An improved energy efficient approach for wsn based tracking applications

Tracking systems using a high number of low cost sensor nodes have been proposed for use in diverse applications including civil, military, and wildlife monitoring applications. In tracking applications, each sensor node attempts to send the target's location information to a sink node. Deploying a tracking system with a high number of sensor nodes results in the following limitations: high packet dropping rate, high congestion, transmission delay, and high power-consumption. Data aggregation schemes can reduce the number of messages transmitted over the network, while prediction schemes can decrease the number of activated beacon nodes in the tracking process. Consequently, data aggregation and prediction approaches can reduce the energy consumed during the tracking process. In this paper, we propose and implement an energy efficient approach for WSN-based tracking applications by integrating both a novel data aggregation method with a simple prediction approach. Three metrics are utilized for the evaluation purposes: total number of messages transmitted in the network, overall power-consumption, and the quality of the tracking accuracy. The proposed system is simulated using the NS2 simulation environment

Secure QoS-Aware Data Fusion to Prevent Node Misbehavior in Wireless Sensor Networks

Wireless Sensor Networks(WSNs) are composed of tiny devices with limited computation and energy capacities. Data fusion is an essential technique to achieve power efficiency in sensor nodes. Some nodes misbehave by increasing the defer time which obstruct the data fusion process. In this paper, an efficient Secured Quality of Service(QoS)-Aware Data Fusion(SQDF) for distributed Wireless Sensor Networks is proposed. The key feature of secure data fusion is to detect the misbehavior of a node… Expan

Tracking mobile targets through Wireless Sensor Networks

In recent years, advances in signal processing have led to small, low power, inexpensive Wireless Sensor Network (WSN). The signal processing in WSN is different from the traditional wireless networks in two critical aspects: firstly, the signal processing in WSN is performed in a fully distributed manner, unlike in traditional wireless networks; secondly, due to the limited computation capabilities of sensor networks, it is essential to develop an energy and bandwidth efficient signal processing algorithms. Target localisation and tracking problems in WSNs have received considerable attention recently, driven by the necessity to achieve higher localisation accuracy, lower cost, and the smallest form factor. Received Signal Strength (RSS) based localisation techniques are at the forefront of tracking research applications. Since tracking algorithms have been attracting research and development attention recently, prolific literature and a wide range of proposed approaches regarding the topic have emerged. This thesis is devoted to discussing the existing WSN-based localisation and tracking approaches. This thesis includes five studies. The first study leads to the design and implementation of a triangulation-based localisation approach using RSS technique for indoor tracking applications. The presented work achieves low localisation error in complex environments by predicting the environmental characteristics among beacon nodes. The second study concentrates on investigating a fingerprinting localisation method for indoor tracking applications. The proposed approach offers reasonable localisation accuracy while requiring a short period of offline computation time. The third study focuses on designing and implementing a decentralised tracking approach for tracking multiple mobile targets with low resource requirements. Despite the interest in target tracking and localisation issues, there are few systems deployed using ZigBee network standard, and no tracking system has used the full features of the ZigBee network standard. Tracking through the ZigBee is a challenging task when the density of router and end-device nodes is low, due to the limited communication capabilities of end-device nodes. The fourth study focuses on developing and designing a practical ZigBee-based tracking approach. To save energy, different strategies were adopted. The fifth study outlines designing and implementing an energy-efficient approach for tracking applications. This study consists of two main approaches: a data aggregation approach, proposed and implemented in order to reduce the total number of messages transmitted over the network; and a prediction approach, deployed to increase the lifetime of the WSN. For evaluation purposes, two environmental models were used in this thesis: firstly, real experiments, in which the proposed approaches were implemented on real sensor nodes, to test the validity for the proposed approaches; secondly, simulation experiments, in which NS-2 was used to evaluate the power-consumption issues of the two approaches proposed in this thesis

Multi-target Data Aggregation and Tracking in Wireless Sensor Networks

Abstract — This paper presents the results of a study on the effects of data aggregation for multi-target tracking in wireless sensor networks. Wireless sensor networks are normally limited in communication bandwidth. The nodes implementing the wireless sensor network are themselves limited in computing power and usually have a limited battery life. These observations are recognized and combined to come to efficient target tracking approaches. The main question to be answered is how to accurately track multiple targets crossing an area observed by a wireless sensor network, while limiting the amount of network traffic. Limiting the amount of network traffic reduces the required bandwidth and reduces the required energy. Various computing power aware data aggregation strategies are researched. They have been tested in a simulation environment and compared with each other. The results of the simulations clearly show the benefit of the new data aggregation strategies in terms of energy consumption and tracking accuracy. Index Terms — Wireless sensor networks, multi-target tracking, distributed tracking, data aggregation