Adaptive Calibration for Fusion-based Wireless Sensor Networks

摘要: Wireless sensor networks (WSNs) are typically composed of low-cost sensors that deeply integrated with physical environments. As a result, the sensing performance WSN is inevitably undermined by various uncertainties, which include stochastic noises, unpredictable environment changes and dynamics monitored phenomenon. Traditional solutions (e.g., calibration collaborative signal processing) work in an open-loop fashion hence fail to adapt these uncertainties after system deployment. In this paper, we propose adaptive system-level approach for class employ data fusion improve performance. Our features feedback control loop exploits heterogeneity deal aforementioned calibrating contrast existing heuristic based solutions, our control-theoretical algorithm can ensure provable stability convergence. We also systematically analyze impacts communication reliability delay, optimal routing minimizes impact packet loss on stability. evaluated both experiments testbed Tmotes as well extensive simulations traces gathered from real vehicle detection experiment. The results demonstrate enables network maintain presence environmental dynamics.