Understanding the optimal usage of fluctuating renewable energy in Wireless Sensor Networks (WSNs) iscomplex. Lexicographic Max-min (LM) rate allocation is a good solution, but is non-trivial for multi-hopWSNs, as both fairness and sensing rates have to be optimized through the exploration of all possibleforwarding routes in the network. All current optimal approaches to this problem are centralized andoff-line, suffering from low scalability and large computational complexity; typically solving O(N2) linearprogramming problems for N-node WSNs. This paper presents the first optimal distributed solution tothis problem with much lower complexity. We apply it to Solar Powered WSNs (SP-WSNs) to achieveboth LM optimality and sustainable operation. Based on realistic models of both time-varying solar powerand photovoltaic-battery hardware, we propose an optimization framework that integrates a local powermanagement algorithm with a global distributed LM rate allocation scheme. The optimality, convergence,and efficiency of our approaches are formally proven. We also evaluate our algorithms via experimentson both solar-powered MicaZ motes and extensive simulations using real solar energy data and practicalpower parameter settings. The results verify our theoretical analysis and demonstrate how our approachoutperforms both the state-of-the-art centralized optimal and distributed heuristic solutions.
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