Wireless sensors can integrate rechargeable batteries and energy-harvesting (EH) devices to enable long-term, autonomous operation, thus requiring intelligent energy management to limit the adverse impact of energy outages. This work considers a network of EH wireless sensors, which report packets with random utility value to a fusion center (FC) over a shared wireless channel. We design decentralized access schemes, where each node performs a local decision to transmit/discard the packet, based on an estimate of the packet's utility, its own energy level, and the scenario state of the EH process, with the objective to maximize the average long-term aggregate utility of the packets received at the FC. Due to the non-convex structure of the problem, we develop an approximate optimization by resorting to a mathematical artifice based on a game theoretic formulation of the multiaccess problem, where the nodes do not behave strategically, but rather attempt to maximize a common network utility with respect to their own policy. We characterize the symmetric Nash-equilibrium (SNE), where all nodes employ the same policy, we prove its uniqueness, and show that it is a local maximum of the original problem. We present an algorithm to compute the SNE and propose a heuristic scheme, which is optimal for large battery capacity. We show numerically that the SNE achieves near-optimal performance, within 3% of the optimal policy, at a fraction of the complexity, and identify two operational regimes of EHnetworks: an energy-limited scenario, where energy is scarce and the channel is under-utilized, and a network-limited scenario, where energy is abundant and the shared wireless channel represents the bottleneck of the system.
al. REF have considered maximizing the long term data utility at the fusion center by scheduling the data transmission and discarding process based on the information of energy level and the energy harvesting process.
Optimal Adaptive Random Multiaccess in Energy Harvesting Wireless Sensor Networks
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