Data Mining Techniques for Wireless Sensor Networks: A Survey
---
paper_title: Survey of the study on frequent pattern mining in data streams

paper_content:

Data mining and knowledge discovery in data streams have recently attracted more attentions for their applications to numerous types of data, including Web clickstreams, sensor networks, etc. Because of some special characteristics, such as continuous arrival in multiple, rapid, time-varying, possibly unpredictable and unbounded, data streams have yielded some fundamentally new research problems. Among the various topics in this research field, it is paramount to find frequent patterns in data streams in a single pass, or a small number of passes, while using less space of memory. This survey reviewed the last advances in the study on frequent pattern mining in data streams, especially classified the present mining algorithms for the first time and discussed them in detail, and finally suggested some promising research directions in the future.
---
paper_title: Wireless sensor network survey

paper_content:

A wireless sensor network (WSN) has important applications such as remote environmental monitoring and target tracking. This has been enabled by the availability, particularly in recent years, of sensors that are smaller, cheaper, and intelligent. These sensors are equipped with wireless interfaces with which they can communicate with one another to form a network. The design of a WSN depends significantly on the application, and it must consider factors such as the environment, the application's design objectives, cost, hardware, and system constraints. The goal of our survey is to present a comprehensive review of the recent literature since the publication of [I.F. Akyildiz, W. Su, Y. Sankarasubramaniam, E. Cayirci, A survey on sensor networks, IEEE Communications Magazine, 2002]. Following a top-down approach, we give an overview of several new applications and then review the literature on various aspects of WSNs. We classify the problems into three different categories: (1) internal platform and underlying operating system, (2) communication protocol stack, and (3) network services, provisioning, and deployment. We review the major development in these three categories and outline new challenges.
---
paper_title: On clustering large number of data streams

paper_content:

Data streams and their applications appear in several fields such as physics, finance, medicine, environmental science, etc. As sensor technology improves, sensor data rates continue to increase. Consequently, analyzing data streams becomes ever more challenging. Fast online response is a must for applications that involve multiple data streams, especially when the number of data streams is large. This paper proposes an efficient clustering technique called Multi-way Grid-based join algorithm MG-join to find clusters in multiple data streams. The proposed algorithm uses a Discrete Fourier Transformation DFT to reduce the dimensionality of the streams. Each stream is represented by a point in a multi-dimensional grid in the frequency domain. The MG-join algorithm finds the different clusters in multiple data streams in the frequency domain. Moreover, this paper proposes an incremental update mechanism to avoid the recalculation of DFT coefficients when new readings arrive and thus minimizes the processing time. Experiments on synthetic data streams show that the proposed clustering technique is much faster than traditional clustering techniques and yet its accuracy is as good as that of the traditional clustering techniques. This makes the proposed technique suitable for sensors network environment where computing and power capabilities are limited.
---
paper_title: Efficient instance-based learning on data streams

paper_content:

The processing of data streams in general and the mining of such streams in particular have recently attracted considerable attention in various research fields. A key problem in stream mining is to extend existing machine learning and data mining methods so as to meet the increased requirements imposed by the data stream scenario, including the ability to analyze incoming data in an online, incremental manner, to observe tight time and memory constraints, and to appropriately respond to changes of the data characteristics and underlying distributions, amongst others. This paper considers the problem of classification on data streams and develops an instance-based learning algorithm for that purpose. The experimental studies presented in the paper suggest that this algorithm has a number of desirable properties that are not, at least not as a whole, shared by currently existing alternatives. Notably, our method is very flexible and thus able to adapt to an evolving environment quickly, a point of utmost importance in the data stream context. At the same time, the algorithm is relatively robust and thus applicable to streams with different characteristics.
---
paper_title: Fault Detection by Mining Association Rules from House-keeping Data

paper_content:

This paper proposes a novel anomaly detection method for spacecraft systems based on data-mining techniques. This method automatically constructs a system behavior model in the form of a set of rules by applying pattern clustering and association rule mining to the time-series data obtained in the learning phase, then detects anomalies by checking the subsequent on-line data with the acquired rules. A major advantage of this approach is that it requires little a priori knowledge on the system.
---
paper_title: A survey on clustering algorithms for wireless sensor networks

paper_content:

The past few years have witnessed increased interest in the potential use of wireless sensor networks (WSNs) in applications such as disaster management, combat field reconnaissance, border protection and security surveillance. Sensors in these applications are expected to be remotely deployed in large numbers and to operate autonomously in unattended environments. To support scalability, nodes are often grouped into disjoint and mostly non-overlapping clusters. In this paper, we present a taxonomy and general classification of published clustering schemes. We survey different clustering algorithms for WSNs; highlighting their objectives, features, complexity, etc. We also compare of these clustering algorithms based on metrics such as convergence rate, cluster stability, cluster overlapping, location-awareness and support for node mobility.
---
paper_title: SO MAD: SensOr Mining for Anomaly Detection in railway data

paper_content:

Today, many industrial companies must face problems raised by maintenance. In particular, the anomaly detection problem is probably one of the most challenging. In this paper we focus on the railway maintenance task and propose to automatically detect anomalies in order to predict in advance potential failures. We first address the problem of characterizing normal behavior. In order to extract interesting patterns, we have developed a method to take into account the contextual criteria associated to railway data (itinerary, weather conditions, etc.). We then measure the compliance of new data, according to extracted knowledge, and provide information about the seriousness and possible causes of a detected anomaly.
---
paper_title: A fuzzy approach for interpretation of ubiquitous data stream clustering and its application in road safety

paper_content:

Ubiquitous Data Mining is the process of analysing data emanating from distributed and heterogeneous sources in the form of a continuous stream with mobile and/or embedded devices. Unsupervised learning is clearly beneficial for initial understanding of data streams, and consequently various clustering algorithms have been developed and applied in UDM systems for the purpose of mining data streams. However, unsupervised data mining techniques require human intervention for further understanding and analysis of the clustering results. This becomes an issue as UDM applications aim to support mobile and highly dynamic users/applications and there is a need for real-time decision making and interpretation of results. In this paper we present an approach to automate the annotation of results obtained from ubiquitous data stream clustering to facilitate interpreting and use of the results to enable real-time, mobile decision making.
---
paper_title: Anomaly detection in wireless sensor networks: A survey

paper_content:

Since security threats to WSNs are increasingly being diversified and deliberate, prevention-based techniques alone can no longer provide WSNs with adequate security. However, detection-based techniques might be effective in collaboration with prevention-based techniques for securing WSNs. As a significant branch of detection-based techniques, the research of anomaly detection in wired networks and wireless ad hoc networks is already quite mature, but such solutions can be rarely applied to WSNs without any change, because WSNs are characterized by constrained resources, such as limited energy, weak computation capability, poor memory, short communication range, etc. The development of anomaly detection techniques suitable for WSNs is therefore regarded as an essential research area, which will enable WSNs to be much more secure and reliable. In this survey paper, a few of the key design principles relating to the development of anomaly detection techniques in WSNs are discussed in particular. Then, the state-of-the-art techniques of anomaly detection in WSNs are systematically introduced, according to WSNs' architectures (Hierarchical/Flat) and detection technique categories (statistical techniques, rule based, data mining, computational intelligence, game theory, graph based, and hybrid, etc.). The analyses and comparisons of the approaches that belong to a similar technique category are represented technically, followed by a brief discussion towards the potential research areas in the near future and conclusion.
---
paper_title: Anomaly detection: A survey

paper_content:

Anomaly detection is an important problem that has been researched within diverse research areas and application domains. Many anomaly detection techniques have been specifically developed for certain application domains, while others are more generic. This survey tries to provide a structured and comprehensive overview of the research on anomaly detection. We have grouped existing techniques into different categories based on the underlying approach adopted by each technique. For each category we have identified key assumptions, which are used by the techniques to differentiate between normal and anomalous behavior. When applying a given technique to a particular domain, these assumptions can be used as guidelines to assess the effectiveness of the technique in that domain. For each category, we provide a basic anomaly detection technique, and then show how the different existing techniques in that category are variants of the basic technique. This template provides an easier and more succinct understanding of the techniques belonging to each category. Further, for each category, we identify the advantages and disadvantages of the techniques in that category. We also provide a discussion on the computational complexity of the techniques since it is an important issue in real application domains. We hope that this survey will provide a better understanding of the different directions in which research has been done on this topic, and how techniques developed in one area can be applied in domains for which they were not intended to begin with.
---
paper_title: A survey on algorithms for mining frequent itemsets over data streams

paper_content:

The increasing prominence of data streams arising in a wide range of advanced applications such as fraud detection and trend learning has led to the study of online mining of frequent itemsets (FIs). Unlike mining static databases, mining data streams poses many new challenges. In addition to the one-scan nature, the unbounded memory requirement and the high data arrival rate of data streams, the combinatorial explosion of itemsets exacerbates the mining task. The high complexity of the FI mining problem hinders the application of the stream mining techniques. We recognize that a critical review of existing techniques is needed in order to design and develop efficient mining algorithms and data structures that are able to match the processing rate of the mining with the high arrival rate of data streams. Within a unifying set of notations and terminologies, we describe in this paper the efforts and main techniques for mining data streams and present a comprehensive survey of a number of the state-of-the-art algorithms on mining frequent itemsets over data streams. We classify the stream-mining techniques into two categories based on the window model that they adopt in order to provide insights into how and why the techniques are useful. Then, we further analyze the algorithms according to whether they are exact or approximate and, for approximate approaches, whether they are false-positive or false-negative. We also discuss various interesting issues, including the merits and limitations in existing research and substantive areas for future research.
---
paper_title: An Efficient Data Extraction Mechanism for Mining Association Rules from Wireless Sensor Networks

paper_content:

With the advances of wireless sensor networks and their ability to generate a large amount of data, data mining techniques to extract useful knowledge regarding the underlying network have recently received a great deal of attention. However, the stream nature of the data, the limited resources, and the distributed nature of sensor networks bring new challenges for the mining techniques that need to be address. In this paper, we introduce a new formulation for the association rules, a well known data mining technique, that is able to generate the time relations between sensor devices in a particular sensor network. This new formulation will allow traditional data mining algorithms proposed to solve the classical association rules mining problem to be applied on sensor based class of applications that generate and use sensor data. The generated rules will give a clear picture about the correlations between sensors in the network and can be used to make decisions about the network performance, or it can be used to predict the sources of future events. In order to prepare for the data needed in the mining process and to maximize the network lifetime, a distributed extraction methodology is introduced, in this distributed methodology sensors perform optimization based on local computation to decide whether it will participate in sending data or not. Experimental results have shown that the distributed extraction solution is able to reduce the number of exchanged messages and the data size by 50% compared to a direct transmission of the data.
---
paper_title: Habitat monitoring with sensor networks

paper_content:

M ONITORING WITH S ENSOR N ETWORKS By Robert Szewczyk, Eric Osterweil, Joseph Polastre, Michael Hamilton, Alan Mainwaring, and Deborah Estrin These networks deliver to ecologists data on localized environmental conditions at the scale of individual organisms to help settle large-scale land-use issues affecting animals, plants, and people. istorically, the study of micro- climate and habitat utilization have been largely observa- tional, with climatic and behavioral variables being extrapolated from a few or even individual measurement sites. Today, densely deployed sensor networks are being scaled to the size of the organisms under study, sampling phenomena at fre- quencies the organisms encounter, and dispersed in patterns that capture the full range of environmen- tal exposures to provide the fine-grain information needed for accurate modeling and prediction. Ranging in size from tens to potentially thousands of nodes within a habitat patch, these networks are beginning to provide a view of often subtle changes in a given landscape at unprecedented spatial and temporal resolution. The technological challenges for developing and deploying them are daunting. They must be unobtrusive yet durable under a range of environmental stresses, including damage caused by the organisms themselves. They must be so energy efficient that they can remain in situ with little human interaction and be maintenance-free for years at a time. They must also reliably interconnect with a cyber infrastructure that permits frequent network H ABITAT H access for data upload, device programming, and management. Here, we survey the components of a complete habitat-monitoring system, from miniature data-collection sensor nodes to data-processing back- ends containing millions of observations, showing how they fit into a unified architecture, deriving our data and conclusions from several case studies (see the sidebar “Sensing the Natural Environment”). Few themes permeate basic and applied ecological research to such an extent as the relationship of microclimate and ecological patterns, processes, physiology, and biological diversity. Microclimate can be defined as the climate close to surfaces, upon and beneath soils, under snow, or in water, on living things (such as trees), or even on individual animals. Individuals may disperse across broad areas, but per- sistence, growth, and reproduction depend on the existence of narrow ranges of key environmental con- ditions that vary over narrow spatial gradients. For example, we see only the stand of trees that reached the right microclimate as seeds but not the tens of thousands of seeds that perished or simply failed to take root because they germinated in areas outside the range of their tolerance. Through their presence and activity, organisms alter their surroundings in important ways. Tree shape, physiology, and canopy structure can produce June 2004/Vol. 47, No. 6 COMMUNICATIONS OF THE ACM
---
paper_title: Clustering distributed sensor data streams using local processing and reduced communication

paper_content:

