Thermal-aware sensor scheduling for distributed estimation

• Authors: Domenic Forte;Ankur Srivastava
• Venue: ACM Transactions on Sensor Networks (TOSN)
• Year: 2013

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. In this article, we present unique management opportunities for distributed estimation tasks in sensor networks consisting of high-end devices prone to thermal issues. We attempt to balance both thermal- and performance-related constraints by examining trade-offs between sensor sampling rate, number of sensors, node temperature, and state estimation error. Initially, we devise a scheduling algorithm which can achieve a desired real-time performance constraint while maintaining a thermal limit on temperature assuming identical nodes in the network. Then, we extend the concept to a network consisting of heterogeneous sensor nodes. Analytical results and simulation experiments are done for state estimation with a Kalman filter for simplicity, but our main contributions should easily extend to any form of estimation with measurable error. Results show that our policies can successfully balance the trade-offs between thermal- and performance-related constraints. Note that our analyses, schemes, and results are less applicable to low-end sensors whose operation does not cause high node temperature. This work is most suited for high-performance sensors and upper-tier sensors which experience greater workloads.