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In recent years, the demand for high-precision tracking systems has significantly increased in the field of Wireless Sensor Network (WSN). A new tracking system based on exploitation of Received Signal Strength Indicator (RSSI) measurements in WSN is proposed. The proposed system is designed in particular for WSNs that are deployed in close proximity and can transmit data at a high transmission rate. The close proximity and an optimized transmit power level enable accurate conversion of RSSI measurements to range estimates. Having an adequate transmission rate enables spatial-temporal correlation between consecutive RSSI measurements. In addition, advanced statistical and signal processing methods are used to mitigate channel distortion and to compensate for packet loss. The system is evaluated in indoor conditions and achieves tracking resolution of a few centimeters which is compatible with theoretical bounds.

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Abstract-In this paper we develop a continuous high-precision tracking system based on Received Signal Strength Indicator (RSSI) measurements for small ranges. The proposed system uses minimal number of sensor nodes with RSSI capabilities to track a moving object in close-proximity and high transmission rate. The close-proximity enables conversion of RSSI measurements to range estimates and the high transmission rate enables continuous tracking of the moving object. The RSSI-based tracking system includes calibration, range estimation, location estimation and refinement. We use advanced statistical and signal processing methods to mitigate channel distortion and packet loss. The system is evaluated in indoor settings and achieves tracking resolution of few centimeters. Therefore, it becomes the motion trackers of notice in many applications.

Abstract-Cooperative and reliable packet forwarding presents a formidable challenge in mobile ad hoc networks (MANET), due to special network characteristics; e.g., mobility, dynamic topology and absence of centralized management. Lack of cooperation, due to misbehavior caused by selfishness or malice, may severely degrade the performance of the network.Previous studies, relying on reputation systems, have demonstrated solutions designed for Dynamic Source Routing (DSR) protocol.This paper highlights various aspects of cooperation enforcement and reliability, when AODV is the underlying protocol. Furthermore, it presents a scalable protocol that combines a reputation system with AODV that addresses reputation fading, second-chance, robustness against liars and load balancing.

Abstract. Distributed inference schemes for detection, estimation and learning comprise an attractive approach to Wireless Sensor Networks (WSNs), because of properties such as asynchronous operation and robustness in the face of failures. Belief Propagation (BP) is a method for distributed inference which provides accurate results with rapid convergence properties. However, applying a BP algorithm to WSN is not trivial, due to the unique characteristics of WSN networks. Many papers which have proposed using BP for WSNs do not consider all of the constraints which these networks impose. This paper first undertakes a thorough study of the practical challenges of WSNs which are raised in the context of distributed inference. It then presents a framework which implements both localized and data-centric approaches to improve the effectiveness and the robustness of this algorithm in the WSN environment. The proposed solution is empirically evaluated, as applied to the clustering problem, and it can be easily extended to suit many other applications that use BP as an underlying algorithm.