A Survey on Gas Leakage Source Detection and Boundary Tracking with Wireless Sensor Networks
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paper_title: Wireless Gas Leak Detection and Localization

paper_content:

Thousands of industrial gas leaks occur every year, with many leading to injuries, deaths, equipment damage, and a disastrous environmental effect. There have been many attempts at solving this problem, but with limited success. This paper proposes a wireless gas leak detection and localization solution. With a monitoring network of 20 wireless devices covering $200\;\text{m}^2$ , 60 propane releases are performed. The detection and localization algorithms proposed here are applied to the collected concentration data, and the methodology is evaluated. A detection rate of 91% is achieved, with seven false alarms recorded over 3 days, and an average detection delay of 108 s. The localization results show an accuracy of 5 m. Recommendations for future explosive gas sensor design are then presented.
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paper_title: Distributed robotics approach to chemical plume tracing

paper_content:

This paper presents an application of a physics-based framework for distributed control of autonomous vehicles. The autonomous swarm uses local information to self-organize into dynamic sensing and computation grids during localization of the source of a toxic plume. Using physics of fluid flow, we develop a new plume-tracing algorithm, and then use computational fluid dynamics simulations to show that the new approach outperforms the leading biomimetic competitors for this task.
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paper_title: Plume mapping via hidden Markov methods

paper_content:

This paper addresses the problem of mapping likely locations of a chemical source using an autonomous vehicle operating in a fluid flow. The paper reviews biological plume-tracing concepts, reviews previous strategies for vehicle-based plume tracing, and presents a new plume mapping approach based on hidden Markov methods (HMM). HMM provide efficient algorithms for predicting the likelihood of odor detection versus position, the likelihood of source location versus position, the most likely path taken by the odor to a given location, and the path between two points most likely to result in odor detection. All four are useful for solving the odor source localization problem using an autonomous vehicle. The vehicle is assumed to be capable of detecting above threshold chemical concentration and sensing the fluid flow velocity at the vehicle location. The fluid flow is assumed to vary with space and time, and to have a high Reynolds number (Re>10).
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paper_title: Distributed robotics approach to chemical plume tracing

paper_content:

This paper presents an application of a physics-based framework for distributed control of autonomous vehicles. The autonomous swarm uses local information to self-organize into dynamic sensing and computation grids during localization of the source of a toxic plume. Using physics of fluid flow, we develop a new plume-tracing algorithm, and then use computational fluid dynamics simulations to show that the new approach outperforms the leading biomimetic competitors for this task.
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paper_title: Wireless Gas Leak Detection and Localization

paper_content:

Thousands of industrial gas leaks occur every year, with many leading to injuries, deaths, equipment damage, and a disastrous environmental effect. There have been many attempts at solving this problem, but with limited success. This paper proposes a wireless gas leak detection and localization solution. With a monitoring network of 20 wireless devices covering $200\;\text{m}^2$ , 60 propane releases are performed. The detection and localization algorithms proposed here are applied to the collected concentration data, and the methodology is evaluated. A detection rate of 91% is achieved, with seven false alarms recorded over 3 days, and an average detection delay of 108 s. The localization results show an accuracy of 5 m. Recommendations for future explosive gas sensor design are then presented.
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paper_title: Hazardous gas model evaluation with field observations

paper_content:

Fifteen hazardous gas models were evaluated using data from eight field experiments. The models include seven publicly available models (AFTOX, DEGADIS, HEGADAS, HGSYSTEM, INPUFF, OB/DG and SLAB), six proprietary models (AIRTOX, CHARM, FOCUS, GASTAR, PHAST and TRACE), and two “benchmark” analytical models (the Gaussian Plume Model and the analytical approximations to the Britter and McQuaid Workbook nomograms). The field data were divided into three groups—continuous dense gas releases (Burro LNG, Coyote LNG, Desert Tortoise NH3-gas and aerosols, Goldfish HF-gas and aerosols, and Maplin Sands LNG), continuous passive gas releases (Prairie Grass and Hanford), and instantaneous dense gas releases (Thorney Island freon). The dense gas models that produced the most consistent predictions of plume centerline concentrations across the dense gas data sets are the Britter and McQuaid, CHARM, GASTAR, HEGADAS, HGSYSTEM, PHAST, SLAB and TRACE models, with relative mean biases of about ±30% or less and magnitudes of relative scatter that are about equal to the mean. The dense gas models tended to overpredict the plume widths and underpredict the plume depths by about a factor of two. All models except GASTAR, TRACE, and the area source version of DEGADIS perform fairly well with the continuous passive gas data sets. Some sensitivity studies were also carried out. It was found that three of the more widely used publicly-available dense gas models (DEGADIS, HGSYSTEM and SLAB) predicted increases in concentration of about 70% as roughness length decreased by an order of magnitude for the Desert Tortoise and Goldfish field studies. It was also found that none of the dense gas models that were considered came close to simulating the observed factor of two increase in peak concentrations as averaging time decreased from several minutes to 1 s. Because of their assumption that a concentrated dense gas core existed that was unaffected by variations in averaging time, the dense gas models predicted, at most, a 20% increase in concentrations for this variation in averaging time.
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paper_title: Application of Gaussian plume models for air pollution simulation at instantaneous emissions

paper_content:

Abstract In this paper there is proposed analytical Gaussian model for diagnosis and prognosis of atmospheric pollution level at damage emissions. The models of Gauss are very widely used to estimate local pollution levels. The program complex has been realized on FORTRAN language of high level. The model reads its input data from GIS files and produces its outputs in a format acceptable by GIS.
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paper_title: Odor Source Localization Algorithm Based on Spatially Distributed Sensors Array

paper_content:

Based on the turbulence model,this paper proposes an approach of odor source localization which is designed to find the static odor source in stable wind field.The spatially distributed sensors array is adopted to monitor odor concentration on multiple spots.And the odor source can be localized depending on the measuring results of sensors array.The optimal solution of the source position is estimated with location algorithm in a predetermined range.By using traversal calculation,the odor source localization problem is converted into the optimal solution based on least-square.Comparing with other odor source location algorithms,the new algorithm can operate without known wind speed and diffusion coefficient.And the traversal calculation avoids being trapped in local optimum.Simulation experiments are performed to verify the localization algorithm.The metal oxide semiconductor sensors array and the sampling system are designed.And the ethanol odor localization experiments are operated in the laboratory.
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paper_title: Simulation of Release and Dispersion of Gas in Sealed Interspace

paper_content:

On the base of 3-D Finite Element Model (FEM3), the discharging diffus ion process of oxygen in sealed interspace is simulated dynamically, in which th e pre-processing function of finite element software ViziCAD and the computation al and visual technology of MATLAB5.3 software are used. The diffusion result an d concentration curves are also provided. A new method for study on release and diffusion of gas is thus explored.
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paper_title: Evaluation of time-average dispersion models for estimating pheromone concentration in a deciduous forest

paper_content:

The Sutton and more recent Gaussian plume models of atmospheric dispersion were used to estimate downwind concentrations of pheromone in a deciduous forest. Wind measurements from two bivane anemometers were recorded every 12 sec and the pheromone was emitted from a point source 1.6 m above ground level at known rates. The wingfanning response of individually caged male gypsy moths (Lymantria dispar) at 15 sites situated 20 to 80 m downwind was used to monitor when pheromone levels were above threshold over a 15-min interval. Predicted concentrations from these Gaussian-type models at locations where wing fanning occurred were often several orders of magnitude below the known behavioral thresholds determined from wind tunnel tests. Probit analyses of dose-response relationships with these models showed no relationship between predicted dose and actual response. The disparity between the predictions of concentration from these models and the actual response patterns of the male gypsy moth in the field was not unexpected. These time-average models predict concentrations for a fixed position over 3-min or longer intervals, based upon the dispersion coefficients. Thus the models estimate pheromone concentrations for time intervals appreciably longer than required for behavioral response.
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paper_title: Application of Gaussian plume models for air pollution simulation at instantaneous emissions

paper_content:

Abstract In this paper there is proposed analytical Gaussian model for diagnosis and prognosis of atmospheric pollution level at damage emissions. The models of Gauss are very widely used to estimate local pollution levels. The program complex has been realized on FORTRAN language of high level. The model reads its input data from GIS files and produces its outputs in a format acceptable by GIS.
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paper_title: Application of Gaussian plume models for air pollution simulation at instantaneous emissions

paper_content:

Abstract In this paper there is proposed analytical Gaussian model for diagnosis and prognosis of atmospheric pollution level at damage emissions. The models of Gauss are very widely used to estimate local pollution levels. The program complex has been realized on FORTRAN language of high level. The model reads its input data from GIS files and produces its outputs in a format acceptable by GIS.
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paper_title: Evaluation of roadway Gaussian plume models with large-scale measurement campaigns

paper_content:

Abstract. Gaussian models are commonly used to simulate atmospheric pollutant dispersion near sources because they provide an efficient compromise between reasonable accuracy and manageable computational time. The Gaussian dispersion formula provides an exact solution to the atmospheric diffusion equation for the dispersion of a pollutant emitted from a point source. However, the Gaussian dispersion formula for a line source, which is convenient to model emissions from on-road traffic, is exact only when the wind is perpendicular to the line source. A novel approach that reduces the error in the line source formula when the wind direction is not perpendicular to the road was recently developed. This model is used to simulate NOx concentrations in a large case study (1371 road sections representing about 831 km). NO2, NO and O3 concentrations are then computed using the photostationary-state approximation. NO2 concentrations are compared with measurements made at 242 locations in the domain area. Model performance is satisfactory with mean normalised errors of 22% (winter month) to 31% (summer month). Results obtained here are also compared with those obtained with a previous formulation and with a standard model used for regulatory applications, ADMS-Urban. Discrepancies among the results obtained with those models are discussed.
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paper_title: Achievable accuracy in parameter estimation of a Gaussian plume dispersion model

paper_content:

The Gaussian plume model is the core of most regulatory atmospheric dispersion models. The parameters of the model include the source characteristics (e.g. location, strength, size) and environmental parameters (wind speed, direction, atmospheric stability conditions). A sensor network is at disposal to measure the concentration of biological pathogen or chemical substance within the plume. This paper presents a theoretical analysis of the best achievable accuracy in estimation of Gaussian plume model parameters. Numerical results illustrate how parameter estimation accuracy depends on sensor measurement accuracy, the density of sensors and the quality of (prior) meteorological advice. The theoretical bounds are compared with empirical errors obtained using an importance sampling parameter estimation technique.
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paper_title: Research on Toxic Gas Diffusion Simulation in Urban Areas Based on GIS

paper_content:

Research on the Neutral Gas Diffusion simulation, improved the Gaussian plume model which simulate under continuous leakage status. Add the terrain factor to the model; calculate with Digital Elevation Model (DEM) in order to verify the idea. Using Geographic Information System (GIS) Control technology developed Chemical Gas leakage Simulate system, take the ammonia Diffusion for instance, Dynamic Simulate its Diffusion process, demonstrate the improved model is correct, make the Gaussian plume adapted to the Gas Diffusion Simulation under the Urban Areas, and also improve the calculation accuracy of the model, provide more reliable decision support for Urban Emergency response.
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paper_title: Interactive methane monitoring system modeling and simulation for coal mines

paper_content:

The methane monitoring system for coal mines is capable of detecting apparent changes in the value of the mine gas composition. It could provide guidance for coal miners to escape following the safest and fastest route while a mine disaster was occurring. In this paper, the coal mine network topology model was established. Furthermore, it is desirable to apply XML-based mechanism for data storage in order to enhance the interoperability. In following, Gaussian puff model was built for the sake of simulating the process of methane gas diffusion with model parameters adjusted according to the coal mine network topology. Application of this model was of great significance for realizing the Markov-chain based simulation engine. The escape routes for coal miners were generated and optimized using the Dijkstra algorithm. The experiment results suggest that the methane gas concentration proved to be the major precursor indicating the gas emission hazard. In addition, the correctness and validity of the proposed model was verified though modeling and simulation of the Daping coal mine in China.
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paper_title: Tracking of multiple contaminant clouds

paper_content:

In this paper, we address the problem of detection and tracking of multiple contaminant clouds. We develop a stochastic extension of the Gaussian puff model to characterize evolution of the average atmospheric pollutant concentration. To perform the sequential inference on this difficult problem, we propose a Markov Chain Monte Carlo (MCMC)-based Particle algorithm. Numerical simulations illustrate the ability of the algorithm to detect and track multiple contaminant clouds.
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paper_title: Research on evacuation in the subway station in China based on the Combined Social Force Model

paper_content:

With the increasing number of subway stations, more and more attention has been paid to their emergency evacuation, as it plays an important part in urban emergency management. The present paper puts forward a method of crowd evacuation simulation for bioterrorism in subway station environment using the basic theory of the Social Force Model combined with the Gaussian Puff Model. A Combined Social Force Model is developed which is suitable for a real situation where there is a sudden toxic gas event. The model can also be used to demonstrate some individual behaviors in evacuation, such as competitive, grouping and herding. At last a series of experiments are conducted and the results are as follows. (1) When there is a toxic gas terroristic attack in subway stations, the influence on passengers varies according to the position that the gas source lies in and the numbers of gas sources. (2) More casualties will occur if managers do not detect the toxic gas danger and inform passengers about it. (3) The larger the wind speed is, the smaller the number of injured passengers will be. With the experiments, the number of people affected and other parameters like gas concentration can be estimated, which could support rapid and efficient emergency decisions.
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paper_title: Uncertainty propagation in puff-based dispersion models using polynomial chaos

paper_content:

Atmospheric dispersion is a complex nonlinear physical process with numerous uncertainties in model parameters, inputs, source parameters, initial and boundary conditions. Accurate propagation of these uncertainties through the dispersion models is crucial for a reliable prediction of the probability distribution of the states and assessment of risk. A simple three-dimensional Gaussian puff-based dispersion model is used as a test case to study the effect of uncertainties in the model parameters and initial conditions on the output concentration. A polynomial chaos based approach is used to numerically investigate the evolution of the model output uncertainties due to initial condition and parametric uncertainties. The polynomial chaos solution is found to be an accurate approximation to ground truth, established by Monte Carlo simulation, while offering an efficient computational approach for large nonlinear systems with a relatively small number of uncertainties.
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paper_title: 3D Simulation of Leakage and Diffusion of Gas in Complex and Sealed Space

paper_content:

The leakage and diffusion accident of inflammable and explosive toxic gas has become a serious problem in the field of environment and security. Based on the optimized three-dimensional finite element model (FEM3) and the actual condition in complex and sealed space, a three-dimensional simulation system is developed to simulate the dynamic process of leakage and diffusion of gas in complex and sealed space. Changes of gas concentration in dynamic diffusion process are accurately simulated in the meanwhile. Experiment results show that this system can accurately simulate the changing condition of gas concentration in leakage and diffusion process. It provides a new method for simulation of gas leakage and diffusion.
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paper_title: Model of drain diffusion of chloroethylene

paper_content:

The mathematical models of the leakage diffusion of poisonous molecules were reviewed in this paper,in which include Gaussian model、BM model、Sutton model and FEM 3 model.According to the characteristic of Chloroethylene,the diffusion process was described with the simplified 3-dimenssion diffusion model at the stable and instable atmosphere.Next,the relationship of the diffusion concentration with the distance was discussed.The results showed that when the leakage rate was 1 kg/s,the dangerous area was in the drain radius of 500 m at the instable atmosphere and the unsafe space was in the drain radius of 1000 m at the instable atmosphere for toxic gas Chloroethylene.
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paper_title: Simulation of Release and Dispersion of Gas in Sealed Interspace

paper_content:

On the base of 3-D Finite Element Model (FEM3), the discharging diffus ion process of oxygen in sealed interspace is simulated dynamically, in which th e pre-processing function of finite element software ViziCAD and the computation al and visual technology of MATLAB5.3 software are used. The diffusion result an d concentration curves are also provided. A new method for study on release and diffusion of gas is thus explored.
---
paper_title: Hazardous gas model evaluation with field observations

paper_content:

Fifteen hazardous gas models were evaluated using data from eight field experiments. The models include seven publicly available models (AFTOX, DEGADIS, HEGADAS, HGSYSTEM, INPUFF, OB/DG and SLAB), six proprietary models (AIRTOX, CHARM, FOCUS, GASTAR, PHAST and TRACE), and two “benchmark” analytical models (the Gaussian Plume Model and the analytical approximations to the Britter and McQuaid Workbook nomograms). The field data were divided into three groups—continuous dense gas releases (Burro LNG, Coyote LNG, Desert Tortoise NH3-gas and aerosols, Goldfish HF-gas and aerosols, and Maplin Sands LNG), continuous passive gas releases (Prairie Grass and Hanford), and instantaneous dense gas releases (Thorney Island freon). The dense gas models that produced the most consistent predictions of plume centerline concentrations across the dense gas data sets are the Britter and McQuaid, CHARM, GASTAR, HEGADAS, HGSYSTEM, PHAST, SLAB and TRACE models, with relative mean biases of about ±30% or less and magnitudes of relative scatter that are about equal to the mean. The dense gas models tended to overpredict the plume widths and underpredict the plume depths by about a factor of two. All models except GASTAR, TRACE, and the area source version of DEGADIS perform fairly well with the continuous passive gas data sets. Some sensitivity studies were also carried out. It was found that three of the more widely used publicly-available dense gas models (DEGADIS, HGSYSTEM and SLAB) predicted increases in concentration of about 70% as roughness length decreased by an order of magnitude for the Desert Tortoise and Goldfish field studies. It was also found that none of the dense gas models that were considered came close to simulating the observed factor of two increase in peak concentrations as averaging time decreased from several minutes to 1 s. Because of their assumption that a concentrated dense gas core existed that was unaffected by variations in averaging time, the dense gas models predicted, at most, a 20% increase in concentrations for this variation in averaging time.
---
paper_title: Evaluation of time-average dispersion models for estimating pheromone concentration in a deciduous forest

paper_content:

The Sutton and more recent Gaussian plume models of atmospheric dispersion were used to estimate downwind concentrations of pheromone in a deciduous forest. Wind measurements from two bivane anemometers were recorded every 12 sec and the pheromone was emitted from a point source 1.6 m above ground level at known rates. The wingfanning response of individually caged male gypsy moths (Lymantria dispar) at 15 sites situated 20 to 80 m downwind was used to monitor when pheromone levels were above threshold over a 15-min interval. Predicted concentrations from these Gaussian-type models at locations where wing fanning occurred were often several orders of magnitude below the known behavioral thresholds determined from wind tunnel tests. Probit analyses of dose-response relationships with these models showed no relationship between predicted dose and actual response. The disparity between the predictions of concentration from these models and the actual response patterns of the male gypsy moth in the field was not unexpected. These time-average models predict concentrations for a fixed position over 3-min or longer intervals, based upon the dispersion coefficients. Thus the models estimate pheromone concentrations for time intervals appreciably longer than required for behavioral response.
---
paper_title: Odor Source Localization Algorithm Based on Spatially Distributed Sensors Array

paper_content:

Based on the turbulence model,this paper proposes an approach of odor source localization which is designed to find the static odor source in stable wind field.The spatially distributed sensors array is adopted to monitor odor concentration on multiple spots.And the odor source can be localized depending on the measuring results of sensors array.The optimal solution of the source position is estimated with location algorithm in a predetermined range.By using traversal calculation,the odor source localization problem is converted into the optimal solution based on least-square.Comparing with other odor source location algorithms,the new algorithm can operate without known wind speed and diffusion coefficient.And the traversal calculation avoids being trapped in local optimum.Simulation experiments are performed to verify the localization algorithm.The metal oxide semiconductor sensors array and the sampling system are designed.And the ethanol odor localization experiments are operated in the laboratory.
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paper_title: Estimation of Gas-Source Location Using Gas Sensors and Ultrasonic Anemometer

paper_content:

This paper reports on a new method to search for a gas source. In the previous works on gas-source localization, robotic or human searchers were navigated using gas sensors and anemometers in order to track gas plumes and find their source locations. Instead, the proposed method provides a map showing the certainty of the gas-source location. In the proposed method, the area under consideration is meshed into a two dimensional grid of cells. At each observation of the gas concentration, the certainty of having a gas source with a specific strength is calculated for each cell using a turbulent diffusion model of gas distribution. The results are aggregated after repeating the observations at multiple points. The gas source is estimated to be in the cell for which high certainty values are consistently obtained. Successful demonstrations on estimating the gas-source location are presented to show the validity of the proposed method.
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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.
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paper_title: Efficient semidefinite relaxation for energy-based source localization in sensor networks

paper_content:

Recently, energy-based localization using acoustic energy measurements has received much attention in wireless sensor networks. Since the objective function of the energy-based maximum likelihood (ML) localization is non-convex, the global solutions are hardly obtained without good initial estimates. In this paper, we relax this non-convex problem as a convex semidefinite programming (SDP), based on which a good estimate can be obtained and be improved by a procedure called randomization. Simulation results show that the proposed method is effective and outperforms the existing methods.
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paper_title: Diffusion based projection method for distributed source localization in wireless sensor networks

paper_content:

Source localization based on energy measurements is an important problem in wireless sensor networks (WSNs). It is well known that the associated objective function is not convex and may have multiple local optima and saddle points. Most of the existing algorithms can not achieve global optima. In this paper, we formulate the source localization as a convex feasibility problem (CFP) and propose a diffusion based projection method as a solution. In the proposed method, no fusion center is required; the sensor nodes need to communicate only with their closest neighbors and all the sensors update their estimations simultaneously and finally they are able to achieve consensus on a possible minimizer asymptotically. The proposed method has low complexity and achieves global optimality. Theoretical analysis and simulation results show that the proposed method has good estimation performance whether or not the CFP is consistent or inconsistent.
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paper_title: Distributed energy-based multi-source localization in wireless sensor network

paper_content:

Multi-source localization is an open and challenging research problem in the energy-based wireless sensor network (WSN) of acoustic sensors. Classic maximum likelihood (ML) algorithm can not work well due to the high computation demand. An expectation maximization (EM) algorithm was proposed in our previous paper to approximate the optimal solution with lower computational complexity. However that algorithm is centralized in nature in which all the observed data from each sensor node must be sent to the fusion centre. It has several drawbacks including the relying on the centre which may be damaged or shut down, poor scalability with the increasing of network size, and the high communication overhead requirement when a sensor is far away from the centre. In this paper we propose a distributed EM algorithm for multi-source localization in energy-based WSN. It can achieve satisfactory localization accuracy with significantly low communication and computation cost.
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paper_title: A new algorithm for multiple-source localization based On acoustic energy in wieless sensor networks

paper_content:

Based on wireless sensor network, this paper discussed the methods to estimate acoustic source location with the energy received. Multiple resolution (M-R) and Tabu heuristic algorithms are expounded in details. Then, a new algorithm is proposed to attain the balance between accuracy and calculation complexity. Comparison among the different methods was made according to simulation data and curve. The result shows shat the algorithm proposed in this paper is valid and proper for acoustic source localization.
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paper_title: An Efficient EM Algorithm for Energy-Based Multisource Localization in Wireless Sensor Networks

paper_content:

Energy-based multisource localization is an important research problem in wireless sensor networks (WSNs). Existing algorithms for this problem, such as multiresolution (MR) search and exhaustive search methods, are of either high computational complexity or low estimation accuracy. In this paper, an efficient expectation-maximization (EM) algorithm for maximum-likelihood (ML) estimation is presented for energy-based multisource localization in WSNs using acoustic sensors. The basic idea of the algorithm is to decompose each sensor's energy measurement, which is a superimposition of energy signals emitted from multiple sources, into components, each of which corresponds to an individual source, and then estimate the source parameters, such as source energy and location, as well as the decay factor of the signal during propagation. An efficient sequential dominant-source (SDS) initialization scheme and an incremental parameterized search refinement scheme are introduced to speed up the algorithm and improve the estimation accuracy. Theoretic analyses on the algorithm convergence rate, the Cramer-Rao lower bound (CRLB) for localization accuracy, and the computational complexity of the algorithm are also given. The simulation results show that the proposed EM algorithm provides a good tradeoff between estimation accuracy and computational complexity.
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paper_title: Maximum Likelihood Localization of a Diffusive Point Source Using Binary Observations

paper_content:

In this paper, we investigate the problem of localization of a diffusive point source of gas based on binary observations provided by a distributed chemical sensor network. We motivate the use of the maximum likelihood (ML) estimator for this scenario by proving that it is consistent and asymptotically efficient, when the density of the sensors becomes infinite. We utilize two different estimation approaches, ML estimation based on all the observations (i.e., batch processing) and approximate ML estimation using only new observations and the previous estimate (i.e., real time processing). The performance of these estimators is compared with theoretical bounds and is shown to achieve excellent performance, even with a finite number of sensors
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paper_title: Maximum likelihood diffusive source localization based on binary observations

paper_content:

In this paper, we construct the maximum likelihood (ML) estimator of diffusive source location based on binary observations. We utilize two different estimation approaches, ML estimation based on all the observations (i.e.. batch processing) and approximated ML estimation using only new observations and the previous estimate (i.e., real time processing). The performance of these estimators are compared with theoretical bounds and are shown to achieve excellent performance.
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paper_title: Source localization based on particle swarm optimization for wireless sensor network

paper_content:

In this paper, a particle swarm optimization approach for the energy-based acoustic source localization of a wireless sensor network is presented. For this work, it is assumed that there is one acoustic source with unknown localizations which transmit acoustic signals that can be received by the nodes. The only available information to the system is the received signal energy which is not very accurate in general because of the attenuation in the process of propagation. To obtain better estimated localization of the acoustic source, maximum likelihood method is applied to transform it into extremal function, the particle swarm optimization scheme searches the optimal solution. Experimental results show that the proposed approach has the advantages of higher precision and lower computational complexity.
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paper_title: An energy efficient iterative method for source localization in wireless sensor networks

paper_content:

In this paper, we study the source localization problem in wireless sensor networks. Sensors transmit their quantized signal amplitude measurements to the fusion center and source location is estimated based on these quantized measurements. In this paper, we propose an energy efficient iterative localization scheme, where the algorithm starts with a coarse location estimate obtained from a set of anchor sensors. At each consecutive iteration, some of the non anchor sensors are activated which minimize the Posterior Cramer Rao Lower Bound (PCRLB). Then, using the available information received at previous iterations as side information, the quantized data of each activated sensor is further compressed to conserve energy using distributed data compression techniques prior to transmission to the fusion center. Simulation results show that the proposed iterative method achieves the same estimation performance as when all the sensors transmit their quantized data to the fusion center within a few iterations, while at the same time significantly reducing the communication requirements resulting in energy savings.
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paper_title: Robust Maximum Likelihood Acoustic Source Localization in Wireless Sensor Networks

paper_content:

Sensor measurements in a wireless sensor network (WSN) may significantly deviate from a commonly used Gaussian noise model due to harsh operating conditions, unreliable wireless communication links, or sensor failures. In this work, a mixed Gaussian and impulse noise model is proposed to more accurately model these types of non-Gaussian noise. However, existing maximum likelihood (ML) acoustic energy based source localization algorithms are very sensitive to non-Gaussian noise perturbations. To mitigate this shortcoming, a novel M-estimate based robust estimation formulation is derived. Extensive simulation results demonstrated superior and consistent performance advantage of this robust estimation approach compared to conventional ML estimates over a wide range of practical scenarios.
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paper_title: First characterization results obtained in a wind tunnel designed for indoor gas source detection

paper_content:

This paper presents the preliminary characterization results of a custom wind tunnel for designed for performing experiments on locating a volatile gas source with a mobile robot. Such experiments require a previous characterization of the wind tunnel as well as the definition of the configurable agents which are present during the experiments. This paper presents the experimental data gathered from the real environments. This paper shows the behavior of the evolution and diffusion of the gas depending on the gas injection rate, the mobile robot position, and the wind force. The mobile robot is equipped with a LIDAR for self localization, with a photo ionization detector (PID) for gas measurement, and with an anemometer for wind measurement. This paper shows the results obtained in static and dynamic experiments.
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paper_title: On Energy-Based Acoustic Source Localization for Sensor Networks

paper_content:

In this paper, energy-based localization methods for source localization in sensor networks are examined. The focus is on least-squares-based approaches due to a good tradeoff between performance and complexity. A suite of methods are developed and compared. First, two previously proposed methods (quadratic elimination and one step) are shown to yield the same location estimate for a source. Next, it is shown that, as the errors which perturb the least-squares equations are nonidentically distributed, it is more appropriate to consider weighted least-squares methods, which are observed to yield significant performance gains over the unweighted methods. Finally, a new weighted direct least-squares formulation is presented and shown to outperform the previous methods with much less computational complexity. Unlike the quadratic elimination method, the weighted direct least-squares method is amenable to a correction technique which incorporates the dependence of unknown parameters leading to further performance gains. For a sufficiently large number of samples, simulations show that the weighted direct solution with correction (WDC) can be more accurate with significantly less computational complexity than the maximum-likelihood estimator and approaches Cramer-Rao bound (CRB). Furthermore, it is shown that WDC attains CRB for the case of a white source.
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paper_title: Energy based collaborative source localization using acoustic micro-sensor array

paper_content:

A novel sensor network source localization method based on acoustic energy measurements is presented. This method makes use of the characteristics that the acoustic energy decays exponentially with respect to the distance from an omni-directional acoustic source. By comparing energy readings measured at surrounding acoustic sensors during the same time interval can be accurately estimated. We show that the potential target location is restricted to a hyper-sphere in the sensor field given the acoustic energy reading at a pair of sensors. Given multiple sensor acoustic energy readings, the target location is solved as the location that is closest (in the least square sense) to all the corresponding hyper-spheres. We further simplified this nonlinear least square problem to an unconstrained linear least square problem that yields a closed form solution. Experiment results using military vehicle acoustic data show great promise of this novel approach.
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paper_title: Mobile robot gas source localization via top-down visual attention mechanism and shape analysis

paper_content:

A novel mobile robot based gas source localization method in which the top-down visual attention mechanism (TDVAM) is combined with shape analysis is proposed. At each location, three different images which cover the scene in front of the robot are captured via changing the horizontal angle of an onboard pan/tilt camera. In each image, three salient regions are computed using TDVAM model, and maximal one plausible gas source from three salient regions is identified by using the shape analysis. The positions of recognized plausible gas sources are determined with a laser range scanner. The robot is navigated to the plausible gas sources one by one and gas concentration information is used to judge if the plausible sources are real ones. Experimental results in a complex indoor airflow environment in which valves are used to simulate gas sources demonstrate the feasibility and high efficiency of the proposed gas source localization approach.
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paper_title: Robot assisted gas tomography — Localizing methane leaks in outdoor environments

paper_content:

In this paper we present an inspection robot to produce gas distribution maps and localize gas sources in large outdoor environments. The robot is equipped with a 3D laser range finder and a remote ...
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paper_title: Source localization by spatially distributed electronic noses for advection and diffusion

paper_content:

Based on continuous concentration measurements from spatially distributed electronic noses, the location of a point source is to be determined. It is assumed that the emitted substance is transported by advection caused by a known homogeneous wind field and by isotropic diffusion. A new two-step approach for solving the source localization problem is presented. In the first step, for each sensor i, the set of points P/sub i/ is determined, on which the source can lie, taking only the specific concentration measurement C/sub i/ at sensor i into account. In the second step, an estimate for the source position is evaluated by intersecting the sets P/sub i/. The new approach overcomes the problem of poor convergence of iterative algorithms, which try to minimize the least squares output error. Finally, experimental results showing the capability of the new approach are presented.
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paper_title: A Semidefinite Relaxation Method for Energy-Based Source Localization in Sensor Networks

paper_content:

In this paper, the energy-based localization problem in wireless sensor networks is addressed. We focus on the weighted least squares (WLS) estimation of the source location. Due to the nonconvex nature of the WLS formulation, its global solution is hard to obtain without a good initial estimate. We propose a semidefinite relaxation method for this localization problem. To do so, we transform the original WLS formulation into a nonconvex approximate WLS (AWLS) formulation, which is then relaxed as a semidefinite programming (SDP). We show that it is possible for the SDP to be tight, i.e., the SDP solves the original AWLS problem. For the cases where the SDP is not tight, a procedure called Gaussian randomization is applied to further refine the SDP solution. Simulation results show that the proposed method can outperform the existing methods at high noise levels.
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paper_title: Plume Source Localizing in Different Distributions and Noise Types Based on WSN

paper_content:

Accidental gas leaks from unknown sites will cause the serious environmental pollution. One of the efficient methods to solve the problem is tracking and locating the plume source position. This paper presents a wireless sensor network installed with the gas sensor to on-line monitor the environment and estimate the location of a gas source based on the concentration readings at the wireless sensor nodes. Nonlinear Least Squares Method (NLS) was proposed for localization. The effect of the estimation error, with different distributions of the sensor nodes, different sensor number and different types of the back ground noises, is researched by simulations. The simulation results show that when the number of the nodes is more, the effect of the different distributions is not distinct. While the number of the nodes is less, the estimation error under the uniform distribution is more stable than under the random distribution. The suitable deployed method is discussed based on the simulation results. We also discussed the impact of the different noise types to the estimation error.
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paper_title: Distributed source localization in wireless sensor networks

paper_content:

We propose a fully distributed algorithm for source localization based on TOA measurements in a multi-hop wireless sensor networks. In contrast to centralized methods where all measurements are transmitted to and processed at a central location (the sink), the proposed method distributes the processing among the relay nodes on the routes to the sink such that the raw and intermediate data are progressively processed and only the refined results are further relayed. As a result, the proposed scheme has improved flexibility and scalability since it does not impose any special requirements on the sink node. The proposed distributed strategy also has the potential to save energy and bandwidth due to reduced radio transmissions.
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paper_title: Efficient Convex Relaxation Methods for Robust Target Localization by a Sensor Network Using Time Differences of Arrivals

paper_content:

We consider the problem of target localization by a network of passive sensors. When an unknown target emits an acoustic or a radio signal, its position can be localized with multiple sensors using the time difference of arrival (TDOA) information. In this paper, we consider the maximum likelihood formulation of this target localization problem and provide efficient convex relaxations for this nonconvex optimization problem. We also propose a formulation for robust target localization in the presence of sensor location errors. Two Cramer-Rao bounds are derived corresponding to situations with and without sensor node location errors. Simulation results confirm the efficiency and superior performance of the convex relaxation approach as compared to the existing least squares based approach when large sensor node location errors are present.
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paper_title: An Importance Sampling Method for TDOA-Based Source Localization

paper_content:

We consider the source localization problem using time-difference-of-arrival (TDOA) measurements in sensor networks. The maximum likelihood (ML) estimation of the source location can be cast as a nonlinear/nonconvex optimization problem, and its global solution is hardly obtained. In this paper, we resort to the Monte Carlo importance sampling (MCIS) technique to find an approximate global solution to this problem. To obtain an efficient importance function that is used in the technique, we construct a Gaussian distribution and choose its probability density function (pdf) as the importance function. In this process, an initial estimate of the source location is required. We reformulate the problem as a nonlinear robust least squares (LS) problem, and relax it as a second-order cone programming (SOCP), the solution of which is used as the initial estimate. Simulation results show that the proposed method can achieve the Cramer-Rao bound (CRB) accuracy and outperforms several existing methods.
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paper_title: Diffusion based projection method for distributed source localization in wireless sensor networks

paper_content:

Source localization based on energy measurements is an important problem in wireless sensor networks (WSNs). It is well known that the associated objective function is not convex and may have multiple local optima and saddle points. Most of the existing algorithms can not achieve global optima. In this paper, we formulate the source localization as a convex feasibility problem (CFP) and propose a diffusion based projection method as a solution. In the proposed method, no fusion center is required; the sensor nodes need to communicate only with their closest neighbors and all the sensors update their estimations simultaneously and finally they are able to achieve consensus on a possible minimizer asymptotically. The proposed method has low complexity and achieves global optimality. Theoretical analysis and simulation results show that the proposed method has good estimation performance whether or not the CFP is consistent or inconsistent.
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paper_title: Energy-based sensor network source localization via projection onto convex sets

paper_content:

This correspondence addresses the problem of locating an acoustic source using a sensor network in a distributed manner, i.e., without transmitting the full data set to a central point for processing. This problem has been traditionally addressed through the maximum-likelihood framework or nonlinear least squares. These methods, even though asymptotically optimal under certain conditions, pose a difficult global optimization problem. It is shown that the associated objective function may have multiple local optima and saddle points, and hence any local search method might stagnate at a suboptimal solution. In this correspondence, we formulate the problem as a convex feasibility problem and apply a distributed version of the projection-onto-convex-sets (POCS) method. We give a closed-form expression for the projection phase, which usually constitutes the heaviest computational aspect of POCS. Conditions are given under which, when the number of samples increases to infinity or in the absence of measurement noise, the convex feasibility problem has a unique solution at the true source location. In general, the method converges to a limit point or a limit cycle in the neighborhood of the true location. Simulation results show convergence to the global optimum with extremely fast convergence rates compared to the previous methods
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paper_title: Event Region for Effective Distributed Acoustic Source Localization in Wireless Sensor Networks

paper_content:

Compared to a centralized system for acoustic source localization using WSN, a distributed system has many advantages in terms of scalability, power consumption, and response time. Complexity is an important factor to consider when implementing a distributed system because of the limited resource of WSN devices. The complexity of the source localization algorithm is related to the area required for location estimation. Since reducing the size of the estimation area influences the accuracy of the source localization system, a policy should be carefully designed to reduce the size. In this paper, the authors use a Voronoi diagram to define an estimation area. The Voronoi diagram-based mechanism may yield an optimized estimation area and accuracy, but creating a Voronoi diagram is not suitable for WSN devices due to the high implementation overhead. This paper proposes an efficient mechanism to create the estimation area, which can be implemented in WSN devices and achieve reasonable accuracy, compared to the ideal Voronoi diagram-based one. The proposed algorithm has been applied to a distributed source localization algorithm, and compared with a Voronoi diagram through extensive simulations.
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paper_title: Distributed Sequential Bayesian Estimation of a Diffusive Source in Wireless Sensor Networks

paper_content:

We develop an efficient distributed sequential Bayesian estimation method for applications relating to diffusive sources-localizing a diffusive source, determining its space-time concentration distribution, and predicting its cloud envelope evolution using wireless sensor networks. Potential applications include security, environmental and industrial monitoring, as well as pollution control. We first derive the physical model of the substance dispersion by solving the diffusion equations under different environment scenarios and then integrate the physical model into the distributed processing technologies. We propose a distributed sequential Bayesian estimation method in which the state belief is transmitted in the wireless sensor networks and updated using the measurements from the new sensor node. We propose two belief representation methods: a Gaussian density approximation and a new LPG function (linear combination of polynomial Gaussian density functions) approximation. These approximations are suitable for the distributed processing in wireless sensor networks and are applicable to different sensor network situations. We implement the idea of information-driven sensor collaboration and select the next sensor node according to certain criterions, which provides an optimal subset and an optimal order of incorporating the measurements into our belief update, reduces response time, and saves energy consumption of the sensor network. Numerical examples demonstrate the effectiveness and efficiency of the proposed methods
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paper_title: Study of Plume Source Localization Based on Force-Directed QPSO Algorithm

paper_content:

Based on the attenuation model of a plume source in the wind field,this paper firstly apply a quantum particle swarm optimization(QPSO) to solve the plume source localization problem in wireless sensor networks,according to the fact that the plume source can only be detected when the sensor measured concentration is lager than a threshold,we adopt the force-directed heuristics that those sensors exert virtual forces which affect the updated position of each particle in QPSO,make the particles random roaming more purposive,direct the updating of particles for improving the convergence speed.Simulation results show that compared with QPSO in the different noise conditions,the virtual force-directed QPSO is more robust,can more quickly find the optimal solution of problem and the precision is higher.
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paper_title: An Accurate Algebraic Closed-Form Solution for Energy-Based Source Localization

paper_content:

The localization of an acoustic source can be based on the energy measurements at a number of spatially separated microphones. This is because the amount of source energy attenuation at a microphone is proportional to the square of the distance between the source and the microphone. This paper develops an algebraic closed-form solution for the acoustic source localization problem using energy measurements, under the condition of direct line-of-sight and free space propagation. First-order analysis is applied to the proposed solution to study its performance, where only the linear noise terms are kept in obtaining the mean-square localization error. The first-order analytical results show that the proposed solution reaches the Cramer-Rao lower bound (CRLB) accuracy for Gaussian noise as the signal-to-noise ratio tends to infinity. In addition, the proposed solution provides much better accuracy than other closed-form solutions available in literature. Improvement on the proposed solution that extends its operating range beyond the threshold noise level was made by imposing nonnegative constraints. Simulations are included to corroborate the performance of the proposed method.
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paper_title: Low complexity multiple acoustic source localization in sensor networks based on energy measurements

paper_content:

This work addresses the problem of estimating the locations of multiple acoustic sources by a network of distributed energy measuring sensors. The maximum likelihood (ML) solution to this problem is related to the optimization of a non-convex function of, usually, many variables. Thus, search-based methods of high complexity are required in order to yield an accurate solution. Considerable reduction of the complexity can be achieved by means of an alternating projection (AP) algorithm that decomposes the original problem into a number of simpler, yet also non-convex, optimization steps. The particular form of the derived cost functions of each such optimization step indicates that, in some cases, an approximate form of these cost functions can be used. These approximate cost functions can be evaluated using considerably lower computational complexity. Thus, a low-complexity version of the AP algorithm is proposed. Extensive simulation results demonstrate that the proposed algorithm offers a performance close to that of the exact AP implementation, and in some cases, similar performance to that of the ML estimator.
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paper_title: Sensor network based localization and target tracking through hybridization in the operational domains of beamforming and dynamic space-time clustering

paper_content:

The severe power, time and processing constraints on ad hoc wireless sensor networks for area surveillance require in-situ adaptations to conserve resources and optimize performance. In particular, it may be necessary to make dynamic tradeoffs between centralized processing algorithms, like beamforming, and knowledge based distributed processing algorithms like dynamic space-time clustering (DSTC) that rely on local processing of raw sensor data. Beamforming methods can achieve high levels of accuracy in estimating direction of arrival with a sound wave even when the source is in the far field. Hence accurate localization can be achieved with a relatively sparse sensor network. However, beamforming has severe limitations when the number of nodes increases. It requires orders of magnitude higher energy for transporting the entire time series over the network. DSTC methods, on the other hand, work well when the number of nodes is large because clusters can be formed within a smaller space-time window. This work examines the operational domains of the two centralized and distributed algorithms by analyzing sources of error, dependence on sensor density, sensor geometries, energy usage, control logic for data processing and the effects of network topology on the two algorithms. Based on this analysis, we develop hybrid algorithms that take advantage of the operational characteristics of each one in designing a high performance sensor network.
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paper_title: A Monte Carlo based energy efficient source localization method for wireless sensor networks

paper_content:

In this paper, we study the source localization problem in wireless sensor networks where the location of the source is estimated according to the quantized measurements received from sensors in the field. We propose an energy efficient iterative source localization scheme, where the algorithm begins with a coarse location estimate obtained from a set of anchor sensors. Based on the available data at each iteration, we approximate the posterior probability density function (pdf) of the source location using a Monte Carlo method and we use this information to activate a number of non-anchor sensors that minimize the Conditional Posterior Cramer Rao Lower Bound (C-PCRLB). Then we also use the Monte Carlo approximation of the posterior pdf of the source location to compress the quantized data of each activated sensor using distributed data compression techniques. Simulation results show that the proposed iterative method achieves the mean squared error that gets close to the unconditional Posterior Cramer Rao Lower Bound (PCRLB) for a Bayesian estimate based on quantized data from all the sensors within a few iterations. By selecting only the most informative sensors, the iterative approach also reduces the communication requirements significantly and resulting in energy savings.
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paper_title: Multiple Source Detection and Localization in Advection-Diffusion Processes Using Wireless Sensor Networks

paper_content:

This paper concerns the use of large-scale wireless sensor networks to detect and locate leaks of specified gases in the presence of time-varying advection (air currents) and diffusion. We show that when leaks are rare but constant for long periods, Kalman filtering combined with binary hypothesis testing provides an effective alternative to full-scale hypothesis testing covering all possible combinations of leaks and leak intensities. To reduce energy consumption and use of communication bandwidth, a two-tiered strategy is proposed in which a reduced number of sensors (Tier 1) provides coarse-grid sensing. When a leak is detected by the Tier 1 strategy, fine-grained grids of sensors (Tier 2) are activated around the vicinities of the detected leak areas to provide more precise detection and localization. Energy consumption is further reduced by applying an information versus energy-based dynamic sensor selection technique. Details of a laboratory implementation are presented and simulation results illustrate the approach and demonstrate its effectiveness.
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paper_title: Distributed gas-source localization based on MMSE estimation with cooperative sensor networks

paper_content:

Sensor network based gas source localization (GSL) has great application prospect in the fields such as environmental monitoring, security protection and pollution control. A novel GSL algorithm based on distributed minimum mean squared error (MMSE) estimation with sensor node cooperation is proposed. In the proposed distributed GSL algorithm, an information fusion objective function which combines the information utility measure and the communication cost between sensor nodes is constructed. The sensor-node scheduling scheme is designed by optimizing the information fusion objective function. For each scheduling sensor node, the MMSE is updated based on its own observation and the estimated result of the previous node. The proposed distributed GSL algorithm is implemented via global and local searches. Simulation results show that both the global search and local search could localize the gas source, however, the local-search scheme is suggested because it can get a significant reduction in the required number of processing nodes to achieve the desired performance. In other words, the local search shows potential advantages in decreasing the congestion, energy consumption and time latency in communication.
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paper_title: Functional quantizer design for source localization in sensor networks

paper_content:

In this paper, we address the problem of quantizer design optimized for a source localization application in acoustic sensor networks where physically separated sensors make measurements of acoustic signal energy, quantize them, and transmit the quantized data to a fusion node, which then produces an estimate of the source location. We propose an iterative regular quantizer design algorithm that minimizes the localization error. To construct regular quantization partitions, we suggest the average distance error as a metric in the functional quantization since the distance is monotonic in each sensor reading. Furthermore, to guarantee minimization of the localization error, we propose a new technique to update the codewords and prove that the localization error can be reduced at each iteration while the average distance error remains nonincreasing by applying our update technique. Our experiments show that our proposed algorithm yields significantly improved performance as compared with traditional quantizer designs.
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paper_title: Sequential acoustic energy based source localization using particle filter in a distributed sensor network

paper_content:

A sequential source localization method using a particle filter is presented to estimate and track multiple-target locations. This method is designed to make use of an acoustic signal measured at multiple acoustic sensors randomly deployed in a wireless distributed sensor network. By using the particle filter, a non-Gaussian probability density function of the target locations is represented by a discrete set of "particles". The positions of these particles are propagated sequentially using known state transition equation, and updated using new location estimates via the observation equation. Compared to a previously proposed maximum likelihood source localization algorithm, this new approach is computationally effective and more robust to parameter perturbation.
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paper_title: Dynamic cluster structure for object detection and tracking in wireless ad-hoc sensor networks

paper_content:

Wireless ad-hoc sensor networks are being developed to carry out tasks such as target detection and tracking, environment monitoring, and data collection across the area of deployment. We explore the problem of using sensor networks to detect and track continuous objects, such as wild fire and bio-chemical material. The continuous objects are different from traditional one or many individual targets in that they are continuously distributed across a region and usually occupy a large area. These continuous objects tend to diffuse, increase in size, change in shape, or even split into multiple relatively smaller continuous objects. The fusion and dissemination of local boundary information becomes a very challenging problem. In the paper, we propose a dynamic cluster-based structure to track the movement of boundaries and facilitate the fusion and dissemination of boundary information in a sensor network.
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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: An Algorithm for Continuous Object Tracking in WSNs

paper_content:

Object tracking is among the imperative applications of WSNs. Tracking can be classified as single-target tracking, multiple-target tracking or continuous object tracking according to the type of targets. Contemporary researchers have mainly focused on single-target or multiple-target tracking, so that there has been little research on continuous object tracking. As a consequence, based on the protocols previously known in the field of continuous object tracking, we propose the Continuous Object Tracking Algorithm (COTA) which can adaptively schedule a sensor’s activity on the grounds of an object’s movement. Simulation has shown that the COTA protocol can steadily detect and track objects of any type-one object splitting into separated parts, several targets merged into one, or an object moving in 2-dimensional space.
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paper_title: A Mobile Anchor Assisted Localization Algorithm Based on Regular Hexagon in Wireless Sensor Networks

paper_content:

Localization is one of the key technologies in wireless sensor networks (WSNs), since it provides fundamental support for many location-aware protocols and applications. Constraints of cost and power consumption make it infeasible to equip each sensor node in the network with a global position system (GPS) unit, especially for large-scale WSNs. A promising method to localize unknown nodes is to use several mobile anchors which are equipped with GPS units moving among unknown nodes and periodically broadcasting their current locations to help nearby unknown nodes with localization. This paper proposes a mobile anchor assisted localization algorithm based on regular hexagon (MAALRH) in two-dimensional WSNs, which can cover the whole monitoring area with a boundary compensation method. Unknown nodes calculate their positions by using trilateration. We compare the MAALRH with HILBERT, CIRCLES, and S-CURVES algorithms in terms of localization ratio, localization accuracy, and path length. Simulations show that the MAALRH can achieve high localization ratio and localization accuracy when the communication range is not smaller than the trajectory resolution.
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paper_title: A multisink-based Continuous Object Tracking in wireless sensor networks by GIS

paper_content:

Continuous Object Tracking (COT) is an important application in wireless sensor networks (WSNs) for smart grid, e.g. detecting forest fire, monitoring mudrock flow phenomena etc. The early warning can effectively reduce the impact of geological disasters for the power system. However, due to the constrained resource of WSN, it is a challenging issue to monitor phenomena in real time for excessive amount of message exchanges between sensors and the sink. In this paper, we propose a novel multisink architecture which consists of a static sink and a mobile sink guided by Geographic Information System (GIS). The two sinks can cooperatively work in an intelligent model in which the mobile sink can calculate the right position to collect the information from boundary nodes; in which the static one can reduce much message transmission as well as energy consumption based on centroid algorithm. Meanwhile, we present a novel algorithm which can track the object more accurately. The simulation result shows that our architecture can distinctly prolong the network life time than single sink network in continuous object tracking.
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paper_title: Static cluster and dynamic cluster head (SCDCH) adaptive prediction-based algorithm for target tracking in wireless sensor networks

paper_content:

Wireless sensor networks consist of large number of sensor nodes that have an ability to interact with the surrounding environment. The importance of finding the suitable protocol to estimate the trajectory for any moving target in a certain environment is a case of study for researchers. In this paper, new and accurate protocol for target tracking with minimum energy consumption is proposed. The proposed approach uses the static cluster and dynamic cluster head mechanism (SCDCH) to collect data from the active nodes, and then forwards it to the cluster head (CH), which in turn will send the collected data to the base station, without changing cluster border during the network's lifetime. Simulation results show that the proposed protocol has higher accuracy and save more energy compared with static clustering protocols which in turn increases the network lifetime.
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paper_title: Dynamic clustering for acoustic target tracking in wireless sensor networks

paper_content:

In the paper, 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 will 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 cooperate with one another to ensure that only one CH (preferably the CH that is closest to the target) is active with high probability, (12) 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 (13) 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 show with the use of Voronoi diagram, the CH that is usually closest 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 Task Allocation Algorithm Based on Score Incentive Mechanism for Wireless Sensor Networks

paper_content:

A wireless sensor network (WSN) consists of many resource constraint sensor nodes, which are always deployed in unattended environment. Therefore, the sensor nodes are vulnerable to failure and malicious attacks. The failed nodes have a heavily negative impact on WSNs' real-time services. Therefore, we propose a task allocation algorithm based on score incentive mechanism (TASIM) for WSNs. In TASIM, the score is proposed to reward or punish sensor nodes' task execution in cluster-based WSNs, where cluster heads are responsible for task allocation and scores' calculation. Based on the task scores, cluster members can collaborate with each other to complete complex tasks. In addition, the uncompleted tasks on failed nodes can be timely migrated to other cluster members for further execution. Furthermore, the uncompleted tasks on death nodes can be reallocated by cluster heads. Simulation results demonstrate that TASIM is quite suitable for real-time task allocation. In addition, the performance of the TASIM is clearly better than that of conventional task allocation algorithms in terms of both network load balance and energy consumption.
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paper_title: Scalable continuous object detection and tracking in sensor networks

paper_content:

With the advancement of MEMS technologies, sensor networks have opened up broad application prospects. An important issue in wireless sensor networks is object detection and tracking, which typically involves two basic components, collaborative data processing and object location reporting. The former aims to have sensors collaborating in determining a concise digest of object location information, while the latter aims to transport a concise digest to sink in a timely manner. This issue has been intensively studied in individual objects, such as intruders. However, the characteristic of continuous objects has posed new challenges to this issue. Continuous objects can diffuse, increase in size, or split into multiple continuous objects, such as a noxious gas. In this paper, a scalable, topology-control-based approach for continuous object detection and tracking is proposed. Extensive simulations are conducted, which show a significant improvement over existing solutions.
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paper_title: Localized Edge Detection in Sensor Fields

paper_content:

A wireless sensor network that studies relatively widespread phenomena (such as a contaminant flow or a seismic disturbance) may be called upon to provide a description of the boundary of the phenomenon (either a contour or some bounding box). In such cases, it may be necessary for each node to locally determine whether it lies at (or near) the edge of the phenomenon. In this paper, we show that such localized edge detection techniques are non-trivial to design in an arbitrarily deployed sensor network. We define the notion of an edge and develop performance metrics for evaluating localized edge detection algorithms. We propose three different approaches for localized edge detection and present one example scheme for each. In all our approaches, each sensor gathers information from its local neighborhood and determines whether or not it is an edge sensor. We evaluate the performance! of each of the example schemes and compare them with respect to the developed metrics.
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paper_title: Position-based adaptive quantization for target location estimation in wireless sensor networks using one-bit data

paper_content:

The problem of target location estimation in a wireless sensor network is considered, where due to the bandwidth and power constraints, each sensor only transmits one-bit information to its fusion center. To improve the performance of estimation, a position-based adaptive quantization scheme for target location estimation in wireless sensor networks is proposed to make a good choice of quantizer' thresholds. By the proposed scheme, each sensor node dynamically adjusts its quantization threshold according to a kind of position-based information sequences and then sends its one-bit quantized version of the original observation to a fusion center. The signal intensity received at local sensors is modeled as an isotropic signal intensity attenuation model. The position-based maximum likelihood estimator as well as its corresponding position-based Cramer-Rao lower bound are derived. Numerical results show that the position-based maximum likelihood estimator is more accurate than the classical fixed-quantization maximum likelihood estimator and the position-based Cramer-Rao lower bound is less than its fixed-quantization Cramer-Rao lower bound. Copyright © 2015 John Wiley & Sons, Ltd.
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paper_title: Localized algorithms for coverage boundary detection in wireless sensor networks

paper_content:

Connected coverage, which reflects how well a target field is monitored under the base station, is the most important performance metric used to measure the quality of surveillance that wireless sensor networks (WSNs) can provide. To facilitate the measurement of this metric, we propose two novel algorithms for individual sensor nodes to identify whether they are on the coverage boundary, i.e., the boundary of a coverage hole or network partition. Our algorithms are based on two novel computational geometric techniques called localized Voronoi and neighbor embracing polygons. Compared to previous work, our algorithms can be applied to WSNs of arbitrary topologies. The algorithms are fully distributed in the sense that only the minimal position information of one-hop neighbors and a limited number of simple local computations are needed, and thus are of high scalability and energy efficiency. We show the correctness and efficiency of our algorithms by theoretical proofs and extensive simulations.
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paper_title: Distributed edge detection with composite hypothesis test in wireless sensor networks

paper_content:

A statistical approach to distributed edge sensor detection with wireless sensor networks is proposed in this research. The objective is to determine whether a target sensor is located in the edge area of a certain phenomenon or not, based on data from its neighboring sensors. Due to the nature of wireless sensor networks, it is desirable to have a distributed algorithm that has a low computational complexity and a low data communication cost among sensors. With some reasonable assumptions and the aid of composite hypothesis testing, we propose data fusion as well as decision fusion methods for edge sensor detection to fulfill the aforementioned constraints. Numerical experiments are used to demonstrate the efficiency of the proposed algorithm.
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paper_title: Selective wakeup discipline for continuous object tracking in grid-based wireless sensor networks

paper_content:

A principal issue for wireless sensor networks is energy efficiency, which is necessary for prolonging applications to track continuous object, such as wild fire and poison gas. Selective wakeup approach is an effective way to save energy in such networks. However, most previous studies on selective wakeup schemes have concentrated on individual objects, such as intruders and tanks, and they cannot be applied for tracking continuous objects. This is because the continuous object is pretty flexible and dynamic due to its sensitiveness to surrounding natural effects so that movable area cannot be correctly estimated by the just spatiotemporal mechanism. Therefore, we propose a grid-based protocol for applying the efficient and accurate selective wakeup technique to tracking continuous objects. When an object is detected in some specific area which tightly surrounds the object, a pre-designated area where the target may move is activated to prepare the target without any complex calculations. Moreover, our protocol is simple and asynchronous, which makes it more suitable for wireless sensor networks.
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paper_title: Energy-efficient predictive tracking for continuous objects in wireless sensor networks

paper_content:

Due to the severe resource constraints of sensor hardware, energy efficiency is one of critical factors for monitoring the movement of the large-scale phenomena such as wild fire and hazardous bio-chemical material, denoted by continuous objects. In order to save energy, most of existing research on tracking the continuous objects focuses on finding the ways to minimize the communication cost through the effective data delivery such as data aggregation and reducing the number of reporting nodes, and not much work has been done on sensor state scheduling. Energy efficiency is expected to improve if only sensor nodes near the boundary of continuous object actively participate in tracking process, while other sensor nodes stay on sleep state for energy saving. In this paper, we propose a predictive continuous object tracking scheme, called PRECO, which uses minimum set of active sensing nodes to reduce energy consumption. The proposed scheme predicts the future boundary line, which provides the knowledge for a wake-up mechanism to decide which sleeping nodes need to be activated for future tracking. The proposed algorithm is verified with simulation results that total energy consumption can be dramatically reduced under acceptable boundary detection accuracy.
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paper_title: A Cluster-Based Continuous Object Tracking Scheme in Wireless Sensor Networks

paper_content:

Due to the severe resource constraints of sensor hardware, energy efficiency is a principal factor for detecting and tracking the movement of the large-scale phenomena such as wild fire, poison gas and hazardous bio-chemical material, denoted by continuous objects. In order to save the energy, the selective wakeup approach is effective way in the wireless sensor networks. However, most previous researches concentrated on individual objects such as intruders, tanks, and animals cannot be applied to the continuous object tracking because it is hard to expect that the diffusion of continuous object has the uniform velocity or acceleration. Recently, a prediction-based selective wakeup algorithm, denoted by PRECO is proposed. Nevertheless, this mechanism is still not acceptable. First, its prediction result is very inaccurate because a sensor node calculates next boundary line with only a few data while the continuous objects are pretty flexible and vicissitudinous. Second, its prediction tasks among current boundary nodes should be operated at the same time but it is a hard problem for the nodes to have synchronization in the wireless sensor networks. Therefore, we propose a novel prediction and selective wakeup scheme for energy efficient and accuracy continuous object tracking by using structured clustering. Hence, when objects are detected in some specific area, appointed areas where the target may move activate to keep guard without any complex calculations. Moreover, our scheme is asynchronous so that it is suitable for the sensor networks.
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paper_title: An energy-efficient clustered distributed coding for large-scale wireless sensor networks

paper_content:

A wireless sensor network (WSN) usually consists of a large number of battery-powered low-cost sensors with limited data collection and processing capacity. In order to prolong the lifetime of the WSN with a target error performance, a novel clustered distributed coding framework, referred to as distributed multiple-sensor cooperative turbo coding (DMSCTC), is developed for a large-scale WSN with sensor grouped in cooperative cluster. In the proposed DMSCTC scheme, a simple forward error correction is employed at each sensor and a simple multi-sensor joint coding is adopted at the cluster head, while complicated joint iterative decoding is implemented only at the data collector. The proposed DMSCTC scheme achieves extra distributed coding gain and cooperative spatial diversity without introducing extra complexity burden on the sensors by transferring the complicated joint decoding process to the data collector. With the proposed scheme, the WSN can achieve the target error performance with less power consumption, thus prolonging its lifetime. The error performance and energy efficiency of the proposed DMSCTC scheme are analyzed, and followed by Monte Carlo simulations. Both analytical and simulation results show that the DMSCTC can substantially improve the energy efficiency of the clustered WSN.
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paper_title: DEMOCO: Energy-Efficient Detection and Monitoring for Continuous Objects in Wireless Sensor Networks

paper_content:

The proliferation of research on target detection and tracking in wireless sensor networks has kindled development of monitoring continuous objects such as fires and hazardous bio-chemical material diffusion. In this paper, we propose an energy-efficient algorithm that monitors a moving event region by selecting only a subset of nodes near object boundaries. The paper also shows that we can effectively reduce report message size. It is verified with performance analysis and simulation results that total average report message size as well as the number of nodes which transmit the report messages to the sink can be greatly reduced, especially when the density of nodes over the network field is high.
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paper_title: EUCOW: Energy-efficient boundary monitoring for unsmoothed continuous objects in wireless sensor network

paper_content:

The proliferation of Continuous boundary monitoring in large-scale wireless sensor networks in various applications such as fires and hazardous bio-chemical material diffusion, soil moisture draws tremendous significant attentions. In this paper, we propose an energy-efficient EUCOW algorithm that monitors both interior and exterior boundary of unsmoothed object by selecting only the nodes are within transmission range r distance far from event boundary and a minimum set of representative nodes that actually report to the sink. The simulation results significantly verify that our algorithm can achieve a thinner accurate boundary with less boundary nodes as well as greatly reduce the report messages to save energy, especially when numerous event objects emerge into less number of objects.
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paper_title: Localized fault-tolerant event boundary detection in sensor networks

paper_content:

This paper targets the identification of faulty sensors and detection of the reach of events in sensor networks with faulty sensors. Typical applications include the detection of the transportation front line of a contamination and the diagnosis of network health. We propose and analyze two novel algorithms for faulty sensor identification and fault-tolerant event boundary detection. These algorithms are purely localized and thus scale well to large sensor networks. Their computational overhead is low, since only simple numerical operations are involved. Simulation results indicate that these algorithms can clearly detect the event boundary and can identify faulty sensors with a high accuracy and a low false alarm rate when as many as 20% sensors become faulty. Our work is exploratory in that the proposed algorithms can accept any kind of scalar values as inputs, a dramatic improvement over existing works that take only 0/1 decision predicates. Therefore, our algorithms are generic. They can be applied as long as the "events" can be modelled by numerical numbers. Though designed for sensor networks, our algorithms can be applied to the outlier detection and regional data analysis in spatial data mining.
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paper_title: Dynamic Rectangle Zone-Based Collaboration Mechanism for Tracking Continuous Objects in Wireless Sensor Networks

paper_content:

Most existing routing protocols on the object detection and tracking in sensor networks concentrates on finding ways to detect and track one or more individual objects, such as people, animals, and vehicles, and not many protocols have been done on detecting and tracking continuous objects, such as poison gas, biochemical, and chemical liquid. These continuous objects are quite different from the individual objects in that they continuously distributed across a region and usually occupy a large area. Accordingly, they are detected and sensed by many sensor nodes, and their sensing data are redundant and highly correlated. Hence, there need any efficient scheme on collecting and aggregating locally their sensing data and generating the data report. The continuous objects also tend to diffuse, changes in shape, increases the size, even splits into multiple smaller continuous objects, or join together one continuous object. Accordingly, there also need any efficient scheme to manage efficiently the dynamic change of shape of the continuous objects. Therefore, we introduce dynamic rectangle zone-based collaboration mechanism for detecting, tracking, and monitoring the continuous objects taking into account their properties. One center node in the zone collects and aggregates the sensing data from sensor nodes which detect the continuous object. The dynamic rectangle zone change newly according to dynamic change of the continuous object and the center node is also altered by another node to minimize the energy consumption for collecting the sensing data.
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paper_title: Energy-Efficiency Continuous Object Tracking via Automatically Adjusting Sensing Range in Wireless Sensor Network

paper_content:

Wireless sensor networks (WSNs) can be used in many applications. Object tracking is one of the most important applications. There are many researches about tracking individual object but few researches on tracking continuous object. Continuous object affects a large number of nodes so needs more communication and cooperation to track it comparing with individual object. In this paper we propose an energy-efficiency continuous object tracking (ECOT) protocol. In ECOT, a sensor node identify itself whether a boundary node or not via automatically adjusting its sensing range instead of communicating with neighboring nodes since radio communication which consumes much more energy than adjusting sensing range. Furthermore just a subset of boundary nodes will send data back to sink node with a small-size message by a representative node selection mechanism. Finally the sink node can estimate the object’s position and shape by locating object mechanism. The analysis and simulation results demonstrate that ECOT outperform than previous work and achieves high fidelity and significantly reduces the amount of communication messages.
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paper_title: A Novel Continuous Object Tracking Scheme for Energy-Constrained Wireless Sensor Networks

paper_content:

In order to monitor continuously moving phenomena such as wile fire and hazardous bio-chemical material in wireless sensor network, boundary tracking approach has been widely used by reason of its huge scale and extensive diffusion property. With the boundary tracking scheme, the energy efficiency is expected to improve if only sensor nodes near the boundary of continuous object actively participate in tracking process, while other sensor nodes stay in sleep mode for energy saving. In this paper, we propose a predictive continuous object tracking scheme, which uses minimum set of active sensing nodes to reduce energy consumption. The proposed scheme predicts the future boundary line, which provides the knowledge for a wake-up mechanism to decide which sleeping nodes need to be activated for future tracking. The proposed algorithm is verified with simulation results that total energy consumption can be dramatically reduced under acceptable boundary detection accuracy.
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paper_title: Efficient Continuous Object Tracking with Virtual Grid in Wireless Sensor Networks

paper_content:

For tracking a continuous object, mass communications are generated in the sensor field because of their huge scale and extensive diffusing property. Due to the severe resource constraints of sensor nodes, the redundant information and communications should be reduced in order to prolong the life time of WSNs. Although there have been numerous studies on continuous object tracking, most of the proposed schemes have focused on reducing the redundant boundary nodes and control messages through static/dynamic clustering in space domain. However, it is not enough to reduce the redundancy only with space domain. Redundantly sampled reporting packets could be a cause of generating redundant long range transmission, which is an important source of energy consumption. For reducing the redundancy, we need to consider not only the method to reduce the redundancy in space domain but also an adaptive sampling scheme in time domain. However, adaptive sampling is difficult to design in continuous object tracking because local sensor nodes hard to figure out how far the continuous object is diffused. To solve this problem, we suggest a new scheme which picturizes the diffusing object by dividing the sensor field into several cells like a pixel on TV and samples reporting time based on pixel image. In addition, we use pictured image of diffusing object for selecting the boundary information of diffusing object in space domain to reduce the redundant boundary information. To present the sensor field as an image, we adopt a virtual grid which divides the sensor field into several cells under the static clustering WSN architecture. The performance of proposed scheme is verified with simulation and the results show that the total number of control messages, reporting packets and boundary nodes can be reduced.
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paper_title: A continuous object tracking protocol suitable for practical wireless sensor networks

paper_content:

The information of boundary area is important to track the continuous objects such as forest fires, radioactive matter, and oil spills. Recently, many of protocols are proposed for the continuous object tracking. They predict the future boundary shape of the continuous objects and activate only sensors in the predicted area of the objects. It is very efficient strategy in a uniformly deployed wireless sensor networks. However, it should be needed more advanced algorithm that is suitable for a randomly deployed practical sensor networks with voids. If the void area is in the predicted area, the activation message cannot reach and the predicted area cannot be activated at the right time. It is a major cause of many detection errors for the continuous object. Moreover, if activation control is once foiled then the next activation control might be continuously failed. The detection errors can be result in serious harm to people, because most of continuous objects are dangerous substances. In this paper, we propose a chaining selective wakeup scheme suitable for continuous object tracking in wireless sensor networks. In our protocol, we collect the information of a void area during the network configuration time; if the next boundary area is in the void area, we activate the chained area surrounding the void area with activation control message.
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paper_title: Detection and Tracking of Continuous Objects for Flexibility and Reliability in Sensor Networks

paper_content:

This paper proposes a novel detection and tracking scheme for continuous objects in wireless sensor networks, such as wild fire and bio-chemical material. Unlike previous works, the scheme takes into account a two-tier grid structure in order to achieve flexible and reliable detection and tracking. Firstly, for flexibility, the scheme proactively constructs a coarse-grained grid structure and then, once a continuous object appears, fine-grained grid structures are reactively established within coarse-grained grid cells only around the continuous object. Secondly, with respect to reliability, minute grid cells of the fine-grained grid structures are able to provide detailed shape of boundary of the continuous object; to quickly deal with diffusion of the continuous object the scheme prepares the fine-grained grid structures into next coarse-grained grid cells toward diffusion direction of the continuous object.
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paper_title: Using Directional Antenna for Continuous Moving Object Tracking in WSN with Uncovered Holes

paper_content:

As a distinguished target monitoring system, wire- less sensor networks have spawned great advances. Most existing work concentrates on improving the accuracy and energy saving in a fully covered area by making full use of omnidirectional antenna. In reality, however, due to the death of sensors caused by harsh environment or energy depletion, the target may enter into a hole that is not covered by sensors. On the other hand, representative researches on boundary detection do not take target tracking into consideration. To address this issue, we introduce the idea of applying directional antenna to accomplish mobile target tracking in WSN with uncovered holes. We propose a collaborative signaling protocol to achieve the goal of continuous target tracking with low-energy-consumption. Simulation results have shown that the proposed approach can realize the real- time detection of moving object when it runs into and out of the hole, and at the same time, consume much less energy than the omnidirectional antenna based methods.
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paper_title: Continuous Energy Minimization for Multitarget Tracking

paper_content:

Many recent advances in multiple target tracking aim at finding a (nearly) optimal set of trajectories within a temporal window. To handle the large space of possible trajectory hypotheses, it is typically reduced to a finite set by some form of data-driven or regular discretization. In this work, we propose an alternative formulation of multitarget tracking as minimization of a continuous energy. Contrary to recent approaches, we focus on designing an energy that corresponds to a more complete representation of the problem, rather than one that is amenable to global optimization. Besides the image evidence, the energy function takes into account physical constraints, such as target dynamics, mutual exclusion, and track persistence. In addition, partial image evidence is handled with explicit occlusion reasoning, and different targets are disambiguated with an appearance model. To nevertheless find strong local minima of the proposed nonconvex energy, we construct a suitable optimization scheme that alternates between continuous conjugate gradient descent and discrete transdimensional jump moves. These moves, which are executed such that they always reduce the energy, allow the search to escape weak minima and explore a much larger portion of the search space of varying dimensionality. We demonstrate the validity of our approach with an extensive quantitative evaluation on several public data sets.
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paper_title: Localized mechanism for continuous objects tracking and monitoring in wireless sensor networks

paper_content:

This paper addresses issues for tracking and monitoring continuous objects, such as poison gas, biochemical, and chemical liquid in wireless sensor networks. These continuous objects are quite different from the individual objects, such as people, animals, and vehicles in that they are continuously distributed across a region and usually occupy a large area. Accordingly, they are detected and sensed by many sensor nodes, and their sensing data are redundant and highly correlated. Hence, there needs any efficient scheme on collecting and aggregating locally their sensing data and generating the data report. The continuous objects also tend to diffuse, changes in shape, increases the size, even splits into multiple smaller continuous objects, or join together one continuous object. Accordingly, there also need any efficient scheme to manage efficiently the dynamic change of shape of the continuous objects. Therefore, we introduce Dynamic Rectangle Zone-based Collaboration Mechanism for detecting, tracking, and monitoring the continuous objects taking into account the dynamic change of their shape. The proposed mechanism constructs a dynamic rectangle zone included the area occupied by one continuous object. One center node in the zone collects and aggregates the sensing data from sensor nodes which detect the continuous object. The dynamic rectangle zone change newly according to dynamic change of the continuous object and the center node is also altered by another node to minimize the energy consumption for collecting the sensing data. Through simulation results, we also evaluate how environmental factors and control parameters affect the performance of the proposed mechanism.
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Title: A Survey on Gas Leakage Source Detection and Boundary Tracking with Wireless Sensor Networks

Section 1: INTRODUCTION
Description 1: This section introduces the paper, explaining the significance of gas leak detection and the need for gas leakage source estimation and boundary tracking in industrial environments.

Section 2: GAS DIFFUSION MODEL
Description 2: This section provides an overview of different gas diffusion models used in gas leakage detection, including static and dynamic environment-based models.

Section 3: GAS SOURCE LOCALIZATION
Description 3: This section describes various algorithms and methods used for gas leakage source localization, focusing on precision, robustness, and energy consumption.

Section 4: BOUNDARY DETECTION AND TRACKING IN WSNs
Description 4: This section discusses methods for boundary detection and tracking of continuous objects in wireless sensor networks, covering dynamic clustering, topology control, and routing protocols.

Section 5: CONCLUSION
Description 5: This section summarizes the paper, highlighting the main findings and suggesting future research directions in the field of gas leakage detection and boundary tracking.