Adaptive multi-target tracking in heterogeneous wireless sensor networks

Energy efficient multiple-target tracking is an important application of Wireless Sensor Networks (WSNs). Most prior studies consider tracking multiple tar- gets as an extension of executing a single target tracking algorithm multiple times, and use a single parameter for energy efficiency. We consider various factors such as mul- tiple targets tracked by the sensor, remaining energy of the sensor and relative location of the sensor with respect to a target's motion, in order to decide the tracking state of a sensor in a distributed environment. Further, we explore and identify the effective combination of these parameters to optimize energy usage, depending on specific network conditions. We then propose the Adaptive Multi-Target Tracking (AMTT) algorithm that can recognize the network condition based on local information without centralized coordination, and uses effective parameters to achieve en- ergy efficiency

Energy efficient multi-target tracking in heterogeneous wireless sensor networks

Title from PDF of title page, viewed on June 3, 2011VitaIncludes bibliographical references (p. 30-32)Thesis (M.S)--School of Computing and Engineering. University of Missouri--Kansas City, 2011Tracking multiple targets in an energy efficient way is an important challenge in wireless sensor networks (WSNs). While most of the prior work consider tracking multiple targets as execution of single target tracking algorithms multiple times and utilize only single parameters for efficient energy consumption, we identify multiple parameters that can influence the energy efficiency of sensors in the WSN. We observe that there are several impacting parameters that can affect the energy efficiency of the sensors in the WSN which are: the relative location of the sensor with respect to the target's motion, multiple targets tracked by the sensor, and the remaining energy in the sensor. These impacting parameters are used to decide the tracking state of the sensors and further, our observations reveal the implications of combining these parameters and we identify that the optimal energy consumption is governed by their usage in particular network conditions. Based on these observations we proceed to propose our Adaptive Multi-Target Tracking (AMTT) algorithm that can identify the local network conditions for individual sensors in distributed environment without any centralized co-ordination, and uses required combination of impacting parameters to achieve energy efficiency.Introduction -- Related work -- Proposed multi-target tracking system -- Simulation setup and results -- Conclusions and future wor

Markov Decision Processes with Applications in Wireless Sensor Networks: A Survey

Wireless sensor networks (WSNs) consist of autonomous and resource-limited devices. The devices cooperate to monitor one or more physical phenomena within an area of interest. WSNs operate as stochastic systems because of randomness in the monitored environments. For long service time and low maintenance cost, WSNs require adaptive and robust methods to address data exchange, topology formulation, resource and power optimization, sensing coverage and object detection, and security challenges. In these problems, sensor nodes are to make optimized decisions from a set of accessible strategies to achieve design goals. This survey reviews numerous applications of the Markov decision process (MDP) framework, a powerful decision-making tool to develop adaptive algorithms and protocols for WSNs. Furthermore, various solution methods are discussed and compared to serve as a guide for using MDPs in WSNs

Distributed Object Tracking Using a Cluster-Based Kalman Filter in Wireless Camera Networks

Local data aggregation is an effective means to save sensor node energy and prolong the lifespan of wireless sensor networks. However, when a sensor network is used to track moving objects, the task of local data aggregation in the network presents a new set of challenges, such as the necessity to estimate, usually in real time, the constantly changing state of the target based on information acquired by the nodes at different time instants. To address these issues, we propose a distributed object tracking system which employs a cluster-based Kalman filter in a network of wireless cameras. When a target is detected, cameras that can observe the same target interact with one another to form a cluster and elect a cluster head. Local measurements of the target acquired by members of the cluster are sent to the cluster head, which then estimates the target position via Kalman filtering and periodically transmits this information to a base station. The underlying clustering protocol allows the current state and uncertainty of the target position to be easily handed off among clusters as the object is being tracked. This allows Kalman filter-based object tracking to be carried out in a distributed manner. An extended Kalman filter is necessary since measurements acquired by the cameras are related to the actual position of the target by nonlinear transformations. In addition, in order to take into consideration the time uncertainty in the measurements acquired by the different cameras, it is necessary to introduce nonlinearity in the system dynamics. Our object tracking protocol requires the transmission of significantly fewer messages than a centralized tracker that naively transmits all of the local measurements to the base station. It is also more accurate than a decentralized tracker that employs linear interpolation for local data aggregation. Besides, the protocol is able to perform real-time estimation because our implementation takes into consideration the sparsit- - y of the matrices involved in the problem. The experimental results show that our distributed object tracking protocol is able to achieve tracking accuracy comparable to the centralized tracking method, while requiring a significantly smaller number of message transmissions in the network

An objective based classification of aggregation techniques for wireless sensor networks

Wireless Sensor Networks have gained immense popularity in recent years due to their ever increasing capabilities and wide range of critical applications. A huge body of research efforts has been dedicated to find ways to utilize limited resources of these sensor nodes in an efficient manner. One of the common ways to minimize energy consumption has been aggregation of input data. We note that every aggregation technique has an improvement objective to achieve with respect to the output it produces. Each technique is designed to achieve some target e.g. reduce data size, minimize transmission energy, enhance accuracy etc. This paper presents a comprehensive survey of aggregation techniques that can be used in distributed manner to improve lifetime and energy conservation of wireless sensor networks. Main contribution of this work is proposal of a novel classification of such techniques based on the type of improvement they offer when applied to WSNs. Due to the existence of a myriad of definitions of aggregation, we first review the meaning of term aggregation that can be applied to WSN. The concept is then associated with the proposed classes. Each class of techniques is divided into a number of subclasses and a brief literature review of related work in WSN for each of these is also presented