Distributed Fusion of Optimally Quantized Local Tracker Estimates for Underwater Wireless Sensor Networks

Abstract

Multi-sensor underwater surveillance has been a significant research problem for civilian and naval applications. Due to limited bandwidth considerations, the underwater wireless sensor networks (UWSNs) use measurement quantization to transmit information from individual sensors to the fusion center to perform centralized tracking/fusion. However, at the measurement level, quantization of azimuth information is complex due to its non-linear behavior. To address this problem, this paper proposes to perform the distributed tracking and quantizing the local estimates (state and covariance) to provide improved bandwidth and reduce computational load. The local tracker estimates the updated state and covariance of a target's time-varying dynamics in the given surveillance from the obtained measurements using extended Kalman filter (EKF) and global nearest neighbor (GNN) data association. The measurement model contains both detections of target and false alarms. This paper uses optimal quantization rather than linear quantization owing to its minimal bandwidth requirement. Once the quantized local tracks are obtained at the fusion center, these tracks are quantified using track-to-track association (T2TA) in the S-D assignment framework. The associated tracks are fused using correlation-free fusion algorithms like covariance intersect (CI), sampling covariance intersects (SCI), ellipsoidal intersect (EI), and arithmetic average (AA) algorithms to achieve the global track. The position root mean square error (PRMSE), bandwidth, and error ellipses are used to quantify the performance of the proposed framework. The simulation results show that the PRMSE of the optimally quantized fusion estimates yields good agreement with the unquantized method. Simulation results further reveals that, optimal quantization utilizes lower bandwidth compared to linear quantization. In addition, optimally quantized local estimates accomplishes promising covariance regions at the fusion center.INDEX TERMS Correlation-free fusion, extended Kalman filter, state quantization, target tracking, trackto-track association.

Task level energy and performance assurance workload scheduling model in distributed computing environment

“…To address the above problem, this paper presents a task level energy and performance assurance-workload scheduling (TLEPA-WS) technique. The TLEPA-WS assures SLA at task level [17], [18]. Research contribution for this research is mentioned as: i) the TLEPA-WS is efficient in minimizing energy with performance assurance of workload task level SLA prerequisite.…”