified during the training phase. With the HMM matrices, a forward-only algorithm can be utilized for real time sight conditions identification. The target is localized by extended Kalman Filter (EKF) by suitably combining with the sight conditions. Simulation results show that our proposed localization strategy can provide good identification to channel sight conditions, hence results in a better localization estimation.
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paper_title: Convex position estimation in wireless sensor networks

paper_content:

A method for estimating unknown node positions in a sensor network based exclusively on connectivity-induced constraints is described. Known peer-to-peer communication in the network is modeled as a set of geometric constraints on the node positions. The global solution of a feasibility problem for these constraints yields estimates for the unknown positions of the nodes in the network. Providing that the constraints are tight enough, simulation illustrates that this estimate becomes close to the actual node positions. Additionally, a method for placing rectangular bounds around the possible positions for all unknown nodes in the network is given. The area of the bounding rectangles decreases as additional or tighter constraints are included in the problem. Specific models are suggested and simulated for isotropic and directional communication, representative of broadcast-based and optical transmission respectively, though the methods presented are not limited to these simple cases.
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paper_title: Walkie-Markie: Indoor Pathway Mapping Made Easy

paper_content:

We present Walkie-Markie - an indoor pathway mapping system that can automatically reconstruct internal pathway maps of buildings without any a-priori knowledge about the building, such as the floor plan or access point locations. Central to Walkie-Markie is a novel exploitation of the WiFi infrastructure to define landmarks (WiFi-Marks) to fuse crowdsourced user trajectories obtained from inertial sensors on users' mobile phones. WiFi-Marks are special pathway locations at which the trend of the received WiFi signal strength changes from increasing to decreasing when moving along the pathway. By embedding these WiFi-Marks in a 2D plane using a newly devised algorithm and connecting them with calibrated user trajectories, Walkie-Markie is able to infer pathway maps with high accuracy. Our experiments demonstrate that Walkie-Markie is able to reconstruct a high-quality pathway map for a real office-building floor after only 5-6 rounds of walks, with accuracy gradually improving as more user data becomes available. The maximum discrepancy between the inferred pathway map and the real one is within 3m and 2.8m for the anchor nodes and path segments, respectively.
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paper_title: Anchor-Based Localization via Interval Analysis for Mobile Ad-Hoc Sensor Networks

paper_content:

Location awareness is a fundamental requirement for many applications of sensor networks. This paper proposes an original technique for self-localization in mobile ad-hoc networks. This method is adapted to the limited computational and memory resources of mobile nodes. The localization problem is solved in an interval analysis framework. The propagation of the estimation errors is based on an interval formulation of a state space model, where observations consist of anchor-based connectivities. The problem is then formulated as a constraint satisfaction problem where a simple Waltz algorithm is applied in order to contract the solution. This technique yields a guaranteed and robust online estimation of the mobile node positions. Observation errors as well as anchor node imperfections are taken into consideration in a simple and computational-consistent way. Multihop anchor-based and backpropagated localizations are also made possible in our method. Simulation results on mobile node trajectories corroborate the efficiency of the proposed technique and show that it outperforms the particle filtering methods.
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paper_title: Localization from mere connectivity

paper_content:

It is often useful to know the geographic positions of nodes in a communications network, but adding GPS receivers or other sophisticated sensors to every node can be expensive. We present an algorithm that uses connectivity information who is within communications range of whom to derive the locations of the nodes in the network. The method can take advantage of additional information, such as estimated distances between neighbors or known positions for certain anchor nodes, if it is available. The algorithm is based on multidimensional scaling, a data analysis technique that takes O(n3) time for a network of n nodes. Through simulation studies, we demonstrate that the algorithm is more robust to measurement error than previous proposals, especially when nodes are positioned relatively uniformly throughout the plane. Furthermore, it can achieve comparable results using many fewer anchor nodes than previous methods, and even yields relative coordinates when no anchor nodes are available.
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paper_title: Locating in fingerprint space: wireless indoor localization with little human intervention

paper_content:

Indoor localization is of great importance for a range of pervasive applications, attracting many research efforts in the past decades. Most radio-based solutions require a process of site survey, in which radio signatures of an interested area are annotated with their real recorded locations. Site survey involves intensive costs on manpower and time, limiting the applicable buildings of wireless localization worldwide. In this study, we investigate novel sensors integrated in modern mobile phones and leverage user motions to construct the radio map of a floor plan, which is previously obtained only by site survey. On this basis, we design LiFS, an indoor localization system based on off-the-shelf WiFi infrastructure and mobile phones. LiFS is deployed in an office building covering over 1600m2, and its deployment is easy and rapid since little human intervention is needed. In LiFS, the calibration of fingerprints is crowdsourced and automatic. Experiment results show that LiFS achieves comparable location accuracy to previous approaches even without site survey.
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paper_title: Anchor-Free Distributed Localization in Sensor Networks

paper_content:

Many sensor network applications require that each node’s sensor stream be annotated with its physical location in some common coordinate system. Manual measurement and configuration methods for obtaining location don’t scale and are error-prone, and equipping sensors with GPS is often expensive and does not work in indoor and urban deployments. Sensor networks can therefore benefit from a self-configuring method where nodes cooperate with each other, estimate local distances to their neighbors, and converge to a consistent coordinate assignment. This paper describes a fully decentralized algorithm called AFL (Anchor-Free Localization) where nodes start from a random initial coordinate assignment and converge to a consistent solution using only local node interactions. The key idea in AFL is fold-freedom, where nodes first configure into a topology that resembles a scaled and unfolded version of the true configuration, and then run a force-based relaxation procedure. We show using extensive simulations under a variety of network sizes, node densities, and distance estimation errors that our algorithm is superior to previously proposed methods that incrementally compute the coordinates of nodes in the network, in terms of its ability to compute correct coordinates under a wider variety of conditions and its robustness to measurement errors.
---
paper_title: GPS-less Low Cost Outdoor Localization For Very Small Devices

paper_content:

Instrumenting the physical world through large networks of wireless sensor nodes, particularly for applications like environmental monitoring of water and soil, requires that these nodes be very small, lightweight, untethered, and unobtrusive. The problem of localization, that is, determining where a given node is physically located in a network, is a challenging one, and yet extremely crucial for many of these applications. Practical considerations such as the small size, form factor, cost and power constraints of nodes preclude the reliance on GPS of all nodes in these networks. We review localization techniques and evaluate the effectiveness of a very simple connectivity metric method for localization in outdoor environments that makes use of the inherent RF communications capabilities of these devices. A fixed number of reference points in the network with overlapping regions of coverage transmit periodic beacon signals. Nodes use a simple connectivity metric, which is more robust to environmental vagaries, to infer proximity to a given subset of these reference points. Nodes localize themselves to the centroid of their proximate reference points. The accuracy of localization is then dependent on the separation distance between two-adjacent reference points and the transmission range of these reference points. Initial experimental results show that the accuracy for 90 percent of our data points is within one-third of the separation distance. However, future work is needed to extend the technique to more cluttered environments.
---
paper_title: Gaussian Process Regression for Fingerprinting based Localization

paper_content:

Abstract In this paper, Gaussian process regression (GPR) for fingerprinting based localization is presented. In contrast to general regression techniques, the GPR not only infers the posterior received signal strength (RSS) mean but also the variance at each fingerprint location. The GPR does take into account the variance of input i.e., noisy RSS data. The hyper-parameters of GPR are estimated using trust-region-reflective algorithm. The Cramer-Rao bound is analysed to highlight the performance of the parameter estimator. The posterior mean and variance of RSS data is utilized in fingerprinting based localization. The principal component analysis is employed to choose the k strongest wi-fi access points (APs). The performance of the proposed algorithm is validated using using real field deployments. Accuracy improvements of 10% and 30% are observed in two sites compared to the Horus fingerprinting approach.
---
paper_title: WiFi-SLAM Using Gaussian Process Latent Variable Models

paper_content:

WiFi localization, the task of determining the physical location of a mobile device from wireless signal strengths, has been shown to be an accurate method of indoor and outdoor localization and a powerful building block for location-aware applications. However, most localization techniques require a training set of signal strength readings labeled against a ground truth location map, which is prohibitive to collect and maintain as maps grow large. In this paper we propose a novel technique for solving the WiFi SLAM problem using the Gaussian Process Latent Variable Model (GPLVM) to determine the latent-space locations of unlabeled signal strength data. We show how GPLVM, in combination with an appropriate motion dynamics model, can be used to reconstruct a topological connectivity graph from a signal strength sequence which, in combination with the learned Gaussian Process signal strength model, can be used to perform efficient localization.
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paper_title: Improved MDS-based localization

paper_content:

