ENERGY-LATENCY TRADE-OFFS IN REAL-TIME WIRELESS SENSOR NETWORKS

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.

Autorentext

Lei Miao received his Ph.D. degree from Boston University, Boston, MA, in 2006. He is currently with Nortel Networks at Billerica, MA. He specializes in the areas of discrete event systems and optimal control, with applications to wireless sensor networks, computer networks, and Ethernet switching systems.

Klappentext

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.