Nowadays applications produce infinite streams of data distributed across wide sensor networks. In this work we study the problem of continuously maintain a cluster structure over the data points generated by the entire network. Usual techniques operate by forwarding and concentrating the entire data in a central server, processing it as a multivariate stream. In this paper, we propose DGClust, a new distributed algorithm which reduces both the dimensionality and the communication burdens, by allowing each local sensor to keep an online discretization of its data stream, which operates with constant update time and (almost) fixed space. Each new data point triggers a cell in this univariate grid, reflecting the current state of the data stream at the local site. Whenever a local site changes its state, it notifies the central server about the new state it is in. This way, at each point in time, the central site has the global multivariate state of the entire network. To avoid monitoring all possible states, which is exponential in the number of sensors, the central site keeps a small list of counters of the most frequent global states. Finally, a simple adaptive partitional clustering algorithm is applied to the frequent states central points in order to provide an anytime definition of the clusters centers. The approach is evaluated in the context of distributed sensor networks, focusing on three outcomes: loss to real centroids, communication prevention, and processing reduction. The experimental work on synthetic data supports our proposal, presenting robustness to a high number of sensors, and the application to real data from physiological sensors exposes the aforementioned advantages of the system.
---
paper_title: Learning Models of Human Behaviour with Sequential Patterns

paper_content:

The Independent Lifestyle Assistant (I.L.S.A.) is agent-based system to aid elderly people to live longer in their homes. One of the biggest challenges for I.L.S.A. is that every installation will (1) be in a home with a different layout and suite of sensor and actuator capabilities, and (2) supporting a technophobic client with unique capabilities, needs and care-giving support network. I.L.S.A. therefore must be able to adapt to its environment over time. This paper describes our approach to modelling one particular aspect of the I.L.S.A. domain: using sequential pattern learning to learn models of human behaviour. We describe the problem, our enhancements to the basic algorithm, and present experimental results collected from four test sites ~
---
paper_title: An active learning system for mining time-changing data streams

paper_content:

Mining time-changing data streams is of great interest. The fundamental problems are how to effectively identify the significant changes and organize new training data to adjust the outdated model. In this paper, we propose an active learning system to address these issues. Without need knowing any true labels of the new data, we devise an active approach to detecting the possible changes. Whenever the suspected changes are indicated, it exploits a light-weight uncertainty sampling algorithm to choose the most informative instances to label. With these labeled instances, it further tests the truth of the suspected changes. If the changes indeed cause significant performance deterioration of the current model, it evolves the old model. Thus, our method is sensitive to significant changes and robust to noisy changes, and can quickly adapt to concept-drift. Experimental results from both synthetic and real-world data confirm the advantages of our system.
---
paper_title: MavHome: an agent-based smart home

paper_content:

The goal of the MavHome (Managing An Intelligent Versatile Home) project is to create a home that acts as an intelligent agent. In this paper we introduce the MavHome architecture. The role of prediction algorithms within the architecture is discussed, and a meta-predictor is presented which combines the strengths of multiple approaches to inhabitant action prediction. We demonstrate the effectiveness of these algorithms on smart home data.
---
paper_title: Novelty detection with application to data streams

paper_content:

This paper presents and evaluates an approach to novelty detection that addresses it as the problem of identifying novel concepts in a continuous learning scenario, as an extension to a single-class classification problem. OLINDDA, an OnLIne Novelty and Drift Detection Algorithm that implements this approach, uses efficient standard clustering algorithms to continuously generate candidate clusters among examples that were not explained by the current known concepts. Clusters complying with a validation criterion that takes cohesiveness and representativeness into account are initially identified as concepts. By merging similar concepts, OLINDDA may enhance the representation of some concepts as it advances toward its final goal of describing novel emerging concepts in an unsupervised way. The proposed approach is experimentally evaluated by the use of several measures taken throughout the learning process. Results show that it is capable of identifying novel concepts that are pure and correspond to real classes, disregarding unrepresentative clusters and outliers.
---
paper_title: Self-organizing sensor networks for integrated target surveillance

paper_content:

Self-organization is critical for a distributed wireless sensor network due to the spontaneous and random deployment of a large number of sensor nodes over a remote area. Such a network is often characterized by its abilities to form an organizational structure without much centralized intervention. An important design goal for a smart sensor network is to be able have an energy-efficient, self-organized configuration of sensor nodes that can scan, detect, and track targets of interest in a distributed manner. In this paper, we propose a novel self-organization protocol and describe other relevant, indigenous building blocks that can be combined to build integrated surveillance applications for self-organized sensor networks. Experiments in both simulated and real-world platforms indicate that this protocol can be useful for tracking targets that follow a predictable course
---
paper_title: Moment: maintaining closed frequent itemsets over a stream sliding window

paper_content:

This paper considers the problem of mining closed frequent itemsets over a sliding window using limited memory space. We design a synopsis data structure to monitor transactions in the sliding window so that we can output the current closed frequent itemsets at any time. Due to time and memory constraints, the synopsis data structure cannot monitor all possible itemsets. However, monitoring only frequent itemsets make it impossible to detect new itemsets when they become frequent. In this paper, we introduce a compact data structure, the closed enumeration tree (CET), to maintain a dynamically selected set of item-sets over a sliding-window. The selected itemsets consist of a boundary between closed frequent itemsets and the rest of the itemsets. Concept drifts in a data stream are reflected by boundary movements in the CET. In other words, a status change of any itemset (e.g., from non-frequent to frequent) must occur through the boundary. Because the boundary is relatively stable, the cost of mining closed frequent item-sets over a sliding window is dramatically reduced to that of mining transactions that can possibly cause boundary movements in the CET. Our experiments show that our algorithm performs much better than previous approaches.
---
paper_title: Outlier Detection Techniques for Wireless Sensor Networks: A Survey

paper_content:

In the field of wireless sensor networks, those measurements that significantly deviate from the normal pattern of sensed data are considered as outliers. The potential sources of outliers include noise and errors, events, and malicious attacks on the network. Traditional outlier detection techniques are not directly applicable to wireless sensor networks due to the nature of sensor data and specific requirements and limitations of the wireless sensor networks. This survey provides a comprehensive overview of existing outlier detection techniques specifically developed for the wireless sensor networks. Additionally, it presents a technique-based taxonomy and a comparative table to be used as a guideline to select a technique suitable for the application at hand based on characteristics such as data type, outlier type, outlier identity, and outlier degree.
---
paper_title: Knowledge discovery from data streams

paper_content:

Since the beginning of the Internet age and the increased use of ubiquitous computing devices, the large volume and continuous flow of distributed data have imposed new constraints on the design of learning algorithms. Exploring how to extract knowledge structures from evolving and time-changing data, Knowledge Discovery from Data Streams presents a coherent overview of state-of-the-art research in learning from data streams. The book covers the fundamentals that are imperative to understanding data streams and describes important applications, such as TCP/IP traffic, GPS data, sensor networks, and customer click streams. It also addresses several challenges of data mining in the future, when stream mining will be at the core of many applications. These challenges involve designing useful and efficient data mining solutions applicable to real-world problems. In the appendix, the author includes examples of publicly available software and online data sets. This practical, up-to-date book focuses on the new requirements of the next generation of data mining. Although the concepts presented in the text are mainly about data streams, they also are valid for different areas of machine learning and data mining.
---
paper_title: A Survey of Applications of Wireless Sensors and Wireless Sensor Networks

paper_content:

Wireless sensors and wireless sensor networks have come to the forefront of the scientific community recently. This is the consequence of engineering increasingly smaller sized devices, which enable many applications. The use of these sensors and the possibility of organizing them into networks have revealed many research issues and have highlighted new ways to cope with certain problems. In this paper, different applications areas where the use of such sensor networks has been proposed are surveyed
---
paper_title: Wireless sensor networks for emergency navigation

paper_content:

In an emergency, wireless network sensors combined with a navigation algorithm could help safely guide people to a building exit while helping them avoid hazardous areas. We propose a distributed navigation algorithm for emergency situations. At normal time, sensors monitor the environment. When the sensors detect emergency events, our protocol quickly separates hazardous areas from safe areas, and the sensors establish escape paths. Simulation and implementation results show that our scheme achieves navigation safety and quick convergence of the navigation directions. We based our protocol on the temporally ordered routing algorithm for mobile ad hoc networks. TORA assigns mobile nodes temporally ordered sequence numbers to support multipath routing from a source to a specific destination node
---
paper_title: Efficiently mining long patterns from databases

paper_content:

We present a pattern-mining algorithm that scales roughly linearly in the number of maximal patterns embedded in a database irrespective of the length of the longest pattern. In comparison, previous algorithms based on Apriori scale exponentially with longest pattern length. Experiments on real data show that when the patterns are long, our algorithm is more efficient by an order of magnitude or more.
---
paper_title: Incremental mining of frequent patterns without candidate generation or support constraint

paper_content:

In this paper, we propose a novel data structure called CATS Tree. CATS Tree extends the idea of FPTree to improve storage compression and allow frequent pattern mining without generation of candidate item sets. The proposed algorithms enable frequent pattern mining with different supports without rebuilding the tree structure. Furthermore, the algorithms allow mining with a single pass over the database as well as efficient insertion or deletion of transactions at any time.
---
paper_title: CFI-Stream: mining closed frequent itemsets in data streams

paper_content:

Mining frequent closed itemsets provides complete and condensed information for non-redundant association rules generation. Extensive studies have been done on mining frequent closed itemsets, but they are mainly intended for traditional transaction databases and thus do not take data stream characteristics into consideration. In this paper, we propose a novel approach for mining closed frequent itemsets over data streams. It computes and maintains closed itemsets online and incrementally, and can output the current closed frequent itemsets in real time based on users' specified thresholds. Experimental results show that our proposed method is both time and space efficient, has good scalability as the number of transactions processed increases and adapts very rapidly to the change in data streams.
---
paper_title: L.: Estimating Missing Data in Data Streams

paper_content:

Networks of thousands of sensors present a feasible and economic solution to some of our most challenging problems, such as real-time traffic modeling, military sensing and tracking. Many research projects have been conducted by different organizations regarding wireless sensor networks; however, few of them discuss how to estimate missing sensor data. In this research we present a novel data estimation technique based on association rules derived from closed frequent itemsets generated by sensors. Experimental results compared with the existing techniques using real-life sensor data show that closed itemset mining effectively imputes missing values as well as achieves time and space efficiency.
---
paper_title: Mining Frequent Patterns without Candidate Generation: A Frequent-Pattern Tree Approach

paper_content:

Mining frequent patterns in transaction databases, time-series databases, and many other kinds of databases has been studied popularly in data mining research. Most of the previous studies adopt an Apriori-like candidate set generation-and-test approach. However, candidate set generation is still costly, especially when there exist a large number of patterns and/or long patterns. ::: ::: In this study, we propose a novel frequent-pattern tree (FP-tree) structure, which is an extended prefix-tree structure for storing compressed, crucial information about frequent patterns, and develop an efficient FP-tree-based mining method, FP-growth, for mining the complete set of frequent patterns by pattern fragment growth. Efficiency of mining is achieved with three techniques: (1) a large database is compressed into a condensed, smaller data structure, FP-tree which avoids costly, repeated database scans, (2) our FP-tree-based mining adopts a pattern-fragment growth method to avoid the costly generation of a large number of candidate sets, and (3) a partitioning-based, divide-and-conquer method is used to decompose the mining task into a set of smaller tasks for mining confined patterns in conditional databases, which dramatically reduces the search space. Our performance study shows that the FP-growth method is efficient and scalable for mining both long and short frequent patterns, and is about an order of magnitude faster than the Apriori algorithm and also faster than some recently reported new frequent-pattern mining methods.
---
paper_title: Beyond market baskets: generalizing association rules to correlations

paper_content:

One of the most well-studied problems in data mining is mining for association rules in market basket data. Association rules, whose significance is measured via support and confidence, are intended to identify rules of the type, “A customer purchasing item A often also purchases item B.” Motivated by the goal of generalizing beyond market baskets and the association rules used with them, we develop the notion of mining rules that identify correlations (generalizing associations), and we consider both the absence and presence of items as a basis for generating rules. We propose measuring significance of associations via the chi-squared test for correlation from classical statistics. This leads to a measure that is upward closed in the itemset lattice, enabling us to reduce the mining problem to the search for a border between correlated and uncorrelated itemsets in the lattice. We develop pruning strategies and devise an efficient algorithm for the resulting problem. We demonstrate its effectiveness by testing it on census data and finding term dependence in a corpus of text documents, as well as on synthetic data.
---
paper_title: Mining association rules between sets of items in large databases

paper_content:

We are given a large database of customer transactions. Each transaction consists of items purchased by a customer in a visit. We present an efficient algorithm that generates all significant association rules between items in the database. The algorithm incorporates buffer management and novel estimation and pruning techniques. We also present results of applying this algorithm to sales data obtained from a large retailing company, which shows the effectiveness of the algorithm.
---
paper_title: Online Algorithms for Mining Inter-Stream Associations from Large Sensor Networks

paper_content:

