A sensor network is a distributed system where sensor nodes autonomously collect local data and collaborate to solve global problems. Recent work has shown that sensor functionality varies with node temperature. Extreme temperatures can decrease node/network lifetime by leading to premature hardware failure and reducing battery capacity. Furthermore, high temperatures can increase sensor measurement noise and disrupt communication between overheated sensor nodes, thereby interfering with their ability to contribute valuable information to collaborative tasks. In the past, sensor networks only consisted of low-end devices with limited power, computational capabilities, and available bandwidth. Such devices would only experience high temperatures in harsh environments.However, sensor networks are now envisioned for applications that require higher-end devices such as smart cameras, smart phones, and laptops. The power dissipated by such devices is much larger than low-end sensors and can create thermal emergencies in sensor hardware even in calm environments
Our work is among the first to consider state estimation under sensor thermal constraints. More specifically, we looked at several problem instances where a Kalman filter was used in a distributed sensor network consisting of high-end sensors prone to thermal issues. We examined the tradeoffs between sensor sampling rate, number of sensors, node temperature, and state estimation error, and we found that it was possible to balance both thermal and performance-related constraints. We proposed several novel algorithms that schedule sensors based on their thermal and noise characteristics in order to keep nodes from violating thermal constraints while maintaining desired estimation accuracy. Simulation results showed that our algorithms could keep sensors at safe operating temperatures in all problem instances while obtaining better performance than naive schemes.
Our Conference and Journal Papers
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- D. Forte, A. Srivastava, “Thermal-Aware Sensor Scheduling for Distributed Estimation”, ACM Transactions on Sensor Networks (TOSN), Vol. 9, No. 4, Nov. 2013. [link]
- D. Forte, A. Srivastava, “Thermal-Aware Sensor Scheduling for Distributed Estimation”, International Conference on Distributed Computing in Sensor Systems (DCOSS), June 2010. [link]