It is often useful to know the geographic positions of nodes in a communications network, but adding GPS receivers or other sophisticated sensors to every node can be expensive. MDS-MAP is a recent localization method based on multidimensional scaling (MDS). It uses connectivity information - who is within communications range of whom - to derive the locations of the nodes in the network, and can take advantage of additional data, such as estimated distances between neighbors or known positions for certain anchor nodes, if they are available. However, MDS-MAP is an inherently centralized algorithm and is therefore of limited utility in many applications. In this paper, we present a new variant of the MDS-MAP method, which we call MDS-MAP(P) standing for MDS-MAP using patches of relative maps, that can be executed in a distributed fashion. Using extensive simulations, we show that the new algorithm not only preserves the good performance of the original method on relatively uniform layouts, but also performs much better than the original on irregularly-shaped networks. The main idea is to build a local map at each node of the immediate vicinity and then merge these maps together to form a global map. This approach works much better for topologies in which the shortest path distance between two nodes does not correspond well to their Euclidean distance. We also discuss an optional refinement step that improves solution quality even further at the expense of additional computation.
---
paper_title: No need to war-drive: Unsupervised indoor localization

paper_content:

We propose UnLoc, an unsupervised indoor localization scheme that bypasses the need for war-driving. Our key observation is that certain locations in an indoor environment present identifiable signatures on one or more sensing dimensions. An elevator, for instance, imposes a distinct pattern on a smartphone’s accelerometer; a corridor-corner may overhear a unique set of WiFi access points; a specific spot may experience an unusual magnetic fluctuation. We hypothesize that these kind of signatures naturally exist in the environment, and can be envisioned as internal landmarks of a building. Mobile devices that “sense” these landmarks can recalibrate their locations, while dead-reckoning schemes can track them between landmarks. Results from 3 different indoor settings, including a shopping mall, demonstrate median location errors of 1.69m. War-driving is not necessary, neither are floorplans ‐ the system simultaneously computes the locations of users and landmarks, in a manner that they converge reasonably quickly. We believe this is an unconventional approach to indoor localization, holding promise for real-world deployment.
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paper_title: Simulated Annealing based Wireless Sensor Network Localization with Flip Ambiguity Mitigation

paper_content:

Accurate self-localization capability is highly desirable in wireless sensor networks. A major problem in wireless sensor network localization is the flip ambiguity, which introduces large errors in the location estimates. In this paper, we propose a two phase simulated annealing based localization (SAL) algorithm to address the issue. Simulated annealing (SA) is a technique for combinatorial optimization problems and it is robust against being trapped into local minima. In the first phase of our algorithm, simulated annealing is used to obtain an accurate estimate of location. Then a second phase of optimization is performed only on those nodes that are likely to have flip ambiguity problem. Based on the neighborhood information of nodes, those nodes likely to have affected by flip ambiguity are identified and moved to the correct position. The proposed scheme is tested using simulation on a sensor network of 200 nodes whose distance measurements are corrupted by Gaussian noise. Simulation results show that the proposed scheme gives accurate and consistent location estimates of the nodes and mitigate errors due to flip ambiguities.
---
paper_title: Fusion of Radio and Camera Sensor Data for Accurate Indoor Positioning

paper_content:

Indoor positioning systems have received a lot of attention recently due to their importance for many location-based services, e.g. indoor navigation and smart buildings. Lightweight solutions based on WiFi and inertial sensing have gained popularity, but are not fit for demanding applications, such as expert museum guides and industrial settings, which typically require sub-meter location information. In this paper, we propose a novel positioning system, RAVEL (Radio And Vision Enhanced Localization), which fuses anonymous visual detections captured by widely available camera infrastructure, with radio readings (e.g. WiFi radio data). Although visual trackers can provide excellent positioning accuracy, they are plagued by issues such as occlusions and people entering/exiting the scene, preventing their use as a robust tracking solution. By incorporating radio measurements, visually ambiguous or missing data can be resolved through multi-hypothesis tracking. We evaluate our system in a complex museum environment with dim lighting and multiple people moving around in a space cluttered with exhibit stands. Our experiments show that although the WiFi measurements are not by themselves sufficiently accurate, when they are fused with camera data, they become a catalyst for pulling together ambiguous, fragmented, and anonymous visual tracklets into accurate and continuous paths, yielding typical errors below 1 meter.
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paper_title: Improving Simultaneous Localization and Mapping for pedestrian navigation and automatic mapping of buildings by using online human-based feature labeling

paper_content:

In this paper we present an extension to odometry based SLAM for pedestrians that incorporates human-reported measurements of recognizable features, or “places” in an environment. The method which we have called “PlaceSLAM” builds on the Simultaneous Localization and Mapping (SLAM) principle in that a spatial representation of such places can be built up during the localization process. We see an important application to be in mapping of new areas by volunteering pedestrians themselves, in particular to improve the accuracy of “FootSLAM” which is based on human step estimation (odometry). We present a description of various flavors of PlaceSLAM and derive a Bayesian formulation and particle filtering implementation for the most general variant. In particular we distinguish between two important cases which depend on whether the pedestrian is required to report a place's identifier or not. Our results based on experimental data show that our approach can significantly improve the accuracy and stability of FootSLAM and this with very little additional complexity. After mapping has been performed, users of such improved FootSLAM maps need not report places themselves.
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paper_title: The horus wlan location determination system

paper_content:

This report presents a general analysis for the performance of WLAN location determination systems. In particular, we present an analytical method for calculating the average distance error and probability of error of WLAN location determination systems. These expressions are obtained with no assumptions regarding the distribution of signal strength or the probability of the user being at a specific location, which is usually taken to be a uniform distribution over all the possible locations in current WLAN location determination systems. We use these expressions to find the optimal strategy to estimate the user location and to prove formally that probabilistic techniques give more accuracy than deterministic techniques, which has been taken for granted without proof for a long time. The analytical results are validated through simulation experiments. We also study the effect of the assumption that the user position follows a uniform distribution over the set of possible locations on the accuracy of WLAN location determination systems. The results show that knowing the probability distribution of the user position can reduce the number of access points required to obtain a given accuracy. However, with a high density of access points, the performance of a WLAN location determination system is consistent under different probability distributions for the user position.
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paper_title: Place Lab: Device Positioning Using Radio Beacons in the Wild

paper_content:

Location awareness is an important capability for mobile computing. Yet inexpensive, pervasive positioning—a requirement for wide-scale adoption of location-aware computing—has been elusive. We demonstrate a radio beacon-based approach to location, called Place Lab, that can overcome the lack of ubiquity and high-cost found in existing location sensing approaches. Using Place Lab, commodity laptops, PDAs and cell phones estimate their position by listening for the cell IDs of fixed radio beacons, such as wireless access points, and referencing the beacons' positions in a cached database. We present experimental results showing that 802.11 and GSM beacons are sufficiently pervasive in the greater Seattle area to achieve 20-30 meter median accuracy with nearly 100% coverage measured by availability in people's daily lives.
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paper_title: Crowdsourced indoor localization for diverse devices through radiomap fusion

paper_content:

Crowdsourcing is an emerging field that allows to tackle difficult problems by soliciting contributions from common people, rather than trained professionals. In the post-pc era, where smartphones dominate the personal computing market offering both constant mobility and large amounts of spatiotemporal sensory data, crowdsourcing is becoming increasingly popular. In this context, crowdsourcing stands as the only viable solution for collecting the large amount of location-related network data required to support location-based services, e.g., the signal strength radiomap of a fingerprinting localization system inside a multi-floor building. However, this benefit does not come for free, because crowdsourcing also poses new challenges in radiomap creation. We focus on the problem of device diversity that occurs frequently as the contributors usually carry heterogeneous mobile devices that report network measurements very differently. We demonstrate with simulations and experimental results that the traditional signal strength values from the surrounding network infrastructure are not suitable for crowdsourcing the radiomap. Moreover, we present an alternative approach, based on signal strength differences, that is far more robust to device variations and maintains the localization accuracy regardless of the number of contributing devices.
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paper_title: Hybrid maximum depth-kNN method for real time node tracking using multi-sensor data

paper_content:

In this paper, a hybrid maximum depth - k Nearest Neighbour (hybrid MD-kNN) method for real time sensor node tracking and localization is proposed. The method combines two individual location hypothesis functions obtained from generalized maximum depth and generalized kNN methods. The individual location hypothesis functions are themselves obtained from multiple sensors measuring visible light, humidity, temperature, acoustics, and link quality. The hybridMD-kNN method therefore combines the lower computational power of maximum depth and outlier rejection ability of kNN method to realize a robust real time tracking method. Additionally, this method does not require the assumption of an underlying distribution under non-line-of-sight (NLOS) conditions. Additional novelty of this method is the utilization of multivariate data obtained from multiple sensors which has hitherto not been used. The affine invariance property of the hybrid MD-kNN method is proved and its robustness is illustrated in the context of node localization. Experimental results on the Intel Berkeley research data set indicates reasonable improvements over conventional methods available in literature.
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paper_title: Indoor location fingerprinting with heterogeneous clients

paper_content:

Heterogeneous wireless clients measure signal strength differently. This is a fundamental problem for indoor location fingerprinting, and it has a high impact on the positioning accuracy. Mapping-based solutions have been presented that require manual and error-prone calibration for each new client. This article presents hyperbolic location fingerprinting, which records fingerprints as signal strength ratios between pairs of base stations instead of absolute signal strength values. This article also presents an automatic mapping-based method that avoids calibration by learning from online measurements. The evaluation shows that the solutions can address the signal strength heterogeneity problem without requiring extra manual calibration.
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paper_title: RADAR: an in-building RF-based user location and tracking system

paper_content:

The proliferation of mobile computing devices and local-area wireless networks has fostered a growing interest in location-aware systems and services. In this paper we present RADAR, a radio-frequency (RF)-based system for locating and tracking users inside buildings. RADAR operates by recording and processing signal strength information at multiple base stations positioned to provide overlapping coverage in the area of interest. It combines empirical measurements with signal propagation modeling to determine user location and thereby enable location-aware services and applications. We present experimental results that demonstrate the ability of RADAR to estimate user location with a high degree of accuracy.
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paper_title: Localization algorithms of Wireless Sensor Networks: a survey

paper_content:

In Wireless Sensor Networks (WSNs), localization is one of the most important technologies since it plays a critical role in many applications, e.g., target tracking. If the users cannot obtain the accurate location information, the related applications cannot be accomplished. The main idea in most localization methods is that some deployed nodes (landmarks) with known coordinates (e.g., GPS-equipped nodes) transmit beacons with their coordinates in order to help other nodes localize themselves. In general, the main localization algorithms are classified into two categories: range-based and range-free. In this paper, we reclassify the localization algorithms with a new perspective based on the mobility state of landmarks and unknown nodes, and present a detailed analysis of the representative localization algorithms. Moreover, we compare the existing localization algorithms and analyze the future research directions for the localization algorithms in WSNs.
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paper_title: A Particle-Filtering Approach for Vehicular Tracking Adaptive to Occlusions

paper_content:

In this paper, we propose a new particle-filtering approach for handling partial and total occlusions in vehicular tracking situations. Our proposed method, which is named adaptive particle filter (APF), uses two different operation modes. When the tracked vehicle is not occluded, the APF uses a normal probability density function (pdf) to generate the new set of particles. Otherwise, when the tracked vehicle is under occlusion, the APF generates the new set of particles using a Normal-Rayleigh pdf. Our approach was designed to detect when a total occlusion starts and ends and to resume vehicle tracking after disocclusions. We have tested our APF approach in a number of traffic surveillance video sequences with encouraging results. Our proposed approach tends to be more accurate than comparable methods in the literature, and at the same time, it tends to be more robust to target occlusions.
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paper_title: Survey of Target Tracking Protocols Using Wireless Sensor Network

paper_content:

Target tracking is one of the non trivial applications of wireless sensor network which is set up in the areas of field surveillance, habitat monitoring, indoor buildings, and intruder tracking. Various approaches have been investigated for tracking the targets, considering diverse metrics like scalability, overheads, energy consumption and target tracking accuracy. This paper for the first time contributes a survey of target tracking protocols for sensor networks and presents their classification in a precise manner. The five main categories explored in this paper are, hierarchical, tree-based, prediction- based, mobicast message-based tracking and hybrid methods. To be more precise, the survey promotes overview of recent research literature along with their performance comparison and evaluation based on simulation with real data. Certainly this task is challenging and not straight forward due to differences in estimations, parameters and performance metrics, therefore the paper concludes with open research challenges.
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paper_title: Dynamic clustering for acoustic target tracking in wireless sensor networks

paper_content:

We devise and evaluate a fully decentralized, light-weight, dynamic clustering algorithm for target tracking. Instead of assuming the same role for all the sensors, we envision a hierarchical sensor network that is composed of 1) a static backbone of sparsely placed high-capability sensors which assume the role of a cluster head (CH) upon triggered by certain signal events and 2) moderately to densely populated low-end sensors whose function is to provide sensor information to CHs upon request. A cluster is formed and a CH becomes active, when the acoustic signal strength detected by the CH exceeds a predetermined threshold. The active CH then broadcasts an information solicitation packet, asking sensors in its vicinity to join the cluster and provide their sensing information. We address and devise solution approaches (with the use of Voronoi diagram) to realize dynamic clustering: (I1) how CHs operate with one another to ensure that only one CH (preferably the CH that is closes to the target) is active with high probability, (I2) when the active CH solicits for sensor information, instead of having all the sensors in its vicinity reply, only a sufficient number of sensors respond with nonredundant, essential information to determine the target location, and (I3) both the packets that sensors send to their CHs and packets that CHs report to subscribers do not incur significant collision. Through both probabilistic analysis and ns-2 simulation, we use with the use of Voronoi diagram, the CH that is usually closes to the target is (implicitly) selected as the leader and that the proposed dynamic clustering algorithm effectively eliminates contention among sensors and renders more accurate estimates of target locations as a result of better quality data collected and less collision incurred.
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paper_title: A survey: localization and tracking mobile targets through wireless sensors network

paper_content:

Wireless sensor network applications have been deployed widely. Sensor networks involve sensor nodes which are very small in size. They are low in cost, and have a low battery life. Sensor nodes are capable of solving a variety of collaborative problems, such as, monitoring and surveillance. One of the critical components in wireless sensor networks is the localizing tracking sensor or mobile node. In this paper we will discuss the various location system techniques and categorize these techniques based on the communication between nodes into centralized and decentralized localization techniques. The tracking techniques are categorized into four main types. Each type will be compared and discussed in detail. We will suggest ways of implementing the techniques and finally carry out an evaluation.
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paper_title: Greedy node localization in mobile sensor networks using Doppler frequency shift

paper_content:

The principle of Doppler frequency shift can be utilized for node localization when mobile nodes are introduced into a sensor network. In this paper, a greedy method for mobile node localization using the principle of Doppler frequency shift is presented. A localization framework which accounts for multiple nodes and multiple reception paths is first developed. Subsequently, a greedy approach to anchor path guidance for maximal node localization is proposed. The method is advantageous both in terms of energy consumption and the number of nodes localized. Experiments on mobile node localization are conducted to illustrate the effectiveness of the proposed method.
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paper_title: Distributed particle filter with GMM approximation for multiple targets localization and tracking in wireless sensor network

paper_content:

Two novel distributed particle filters with Gaussian mixer approximation are proposed to localize and track multiple moving targets in a wireless sensor network. The distributed particle filters run on a set of uncorrelated sensor cliques that are dynamically organized based on moving target trajectories. These two algorithms differ in how the distributive computing is performed. In the first algorithm, partial results are updated at each sensor clique sequentially based on partial results forwarded from a neighboring clique and local observations. In the second algorithm, all individual cliques compute partial estimates based only on local observations in parallel, and forward their estimates to a fusion center to obtain final output. In order to conserve bandwidth and power, the local sufficient statistics (belief) is approximated by a low dimensional Gaussian mixture model (GMM) before propagating among sensor cliques. We further prove that the posterior distribution estimated by distributed particle filter convergence almost surely to the posterior distribution estimated from a centralized Bayesian formula. Moreover, a data-adaptive application layer communication protocol is proposed to facilitate sensor self-organization and collaboration. Simulation results show that the proposed DPF with GMM approximation algorithms provide robust localization and tracking performance at much reduced communication overhead.
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paper_title: Classification of Object Tracking Techniques in Wireless Sensor Networks

paper_content:

Object tracking is one of the killer applications for wireless sensor networks (WSN) in which the network of wireless sensors is assigned the task of tracking a particular object. The network employs the object tracking techniques to continuously report the position of the object in terms of Cartesian coordinates to a sink node or to a central base station. A family tree of object tracking techniques has been prepared.In this paper we have summarized the object tracking techniques available so far in wireless sensor networks.
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paper_title: Monte Carlo localization for mobile wireless sensor networks

paper_content:

Localization is crucial to many applications in wireless sensor networks. In this article, we propose a range-free anchor-based localization algorithm for mobile wireless sensor networks that builds upon the Monte Carlo localization algorithm. We concentrate on improving the localization accuracy and efficiency by making better use of the information a sensor node gathers and by drawing the necessary location samples faster. To do so, we constrain the area from which samples are drawn by building a box that covers the region where anchors' radio ranges overlap. This box is the region of the deployment area where the sensor node is localized. Simulation results show that localization accuracy is improved by a minimum of 4% and by a maximum of 73% (average 30%), for varying node speeds when considering nodes with knowledge of at least three anchors. The coverage is also strongly affected by speed and its improvement ranges from 3% to 55% (average 22%). Finally, the processing time is reduced by 93% for a similar localization accuracy.
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paper_title: Distributed tracking in wireless ad hoc sensor networks

paper_content:

Abstract : Target tracking is an important application for wireless ad hoc sensor networks. Because of the energy and communication constraints imposed by the size of the sensors, the processing has to be distributed over the sensor nodes. This paper discusses issues associated with distributed multiple target tracking for ad hoc sensor networks and examines the applicability of tracking algorithms developed for traditional networks of large sensors. when data association is not an issue, the standard pre- predict/update structure in single target tracking can be used to assign individual tracks to the sensor nodes based on their locations. Track ownership will have to be carefully migrated, using for example information driven sensor tasking, to minimize the need for communication when targets move. when data association is needed in tracking multiple interacting targets, clusters of tracks should be assigned to groups of collaborating nodes. Some recent examples of this type of distributed processing are given. Keywords: Wireless ad hoc sensor networks, multiple target tracking, distributed tracking
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paper_title: A Hybrid Cluster-Based Target Tracking Protocol for Wireless Sensor Networks

paper_content:

Target tracking is a typical and important application of wireless sensor networks (WSNs). In consideration of the network scalability and energy efficiency for target tracking in large-scale WSNs, it has been employed as an effective solution by organizing the WSNs into clusters. However, tracking a moving target in cluster-based WSNs suffers a boundary problem when the target moves across or along the boundaries of clusters, as the static cluster membership prevents sensors in different clusters from sharing information. In this paper, we propose a novel mobility management protocol, called hybrid cluster-based target tracking (HCTT), which integrates on-demand dynamic clustering into a cluster-based WSN for target tracking. By constructing on-demand dynamic clusters at boundary regions, nodes from different static clusters that detect the target can temporarily share information, and the tracking task can be handed over smoothly from one static cluster to another. As the target moves, static clusters and on-demand dynamic clusters alternately manage the target tracking task. Simulation results show that the proposed protocol performs better in tracking the moving target when compared with other typical target tracking protocols.
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paper_title: A survey of secure target tracking algorithms for wireless sensor networks

paper_content:

Tracking a target as it moves in monitored area has become an increasingly important application for wireless sensor networks (WSNs). Target tracking algorithms continuously report the position of the target in terms of its coordinates to a sink node or a central base station. Due to the rapid development of WSNs, there is no standardized classification of target tracking algorithms. Some of those classifications consider scalability, energy consumption and accuracy; other classification considers network topology, position of target, mobility of target/object etc. In this paper, we have considered target tracking algorithms of WSNs from the security point of view. We have compared and discussed problem of security in the most important target tracking algorithms for WSNs. To the best of our knowledge, this is the first study that analyses the target tracking algorithms in terms of security.
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paper_title: A protocol for tracking mobile targets using sensor networks

paper_content:

With recent advances in device fabrication technology, economical deployment of large scale sensor networks, capable of pervasive monitoring and control of physical systems have become possible. Scalability, low overhead anti distributed functionality are some of the key requirements for any protocol designed for such large scale sensor networks. In this paper, we present a protocol, Distributed Predictive Tracking, for one of the most likely applications for sensor networks: tracking moving targets. The protocol uses a clustering based approach for scalability and a prediction based tracking mechanism to provide a distributed and energy efficient solution. The protocol is robust against node or prediction failures which may result in temporary loss of the target and recovers from such scenarios quickly and with very little additional energy use. Using simulations we show that the proposed architecture is able to accurately track targets with random movement patterns with accuracy over a wide range of target speeds.
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paper_title: Spatiotemporal multicast in sensor networks

paper_content:

Sensor networks often involve the monitoring of mobile phenomena. We believe this task can be facilitated by a spatiotemporal multicast protocol which we call "mobicast". Mobicast is a novel spatiotemporal multicast protocol that distributes a message to nodes in a delivery zone that evolves over time in some predictable manner. A key advantage of mobicast lies in its ability to provide reliable and just-in-time message delivery to mobile delivery zones on top of a random network topology. Mobicast can in theory achieve good spatiotemporal delivery guarantees by limiting communication to a mobile forwarding zone whose size is determined by the global worst-case value associated with a compactness metric defined over the geometry of the network (under a reasonable set of assumptions). In this work, we first studied the compactness properties of sensor networks with uniform distribution. The results of this study motivate three approaches for improving the efficiency of spatiotemporal multicast in such networks. First, spatiotemporal multicast protocols can exploit the fundamental tradeoff between delivery guarantees and communication overhead in spatiotemporal multicast. Our results suggest that in such networks, a mobicast protocol can achieve relatively high savings in message forwarding overhead by slightly relaxing the delivery guarantee, e.g., by optimistically choosing a forwarding zone that is smaller than the one needed for a 100% delivery guarantee. Second, spatiotemporal multicast may exploit local compactness values for higher efficiency for networks with non uniform spatial distribution of compactness. Third, for random uniformly distributed sensor network deployment, one may choose a deployment density to best support spatiotemporal communication. We also explored all these directions via simulation and results are presented in this paper.
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paper_title: Fusion of WiFi, Smartphone Sensors and Landmarks Using the Kalman Filter for Indoor Localization

paper_content:

Location-based services (LBS) have attracted a great deal of attention recently. Outdoor localization can be solved by the GPS technique, but how to accurately and efficiently localize pedestrians in indoor environments is still a challenging problem. Recent techniques based on WiFi or pedestrian dead reckoning (PDR) have several limiting problems, such as the variation of WiFi signals and the drift of PDR. An auxiliary tool for indoor localization is landmarks, which can be easily identified based on specific sensor patterns in the environment, and this will be exploited in our proposed approach. In this work, we propose a sensor fusion framework for combining WiFi, PDR and landmarks. Since the whole system is running on a smartphone, which is resource limited, we formulate the sensor fusion problem in a linear perspective, then a Kalman filter is applied instead of a particle filter, which is widely used in the literature. Furthermore, novel techniques to enhance the accuracy of individual approaches are adopted. In the experiments, an Android app is developed for real-time indoor localization and navigation. A comparison has been made between our proposed approach and individual approaches. The results show significant improvement using our proposed framework. Our proposed system can provide an average localization accuracy of 1 m.
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paper_title: An Adaptive Particle Filter for Indoor Robot Localization

paper_content:

This paper develops an adaptive particle filter for indoor mobile robot localization, in which two different resampling operations are implemented to adjust the number of particles for fast and reliable computation. Since the weight updating is usually much more computationally intensive than the prediction, the first resampling-procedure so-called partial resampling is adopted before the prediction step, which duplicates the large weighted particles while reserves the rest obtaining better estimation accuracy and robustness. The second resampling, adopted before the updating step, decreases the number of particles through particle merging to save updating computation. In addition to speeding up the filter, sample degeneracy and sample impoverishment are counteracted. Simulations on a typical 1D model and for mobile robot localization are presented to demonstrate the validity of our approach.
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paper_title: Performance analysis based on least squares and extended Kalman filter for localization of static target in wireless sensor networks

paper_content:

Abstract Wireless sensor network localization is an essential problem that has attracted increasing attention due to wide demands such as in-door navigation, autonomous vehicle, intrusion detection, and so on. With the a priori knowledge of the positions of sensor nodes and their measurements to targets in the wireless sensor networks (WSNs), i.e. posterior knowledge, such as distance and angle measurements, it is possible to estimate the position of targets through different algorithms. In this contribution, two commonly-used approaches based on least-squares and Kalman filter are described and analyzed for localization of one static target in the WSNs with distance, angle, or both distance and angle measurements, respectively. Noting that the measurements of these sensors are generally noisy of certain degree, it is crucial and interesting to analyze how the accuracy of localization is affected by the sensor errors and the sensor network, which may help to provide guideline on choosing the specification of sensors and designing the sensor network. In addition, the problem of optimal sensor placement is also addressed to minimize the localization error. To this end, theoretical analysis have been made for the different methods based on three typical types of measurement noise: bounded noise, uniformly distributed noise, and Gaussian white noise. Simulation results illustrate the performance comparison of these different methods, the theoretical analysis and simulations and the optimal sensor geometry which may be meaningful and guideful in practice.
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paper_title: Dynamic clustering for object tracking in wireless sensor networks

paper_content:

Object tracking is an important feature of the ubiquitous society and also a killer application of wireless sensor networks. Nowadays, there are many researches on object tracking in wireless sensor networks under practice, however most of them cannot effectively deal with the trade-off between missing-rate and energy efficiency. In this paper, we propose a dynamic clustering mechanism for object tracking in wireless sensor networks. With forming the cluster dynamically according to the route of moving, the proposed method can not only decrease the missing-rate but can also decrease the energy consumption by reducing the number of nodes that participate in tracking and minimizing the communication cost, thus can enhance the lifetime of the whole sensor networks. The simulation result shows that our proposed method achieves lower energy consumption and lower missing-rate.
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paper_title: Tracking and Predicting Moving Targets in Hierarchical Sensor Networks

paper_content:

Target tracking is an important application of newly developed wireless sensor networks (WSN). Much work has been done on this topic using a plane network architecture. We propose a scheme, namely hierarchical prediction strategy (HPS), for target prediction in hierarchical sensor networks. The network is divided into clusters, which are composed of one cluster-head and many normal nodes, by Voronoi division. For an existing target, cluster-heads only selectively activate nearby sensor nodes to perform tracking. Moreover, Recursive Least Square technique is used to predict the target trajectory and help activate next-round sensor nodes. Extended simulations show the properties of the proposed network architecture and the efficiency of the prediction scheme.
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paper_title: Voronoi-Based Sensor Network Engineering for Target Tracking Using Wireless Sensor Networks

paper_content:

Recent advances in integrated electronic devices motivated the use of wireless sensor networks in many applications including target surveillance and tracking. A number of sensor nodes are scattered within a sensitive region to detect the presence of intruders and forward subsequent events to the analysis center(s). Obviously, the sensor deployment should guarantee an optimal event detection rate. This paper proposes a tracking framework based on Voronoi tessellations. Two mobility models are proposed to control the coverage degree according to target presence. The objective is to set a non-uniform coverage within the monitored zone to allow detecting the target by multiple sensor nodes. Moreover, we introduce an algorithm to discover redundant nodes (which do not provide additional information about target position). This algorithm is shown to be effective in reducing the energy consumption using an activity scheduling approach.
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paper_title: Localization for mobile sensor networks

paper_content:

Many sensor network applications require location awareness, but it is often too expensive to include a GPS receiver in a sensor network node. Hence, localization schemes for sensor networks typically use a small number of seed nodes that know their location and protocols whereby other nodes estimate their location from the messages they receive. Several such localization techniques have been proposed, but none of them consider mobile nodes and seeds. Although mobility would appear to make localization more difficult, in this paper we introduce the sequential Monte Carlo Localization method and argue that it can exploit mobility to improve the accuracy and precision of localization. Our approach does not require additional hardware on the nodes and works even when the movement of seeds and nodes is uncontrollable. We analyze the properties of our technique and report experimental results from simulations. Our scheme outperforms the best known static localization schemes under a wide range of conditions.
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paper_title: A Survey on Target Tracking Techniques in Wireless Sensor Networks

paper_content:

Target Tracking as it moves through a sensor network has become an increasingly important application in Wireless Sensor Networks. This paper examines some of the target tracking techniques in use today. An analysis of each technique is presented along with it advantages, problems and possible improvements. There are seven main categories explored in this paper. The survey promotes overview of recent research literature along with their performance comparison and evaluation.
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paper_title: Distributed tracking and classification of targets with sensor networks

paper_content:

Localization and tracking of target is an important application of the wireless sensor network. In this paper, we propose to apply a classification algorithm to sensor network nodes aimed at target tracking. Localization and tracking is based on a distributed method that enables us to simplify the signal processing and makes a more robust system. Also we address to group sensors, each group or cluster is led by one of them (leader node/sensor). This sensor is responsible for processing all the information about the target and estimate its position. Simulation results show that this classification algorithm reduces the estimate error in tracking targets.
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paper_title: Prediction-based strategies for energy saving in object tracking sensor networks

paper_content:

In order to fully realize the potential of sensor networks, energy awareness should be incorporated into every stage of the network design and operation. In this paper, we address the energy management issue in a sensor network killer application - object tracking sensor networks (OTSNs). Based on the fact that the movements of the tracked objects are sometimes predictable, we propose a prediction-based energy saving scheme, called PES, to reduce the energy consumption for object tracking under acceptable conditions. We compare PES against the basic schemes we proposed in the paper to explore the conditions under which PES is most desired. We also test the effect of some parameters related to the system workload, object moving behavior and sensing operations on PES through extensive simulation. Our results show that PES can save significant energy under various conditions.
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paper_title: Dynamic clustering for acoustic target tracking in wireless sensor networks

paper_content:

We devise and evaluate a fully decentralized, light-weight, dynamic clustering algorithm for target tracking. Instead of assuming the same role for all the sensors, we envision a hierarchical sensor network that is composed of 1) a static backbone of sparsely placed high-capability sensors which assume the role of a cluster head (CH) upon triggered by certain signal events and 2) moderately to densely populated low-end sensors whose function is to provide sensor information to CHs upon request. A cluster is formed and a CH becomes active, when the acoustic signal strength detected by the CH exceeds a predetermined threshold. The active CH then broadcasts an information solicitation packet, asking sensors in its vicinity to join the cluster and provide their sensing information. We address and devise solution approaches (with the use of Voronoi diagram) to realize dynamic clustering: (I1) how CHs operate with one another to ensure that only one CH (preferably the CH that is closes to the target) is active with high probability, (I2) when the active CH solicits for sensor information, instead of having all the sensors in its vicinity reply, only a sufficient number of sensors respond with nonredundant, essential information to determine the target location, and (I3) both the packets that sensors send to their CHs and packets that CHs report to subscribers do not incur significant collision. Through both probabilistic analysis and ns-2 simulation, we use with the use of Voronoi diagram, the CH that is usually closes to the target is (implicitly) selected as the leader and that the proposed dynamic clustering algorithm effectively eliminates contention among sensors and renders more accurate estimates of target locations as a result of better quality data collected and less collision incurred.
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paper_title: Performance evaluation of selective and adaptive heads clustering algorithms over wireless sensor networks

paper_content:

Target tracking in wireless sensor networks can be considered as a milestone of a wide range of applications to permanently report, through network sensors, the positions of a mobile target to the base station during its move across a certain path. While tracking a mobile target, a lot of open challenges arise and need to be investigated and maintained which mainly include energy efficiency and tracking accuracy. In this paper, we propose three algorithms for tracking a mobile target in wireless sensor network utilizing cluster-based architecture, namely adaptive head, static head, and selective static head. Our goal is to achieve a promising tracking accuracy and energy efficiency by choosing the candidate sensor nodes nearby the target to participate in the tracking process while preserving the others in sleep state. Through Matlab simulation, we investigate the performance of the proposed algorithms in terms of energy consumption, tracking error, sensor density, as well as target speed. The results show that the adaptive head is the most efficient algorithm in terms of energy consumption while static and selective static heads algorithms are preferred as far as the tracking error is concerned especially when the target moves rapidly. Furthermore, the effectiveness of our proposed algorithms is verified through comparing their results with those obtained from previous algorithms.
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paper_title: Prediction-based cluster management for target tracking in wireless sensor networks

paper_content:

The key impediments to a successful wireless sensor network (WSN) application are the energy and the longevity constraints of sensor nodes. Therefore, two signal processing oriented cluster management strategies, the proactive and the reactive cluster management, are proposed to efficiently deal with these constraints. The former strategy is designed for heterogeneous WSNs, where sensors are organized in a static clustering architecture. A non-myopic cluster activation rule is realized to reduce the number of hand-off operations between clusters, while maintaining desired estimation accuracy. The proactive strategy minimizes the hardware expenditure and the total energy consumption. On the other hand, the main concern of the reactive strategy is to maximize the network longevity of homogeneous WSNs. A Dijkstra-like algorithm is proposed to dynamically form active cluster based on the relation between the predictive target distribution and the candidate sensors, considering both the energy efficiency and the data relevance. By evenly distributing the energy expenditure over the whole network, the objective of maximizing the network longevity is achieved. The simulations evaluate and compare the two proposed strategies in terms of tracking accuracy, energy consumption and execution time. Copyright © 2010 John Wiley & Sons, Ltd.
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paper_title: CODA: A Continuous Object Detection and Tracking Algorithm for Wireless Ad Hoc Sensor Networks

paper_content:

Wireless sensor networks make possible many new applications in a wide range of application domains. One of the primary applications of such networks is the detection and tracking of continuously moving objects, such as wild fires, biochemical materials, and so forth. This study supports such applications by developing a continuous object detection and tracking algorithm, designated as CODA, based on a hybrid static/dynamic clustering technique. The CODA mechanism enables each sensor node to detect and track the moving boundaries of objects in the sensing field. The numerical results obtained using a Qualnet simulator confirm the effectiveness and robustness of the proposed approach.
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paper_title: Target localization based on energy considerations in distributed sensor networks

paper_content:

Wireless distributed sensor networks (DSNs) are important for a number of strategic applications such as coordinated target detection, surveillance, and localization. Energy is a critical resource in wireless sensor networks and system lifetime needs to be prolonged through the use of energy-conscious sensing strategies during system operation. We propose an energy-aware target detection and localization strategy for cluster-based wireless sensor networks. The proposed method is based on an a posteriori algorithm with a two-step communication protocol between the cluster head and the sensors within the cluster. Based on a limited amount of data received from the sensor nodes, the cluster head executes a localization procedure to determine the subset of sensors that must be queried for detailed target information. This approach reduces both energy consumption and communication bandwidth requirements, and prolongs the lifetime of the wireless sensor network. Simulation results show that a large amount of energy is saved during target localization using this strategy.
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paper_title: A dynamic tracking mechanism for mobile target in wireless sensor networks

paper_content:

How to save energy for target tracking in WSNs is an important issue. The basic idea of energy-saving method is using sleep mechanism. However, if nodes enter sleep state too much, targets may lose easily from the sleeping nodes. It is high consuming the energy to find back these losing targets. Thus, it would notice accuracy of tracing targets when saving energy. In this proposal, it offers a scheme about trace of moving target. It uses cluster scheme and algorithm, which apply different strengths and energy-consuming according to different possibility of losing targets. The possibility of lost is aimed to different acceleration of targets and wake up different number of nodes. Lowering losing rate of target can own energy-saving and improved performance.
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paper_title: Information-driven dynamic sensor collaboration

paper_content:

This article overviews the information-driven approach to sensor collaboration in ad hoc sensor networks. The main idea is for a network to determine participants in a "sensor collaboration" by dynamically optimizing the information utility of data for a given cost of communication and computation. A definition of information utility is introduced, and several approximate measures of the information utility are developed for reasons of computational tractability. We illustrate the use of this approach using examples drawn from tracking applications.
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paper_title: RARE: An Energy-Efficient Target Tracking Protocol for Wireless Sensor Networks

paper_content:

Energy efficiency for target tracking in wireless sensor networks is very important and can be improved by reducing the number of nodes involved in communications. We propose two algorithms, RARE-area and RARE-node to reduce the number of nodes participating in tracking and so increase energy efficiency. The RARE-area algorithm ensures that only nodes that receive a given quality of data participate in tracking and the RARE-node algorithm ensures that any nodes with redundant information do not participate in tracking. Simulation studies show significant energy savings are obtained with implementation of either the RARE-area algorithm alone or both RARE-area and RARE-node algorithms together.
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paper_title: Distributed event localization and tracking with wireless sensors ⋆

paper_content:

In this paper we present the distributed event localization and tracking algorithm DELTA that solely depends on light measurements. Based on this information and the positions of the sensors, DELTA is able to track a moving person equipped with a flashlight by dynamically building groups and electing well located nodes as group leaders. Moreover, DELTA supports object localization. The gathered data is sent to a monitoring entity in a fixed network which can apply pattern recognition techniques to determine the legitimacy of the moving person. DELTA enables object tracking with minimal constraints on both sensor hardware and the moving object. We show the feasibility of the algorithm running on the limited hardware of an existing sensor platform.
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paper_title: Distributed tracking and classification of targets with sensor networks

paper_content:

Localization and tracking of target is an important application of the wireless sensor network. In this paper, we propose to apply a classification algorithm to sensor network nodes aimed at target tracking. Localization and tracking is based on a distributed method that enables us to simplify the signal processing and makes a more robust system. Also we address to group sensors, each group or cluster is led by one of them (leader node/sensor). This sensor is responsible for processing all the information about the target and estimate its position. Simulation results show that this classification algorithm reduces the estimate error in tracking targets.
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paper_title: Survey of Target Tracking Protocols Using Wireless Sensor Network

paper_content:

Target tracking is one of the non trivial applications of wireless sensor network which is set up in the areas of field surveillance, habitat monitoring, indoor buildings, and intruder tracking. Various approaches have been investigated for tracking the targets, considering diverse metrics like scalability, overheads, energy consumption and target tracking accuracy. This paper for the first time contributes a survey of target tracking protocols for sensor networks and presents their classification in a precise manner. The five main categories explored in this paper are, hierarchical, tree-based, prediction- based, mobicast message-based tracking and hybrid methods. To be more precise, the survey promotes overview of recent research literature along with their performance comparison and evaluation based on simulation with real data. Certainly this task is challenging and not straight forward due to differences in estimations, parameters and performance metrics, therefore the paper concludes with open research challenges.
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paper_title: Distributed tracking in wireless ad hoc sensor networks

paper_content:

Abstract : Target tracking is an important application for wireless ad hoc sensor networks. Because of the energy and communication constraints imposed by the size of the sensors, the processing has to be distributed over the sensor nodes. This paper discusses issues associated with distributed multiple target tracking for ad hoc sensor networks and examines the applicability of tracking algorithms developed for traditional networks of large sensors. when data association is not an issue, the standard pre- predict/update structure in single target tracking can be used to assign individual tracks to the sensor nodes based on their locations. Track ownership will have to be carefully migrated, using for example information driven sensor tasking, to minimize the need for communication when targets move. when data association is needed in tracking multiple interacting targets, clusters of tracks should be assigned to groups of collaborating nodes. Some recent examples of this type of distributed processing are given. Keywords: Wireless ad hoc sensor networks, multiple target tracking, distributed tracking
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paper_title: Dual prediction-based reporting for object tracking sensor networks

paper_content:

As one of the wireless sensor network killer applications, object tracking sensor networks (OTSNs) disclose many opportunities for energy-aware system design and implementations. We investigate prediction-based approaches for performing energy efficient reporting in OTSNs. We propose a dual prediction-based reporting mechanism (called DPR), in which both sensor nodes and the base station predict the future movements of the mobile objects. Transmissions of sensor readings are avoided as long as the predictions are consistent with the real object movements. DPR achieves energy efficiency by intelligently trading off multihop/long-range transmissions of sensor readings between sensor nodes and the base station with one-hop/short-range communications of object movement history among neighbor sensor nodes. We explore the impact of several system parameters and moving behavior of tracked objects on DPR performance, and also study two major components of DPR: prediction models and location models through simulations. Our experimental results show that DPR is able to achieve considerable energy savings under various conditions and outperforms existing reporting mechanisms.
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paper_title: Energy-aware location error handling for object tracking applications in wireless sensor networks

paper_content:

Developing an efficient object tracking system has been an interesting challenge in wireless sensor network communities. Due to the severe resource constraints of sensor hardware, the accuracy of the tracking system could be compromised by the processing power or energy consumption. A sophisticated tracking algorithm is therefore not applicable to sensor applications, and any tracking system should explicitly consider the energy issue. In this paper, we present energy-aware location error handling techniques, namely error avoidance and error correction, to prevent and handle errors efficiently. Real situations such as an unexpected change in the mobile event's direction, failure of event detection, or transmission failure of an error message are considered in the design of the proposed mechanisms. The prototype system is built with real sensor hardware, and the functionality is validated in real experiments. The experimental evaluation, together with simulation analysis, shows that the proposed mechanism saves energy while achieving good tracking accuracy.
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paper_title: Optimal tracking interval for predictive tracking in wireless sensor network

paper_content:

An important application of wireless sensor networks is tracking moving objects. Prediction-based techniques have been proposed to reduce the power consumption in wireless sensor networks by limiting the sensor active time. This paper proposes a quantitative method to optimize the power efficiency by analyzing the effect of prediction on the energy consumption in such networks. To our best knowledge, our efforts are the first attempt made to calculate the optimal tracking interval for a given predictive tracking algorithm. Based on this method, the lifetime and power efficiency of a sensor network can be effectively improved.
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paper_title: A protocol for tracking mobile targets using sensor networks

paper_content:

With recent advances in device fabrication technology, economical deployment of large scale sensor networks, capable of pervasive monitoring and control of physical systems have become possible. Scalability, low overhead anti distributed functionality are some of the key requirements for any protocol designed for such large scale sensor networks. In this paper, we present a protocol, Distributed Predictive Tracking, for one of the most likely applications for sensor networks: tracking moving targets. The protocol uses a clustering based approach for scalability and a prediction based tracking mechanism to provide a distributed and energy efficient solution. The protocol is robust against node or prediction failures which may result in temporary loss of the target and recovers from such scenarios quickly and with very little additional energy use. Using simulations we show that the proposed architecture is able to accurately track targets with random movement patterns with accuracy over a wide range of target speeds.
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paper_title: On localized prediction for power efficient object tracking in sensor networks

paper_content:

Energy is one of the most critical constraints for sensor network applications. In this paper, we exploit the localized prediction paradigm for power-efficient object tracking sensor network. Localized prediction consists of a localized network architecture and a prediction mechanism called dual prediction, which achieve power savings by allowing most of the sensor nodes stay in sleep mode and by reducing the amount of long-range transmissions. Performance evaluation, based on mathematical analysis, shows that localized prediction can significantly reduce the power consumption in object tracking sensor networks.
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paper_title: Prediction-based strategies for energy saving in object tracking sensor networks

paper_content:

In order to fully realize the potential of sensor networks, energy awareness should be incorporated into every stage of the network design and operation. In this paper, we address the energy management issue in a sensor network killer application - object tracking sensor networks (OTSNs). Based on the fact that the movements of the tracked objects are sometimes predictable, we propose a prediction-based energy saving scheme, called PES, to reduce the energy consumption for object tracking under acceptable conditions. We compare PES against the basic schemes we proposed in the paper to explore the conditions under which PES is most desired. We also test the effect of some parameters related to the system workload, object moving behavior and sensing operations on PES through extensive simulation. Our results show that PES can save significant energy under various conditions.
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paper_title: DCTC: dynamic convoy tree-based collaboration for target tracking in sensor networks

paper_content:

Most existing work on sensor networks concentrates on finding efficient ways to forward data from the information source to the data centers, and not much work has been done on collecting local data and generating the data report. This paper studies this issue by proposing techniques to detect and track a mobile target. We introduce the concept of dynamic convoy tree-based collaboration, and formalize it as a multiple objective optimization problem which needs to find a convoy tree sequence with high tree coverage and low energy consumption. We propose an optimal solution which achieves 100% coverage and minimizes the energy consumption under certain ideal situations. Considering the real constraints of a sensor network, we propose several practical implementations: the conservative scheme and the prediction-based scheme for tree expansion and pruning; the sequential and the localized reconfiguration schemes for tree reconfiguration. Extensive experiments are conducted to compare the practical implementations and the optimal solution. The results show that the prediction-based scheme outperforms the conservative scheme and it can achieve similar coverage and energy consumption to the optimal solution. The experiments also show that the localized reconfiguration scheme outperforms the sequential reconfiguration scheme when the node density is high, and the trend is reversed when the node density is low.
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paper_title: Mobile object tracking in wireless sensor networks

paper_content:

Wireless sensor network is an emerging technology that enables remote monitoring objects and environment. This paper proposes a protocol to track a mobile object in a sensor network dynamically. The previous researches almost focus on how to track object accurately and they do not consider the query for mobile sources. Additionally, they need not report the tracking information to user. The work is concentrated on mobile user how to query target tracks and obtain the target position effectively. The mobile user can obtain the tracking object position without broadcast query. The user is moving and approaching the target when he/she knows the target's position. Wireless sensor networks can assist user to detect target as well as keep the movement information of the target. Sensor nodes establish face structure to track the designated target and keep target tracks. The source follows the tracks to approaching target. To chase the object quick and maintain an accurate tracking route, the sensors cooperate together to shorten the route between target and source dynamically. A source can quickly approach a target along a shortened route. Finally, we compare the proposed scheme with three flooding-based query methods. By the simulation results, the proposed protocol has better performance than that of flooding-based query methods.
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paper_title: Efficient location tracking using sensor networks

paper_content:

We apply sensor networks to the problem of tracking moving objects. We describe a publish-and-subscribe tracking method, called scalable tracking using networked sensors (STUN), that scales well to large numbers of sensors and moving objects by using hierarchy. We also describe a method, called drain-and-balance (DAB), for building efficient tracking hierarchies, computed from expected characteristics of the objects movement patterns. DAB is shown to perform well by running it on 1D and 2D sensor network topologies, and comparing it to schemes, which do not utilize movement information.
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paper_title: Efficient in-network moving object tracking in wireless sensor networks

paper_content:

The rapid progress of wireless communication and embedded microsensing MEMS technologies has made wireless sensor networks possible. In light of storage in sensors, a sensor network can be considered as a distributed database, in which one can conduct in-network data processing. An important issue of wireless sensor networks is object tracking, which typically involves two basic operations: update and query. This issue has been intensively studied in other areas, such as cellular networks. However, the in-network processing characteristic of sensor networks has posed new challenges to this issue. In this paper, we develop several tree structures for in-network object tracking which take the physical topology of the sensor network into consideration. The optimization process has two stages. The first stage tries to reduce the location update cost based on a deviation-avoidance principle and a highest-weight-first principle. The second stage further adjusts the tree obtained in the first stage to reduce the query cost. The way we model this problem allows us to analytically formulate the cost of object tracking given the update and query rates of objects. Extensive simulations are conducted, which show a significant improvement over existing solutions
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paper_title: Voronoi-Based Sensor Network Engineering for Target Tracking Using Wireless Sensor Networks

paper_content:

Recent advances in integrated electronic devices motivated the use of wireless sensor networks in many applications including target surveillance and tracking. A number of sensor nodes are scattered within a sensitive region to detect the presence of intruders and forward subsequent events to the analysis center(s). Obviously, the sensor deployment should guarantee an optimal event detection rate. This paper proposes a tracking framework based on Voronoi tessellations. Two mobility models are proposed to control the coverage degree according to target presence. The objective is to set a non-uniform coverage within the monitored zone to allow detecting the target by multiple sensor nodes. Moreover, we introduce an algorithm to discover redundant nodes (which do not provide additional information about target position). This algorithm is shown to be effective in reducing the energy consumption using an activity scheduling approach.
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paper_title: Evaluations of target tracking in wireless sensor networks

paper_content:

Target tracking is one of the most important applications of wireless sensor networks. Optimized computation and energy dissipation are critical requirements to maximize the lifetime of the sensor network. There exists a demand for self-organizing and routing capabilities in the sensor network. Existing methods attempting to achieve these requirements, such as the LEACH-based algorithms, however, suffer either redundancy in data and sensor node deployment, or complex computation incurred in the sensor nodes. Those drawbacks result in energy use inefficiency and/or complex computation overhead. OCO, or Optimized Communication and Organization, is an algorithm that ensures maximum accuracy of target tracking, efficient energy dissipation, and low computation overhead on the sensor nodes. Simulation evaluations of OCO are compared with other two methods under various scenarios.
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paper_title: Structures for in-network moving object tracking in wireless sensor networks

paper_content:

One important application of wireless sensor networks is the tracking of moving objects. The recent progress has made it possible for tiny sensors to have more computing power and storage space. Therefore, a sensor network can be considered as a distributed database, on which one can conduct in-network data processing. This paper considers in-network moving object tracking in a sensor network. This typically consists of two operations: location update and query. We propose a message-pruning tree structure that is an extension of the earlier work (H.T. Kung and D. Vlah, March 2003), which assumes the existence of a logical structure to connect sensors in the network. We formulate this problem as an optimization problem. The formulation allows us to take into account the physical structure of the sensor network, thus leading to more efficient solutions than in the previous paper of H.T. Kung and D. Vlah (March 2003) in terms of communication costs. We evaluate updating and querying costs through simulations.
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paper_title: Distributed sensor activation algorithm for target tracking with binary sensor networks

paper_content:

Target tracking with wireless sensor networks (WSNs) has been a hot research topic recently. Many works have been done to improve the algorithms for localization and prediction of a moving target with smart sensors. However, the results are frequently difficult to implement because of hardware limitations. In this paper, we propose a practical distributed sensor activation algorithm (DSA2) that enables reliable tracking with the simplest binary-detection sensors. In this algorithm, all sensors in the field are activated with a probability to detect targets or sleep to save energy, the schedule of which depends on their neighbor sensors' behaviors. Extensive simulations are also shown to demonstrate the effectiveness of the proposed algorithm. Great improvement in terms of energy-quality tradeoff and excellent robustness of the algorithm are also emphasized in the simulations.
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paper_title: Adaptive Sensor Activation Algorithm for Target Tracking in Wireless Sensor Networks

paper_content:

Target tracking is an important application of wireless sensor networks where energy conservation plays an important role. In this paper, we propose an energy-efficient sensor activation protocol based on predicted region technique, called predicted region sensor activation algorithm (PRSA). The proposed algorithm predicts the moving region of target in the next time interval instead of predicting the accurate position, by analyzing current location and velocity of the target. We take these nodes within the predicted region as waiting-activation nodes and establish activation strategy. The fewest essential number of sensor nodes within the predicted region will be activated to monitor the target. Thus, the number of nodes that was involved in tracking the target will be decreased to save energy and prolong the network’s operational lifetime. The simulation results demonstrate the effectiveness of the proposed algorithm.
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paper_title: Quality Tradeoffs in Object Tracking with Duty-Cycled Sensor Networks

paper_content:

Extending the lifetime of wireless sensor networks requires energy-conserving operations such as duty-cycling. However, such operations may impact the effectiveness of high fidelity real-time sensing tasks, such as object tracking, which require high accuracy and short response times. In this paper, we quantify the influence of different duty-cycle schemes on the efficiency of bearings-only object tracking. Specifically, we use the Maximum Likelihood localization technique to analyze the accuracy limits of object location estimates under different response latencies considering variable network density and duty-cycle parameters. Moreover, we study the tradeoffs between accuracy and response latency under various scenarios and motion patterns of the object. We have also investigated the effects of different duty-cycled schedules on the tracking accuracy using acoustic sensor data collected at Aberdeen Proving Ground, Maryland, by the U.S. Army Research Laboratory (ARL).
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paper_title: Space-time Coordinated Distributed Sensing Algorithms for Resource Efficient Narrowband Target Localization and Tracking

paper_content:

Distributed sensing has been used for enhancing signal to noise ratios for space-time localization and tracking of remote objects using phased array antennas, sonar, and radio signals. The use of these technologies in identifying mobile targets in a field, emitting acoustic signals, using a network of low-cost narrow band acoustic micro-sensing devices randomly dispersed over the region of interest, presents unique challenges. The effects of wind, turbulence, and temperature gradients and other environmental effects can decrease the signal to noise ratio by introducing random errors that cannot be removed through calibration. This paper presents methods for dynamic distributed signal processing to detect, identify, and track targets in noisy environments with limited resources. Specifically, it evaluates the noise tolerance of adaptive beamforming and compares it to other distributed sensing approaches. Many source localization and direction-of-arrival (DOA) estimation methods based on beamforming using acoustic sensor array have been proposed. We use the approximate maximum likelihood parameter estimation method to perform DOA estimation of the source in the frequency domain. Generally, sensing radii are large and data from the nodes are transmitted over the network to a centralized location where beamforming is done. These methods therefore depict low tolerance to environmental noise. Knowledge based localized distributed processing methods have also been developed for distributed in-situ localization and target tracking in these environments. These methods, due to their reliance only on local sensing, are not significantly affected by spatial perturbations and are robust in tracking targets in low SNR environments. Specifically, Dynamic Space-time Clustering (DSTC)-based localization and tracking algorithm has demonstrated orders of magnitude improvement in noise tolerance with nominal impact on performance. We also propose hybrid algorithms for energy efficient robust performance in very noisy environments. This paper compares the performance of hybrid algorithms with sparse beamforming nodes supported by randomly dispersed DSTC nodes to that of beamforming and DSTC algorithms. Hybrid algorithms achieve relative high accuracy in noisy environments with low energy consumption. Sensor data from a field test in the Marine base at 29 Palms, CA, were analyzed for validating the results in this paper. The results were compared to “ground truth” data obtained from GPS receivers on the vehicles.
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paper_title: Spatiotemporal multicast in sensor networks

paper_content:

Sensor networks often involve the monitoring of mobile phenomena. We believe this task can be facilitated by a spatiotemporal multicast protocol which we call "mobicast". Mobicast is a novel spatiotemporal multicast protocol that distributes a message to nodes in a delivery zone that evolves over time in some predictable manner. A key advantage of mobicast lies in its ability to provide reliable and just-in-time message delivery to mobile delivery zones on top of a random network topology. Mobicast can in theory achieve good spatiotemporal delivery guarantees by limiting communication to a mobile forwarding zone whose size is determined by the global worst-case value associated with a compactness metric defined over the geometry of the network (under a reasonable set of assumptions). In this work, we first studied the compactness properties of sensor networks with uniform distribution. The results of this study motivate three approaches for improving the efficiency of spatiotemporal multicast in such networks. First, spatiotemporal multicast protocols can exploit the fundamental tradeoff between delivery guarantees and communication overhead in spatiotemporal multicast. Our results suggest that in such networks, a mobicast protocol can achieve relatively high savings in message forwarding overhead by slightly relaxing the delivery guarantee, e.g., by optimistically choosing a forwarding zone that is smaller than the one needed for a 100% delivery guarantee. Second, spatiotemporal multicast may exploit local compactness values for higher efficiency for networks with non uniform spatial distribution of compactness. Third, for random uniformly distributed sensor network deployment, one may choose a deployment density to best support spatiotemporal communication. We also explored all these directions via simulation and results are presented in this paper.
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paper_title: VE-mobicast: A variant-egg-based mobicast routing protocol for sensornets

paper_content:

In this paper, we present a new "spatiotemporal multicast" protocol for supporting applications which require spatiotemporal coordination in sensornets. To simultaneously consider the factors of moving speed and direction, this work mainly investigates a new mobicast routing protocol, called variant-egg-based mobicast (VE-mobicast), by utilizing the variant-egg shape of the forwarding zone to achieve a high predicted accuracy. The contributions of our protocol are summarized as follows: (1) it builds a new shape of a forwarding zone, called the variant-egg, to adaptively and efficiently determine the location and shape of the forwarding zone to maintain the same number of wake-up sensor nodes; (2) it is a fully distributed algorithm which reduces the communication overhead of determining the forwarding zone and the mobicast message forwarding overhead; (3) it can improve the predicted accuracy of the forwarding zone by considering the factors of moving speed and direction. Finally, the simulation results illustrate the performance achievements, compared to existing mobicast routing protocols.
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paper_title: HVE-mobicast: a hierarchical-variant-egg-based mobicast routing protocol for wireless sensornets

paper_content:

In this paper, we propose a new mobicast routing protocol, called the HVE-mobicast (hierarchical-variant-egg-based mobicast) routing protocol, in wireless sensor networks (WSNs). Existing protocols for a spatiotemporal variant of the multicast protocol called a "mobicast" were designed to support a forwarding zone that moves at a constant velocity, $\stackrel{\rightarrow}{v}$ , through sensornets. The spatiotemporal characteristic of a mobicast is to forward a mobicast message to all sensor nodes that are present at time t in some geographic zone (called the forwarding zone) Z, where both the location and shape of the forwarding zone are a function of time over some interval (t start ,t end ). Mobicast routing protocol aims to provide reliable and just-in-time message delivery for a mobile sink node. To consider the mobile entity with the different moving speed, a new mobicast routing protocol is investigated in this work by utilizing the cluster-based approach. The message delivery of nodes in the forwarding zone of the HVE-mobicast routing protocol is transmitted by two phases; cluster-to-cluster and cluster-to-node phases. In the cluster-to-cluster phase, the cluster-head and relay nodes are distributively notified to wake them up. In the cluster-to-node phase, all member nodes are then notified to wake up by cluster-head nodes according to the estimated arrival time of the delivery zone. The key contribution of the HVE-mobicast routing protocol is that it is more power efficient than existing mobicast routing protocols, especially by considering different moving speeds and directions. Finally, simulation results illustrate performance enhancements in message overhead, power consumption, needlessly woken-up nodes, and successful woken-up ratio, compared to existing mobicast routing protocols.
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paper_title: Reliable mobicast via face-aware routing

paper_content:

This paper presents a novel protocol for a spatiotemporal variant of multicast called mobicast, designed to support message delivery in ad hoc sensor networks. The spatiotemporal character of mobicast relates to the obligation to deliver a message to all the nodes that will he present at time t in some geographic zone Z, where both the location and shape of the delivery zone are a function of time over some interval (t start, tend). The protocol, called face-aware routing (FAR), exploits ideas adapted from existing applications of face routing to achieve reliable mobicast delivery. The key features of the protocol are a routing strategy, which uses information confined solely to a node's immediate spatial neighborhood, and a forwarding schedule, which employs only local topological information. Statistical results shows that, in uniformly distributed random disk graphs, the spatial neighborhood size is usually less than 20. This suggests that FAR is likely to exhibit a low average memory cost. An estimation formula for the average size of the spatial neighborhood in a random network is another analytical result reported in this paper. This paper also presents a novel and low cost distributed algorithm for spatial neighborhood discovery
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Title: A Review of Localization and Tracking Algorithms in Wireless Sensor Networks

Section 1: INTRODUCTION
Description 1: This section introduces the challenges, importance, and applications of localization and tracking in wireless sensor networks.

Section 2: BROAD CLASSIFICATION OF LOCALIZATION METHODS
Description 2: This section provides a broad classification of localization methods including range-free and range-based methods, and discusses their differences and applications.

Section 3: Range-based Localization Methods
Description 3: This subsection details the various range-based localization methods such as Received Signal Strength (RSS), Angle-of-Arrival (AOA), Time-of-Arrival (TOA), and Time-Difference-of-Arrival (TDOA).

Section 4: Time-of-Arrival (TOA)-based Localization
Description 4: This subsection explores TOA-based localization methods and their formulation including challenges related to synchronization.

Section 5: Angle-of-Arrival (AOA)-based Localization
Description 5: This subsection discusses AOA-based localization methods and their utilization of angle information with the associated costs and hardware requirements.

Section 6: Received Signal Strength (RSS) based Localization
Description 6: This subsection examines RSS-based localization methods, including model dependencies and error considerations.

Section 7: Range-free Localization Methods
Description 7: This section explains various range-free localization techniques and their subdivision into local techniques and hop-counting methods.

Section 8: OVERVIEW OF IMPLEMENTATION METHODS
Description 8: This section provides an overview of different implementation methods for localization such as machine learning-based methods, centralized and distributed methods, fingerprinting, and multi-sensor data fusion techniques.

Section 9: BROAD CLASSIfiCATION OF TRACKING METHODS
Description 9: This section broadly classifies tracking methods into cluster-based, tree-based, activation-based, and mobicast-based tracking methods.

Section 10: Cluster-based Tracking Methods
Description 10: This subsection delves into cluster-based tracking methods, exploring static, dynamic, and spatio-temporal approaches.

Section 11: Prediction-based Tracking Methods
Description 11: This subsection discusses energy-efficient prediction-based tracking approaches, detailing their architecture and limitations.

Section 12: Tree-based Tracking Methods
Description 12: This subsection covers tree-based tracking methods, focusing on hierarchical organization and convoy trees.

Section 13: Activation-based Tracking Methods
Description 13: This subsection explains different activation-based tracking algorithms and their energy consumption implications.

Section 14: Mobicast-based Tracking Methods
Description 14: This subsection explores spatio-temporal methods for tracking using mobicast and the relevance of geographic zone messaging.

Section 15: EXPERIMENTAL SETUP FOR LOCALIZATION AND TRACKING
Description 15: This section reviews various experimental kits and setups available for localization and tracking applications, highlighting their features and applications.

Section 16: National Instruments Wireless Sensor Nodes
Description 16: This subsection discusses the features and applications of National Instruments Wireless Sensor Nodes.

Section 17: Crossbow Motes
Description 17: This subsection details the Crossbow motes setup and their application in research.

Section 18: SensWiz Networks Kit
Description 18: This subsection covers the SensWiz Networks Kit and its application.

Section 19: Hand-Held Devices
Description 19: This subsection discusses the use of hand-held devices like smartphones and tablets for localization and tracking purposes.

Section 20: CONCLUSION
Description 20: This concluding section summarizes the key points and findings of the survey, discussing the trade-offs and applications of different localization and tracking methods.