We study the problem of mining frequent value sets from a large sensor network. We discuss how sensor stream data could be represented that facilitates efficient online mining and propose the interval-list representation. Based on Lossy Counting, we propose ILB, an interval-list-based online mining algorithm for discovering frequent sensor value sets. Through extensive experiments, we compare the performance of ILB against an application of Lossy Counting (LC) using a weighted transformation method. Results show that ILB outperforms LC significantly for large sensor networks.
---
paper_title: On-board Mining of Data Streams in Sensor Networks

paper_content:

Data streams are generated in large quantities and at rapid rates from sensor networks that typically monitor environmental conditions, traffic conditions and weather conditions among others. A significant challenge in sensor networks is the analysis of the vast amounts of data that are rapidly generated and transmitted through sensing. Given that wired communication is infeasible in the environmental situations outlined earlier, the current method for communicating this data for analysis is through satellite channels. Satellite communication is exorbitantly expensive. In order to address this issue, we propose a strategy for on-board mining of data streams in a resource-constrained environment. We have developed a novel approach that dynamically adapts the data-stream mining process on the basis of available memory resources. This adaptation is algorithm-independent and enables data-stream mining algorithms to cope with high data rates in the light of finite computational resources. We have also developed lightweight data-stream mining algorithms that incorporate our adaptive mining approach for resource constrained environments.
---
paper_title: Approximate Frequency Counts over Data Streams

paper_content:

Research in data stream algorithms has blossomed since late 90s. The talk will trace the history of the Approximate Frequency Counts paper, how it was conceptualized and how it influenced data stream research. The talk will also touch upon a recent development: analysis of personal data streams for improving our quality of lives.
---
paper_title: Efficient mining of association rules from Wireless Sensor Networks

paper_content:

Wireless Sensor Networks (WSNs) produce large scale of data in the form of streams. Recently, data mining techniques have received a great deal of attention in extracting knowledge from WSNs data. Mining association rules on the sensor data provides useful information for different applications. Even though there have been some efforts to address this issue in WSNs, they are not suitable when multiple database scans are the major limitation. In this paper, we propose a new tree-based data structure called Sensor Pattern Tree (SP-tree) to generate association rules from WSNs data with one database scan. The SP-tree is constructed in frequency-descending order, which facilitates an efficient mining using the FP-growth-based [6] mining technique. The experimental results show that SP-tree outperforms related algorithms in generating association rules from WSNs data.
---
paper_title: A Novel Algorithm for Mining Association Rules in Wireless Ad Hoc Sensor Networks

paper_content:

In this paper, we propose a comprehensive framework for mining wireless ad hoc sensor networks (WASNs), which is able to extract patterns regarding the sensors' behaviors. The main goal of determining behavioral patterns is to use them to generate rules that will improve the WASN's quality of service by participating in the resource management process or compensating for the undesired side effects of wireless communication. The proposed framework consists of 1) a formal definition of sensor behavioral patterns and sensor association rules, 2) a novel representation structure that we refer to as the positional lexicographic tree (PLT) that is able to compress the data gathered for the mining process and thus allows the fast and efficient mining of sensor behavioral patterns, and 3) a distributed data extraction mechanism to prepare the data required for mining sensor behavioral patterns. Several experimental studies have been conducted to evaluate our PLT structure and our proposed data extraction algorithms for mining wireless sensor networks.
---
paper_title: Adaptive Mining Techniques for Data Streams Using Algorithm Output Granularity Mohamed

paper_content:

Mining data streams is an emerging area of research given the potentially large number of business and scientific applications. A significant challenge in analyzing/ mining data streams is the high ...
---
paper_title: Using association rules for energy conservation in wireless sensor networks

paper_content:

Frequent radio transmissions among sensors, or from sensors to the basestation, have always been a major energy drain. One of the approaches to reduce the data transmitted to the basestation is to shift the bulk of data processing to networked sensor nodes; for instance, sensors to send only data aggregates to reduce the overall amount of data exchanged. Sensor nodes, however, are quite limited in terms of their energy and processing power, and as such, traditional centralised data mining algorithms are infeasible to be directly implemented on sensors. In this paper, we modify APRIORI to find strong rules from sensor readings in a sensor network and using these rules, autonomously control sensor network operations or supplement sensor operations with a rule knowledge base. For example, triggers activated from the rules could be used to sleep sensors or reduce data transmissions to conserve sensor energy. Our work here includes a detailed implementation of a lightweight rule learning algorithm for a resource-constrainted sensor network, with simulation results for a group node setup running the algorithm.
---
paper_title: A dynamic clustering and scheduling approach to energy saving in data collection from wireless sensor networks

paper_content:

Energy consumption is one of the major con- straints in wireless sensor networks. A highly feasible strat- egy is to aggressively reduce the spatial sampling rate of sensors (i.e., the density of the measure points in a field). By properly scheduling, we want to retain the high quality of data collection. In this paper, we propose a novel dynamic clustering and scheduling approach. Orthogonal to most ex- isting methods which mainly utilize the overlaps of sensing ranges of sensors to reduce the spatial sampling rate, our method is based on a careful analysis of the surveillance data reported by the sensors. We dynamically partition the sensors into groups so that the sensors in the same group have similar surveillance time series. They can share the workload of data collection in the future since their future readings may likely be similar. A generic framework is de- veloped to address several important technical challenges, including how to partition the sensors into groups, how to dynamically maintain the groups, and how to schedule sam- pling for the sensors in a group. We conduct an extensive empirical study to test our method using both a real test bed system and a large-scale synthetic dataset.
---
paper_title: Energy-efficient real-time object tracking in multi-level sensor networks by mining and predicting movement patterns

paper_content:

A number of studies have been written on sensor networks in the past few years due to their wide range of potential applications. Object tracking is an important topic in sensor networks; and the limited power of sensor nodes presents numerous challenges to researchers. Previous studies of energy conservation in sensor networks have considered object movement behavior to be random. However, in some applications, the movement behavior of an object is often based on certain underlying events instead of randomness completely. Moreover, few studies have considered the real-time issue in addition to the energy saving problem for object tracking in sensor networks. In this paper, we propose a novel strategy named multi-level object tracking strategy (MLOT) for energy-efficient and real-time tracking of the moving objects in sensor networks by mining the movement log. In MLOT, we first conduct hierarchical clustering to form a hierarchical model of the sensor nodes. Second, the movement logs of the moving objects are analyzed by a data mining algorithm to obtain the movement patterns, which are then used to predict the next position of a moving object. We use the multi-level structure to represent the hierarchical relations among sensor nodes so as to achieve the goal of keeping track of moving objects in a real-time manner. Through experimental evaluation of various simulated conditions, the proposed method is shown to deliver excellent performance in terms of both energy efficiency and timeliness.
---
paper_title: Energy efficient strategies for object tracking in sensor networks: A data mining approach

paper_content:

In recent years, a number of studies have been done on object tracking sensor networks (OTSNs) due to the wide applications. One important research issue in OTSNs is the energy saving strategy in considering the limited power of sensor nodes. The past studies on energy saving in OTSNs considered the object's movement behavior as randomness. In some real applications, however, the object movement behavior is often based on certain underlying events instead of randomness completely. In this paper, we propose a novel data mining algorithm named TMP-Mine with a special data structure named TMP-Tree for efficiently discovering the temporal movement patterns of objects in sensor networks. To our best knowledge, this is the first work on mining the movement patterns associated with time intervals in OTSNs. Moreover, we propose novel location prediction strategies that utilize the discovered temporal movement patterns so as to reduce the prediction errors for energy savings. Through empirical evaluation on various simulation conditions and real dataset, TMP-Mine and the proposed prediction strategies are shown to deliver excellent performance in terms of scalability, accuracy and energy efficiency.
---
paper_title: High-density wireless sensor networks: a new clustering approach for prediction-based monitoring

paper_content:

We propose a new cluster-based approach that simplifies prediction-based monitoring for homogeneous, high-density wireless sensor networks composed of a large number of small, power-restricted nodes. Prediction-based monitoring can increase the autonomous lifetime of the network by reducing communication. In our clustering approach, the cluster-heads spatio-temporally correlate and predict the measurements of the cluster-members by executing their prediction model. Routing is only done by the gateway nodes at the circumference of the clusters while the nongateway nodes, which are positioned between the cluster-heads and their gateway nodes, are allowed to turn off their radio communication as long as their measurements satisfy the predictions of their cluster-head. Turning off radio communication results in high energy savings and can greatly improve system lifetime. Our main contribution is the description of this clustering approach while the prediction models are beyond the scope of this paper.
---
paper_title: Real time clustering of sensory data in wireless sensor networks

paper_content:

Data mining in wireless sensor networks (WSNs) is a new emerging research area. This paper investigates the problem of real time clustering of sensory data in WSNs. The objective is to cluster the data collected by sensor nodes in real time according to data similarity in a d-dimensional sensory data space. To perform in-network data clustering efficiently, a Hilbert Curves based mapping algorithm, HilbertMap, is proposed to convert a d-dimensional sensory data space into a two-dimensional area covered by a sensor network. Based on this mapping, a distributed algorithm for clustering sensory data, H-Cluster, is proposed. It guarantees that the communications for sensory data clustering mostly occur among geographically nearby sensor nodes and sensory data clustering is accomplished in in-network manner. Extensive simulation experiments were conducted using both real-world datasets and synthetic datasets to evaluate the algorithms. H-Cluster consistently achieves the lowest data loss rate, the highest energy efficiency, and the best clustering quality.
---
paper_title: A Communication-Efficient Distributed Clustering Algorithm for Sensor Networks

paper_content:

Sensor networks usually generate continuous stream of data over time. Clustering sensor data as a core task of mining sensor data plays an essential role in analytical applications of sensor networks. Although several algorithms have been proposed to address the problem of distributed clustering, in the domain of sensor networks these algorithms face major new challenges such as limited communication bandwidth and constraints in power supply, and storage resources. Moreover, previous studies about clustering in sensor networks have mostly focused on clustering sensor nodes and designing better network topology for the purpose of energy conservation rather than clustering sensor data for future analytical purposes. In this paper a communication efficient distributed algorithm is proposed for clustering sensory data. This approach addresses the limited bandwidth issue through summarized transmissions. Furthermore communication efficiency of the algorithm contributes to reduced power consumption. Time efficiency of the algorithm is evaluated through simulation experiments and the results are presented.
---
paper_title: An Efficient Data Extraction Mechanism for Mining Association Rules from Wireless Sensor Networks

paper_content:

With the advances of wireless sensor networks and their ability to generate a large amount of data, data mining techniques to extract useful knowledge regarding the underlying network have recently received a great deal of attention. However, the stream nature of the data, the limited resources, and the distributed nature of sensor networks bring new challenges for the mining techniques that need to be address. In this paper, we introduce a new formulation for the association rules, a well known data mining technique, that is able to generate the time relations between sensor devices in a particular sensor network. This new formulation will allow traditional data mining algorithms proposed to solve the classical association rules mining problem to be applied on sensor based class of applications that generate and use sensor data. The generated rules will give a clear picture about the correlations between sensors in the network and can be used to make decisions about the network performance, or it can be used to predict the sources of future events. In order to prepare for the data needed in the mining process and to maximize the network lifetime, a distributed extraction methodology is introduced, in this distributed methodology sensors perform optimization based on local computation to decide whether it will participate in sending data or not. Experimental results have shown that the distributed extraction solution is able to reduce the number of exchanged messages and the data size by 50% compared to a direct transmission of the data.
---
paper_title: Habitat monitoring with sensor networks

paper_content:

M ONITORING WITH S ENSOR N ETWORKS By Robert Szewczyk, Eric Osterweil, Joseph Polastre, Michael Hamilton, Alan Mainwaring, and Deborah Estrin These networks deliver to ecologists data on localized environmental conditions at the scale of individual organisms to help settle large-scale land-use issues affecting animals, plants, and people. istorically, the study of micro- climate and habitat utilization have been largely observa- tional, with climatic and behavioral variables being extrapolated from a few or even individual measurement sites. Today, densely deployed sensor networks are being scaled to the size of the organisms under study, sampling phenomena at fre- quencies the organisms encounter, and dispersed in patterns that capture the full range of environmen- tal exposures to provide the fine-grain information needed for accurate modeling and prediction. Ranging in size from tens to potentially thousands of nodes within a habitat patch, these networks are beginning to provide a view of often subtle changes in a given landscape at unprecedented spatial and temporal resolution. The technological challenges for developing and deploying them are daunting. They must be unobtrusive yet durable under a range of environmental stresses, including damage caused by the organisms themselves. They must be so energy efficient that they can remain in situ with little human interaction and be maintenance-free for years at a time. They must also reliably interconnect with a cyber infrastructure that permits frequent network H ABITAT H access for data upload, device programming, and management. Here, we survey the components of a complete habitat-monitoring system, from miniature data-collection sensor nodes to data-processing back- ends containing millions of observations, showing how they fit into a unified architecture, deriving our data and conclusions from several case studies (see the sidebar “Sensing the Natural Environment”). Few themes permeate basic and applied ecological research to such an extent as the relationship of microclimate and ecological patterns, processes, physiology, and biological diversity. Microclimate can be defined as the climate close to surfaces, upon and beneath soils, under snow, or in water, on living things (such as trees), or even on individual animals. Individuals may disperse across broad areas, but per- sistence, growth, and reproduction depend on the existence of narrow ranges of key environmental con- ditions that vary over narrow spatial gradients. For example, we see only the stand of trees that reached the right microclimate as seeds but not the tens of thousands of seeds that perished or simply failed to take root because they germinated in areas outside the range of their tolerance. Through their presence and activity, organisms alter their surroundings in important ways. Tree shape, physiology, and canopy structure can produce June 2004/Vol. 47, No. 6 COMMUNICATIONS OF THE ACM
---
paper_title: A Novel Algorithm for Mining Association Rules in Wireless Ad Hoc Sensor Networks

paper_content:

In this paper, we propose a comprehensive framework for mining wireless ad hoc sensor networks (WASNs), which is able to extract patterns regarding the sensors' behaviors. The main goal of determining behavioral patterns is to use them to generate rules that will improve the WASN's quality of service by participating in the resource management process or compensating for the undesired side effects of wireless communication. The proposed framework consists of 1) a formal definition of sensor behavioral patterns and sensor association rules, 2) a novel representation structure that we refer to as the positional lexicographic tree (PLT) that is able to compress the data gathered for the mining process and thus allows the fast and efficient mining of sensor behavioral patterns, and 3) a distributed data extraction mechanism to prepare the data required for mining sensor behavioral patterns. Several experimental studies have been conducted to evaluate our PLT structure and our proposed data extraction algorithms for mining wireless sensor networks.
---
paper_title: The Clustered AGgregation (CAG) technique leveraging spatial and temporal correlations in wireless sensor networks

paper_content:

Sensed data in Wireless Sensor Networks (WSN) reflect the spatial and temporal correlations of physical attributes existing intrinsically in the environment. In this article, we present the Clustered AGgregation (CAG) algorithm that forms clusters of nodes sensing similar values within a given threshold (spatial correlation), and these clusters remain unchanged as long as the sensor values stay within a threshold over time (temporal correlation). With CAG, only one sensor reading per cluster is transmitted whereas with Tiny AGgregation (TAG) all the nodes in the network transmit the sensor readings. Thus, CAG provides energy efficient and approximate aggregation results with small and often negligible and bounded error. ::: In this article we extend our initial work in CAG in five directions: First, we investigate the effectiveness of CAG that exploits the temporal as well as spatial correlations using both the measured and modeled data. Second, we design CAG for two modes of operation (interactive and streaming) to enable CAG to be used in different environments and for different purposes. Interactive mode provides mechanisms for one-shot queries, whereas the streaming mode provides those for continuous queries. Third, we propose a fixed range clustering method, which makes the performance of our system independent of the magnitude of the sensor readings and the network topology. Fourth, using mica2 motes, we perform a large-scale measurement of real environmental data (temperature and light, both indoor and outdoor) and the wireless radio reliability, which were used for both analytical modeling and simulation experiments. Fifth, we model the spatially correlated data using the properties of our real world measurements. ::: Our experimental results show that when we compute the average of sensor readings in the network using the CAG interactive mode with the user-provided error threshold of, 20%, we can save 68.25% of transmissions over TAG with only 2.46% inaccuracy in the result. The streaming mode of CAG can save even more transmissions (up to 70.24% in our experiments) over TAG, when data shows high spatial and temporal correlations. We expect these results to hold in reality, because we used the mica2 radio profile and empirical datasets for our simulation study. CAG is the first system that leverages spatial and temporal correlations to improve energy efficiency of in-network aggregation. This study analytically and empirically validates CAG's effectiveness.
---
paper_title: HEED: a hybrid, energy-efficient, distributed clustering approach for ad hoc sensor networks

paper_content:

Topology control in a sensor network balances load on sensor nodes and increases network scalability and lifetime. Clustering sensor nodes is an effective topology control approach. In this paper, we propose a novel distributed clustering approach for long-lived ad hoc sensor networks. Our proposed approach does not make any assumptions about the presence of infrastructure or about node capabilities, other than the availability of multiple power levels in sensor nodes. We present a protocol, HEED (Hybrid Energy-Efficient Distributed clustering), that periodically selects cluster heads according to a hybrid of the node residual energy and a secondary parameter, such as node proximity to its neighbors or node degree. HEED terminates in O(1) iterations, incurs low message overhead, and achieves fairly uniform cluster head distribution across the network. We prove that, with appropriate bounds on node density and intracluster and intercluster transmission ranges, HEED can asymptotically almost surely guarantee connectivity of clustered networks. Simulation results demonstrate that our proposed approach is effective in prolonging the network lifetime and supporting scalable data aggregation.
---
paper_title: Distributed consensus algorithms for SVM training in wireless sensor networks

paper_content:

This paper studies coordination and consensus mechanisms for Wireless sensor networks in order to train a Support Vector Machine (SVM) classifier in a distributed fashion. We propose two selective gossip algorithms, which take advantage of the sparse representation that SVMs provide for their decision boundary (hyperplane), in order to ensure convergence to an optimal or close-to-optimal classifier, while minimizing the required amount of information exchange between neighboring sensors. The first proposed algorithm calls for the local exchange of support vectors between sensors, while the second technique requires the exchange of all sample vectors that define uniquely and completely the convex hulls of the two classes. Through simulation experiments, we show that the proposed algorithms achieve a consensus close to the desired hyperplane obtained with a centralized SVM-based classifier that uses the entire sensor data.
---
paper_title: A Predictive Energy-Efficient Technique to Support Object-Tracking Sensor Networks

paper_content:

In recent years, we have witnessed an increasing interest in deploying wireless sensor networks (WSNs) for real-life applications. However, before WSNs become a commodity, several challenging issues remain to be resolved. Object-tracking sensor network (OTSN)-based applications are widely viewed as being among the most interesting applications of WSNs. OTSN is mainly used to track certain objects in a monitored area and to report their location to the application's users. However, OTSNs are well known for their energy consumption when compared with other WSN applications. In this paper, we propose a prediction-based tracking technique using sequential patterns (PTSPs) designed to achieve significant reductions in the energy dissipated by the OTSNs while maintaining acceptable missing rate levels. PTSP is tested against basic tracking techniques to determine the appropriateness of PTSP under various circumstances. Our experimental results have shown that PTSP outperforms all the other basic tracking techniques and exhibits significant amounts of savings in terms of the entire network's energy consumption.
---
paper_title: Distributed classification of acoustic targets in wireless audio-sensor networks

paper_content:

Target tracking is an important application for wireless sensor networks. One important aspect of tracking is target classification. Classification helps in selecting particular target(s) of interest. In this paper, we address the problem of classification of moving ground vehicles. The basis of classification are the audible signals produced by these vehicles. We present a distributed framework to classify vehicles based on features extracted from acoustic signals of vehicles. The main features used in our study are based on FFT (fast Fourier transform) and PSD (power spectral density). We propose three distributed algorithms for classification that are based on the k-nearest neighbor (k-NN) classification method. An experimental study has been conducted using real acoustic signals of different vehicles recorded in the city of Edmonton. We compare our proposed algorithms with a naive distributed implementation of the k-NN algorithm. Performance results reveal that our proposed algorithms are energy efficient, and thus suitable for sensor network deployment.
---
paper_title: Attribute-based clustering for information dissemination in wireless sensor networks

paper_content:

Data routing in large wireless sensor networks is challenged by requirements for scalability, robustness, and energy use, as well as the possibility that globally unique identifiers for each node are nonexistent. Moreover, if the sensor network serves multiple functions or users, its routing function must be energy efficient when responding to information requests (inquiries) that can arrive at high rates, require diverse data types, and target subsets of the sensor network. We propose virtual "containment" hierarchies based on relevant attributes as a more efficient mechanism to disseminate inquiries rather than theuse of flooding schemes. We show algorithms that support clustering sensors according to these hierarchies and implement clusterhead failure recovery and load balancing among cluster members. We show that our framework has significant bandwidth gains over a flooding-based scheme under the scenario considered using analytical techniques.
---
paper_title: TRAINING A SVM-BASED CLASSIFIER IN DISTRIBUTED SENSOR NETWORKS

paper_content:

The emergence of smart low-power devices (motes), which have micro-sensing, on-board processing, and wireless communication capabilities, has impelled research in distributed and on-line learning under communication constraints. In this paper, we show how to perform a classification task in a wireless sensor network using distributed algorithms for Support Vector Machines (SVMs), taking advantage of the sparse representation that SVMs provide for the decision boundaries. We present two energy-efficient algorithms that involve a distributed incremental learning for the training of a SVM in a wireless sensor network, both for stationary and non-stationary sample data (concept drift). Through analytical studies and simulation experiments, we show that the two proposed algorithms exhibit similar performance to the traditional centralized SVM training methods, while being much more efficient in terms of energy cost.
---
paper_title: Distributed, hierarchical clustering and summarization in sensor networks

paper_content:

We propose DHCS, a method of distributed, hierarchical clustering and summarization for online data analysis and mining in sensor networks. Different from the acquisition and aggregation of raw sensory data, our method clusters sensor nodes based on their current data values as well as their geographical proximity, and computes a summary for each cluster. Furthermore, these clusters, together with their summaries, are produced in a distributed, bottom-up manner. The resulting hierarchy of clusters and their summaries facilitates interactive data exploration at multiple resolutions. It can also be used to improve the efficiency of data-centric routing and query processing in sensor networks. Our simulation results on real world data sets as well as synthetic data sets show the effectiveness and efficiency of our approach.
---
paper_title: Quarter Sphere Based Distributed Anomaly Detection in Wireless Sensor Networks

paper_content:

Anomaly detection is an important challenge for tasks such as fault diagnosis and intrusion detection in energy constrained wireless sensor networks. A key problem is how to minimise the communication overhead in the network while performing in-network computation when detecting anomalies. Our approach to this problem is based on a formulation that uses distributed, one-class quarter-sphere support vector machines to identify anomalous measurements in the data. We demonstrate using sensor data from the Great Duck Island Project that our distributed approach is energy efficient in terms of communication overhead while achieving comparable accuracy to a centralised scheme.
---
paper_title: The PSP Approach for Mining Sequential Patterns

paper_content:

In this paper, we present an approach, called PSP, for mining sequential patterns embedded in a database. Close to the problem of discovering association rules, mining sequential patterns requires handling time constraints. Originally introduced in [3], the issue is addressed by the GSP approach [10]. Our proposal resumes the general principles of GSP but it makes use of a different intermediary data structure which is proved to be more efficient than in GSP.
---
paper_title: PrefixSpan,: mining sequential patterns efficiently by prefix-projected pattern growth

paper_content:

Sequential pattern mining is an important data mining problem with broad applications. It is challenging since one may need to examine a combinatorially explosive number of possible subsequence patterns. Most of the previously developed sequential pattern mining methods follow the methodology of A priori which may substantially reduce the number of combinations to be examined. Howeve6 Apriori still encounters problems when a sequence database is large andor when sequential patterns to be mined are numerous ano we propose a novel sequential pattern mining method, called Prefixspan (i.e., Prefix-projected - Ettern_ mining), which explores prejxprojection in sequential pattern mining. Prefixspan mines the complete set of patterns but greatly reduces the efforts of candidate subsequence generation. Moreover; prefi-projection substantially reduces the size of projected databases and leads to efJicient processing. Our performance study shows that Prefixspan outperforms both the Apriori-based GSP algorithm and another recently proposed method; Frees pan, in mining large sequence data bases.
---
paper_title: Mining sequential patterns

paper_content:

We are given a large database of customer transactions, where each transaction consists of customer-id, transaction time, and the items bought in the transaction. We introduce the problem of mining sequential patterns over such databases. We present three algorithms to solve this problem, and empirically evaluate their performance using synthetic data. Two of the proposed algorithms, AprioriSome and AprioriAll, have comparable performance, albeit AprioriSome performs a little better when the minimum number of customers that must support a sequential pattern is low. Scale-up experiments show that both AprioriSome and AprioriAll scale linearly with the number of customer transactions. They also have excellent scale-up properties with respect to the number of transactions per customer and the number of items in a transaction. >
---
paper_title: The PSP Approach for Mining Sequential Patterns

paper_content:

In this paper, we present an approach, called PSP, for mining sequential patterns embedded in a database. Close to the problem of discovering association rules, mining sequential patterns requires handling time constraints. Originally introduced in [3], the issue is addressed by the GSP approach [10]. Our proposal resumes the general principles of GSP but it makes use of a different intermediary data structure which is proved to be more efficient than in GSP.
---
paper_title: A Relational Approach to Sensor Network Data Mining

paper_content:

