Energy Management and Cross Layer Optimization for Wireless Sensor Network Powered by Heterogeneous Energy Sources

TL;DR

This paper develops a stochastic cross-layer optimization framework for wireless sensor networks powered by heterogeneous energy sources, balancing data utility and electricity costs under uncertain energy and channel conditions.

Contribution

It introduces a novel system model and a distributed optimization algorithm that accounts for multiple energy sources and stochastic environmental factors in WSNs.

Findings

The proposed algorithm achieves a good trade-off between data utility and energy cost.

The system maintains queue stability under stochastic energy harvesting and channel conditions.

Simulations confirm the theoretical performance guarantees.

Abstract

Recently, utilizing renewable energy for wireless system has attracted extensive attention. However, due to the instable energy supply and the limited battery capacity, renewable energy cannot guarantee to provide the perpetual operation for wireless sensor networks (WSN). The coexistence of renewable energy and electricity grid is expected as a promising energy supply manner to remain function for a potentially infinite lifetime. In this paper, we propose a new system model suitable for WSN, taking into account multiple energy consumptions due to sensing, transmission and reception, heterogeneous energy supplies from renewable energy, electricity grid and mixed energy, and multidimension stochastic natures due to energy harvesting profile, electricity price and channel condition. A discrete-time stochastic cross-layer optimization problem is formulated to achieve the optimal trade-off between the time-average rate utility and electricity cost subject to the data and energy queuing stability constraints. The Lyapunov drift-plus-penalty with perturbation technique and block coordinate descent method is applied to obtain a fully distributed and low-complexity cross-layer algorithm only requiring knowledge of the instantaneous system state. The explicit trade-off between the optimization objective and queue backlog is theoretically proven. Finally, the extensive simulations verify the theoretic claims.