Parallelized Simulated Annealing for Model Updating in Ad-Hoc Wireless Sensing Networks

The engineering community has recently begun to adopt wireless sensing technologies for use in many sensing applications. These low-cost sensors provide an optimal setting for dense sensing networks, and can make large amounts of sensor data available. Also, the computational power embedded within each sensing node allows a wireless network to interrogate data within the network and in real-time. In a monitoring situation, these capabilities can be leveraged to detect and locate changes in system properties that could be an early indication of malfunction within a complex physical system. In this paper, a distributed model updating technique is embedded within the computational core of a wireless sensing network. Using a novel distributed implementation of the simulated annealing method, an ad-hoc network of wireless sensing units can determine updated system properties by iteratively matching data derived from an analytical model of the system with collected sensor data. By comparing updated system properties with those obtained in a baseline state, a wireless sensing network can detect and locate changes in the system. Experimental verification of this technique is provided on a network of wireless sensor prototypes using simulated results from a Navy ship-board chilled water system monitored with a wireless sensor network.