In this chapter a relational framework able to model and analyse the data observed by nodes involved in a sensor network is presented. In particular, we propose a powerful and expressive description language able to represent the spatio-temporal relations appearing in sensor network data along with the environmental information. Furthermore, a general purpose system able to elicit hidden frequent temporal correlations between sensor nodes is presented. The framework has been extended in order to take into account interval-based temporal data by introducing some operators based on a temporal interval logic. A preliminary abstraction step with the aim of segmenting and labelling the real-valued time series into similar subsequences is performed exploiting a kernel density estimation approach. The prposed framework has been evaluated on real world data collected from a wireless sensor network.
---
paper_title: SO MAD: SensOr Mining for Anomaly Detection in railway data

paper_content:

Today, many industrial companies must face problems raised by maintenance. In particular, the anomaly detection problem is probably one of the most challenging. In this paper we focus on the railway maintenance task and propose to automatically detect anomalies in order to predict in advance potential failures. We first address the problem of characterizing normal behavior. In order to extract interesting patterns, we have developed a method to take into account the contextual criteria associated to railway data (itinerary, weather conditions, etc.). We then measure the compliance of new data, according to extracted knowledge, and provide information about the seriousness and possible causes of a detected anomaly.
---
paper_title: Multi-Dimensional Relational Sequence Mining

paper_content:

The issue addressed in this paper concerns the discovery of frequent multi-dimensional patterns from relational sequences. The great variety of applications of sequential pattern mining, such as user profiling, medicine, local weather forecast and bioinformatics, makes this problem one of the central topics in data mining. Nevertheless, sequential information may concern data on multiple dimensions and, hence, the mining of sequential patterns from multi-dimensional information results very important. In a multi-dimensional sequence each event depends on more than one dimension, such as in spatio-temporal sequences where an event may be spatially or temporally related to other events. In literature, the multi-relational data mining approach has been successfully applied to knowledge discovery fromcomplex data. However, there exists no contribution to manage the general case of multi-dimensional data in which, for example, spatial and temporal information may co-exist. This work takes into account the possibility to mine complex patterns, expressed in a first-order language, in which events may occur along different dimensions. Specifically, multidimensional patterns are defined as a set of atomic first-order formulae in which events are explicitly represented by a variable and the relations between events are represented by a set of dimensional predicates. A complete framework and an Inductive Logic Programming algorithm to tackle this problem are presented along with some experiments on artificial and real multi-dimensional sequences proving its effectiveness.
---
paper_title: Mining Sequential Alarm Patterns in a Telecommunication Database

paper_content:

A telecommunication system produces daily a large amount of alarm data which contains hidden valuable information about the system behavior. The knowledge discovered from alarm data can be used in finding problems in networks and possibly in predicting severe faults. In this paper, we devise a solution procedure for mining sequential alarm patterns from the alarm data of a GSM system. First, by observing the features of tile alarm data, we develop operations for data cleaning. Then, we transform the alarm data into a set of alarm sequences. Note that the consecutive alarm events exist in the alarm sequences, and it is complicated to count the occurrence counts of events and extract patterns. Hence, we devise a new procedure to determine the occurrence count of the sequential alarm patterns in accordance with the nature of alarms. By utilizing time constraints to restrict tile time difference between two alarm events, we devise a mining algorithm to discover useful sequential alarm patterns. The proposed mining algorithm is implemented and applied to test against a set of real alarm data provided by a cellular phone company. The quality of knowledge discovered is evaluated. The experimental results show that the proposed operations of data cleaning are able to improve tile execution of our mining algorithm significantly and tile knowledge obtained from the alarm data is very useful from tile perspective of network operators for alarm prediction and alarm control.
---
paper_title: MavHome: an agent-based smart home

paper_content:

The goal of the MavHome (Managing An Intelligent Versatile Home) project is to create a home that acts as an intelligent agent. In this paper we introduce the MavHome architecture. The role of prediction algorithms within the architecture is discussed, and a meta-predictor is presented which combines the strengths of multiple approaches to inhabitant action prediction. We demonstrate the effectiveness of these algorithms on smart home data.
---
paper_title: WSN16-5: Distributed Formation of Overlapping Multi-hop Clusters in Wireless Sensor Networks

paper_content:

Clustering is a standard approach for achieving efficient and scalable performance in wireless sensor networks. Most of the published clustering algorithms strive to generate the minimum number of disjoint clusters. However, we argue that guaranteeing some degree of overlap among clusters can facilitate many applications, like inter-cluster routing, topology discovery and node localization, recovery from cluster head failure, etc. We formulate the overlapping multi-hop clustering problem as an extension to the k-dominating set problem. Then we propose MOCA; a randomized distributed multi-hop clustering algorithm for organizing the sensors into overlapping clusters. We validate MOCA in a simulated environment and analyze the effect of different parameters, e.g. node density and network connectivity, on its performance. The simulation results demonstrate that MOCA is scalable, introduces low overhead and produces approximately equal-sized clusters.
---
paper_title: An energy efficient hierarchical clustering algorithm for wireless sensor networks

paper_content:

A wireless network consisting of a large number of small sensors with low-power transceivers can be an effective tool for gathering data in a variety of environments. The data collected by each sensor is communicated through the network to a single processing center that uses all reported data to determine characteristics of the environment or detect an event. The communication or message passing process must be designed to conserve the limited energy resources of the sensors. Clustering sensors into groups, so that sensors communicate information only to clusterheads and then the clusterheads communicate the aggregated information to the processing center, may save energy. In this paper, we propose a distributed, randomized clustering algorithm to organize the sensors in a wireless sensor network into clusters. We then extend this algorithm to generate a hierarchy of clusterheads and observe that the energy savings increase with the number of levels in the hierarchy. Results in stochastic geometry are used to derive solutions for the values of parameters of our algorithm that minimize the total energy spent in the network when all sensors report data through the clusterheads to the processing center.
---
paper_title: ACE: An Emergent Algorithm for Highly Uniform Cluster Formation

paper_content:

The efficient subdivision of a sensor network into uniform, mostly non-overlapping clusters of physically close nodes is an important building block in the design of efficient upper layer network functions such as routing, broadcast, data aggregation, and query processing.
---
paper_title: A probabilistic clustering algorithm in wireless sensor networks

paper_content:

A wireless sensor network consists of nodes that can communicate with each other via wireless links. One way to support efficient communication between sensors is to organize the network into several groups, called clusters, with each cluster electing one node as the head of cluster. The paper describes a constant time clustering algorithm that can be applied on wireless sensor networks. This approach is an extension to the Younis and Fahmy method (1). The simulation results show that the extension can generate a small number of cluster heads in relatively few rounds, especially in sparse networks.
---
paper_title: A self-reorganizing slot allocation protocol for multi-cluster sensor networks

paper_content:

This paper presents a self-reorganizing slot allocation (SRSA) mechanism for TDMA based medium access control (MAC) in wireless sensor networks. With TDMA, a node can achieve significant energy savings by remaining active only during allocated slots for transmissions and receptions. In multi-cluster networks, it is often necessary for nodes to use either CDMA or FDMA for preventing interference across neighbor clusters. The goal of this paper is to provide an alternative design that can reduce inter-cluster TDMA interference without having to use spectrum expensive CDMA or FDMA. The primary contribution of this paper is to demonstrate that with adaptive slot allocation, it is possible to reduce such interference under low loading conditions, which is often the case for sensor networks with monitoring applications. The second contribution is to design a feedback based adaptive allocation reorganization protocol that can significantly reduce those interferences without relying on any global synchronization mechanisms. We present the design of SRSA and provide a simulation based characterization of the protocol in comparison with TDMA-over-CDMA, TDMA with random slot allocation and CSMA MAC protocols. The results indicate that with moderate cluster overlapping and low traffic, SRSA can significantly reduce inter-cluster TDMA interference while delivering TDMA-over-CDMA like energy efficiency, at the cost of higher delivery latency compared to TDMA-over-CDMA. Assuming its low complexity and narrow-band operation unlike TDMA-over-CDMA, SRSA can be an ideal sensor MAC protocol for applications that can tolerate relatively larger delivery latency but not frequent packet drops.
---
paper_title: A Communication-Efficient Distributed Clustering Algorithm for Sensor Networks

paper_content:

Sensor networks usually generate continuous stream of data over time. Clustering sensor data as a core task of mining sensor data plays an essential role in analytical applications of sensor networks. Although several algorithms have been proposed to address the problem of distributed clustering, in the domain of sensor networks these algorithms face major new challenges such as limited communication bandwidth and constraints in power supply, and storage resources. Moreover, previous studies about clustering in sensor networks have mostly focused on clustering sensor nodes and designing better network topology for the purpose of energy conservation rather than clustering sensor data for future analytical purposes. In this paper a communication efficient distributed algorithm is proposed for clustering sensory data. This approach addresses the limited bandwidth issue through summarized transmissions. Furthermore communication efficiency of the algorithm contributes to reduced power consumption. Time efficiency of the algorithm is evaluated through simulation experiments and the results are presented.
---
paper_title: Mining clustering algorithm in wireless sensor networks

paper_content:

One important critical issue in wireless sensor networks is how to gather sensed information in an energy efficient way since the energy is a scarce resource in a sensor node. Clustering technique has been proven to be an effective approach for data-gathering in wireless sensor networks. However, these data are characteristic of being heavily noisy, exhibiting temporal and spatial correlation. Data mining is a process in which a wide spectrum of data analysis methods is used. In order to extract useful information from such data, in this paper, we propose a novel cluster formation algorithm, which is called ACE-C algorithm according to mining sensor nodes. Compared to the ILP algorithm, the proposed algorithm increase the cluster head election mechanism, and the simulation results show that ACE-CILP algorithm achieves its intention of consuming less energy, equalizing the energy consumption of all the nodes, as well as extending the network lifetime perfectly.
---
paper_title: Using Clustering Information for Sensor Network Localization

paper_content:

Sensor network localization continues to be an important research challenge. The goal of localization is to assign geographic coordinates to each node in the sensor network. Localization schemes for sensor network systems should work with inexpensive off-the-shelf hardware, scale to large networks, and also achieve good accuracy in the presence of irregularities and obstacles in the deployment area. ::: ::: We present a novel approach for localization that can satisfy all of these desired properties. Recent developments in sensor network clustering algorithms have resulted in distributed algorithms that produce highly regular clusters. We propose to make use of this regularity to inform our localization algorithm. The main advantages of our approach are that our protocol requires only three randomly-placed nodes that know their geographic coordinates, and does not require any ranging or positioning equipment (i.e., no signal strength measurement, ultrasound ranging, or directional antennas are needed). So far, only the DV-Hop localization mechanism worked with the same assumptions [1]. We show that our proposed approach may outperform DV-Hop in certain scenarios, in particular when there exist large obstacles in the deployment field, or when the deployment area is free of obstacles but the number of anchors is limited.
---
paper_title: On energy provisioning and relay node placement for wireless sensor networks

paper_content:

Wireless sensor networks that operate on batteries have limited network lifetime. There have been extensive recent research efforts on how to design protocols and algorithms to prolong network lifetime. However, due to energy constraint, even under the most efficient protocols and algorithms, the network lifetime may still be unable to meet the mission's requirements. In this paper, we consider the energy provisioning (EP) problem for a two-tiered wireless sensor network. In addition to provisioning additional energy on the existing nodes, we also consider deploying relay nodes (RNs) into the network to mitigate network geometric deficiencies and prolong network lifetime. We formulate the joint problem of EP and RN placement (EP-RNP) into a mixed-integer nonlinear programming (MINLP) problem. Since an MINLP problem is NP-hard in general, and even state-of-the-art software and techniques are unable to offer satisfactory solutions, we develop a heuristic algorithm, called Smart Pairing and INtelligent Disc Search (SPINDS), to address this problem. We show a number of novel algorithmic design techniques in the design of SPINDS that effectively transform a complex MINLP problem into a linear programming (LP) problem without losing critical points in its search space. Through numerical results, we show that SPINDS offers a very attractive solution and some important insights to the EP-RNP problem.
---
paper_title: HEED: a hybrid, energy-efficient, distributed clustering approach for ad hoc sensor networks

paper_content:

Topology control in a sensor network balances load on sensor nodes and increases network scalability and lifetime. Clustering sensor nodes is an effective topology control approach. In this paper, we propose a novel distributed clustering approach for long-lived ad hoc sensor networks. Our proposed approach does not make any assumptions about the presence of infrastructure or about node capabilities, other than the availability of multiple power levels in sensor nodes. We present a protocol, HEED (Hybrid Energy-Efficient Distributed clustering), that periodically selects cluster heads according to a hybrid of the node residual energy and a secondary parameter, such as node proximity to its neighbors or node degree. HEED terminates in O(1) iterations, incurs low message overhead, and achieves fairly uniform cluster head distribution across the network. We prove that, with appropriate bounds on node density and intracluster and intercluster transmission ranges, HEED can asymptotically almost surely guarantee connectivity of clustered networks. Simulation results demonstrate that our proposed approach is effective in prolonging the network lifetime and supporting scalable data aggregation.
---
paper_title: Load-balanced clustering of wireless sensor networks

paper_content:

