Suzanne Lesecq

A Hybrid Control Law for Energy-Oriented Tasks Scheduling in Wireless Sensor Networks

Energy is a key resource in wireless sensor networks (WSNs), especially when sensor nodes are powered by batteries. This paper investigates how to save energy of the whole WSN, thanks to control strategies, in real time and in a dynamic way. The energy management strategy is based on a hybrid dynamical system approach. This choice is motivated by the hybrid inherent nature of the WSN system when energy management is considered. The hybrid nature basically comes from the combination of continuous physical processes, namely, the charge/discharge of the node batteries, while the discrete part is related to the change in the functioning modes and an unreachable condition of the nodes. This approach provides a decentralized controller with low computational load that reduces the number of switching as compared with existing approaches. The proposed strategy is evaluated and compared in simulation on a realistic test case. Last, they have been implemented on a real test bench, and the obtained results have been discussed.

Feedback scheduling of sensor network activity using a Hybrid Dynamical Systems approach

Wireless sensor nodes are now cheap and reliable enough to be deployed in different environments. However, their limited energy capacity limits their lifespan. In this paper, a power management strategy at network-level for a set of nodes is proposed. The control strategy makes use of a Hybrid Dynamical System approach, where solutions may continuously flow according to some differential equations and may discontinuously jump according to some rules. The goal of the proposed control strategy is to improve the scalability issues while the network lifespan is not decreased when compared to a Model Predictive Control (MPC) approach, leading to an improvement in the network power consumption. The strategy is evaluated in simulation on a realistic benchmark. Simulation results and their comparison to results obtained with an MPC strategy show the effectiveness of the proposed control strategy.