We study the optimal control of a class of resource allocation problems characterized by energy-latency trade-offs in Wireless Sensor Networks (WSN) using the framework of Discrete Event Systems. Our work is based on the observation that energy of wireless nodes can be greatly saved by introducing some delay of task completion time. Specifically, we consider a family of problems motivated by WSN such as Dynamic Transmission Control and Dynamic Voltage Scaling, where the objective is to minimize energy consumption while satisfying real-time operating constraints. Using advanced techniques, such as sample path analysis and Receding Horizon Control, we address both off-line and on-line scenarios and have found better and more efficient solutions than the existing ones in the literature. Our results do not rely on the exact form of the cost function and can be readily applied to other settings as long as the cost function satisfies certain conditions. This work is beneficial to students, scholars, and engineers who are interested in utilizing advanced control and optimization techniques to solve resource allocation problems in wireless networks.
ENERGY-LATENCY TRADE-OFFS IN REAL-TIME WIRELESS SENSOR NETWORKS
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Description
We study the optimal control of a class of resource allocation problems characterized by energy-latency trade-offs in Wireless Sensor Networks (WSN) using the framework of Discrete Event Systems. Our work is based on the observation that energy of wireless nodes can be greatly saved by introducing some delay of task completion time. Specifically, we consider a family of problems motivated by WSN such as Dynamic Transmission Control and Dynamic Voltage Scaling, where the objective is to minimize energy consumption while satisfying real-time operating constraints. Using advanced techniques, such as sample path analysis and Receding Horizon Control, we address both off-line and on-line scenarios and have found better and more efficient solutions than the existing ones in the literature. Our results do not rely on the exact form of the cost function and can be readily applied to other settings as long as the cost function satisfies certain conditions. This work is beneficial to students, scholars, and engineers who are interested in utilizing advanced control and optimization techniques to solve resource allocation problems in wireless networks.
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