Wireless sensor networks have potential to monitor environments for both military and civil applications. Due to inhospitable conditions these sensors are not always deployed uniformly ion the area of interest. Since sensors are generally constrained in on-board energy supply, efficient management of the network is crucial to extend the life of the sensors. Sensors' energy cannot support long haul communication to reach a remote command site and thus requires many levels of hops or a gateway to forward the data on behalf of the sensor. In this paper, we propose an algorithm to network these sensors in to well define clusters with less energy-constrained gateway nodes acting as cluster-heads, and balance load among these gateways. Simulation results show how our approach can balance the load and improve the lifetime of the system.
---
paper_title: Optimal Energy Aware Clustering in Sensor Networks

paper_content:

Sensor networks is among the fastest growing technologies that have the potential of changing our lives drastically. These collaborative, dynamic and distributed computing and communicating systems will be self organizing. They will have capabilities of distributing a task among themselves for efficient computation. There are many challenges in implementation of such systems: energy dissipation and clustering being one of them. In order to maintain a certain degree of service quality and a reasonable system lifetime, energy needs to be optimized at every stage of system operation. Sensor node clustering is another very important optimization problem. Nodes that are clustered together will easily be able to communicate with each other. Considering energy as an optimization parameter while clustering is imperative. In this paper we study the theoretical aspects of the clustering problem in sensor networks with application to energy optimization. We illustrate an optimal algorithm for clustering the sensor nodes such that each cluster (which has a master) is balanced and the total distance between sensor nodes and master nodes is minimized. Balancing the clusters is needed for evenly distributing the load on all master nodes. Minimizing the total distance helps in reducing the communication overhead and hence the energy dissipation. This problem (which we call balanced k-clustering) is modeled as a mincost flow problem which can be solved optimally using existing techniques.
---
paper_title: Energy-efficient communication protocol for wireless microsensor networks

paper_content:

Wireless distributed microsensor systems will enable the reliable monitoring of a variety of environments for both civil and military applications. In this paper, we look at communication protocols, which can have significant impact on the overall energy dissipation of these networks. Based on our findings that the conventional protocols of direct transmission, minimum-transmission-energy, multi-hop routing, and static clustering may not be optimal for sensor networks, we propose LEACH (Low-Energy Adaptive Clustering Hierarchy), a clustering-based protocol that utilizes randomized rotation of local cluster based station (cluster-heads) to evenly distribute the energy load among the sensors in the network. LEACH uses localized coordination to enable scalability and robustness for dynamic networks, and incorporates data fusion into the routing protocol to reduce the amount of information that must be transmitted to the base station. Simulations show the LEACH can achieve as much as a factor of 8 reduction in energy dissipation compared with conventional outing protocols. In addition, LEACH is able to distribute energy dissipation evenly throughout the sensors, doubling the useful system lifetime for the networks we simulated.
---
paper_title: An efficient clustering-based heuristic for data gathering and aggregation in sensor networks

paper_content:

The rapid advances in processor, memory, and radio technology have enabled the development of distributed networks of small, inexpensive nodes that are capable of sensing, computation, and wireless communication. Sensor networks of the future are envisioned to revolutionize the paradigm of collecting and processing information in diverse environments. However, the severe energy constraints and limited computing resources of the sensors, present major challenges for such a vision to become a reality. We consider a network of energy-constrained sensors that are deployed over a region. Each sensor periodically produces information as it monitors its vicinity. The basic operation in such a network is the systematic gathering and transmission of sensed data gathering and transmission of sensed data to a base station for further processing. During data gathering, sensors have the ability to perform in-network aggregation (fusion) of data packets enroute to the base station. The lifetime of such a sensor system is the time during which we can gather information from all the sensors to the base station. A key challenge in data gathering is to maximize the system lifetime, given the energy constraints of the sensors. Given the location of sensors and the base station and the available energy at each sensor, we are interested in finding an efficient manner in which data should be collected from all the sensors and transmitted to the base station, such that the system lifetime is maximized. This is the maximum lifetime data-gathering problem. In this paper, we describe a heuristic to solve the data-gathering problem with aggregation in sensor networks. Our experimental results demonstrate that the proposed algorithm significantly outperform previous methods, in terms of system lifetime.
---
paper_title: Distributed Energy-Efficient Hierarchical Clustering for Wireless Sensor Networks

paper_content:

Since nodes in a sensor network have limited energy, prolonging the network lifetime and improving scalability become important. In this paper, we propose a distributed weight-based energy-efficient hierarchical clustering protocol (DWEHC). Each node first locates its neighbors (in its enclosure region), then calculates its weight which is based on its residual energy and distance to its neighbors. The largest weight node in a neighborhood may become a clusterhead. Neighboring nodes will then join the clusterhead hierarchy. The clustering process terminates in O(1) iterations, and does not depend on network topology or size. Simulations show that DWEHC clusters have good performance characteristics.
---
paper_title: Energy-aware management for cluster-based sensor networks

paper_content:

Networking unattended sensors is expected to have a significant impact on the efficiency of many military and civil applications. Sensors in such systems are typically disposable and expected to last until their energy drains. Therefore, energy is a very scarce resource for such sensor systems and has to be managed wisely in order to extend the life of the sensors for the duration of a particular mission. In this paper, we present a novel approach for energy-aware management of sensor networks that maximizes the lifetime of the sensors while achieving acceptable performance for sensed data delivery. The approach is to dynamically set routes and arbitrate medium access in order to minimize energy consumption and maximize sensor life. The approach calls for network clustering and assigns a less-energy-constrained gateway node that acts as a cluster manager. Based on energy usage at every sensor node and changes in the mission and the environment, the gateway sets routes for sensor data, monitors latency throughout the cluster, and arbitrates medium access among sensors. We also describe a time-based medium access control (MAC) protocol and discuss algorithms for assigning time slots for the communicating sensor nodes. Simulation results show an order of magnitude enhancement in the time to network partitioning, 11% enhancement in network lifetime predictability, and 14% enhancement in average energy consumed per packet.
---
paper_title: Nearest Neighbour Classification for Trajectory Data

paper_content:

Trajectory data mining is an emerging area of research, having a large variety of applications. This paper proposes a nearest neighbour based trajectory data as two-step process. Extensive experiments were conducted using real datasets of moving vehicles in Milan (Italy). In our method first, we build a classifier from the pre-processed 03 days training trajectory data and then we classify 04 days test trajectory data using class label. The resultant figure shows the our experimental investigation yields output as classified test trajectories, significant in terms of correctly classified success rate being 98.2. To measure the agreement between predicted and observed categorization of the dataset is carried out using Kappa statistics.
---
paper_title: A New Algorithm Based On Sequential Pattern Mining For Person Identification In Ubiquitous Environments

paper_content:

This paper presents an approach to person identification in ubiquitous environments. Our approach uses the sequential pattern mining principle to extract frequent patterns in data collected from the different sensors disseminated in the ubiquitous environment. In contrast with existing, intrusive, person identification algorithms that have been proposed in the literature, where the data is basically composed of audiovisual or image files recorded during experiments, our approach is fully non-intrusive and is based on event sequences collected from heterogeneous sensors. Our approach is divided into three main phases: (1) frequent pattern mining, (2) assignment of weights to extracted patterns, and (3) classification. Experiments using data collected in the Domus and Testbed smart homes demonstrate that our approach accurately identifies persons and improves classification results, outperforming two of the approaches reported in the literature.
---
paper_title: Nearest Neighbour Classification for Wireless Sensor Network Data

paper_content:

Advances in wireless technologies have led to the development of sensor nodes that are capable of sensing, processing, and transmitting. They collect large amounts of sensor data in a highly decentralized manner. Classification is an important task in data mining. In this paper a Nearest Neighbour Classification technique is used to classify the Wireless Sensor Network data. Our experimental investigation yields a significant output in terms of the correctly classified success rate being 92.3%.
---
paper_title: Data mining: practical machine learning tools and techniques with Java implementations

paper_content:

1. What's It All About? 2. Input: Concepts, Instances, Attributes 3. Output: Knowledge Representation 4. Algorithms: The Basic Methods 5. Credibility: Evaluating What's Been Learned 6. Implementations: Real Machine Learning Schemes 7. Moving On: Engineering The Input And Output 8. Nuts And Bolts: Machine Learning Algorithms In Java 9. Looking Forward
---
paper_title: Mining sequential patterns

paper_content:

We are given a large database of customer transactions, where each transaction consists of customer-id, transaction time, and the items bought in the transaction. We introduce the problem of mining sequential patterns over such databases. We present three algorithms to solve this problem, and empirically evaluate their performance using synthetic data. Two of the proposed algorithms, AprioriSome and AprioriAll, have comparable performance, albeit AprioriSome performs a little better when the minimum number of customers that must support a sequential pattern is low. Scale-up experiments show that both AprioriSome and AprioriAll scale linearly with the number of customer transactions. They also have excellent scale-up properties with respect to the number of transactions per customer and the number of items in a transaction. >
---
paper_title: A survey on clustering algorithms for wireless sensor networks

paper_content:

The past few years have witnessed increased interest in the potential use of wireless sensor networks (WSNs) in applications such as disaster management, combat field reconnaissance, border protection and security surveillance. Sensors in these applications are expected to be remotely deployed in large numbers and to operate autonomously in unattended environments. To support scalability, nodes are often grouped into disjoint and mostly non-overlapping clusters. In this paper, we present a taxonomy and general classification of published clustering schemes. We survey different clustering algorithms for WSNs; highlighting their objectives, features, complexity, etc. We also compare of these clustering algorithms based on metrics such as convergence rate, cluster stability, cluster overlapping, location-awareness and support for node mobility.
---
paper_title: A Novel Algorithm for Mining Association Rules in Wireless Ad Hoc Sensor Networks

paper_content:

In this paper, we propose a comprehensive framework for mining wireless ad hoc sensor networks (WASNs), which is able to extract patterns regarding the sensors' behaviors. The main goal of determining behavioral patterns is to use them to generate rules that will improve the WASN's quality of service by participating in the resource management process or compensating for the undesired side effects of wireless communication. The proposed framework consists of 1) a formal definition of sensor behavioral patterns and sensor association rules, 2) a novel representation structure that we refer to as the positional lexicographic tree (PLT) that is able to compress the data gathered for the mining process and thus allows the fast and efficient mining of sensor behavioral patterns, and 3) a distributed data extraction mechanism to prepare the data required for mining sensor behavioral patterns. Several experimental studies have been conducted to evaluate our PLT structure and our proposed data extraction algorithms for mining wireless sensor networks.
---
paper_title: Using association rules for energy conservation in wireless sensor networks

paper_content:

Frequent radio transmissions among sensors, or from sensors to the basestation, have always been a major energy drain. One of the approaches to reduce the data transmitted to the basestation is to shift the bulk of data processing to networked sensor nodes; for instance, sensors to send only data aggregates to reduce the overall amount of data exchanged. Sensor nodes, however, are quite limited in terms of their energy and processing power, and as such, traditional centralised data mining algorithms are infeasible to be directly implemented on sensors. In this paper, we modify APRIORI to find strong rules from sensor readings in a sensor network and using these rules, autonomously control sensor network operations or supplement sensor operations with a rule knowledge base. For example, triggers activated from the rules could be used to sleep sensors or reduce data transmissions to conserve sensor energy. Our work here includes a detailed implementation of a lightweight rule learning algorithm for a resource-constrainted sensor network, with simulation results for a group node setup running the algorithm.
---
paper_title: A survey on clustering algorithms for wireless sensor networks

paper_content:

The past few years have witnessed increased interest in the potential use of wireless sensor networks (WSNs) in applications such as disaster management, combat field reconnaissance, border protection and security surveillance. Sensors in these applications are expected to be remotely deployed in large numbers and to operate autonomously in unattended environments. To support scalability, nodes are often grouped into disjoint and mostly non-overlapping clusters. In this paper, we present a taxonomy and general classification of published clustering schemes. We survey different clustering algorithms for WSNs; highlighting their objectives, features, complexity, etc. We also compare of these clustering algorithms based on metrics such as convergence rate, cluster stability, cluster overlapping, location-awareness and support for node mobility.
---
paper_title: Energy efficient strategies for object tracking in sensor networks: A data mining approach

paper_content:

In recent years, a number of studies have been done on object tracking sensor networks (OTSNs) due to the wide applications. One important research issue in OTSNs is the energy saving strategy in considering the limited power of sensor nodes. The past studies on energy saving in OTSNs considered the object's movement behavior as randomness. In some real applications, however, the object movement behavior is often based on certain underlying events instead of randomness completely. In this paper, we propose a novel data mining algorithm named TMP-Mine with a special data structure named TMP-Tree for efficiently discovering the temporal movement patterns of objects in sensor networks. To our best knowledge, this is the first work on mining the movement patterns associated with time intervals in OTSNs. Moreover, we propose novel location prediction strategies that utilize the discovered temporal movement patterns so as to reduce the prediction errors for energy savings. Through empirical evaluation on various simulation conditions and real dataset, TMP-Mine and the proposed prediction strategies are shown to deliver excellent performance in terms of scalability, accuracy and energy efficiency.
---
paper_title: Online Algorithms for Mining Inter-Stream Associations from Large Sensor Networks

paper_content:

