Sustainability Analysis and Resource Management for Wireless Mesh Networks with Renewable Energy Supplies

Abstract

There is a growing interest in the use of renewable energy sources to power wireless networks in order to mitigate the detrimental effects of conventional energy production or to enable deployment in off-grid locations. However, renewable energy sources, such as solar and wind, are by nature unstable in their availability and capacity. The dynamics of energy supply hence impose new challenges for network planning and resource management. In this paper, the sustainable performance of a wireless mesh network powered by renewable energy sources is studied. To address the intermittently available capacity of the energy supply, adaptive resource management and admission control schemes are proposed. Specifically, the goal is to maximize the energy sustainability of the network, or equivalently, to minimize the failure probability that the mesh access points (APs) deplete their energy and go out of service due to the unreliable energy supply. To this end, the energy buffer of a mesh AP is modeled as a G/G/1(/N) queue with arbitrary patterns of energy charging and discharging. Diffusion approximation is applied to analyze the transient evolution of the queue length and the energy depletion duration. Based on the analysis, an adaptive resource management scheme is proposed to balance traffic loads across the mesh network according to the energy adequacy at different mesh APs. A distributed admission control strategy to guarantee high resource utilization and to improve energy sustainability is presented. By considering the first and second order statistics of the energy charging and discharging processes at each mesh AP, it is demonstrated that the proposed schemes outperform some existing state-of-the-art solutions.

DOI: 10.1109/JSAC.2014.141214

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Cite this paper

@article{Cai2014SustainabilityAA, title={Sustainability Analysis and Resource Management for Wireless Mesh Networks with Renewable Energy Supplies}, author={Lin X. Cai and Yongkang Liu and Tom H. Luan and Xuemin Shen and Jon W. Mark and H. Vincent Poor}, journal={IEEE Journal on Selected Areas in Communications}, year={2014}, volume={32}, pages={345-355} }