A fully decentralized multi-sensor system for tracking and surveillance
International Journal of Robotics Research
Temperature-aware microarchitecture: Modeling and implementation
ACM Transactions on Architecture and Code Optimization (TACO)
The Impact of Technology Scaling on Lifetime Reliability
DSN '04 Proceedings of the 2004 International Conference on Dependable Systems and Networks
On Estimating Optimal Performance of CPU Dynamic Thermal Management
IEEE Computer Architecture Letters
Optimal State Estimation: Kalman, H Infinity, and Nonlinear Approaches
Optimal State Estimation: Kalman, H Infinity, and Nonlinear Approaches
An optimal analytical solution for processor speed control with thermal constraints
Proceedings of the 2006 international symposium on Low power electronics and design
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Recent work has shown that rising temperatures are increasing failures and reducing integrated circuit reliability. Although such results have prompted development of thermal management policies for stand-alone processors and on distributed power management, there is an overall lack of research on thermal management policies and their tradeoffs in sensor networks where sensors can overheat due to excessive sampling. Our primary focus in this paper is to examine the relationship between sampling, number of sensors, sensor node temperature, and state estimation error. We devise a scheduling algorithm which can achieve a desired real-time performance constraint while maintaining a thermal limit on temperature at all nodes in a network. Analytical results and experimentation are done for estimation with a Kalman filter for simplicity, but our main contributions should easily extend to any form of estimation with measurable error.