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

• Published in: IEEE transactions on wireless communications Vol. 14; no. 5; pp. 2814 - 2826
• Main Authors: Xu, Weiqiang, Zhang, Yushu, Shi, Qingjiang, Wang, Xiaodong
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.05.2015; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Alternative energy sources; Batteries; block coordinate descent; crosslayer optimization; drift-plus-penalty; Electricity; Electricity distribution; electricity grid; Energy consumption; energy harvesting; energy management; Energy policy; Energy transmission; heterogeneous energy; Lyapunov optimization; Mathematical models; Optimization; Power grids; Radio frequency; Remote sensors; Renewable energy; Renewable resources; Wireless networks; Wireless sensor networks; Wireless sensor networks; Lyapunov optimization; block coordinate descent; drift-plus-penalty; electricity grid; energy harvesting; cross-layer optimization; energy management; heterogeneous energy
• ISSN: 1536-1276; 1558-2248
• DOI: 10.1109/TWC.2015.2394799

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 of WSN 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 multi-dimension 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, through extensive simulations, the theoretic claims are verified, and the impacts of a variety of system parameters on overall objective, rate utility and electricity cost are investigated.