We study the problem of mining frequent value sets from a large sensor network. We discuss how sensor stream data could be represented that facilitates efficient online mining and propose the interval-list representation. Based on Lossy Counting, we propose ILB, an interval-list-based online mining algorithm for discovering frequent sensor value sets. Through extensive experiments, we compare the performance of ILB against an application of Lossy Counting (LC) using a weighted transformation method. Results show that ILB outperforms LC significantly for large sensor networks.
---
paper_title: Energy-efficient real-time object tracking in multi-level sensor networks by mining and predicting movement patterns

paper_content:

A number of studies have been written on sensor networks in the past few years due to their wide range of potential applications. Object tracking is an important topic in sensor networks; and the limited power of sensor nodes presents numerous challenges to researchers. Previous studies of energy conservation in sensor networks have considered object movement behavior to be random. However, in some applications, the movement behavior of an object is often based on certain underlying events instead of randomness completely. Moreover, few studies have considered the real-time issue in addition to the energy saving problem for object tracking in sensor networks. In this paper, we propose a novel strategy named multi-level object tracking strategy (MLOT) for energy-efficient and real-time tracking of the moving objects in sensor networks by mining the movement log. In MLOT, we first conduct hierarchical clustering to form a hierarchical model of the sensor nodes. Second, the movement logs of the moving objects are analyzed by a data mining algorithm to obtain the movement patterns, which are then used to predict the next position of a moving object. We use the multi-level structure to represent the hierarchical relations among sensor nodes so as to achieve the goal of keeping track of moving objects in a real-time manner. Through experimental evaluation of various simulated conditions, the proposed method is shown to deliver excellent performance in terms of both energy efficiency and timeliness.
---
paper_title: Real time clustering of sensory data in wireless sensor networks

paper_content:

Data mining in wireless sensor networks (WSNs) is a new emerging research area. This paper investigates the problem of real time clustering of sensory data in WSNs. The objective is to cluster the data collected by sensor nodes in real time according to data similarity in a d-dimensional sensory data space. To perform in-network data clustering efficiently, a Hilbert Curves based mapping algorithm, HilbertMap, is proposed to convert a d-dimensional sensory data space into a two-dimensional area covered by a sensor network. Based on this mapping, a distributed algorithm for clustering sensory data, H-Cluster, is proposed. It guarantees that the communications for sensory data clustering mostly occur among geographically nearby sensor nodes and sensory data clustering is accomplished in in-network manner. Extensive simulation experiments were conducted using both real-world datasets and synthetic datasets to evaluate the algorithms. H-Cluster consistently achieves the lowest data loss rate, the highest energy efficiency, and the best clustering quality.
---
paper_title: A New Algorithm Based On Sequential Pattern Mining For Person Identification In Ubiquitous Environments

paper_content:

This paper presents an approach to person identification in ubiquitous environments. Our approach uses the sequential pattern mining principle to extract frequent patterns in data collected from the different sensors disseminated in the ubiquitous environment. In contrast with existing, intrusive, person identification algorithms that have been proposed in the literature, where the data is basically composed of audiovisual or image files recorded during experiments, our approach is fully non-intrusive and is based on event sequences collected from heterogeneous sensors. Our approach is divided into three main phases: (1) frequent pattern mining, (2) assignment of weights to extracted patterns, and (3) classification. Experiments using data collected in the Domus and Testbed smart homes demonstrate that our approach accurately identifies persons and improves classification results, outperforming two of the approaches reported in the literature.
---
paper_title: A Communication-Efficient Distributed Clustering Algorithm for Sensor Networks

paper_content:

Sensor networks usually generate continuous stream of data over time. Clustering sensor data as a core task of mining sensor data plays an essential role in analytical applications of sensor networks. Although several algorithms have been proposed to address the problem of distributed clustering, in the domain of sensor networks these algorithms face major new challenges such as limited communication bandwidth and constraints in power supply, and storage resources. Moreover, previous studies about clustering in sensor networks have mostly focused on clustering sensor nodes and designing better network topology for the purpose of energy conservation rather than clustering sensor data for future analytical purposes. In this paper a communication efficient distributed algorithm is proposed for clustering sensory data. This approach addresses the limited bandwidth issue through summarized transmissions. Furthermore communication efficiency of the algorithm contributes to reduced power consumption. Time efficiency of the algorithm is evaluated through simulation experiments and the results are presented.
---
paper_title: Habitat monitoring with sensor networks

paper_content:

M ONITORING WITH S ENSOR N ETWORKS By Robert Szewczyk, Eric Osterweil, Joseph Polastre, Michael Hamilton, Alan Mainwaring, and Deborah Estrin These networks deliver to ecologists data on localized environmental conditions at the scale of individual organisms to help settle large-scale land-use issues affecting animals, plants, and people. istorically, the study of micro- climate and habitat utilization have been largely observa- tional, with climatic and behavioral variables being extrapolated from a few or even individual measurement sites. Today, densely deployed sensor networks are being scaled to the size of the organisms under study, sampling phenomena at fre- quencies the organisms encounter, and dispersed in patterns that capture the full range of environmen- tal exposures to provide the fine-grain information needed for accurate modeling and prediction. Ranging in size from tens to potentially thousands of nodes within a habitat patch, these networks are beginning to provide a view of often subtle changes in a given landscape at unprecedented spatial and temporal resolution. The technological challenges for developing and deploying them are daunting. They must be unobtrusive yet durable under a range of environmental stresses, including damage caused by the organisms themselves. They must be so energy efficient that they can remain in situ with little human interaction and be maintenance-free for years at a time. They must also reliably interconnect with a cyber infrastructure that permits frequent network H ABITAT H access for data upload, device programming, and management. Here, we survey the components of a complete habitat-monitoring system, from miniature data-collection sensor nodes to data-processing back- ends containing millions of observations, showing how they fit into a unified architecture, deriving our data and conclusions from several case studies (see the sidebar “Sensing the Natural Environment”). Few themes permeate basic and applied ecological research to such an extent as the relationship of microclimate and ecological patterns, processes, physiology, and biological diversity. Microclimate can be defined as the climate close to surfaces, upon and beneath soils, under snow, or in water, on living things (such as trees), or even on individual animals. Individuals may disperse across broad areas, but per- sistence, growth, and reproduction depend on the existence of narrow ranges of key environmental con- ditions that vary over narrow spatial gradients. For example, we see only the stand of trees that reached the right microclimate as seeds but not the tens of thousands of seeds that perished or simply failed to take root because they germinated in areas outside the range of their tolerance. Through their presence and activity, organisms alter their surroundings in important ways. Tree shape, physiology, and canopy structure can produce June 2004/Vol. 47, No. 6 COMMUNICATIONS OF THE ACM
---
paper_title: A Novel Algorithm for Mining Association Rules in Wireless Ad Hoc Sensor Networks

paper_content:

In this paper, we propose a comprehensive framework for mining wireless ad hoc sensor networks (WASNs), which is able to extract patterns regarding the sensors' behaviors. The main goal of determining behavioral patterns is to use them to generate rules that will improve the WASN's quality of service by participating in the resource management process or compensating for the undesired side effects of wireless communication. The proposed framework consists of 1) a formal definition of sensor behavioral patterns and sensor association rules, 2) a novel representation structure that we refer to as the positional lexicographic tree (PLT) that is able to compress the data gathered for the mining process and thus allows the fast and efficient mining of sensor behavioral patterns, and 3) a distributed data extraction mechanism to prepare the data required for mining sensor behavioral patterns. Several experimental studies have been conducted to evaluate our PLT structure and our proposed data extraction algorithms for mining wireless sensor networks.
---
paper_title: A Predictive Energy-Efficient Technique to Support Object-Tracking Sensor Networks

paper_content:

In recent years, we have witnessed an increasing interest in deploying wireless sensor networks (WSNs) for real-life applications. However, before WSNs become a commodity, several challenging issues remain to be resolved. Object-tracking sensor network (OTSN)-based applications are widely viewed as being among the most interesting applications of WSNs. OTSN is mainly used to track certain objects in a monitored area and to report their location to the application's users. However, OTSNs are well known for their energy consumption when compared with other WSN applications. In this paper, we propose a prediction-based tracking technique using sequential patterns (PTSPs) designed to achieve significant reductions in the energy dissipated by the OTSNs while maintaining acceptable missing rate levels. PTSP is tested against basic tracking techniques to determine the appropriateness of PTSP under various circumstances. Our experimental results have shown that PTSP outperforms all the other basic tracking techniques and exhibits significant amounts of savings in terms of the entire network's energy consumption.
---
paper_title: Using association rules for energy conservation in wireless sensor networks

paper_content:

Frequent radio transmissions among sensors, or from sensors to the basestation, have always been a major energy drain. One of the approaches to reduce the data transmitted to the basestation is to shift the bulk of data processing to networked sensor nodes; for instance, sensors to send only data aggregates to reduce the overall amount of data exchanged. Sensor nodes, however, are quite limited in terms of their energy and processing power, and as such, traditional centralised data mining algorithms are infeasible to be directly implemented on sensors. In this paper, we modify APRIORI to find strong rules from sensor readings in a sensor network and using these rules, autonomously control sensor network operations or supplement sensor operations with a rule knowledge base. For example, triggers activated from the rules could be used to sleep sensors or reduce data transmissions to conserve sensor energy. Our work here includes a detailed implementation of a lightweight rule learning algorithm for a resource-constrainted sensor network, with simulation results for a group node setup running the algorithm.
---
paper_title: Self-organizing sensor networks for integrated target surveillance

paper_content:

Self-organization is critical for a distributed wireless sensor network due to the spontaneous and random deployment of a large number of sensor nodes over a remote area. Such a network is often characterized by its abilities to form an organizational structure without much centralized intervention. An important design goal for a smart sensor network is to be able have an energy-efficient, self-organized configuration of sensor nodes that can scan, detect, and track targets of interest in a distributed manner. In this paper, we propose a novel self-organization protocol and describe other relevant, indigenous building blocks that can be combined to build integrated surveillance applications for self-organized sensor networks. Experiments in both simulated and real-world platforms indicate that this protocol can be useful for tracking targets that follow a predictable course
---
paper_title: A dynamic clustering and scheduling approach to energy saving in data collection from wireless sensor networks

paper_content:

Energy consumption is one of the major con- straints in wireless sensor networks. A highly feasible strat- egy is to aggressively reduce the spatial sampling rate of sensors (i.e., the density of the measure points in a field). By properly scheduling, we want to retain the high quality of data collection. In this paper, we propose a novel dynamic clustering and scheduling approach. Orthogonal to most ex- isting methods which mainly utilize the overlaps of sensing ranges of sensors to reduce the spatial sampling rate, our method is based on a careful analysis of the surveillance data reported by the sensors. We dynamically partition the sensors into groups so that the sensors in the same group have similar surveillance time series. They can share the workload of data collection in the future since their future readings may likely be similar. A generic framework is de- veloped to address several important technical challenges, including how to partition the sensors into groups, how to dynamically maintain the groups, and how to schedule sam- pling for the sensors in a group. We conduct an extensive empirical study to test our method using both a real test bed system and a large-scale synthetic dataset.
---
paper_title: Distributed, hierarchical clustering and summarization in sensor networks

paper_content:

We propose DHCS, a method of distributed, hierarchical clustering and summarization for online data analysis and mining in sensor networks. Different from the acquisition and aggregation of raw sensory data, our method clusters sensor nodes based on their current data values as well as their geographical proximity, and computes a summary for each cluster. Furthermore, these clusters, together with their summaries, are produced in a distributed, bottom-up manner. The resulting hierarchy of clusters and their summaries facilitates interactive data exploration at multiple resolutions. It can also be used to improve the efficiency of data-centric routing and query processing in sensor networks. Our simulation results on real world data sets as well as synthetic data sets show the effectiveness and efficiency of our approach.
---
paper_title: On clustering large number of data streams

paper_content:

Data streams and their applications appear in several fields such as physics, finance, medicine, environmental science, etc. As sensor technology improves, sensor data rates continue to increase. Consequently, analyzing data streams becomes ever more challenging. Fast online response is a must for applications that involve multiple data streams, especially when the number of data streams is large. This paper proposes an efficient clustering technique called Multi-way Grid-based join algorithm MG-join to find clusters in multiple data streams. The proposed algorithm uses a Discrete Fourier Transformation DFT to reduce the dimensionality of the streams. Each stream is represented by a point in a multi-dimensional grid in the frequency domain. The MG-join algorithm finds the different clusters in multiple data streams in the frequency domain. Moreover, this paper proposes an incremental update mechanism to avoid the recalculation of DFT coefficients when new readings arrive and thus minimizes the processing time. Experiments on synthetic data streams show that the proposed clustering technique is much faster than traditional clustering techniques and yet its accuracy is as good as that of the traditional clustering techniques. This makes the proposed technique suitable for sensors network environment where computing and power capabilities are limited.
---
paper_title: Efficient instance-based learning on data streams

paper_content:

The processing of data streams in general and the mining of such streams in particular have recently attracted considerable attention in various research fields. A key problem in stream mining is to extend existing machine learning and data mining methods so as to meet the increased requirements imposed by the data stream scenario, including the ability to analyze incoming data in an online, incremental manner, to observe tight time and memory constraints, and to appropriately respond to changes of the data characteristics and underlying distributions, amongst others. This paper considers the problem of classification on data streams and develops an instance-based learning algorithm for that purpose. The experimental studies presented in the paper suggest that this algorithm has a number of desirable properties that are not, at least not as a whole, shared by currently existing alternatives. Notably, our method is very flexible and thus able to adapt to an evolving environment quickly, a point of utmost importance in the data stream context. At the same time, the algorithm is relatively robust and thus applicable to streams with different characteristics.
---
paper_title: A New Algorithm Based On Sequential Pattern Mining For Person Identification In Ubiquitous Environments

paper_content:

This paper presents an approach to person identification in ubiquitous environments. Our approach uses the sequential pattern mining principle to extract frequent patterns in data collected from the different sensors disseminated in the ubiquitous environment. In contrast with existing, intrusive, person identification algorithms that have been proposed in the literature, where the data is basically composed of audiovisual or image files recorded during experiments, our approach is fully non-intrusive and is based on event sequences collected from heterogeneous sensors. Our approach is divided into three main phases: (1) frequent pattern mining, (2) assignment of weights to extracted patterns, and (3) classification. Experiments using data collected in the Domus and Testbed smart homes demonstrate that our approach accurately identifies persons and improves classification results, outperforming two of the approaches reported in the literature.
---
paper_title: Fault Detection by Mining Association Rules from House-keeping Data

paper_content:

This paper proposes a novel anomaly detection method for spacecraft systems based on data-mining techniques. This method automatically constructs a system behavior model in the form of a set of rules by applying pattern clustering and association rule mining to the time-series data obtained in the learning phase, then detects anomalies by checking the subsequent on-line data with the acquired rules. A major advantage of this approach is that it requires little a priori knowledge on the system.
---
paper_title: SO MAD: SensOr Mining for Anomaly Detection in railway data

paper_content:

Today, many industrial companies must face problems raised by maintenance. In particular, the anomaly detection problem is probably one of the most challenging. In this paper we focus on the railway maintenance task and propose to automatically detect anomalies in order to predict in advance potential failures. We first address the problem of characterizing normal behavior. In order to extract interesting patterns, we have developed a method to take into account the contextual criteria associated to railway data (itinerary, weather conditions, etc.). We then measure the compliance of new data, according to extracted knowledge, and provide information about the seriousness and possible causes of a detected anomaly.
---
paper_title: A fuzzy approach for interpretation of ubiquitous data stream clustering and its application in road safety

paper_content:

Ubiquitous Data Mining is the process of analysing data emanating from distributed and heterogeneous sources in the form of a continuous stream with mobile and/or embedded devices. Unsupervised learning is clearly beneficial for initial understanding of data streams, and consequently various clustering algorithms have been developed and applied in UDM systems for the purpose of mining data streams. However, unsupervised data mining techniques require human intervention for further understanding and analysis of the clustering results. This becomes an issue as UDM applications aim to support mobile and highly dynamic users/applications and there is a need for real-time decision making and interpretation of results. In this paper we present an approach to automate the annotation of results obtained from ubiquitous data stream clustering to facilitate interpreting and use of the results to enable real-time, mobile decision making.
---
paper_title: An Efficient Data Extraction Mechanism for Mining Association Rules from Wireless Sensor Networks

paper_content:

With the advances of wireless sensor networks and their ability to generate a large amount of data, data mining techniques to extract useful knowledge regarding the underlying network have recently received a great deal of attention. However, the stream nature of the data, the limited resources, and the distributed nature of sensor networks bring new challenges for the mining techniques that need to be address. In this paper, we introduce a new formulation for the association rules, a well known data mining technique, that is able to generate the time relations between sensor devices in a particular sensor network. This new formulation will allow traditional data mining algorithms proposed to solve the classical association rules mining problem to be applied on sensor based class of applications that generate and use sensor data. The generated rules will give a clear picture about the correlations between sensors in the network and can be used to make decisions about the network performance, or it can be used to predict the sources of future events. In order to prepare for the data needed in the mining process and to maximize the network lifetime, a distributed extraction methodology is introduced, in this distributed methodology sensors perform optimization based on local computation to decide whether it will participate in sending data or not. Experimental results have shown that the distributed extraction solution is able to reduce the number of exchanged messages and the data size by 50% compared to a direct transmission of the data.
---
paper_title: Clustering distributed sensor data streams using local processing and reduced communication

paper_content:

Nowadays applications produce infinite streams of data distributed across wide sensor networks. In this work we study the problem of continuously maintain a cluster structure over the data points generated by the entire network. Usual techniques operate by forwarding and concentrating the entire data in a central server, processing it as a multivariate stream. In this paper, we propose DGClust, a new distributed algorithm which reduces both the dimensionality and the communication burdens, by allowing each local sensor to keep an online discretization of its data stream, which operates with constant update time and (almost) fixed space. Each new data point triggers a cell in this univariate grid, reflecting the current state of the data stream at the local site. Whenever a local site changes its state, it notifies the central server about the new state it is in. This way, at each point in time, the central site has the global multivariate state of the entire network. To avoid monitoring all possible states, which is exponential in the number of sensors, the central site keeps a small list of counters of the most frequent global states. Finally, a simple adaptive partitional clustering algorithm is applied to the frequent states central points in order to provide an anytime definition of the clusters centers. The approach is evaluated in the context of distributed sensor networks, focusing on three outcomes: loss to real centroids, communication prevention, and processing reduction. The experimental work on synthetic data supports our proposal, presenting robustness to a high number of sensors, and the application to real data from physiological sensors exposes the aforementioned advantages of the system.
---
paper_title: An active learning system for mining time-changing data streams

paper_content:

Mining time-changing data streams is of great interest. The fundamental problems are how to effectively identify the significant changes and organize new training data to adjust the outdated model. In this paper, we propose an active learning system to address these issues. Without need knowing any true labels of the new data, we devise an active approach to detecting the possible changes. Whenever the suspected changes are indicated, it exploits a light-weight uncertainty sampling algorithm to choose the most informative instances to label. With these labeled instances, it further tests the truth of the suspected changes. If the changes indeed cause significant performance deterioration of the current model, it evolves the old model. Thus, our method is sensitive to significant changes and robust to noisy changes, and can quickly adapt to concept-drift. Experimental results from both synthetic and real-world data confirm the advantages of our system.
---
paper_title: Novelty detection with application to data streams

paper_content:

This paper presents and evaluates an approach to novelty detection that addresses it as the problem of identifying novel concepts in a continuous learning scenario, as an extension to a single-class classification problem. OLINDDA, an OnLIne Novelty and Drift Detection Algorithm that implements this approach, uses efficient standard clustering algorithms to continuously generate candidate clusters among examples that were not explained by the current known concepts. Clusters complying with a validation criterion that takes cohesiveness and representativeness into account are initially identified as concepts. By merging similar concepts, OLINDDA may enhance the representation of some concepts as it advances toward its final goal of describing novel emerging concepts in an unsupervised way. The proposed approach is experimentally evaluated by the use of several measures taken throughout the learning process. Results show that it is capable of identifying novel concepts that are pure and correspond to real classes, disregarding unrepresentative clusters and outliers.
---
paper_title: Moment: maintaining closed frequent itemsets over a stream sliding window

paper_content:

This paper considers the problem of mining closed frequent itemsets over a sliding window using limited memory space. We design a synopsis data structure to monitor transactions in the sliding window so that we can output the current closed frequent itemsets at any time. Due to time and memory constraints, the synopsis data structure cannot monitor all possible itemsets. However, monitoring only frequent itemsets make it impossible to detect new itemsets when they become frequent. In this paper, we introduce a compact data structure, the closed enumeration tree (CET), to maintain a dynamically selected set of item-sets over a sliding-window. The selected itemsets consist of a boundary between closed frequent itemsets and the rest of the itemsets. Concept drifts in a data stream are reflected by boundary movements in the CET. In other words, a status change of any itemset (e.g., from non-frequent to frequent) must occur through the boundary. Because the boundary is relatively stable, the cost of mining closed frequent item-sets over a sliding window is dramatically reduced to that of mining transactions that can possibly cause boundary movements in the CET. Our experiments show that our algorithm performs much better than previous approaches.
---
paper_title: Knowledge discovery from data streams

paper_content:

Since the beginning of the Internet age and the increased use of ubiquitous computing devices, the large volume and continuous flow of distributed data have imposed new constraints on the design of learning algorithms. Exploring how to extract knowledge structures from evolving and time-changing data, Knowledge Discovery from Data Streams presents a coherent overview of state-of-the-art research in learning from data streams. The book covers the fundamentals that are imperative to understanding data streams and describes important applications, such as TCP/IP traffic, GPS data, sensor networks, and customer click streams. It also addresses several challenges of data mining in the future, when stream mining will be at the core of many applications. These challenges involve designing useful and efficient data mining solutions applicable to real-world problems. In the appendix, the author includes examples of publicly available software and online data sets. This practical, up-to-date book focuses on the new requirements of the next generation of data mining. Although the concepts presented in the text are mainly about data streams, they also are valid for different areas of machine learning and data mining.
---
paper_title: Review: Real-time data management on wireless sensor networks: A survey

paper_content:

In the recent past, search in sensor systems focused on node hardware constraints and very limited energy resources. But nowadays, that new applications need data processing with temporal constraints in their tasks; then one of the new challenges faced by wireless sensor networks (WSNs) is handling real-time storage and querying the data they process. Two main approaches to storage and querying data are generally considered warehousing and distributed. The warehousing approach stores data in a central database and then queries may be performed to it. In a distributed approach, sensor devices are considered as local databases and data are managed locally. The data collected by sensors must represent the current state of the environment; for this reason they are subject to logic and time constraints. Then, this paper identifies the main specifications of real-time data management and presents the available real-time data management solutions for WSNs, in order to discuss them and identify some open issues and provide guidelines for further contributions.
---
paper_title: Incremental mining of frequent patterns without candidate generation or support constraint

paper_content:

In this paper, we propose a novel data structure called CATS Tree. CATS Tree extends the idea of FPTree to improve storage compression and allow frequent pattern mining without generation of candidate item sets. The proposed algorithms enable frequent pattern mining with different supports without rebuilding the tree structure. Furthermore, the algorithms allow mining with a single pass over the database as well as efficient insertion or deletion of transactions at any time.
---
paper_title: Using Clustering Information for Sensor Network Localization

paper_content:

Sensor network localization continues to be an important research challenge. The goal of localization is to assign geographic coordinates to each node in the sensor network. Localization schemes for sensor network systems should work with inexpensive off-the-shelf hardware, scale to large networks, and also achieve good accuracy in the presence of irregularities and obstacles in the deployment area. ::: ::: We present a novel approach for localization that can satisfy all of these desired properties. Recent developments in sensor network clustering algorithms have resulted in distributed algorithms that produce highly regular clusters. We propose to make use of this regularity to inform our localization algorithm. The main advantages of our approach are that our protocol requires only three randomly-placed nodes that know their geographic coordinates, and does not require any ranging or positioning equipment (i.e., no signal strength measurement, ultrasound ranging, or directional antennas are needed). So far, only the DV-Hop localization mechanism worked with the same assumptions [1]. We show that our proposed approach may outperform DV-Hop in certain scenarios, in particular when there exist large obstacles in the deployment field, or when the deployment area is free of obstacles but the number of anchors is limited.
---

Title: Data Mining Techniques for Wireless Sensor Networks: A Survey

Section 1: Introduction
Description 1: Introduce the importance of data mining techniques in wireless sensor networks (WSNs) and highlight the main focus and contributions of the survey.

Section 2: Data Mining Process in WSNs
Description 2: Discuss the data mining process in sensor networks and how it differs from traditional data mining processes.

Section 3: Challenges
Description 3: Describe the challenges of applying conventional data mining techniques to WSNs due to constraints like resource limitations and fast data arrival rates.

Section 4: Taxonomy of Data Mining Techniques for WSNs
Description 4: Present a classification scheme for existing data mining techniques tailored for WSNs and categorize them based on data processing approaches and problem-solving attitudes.

Section 5: State of the Art of Data Mining Techniques for WSNs
Description 5: Classify and discuss various data mining techniques for WSNs, focusing on frequent pattern mining, sequential pattern mining, clustering, and classification.

Section 6: Comparison of Data Mining Techniques for WSNs
Description 6: Identify and compare common and different aspects of existing data mining techniques for WSNs using evaluations and metrics.

Section 7: Limitations of Existing Data Mining Techniques for WSNs
Description 7: Outline the limitations and shortcomings of current data mining techniques applied to WSNs.

Section 8: Future Research Directions
Description 8: Suggest future research directions and propose a new hybrid data mining framework to address identified shortcomings in existing techniques.

Section 9: Conclusion
Description 9: Summarize the survey's findings, emphasizing the need for and potential of data mining techniques for enhancing WSN performance and operation.