On energy-balanced backpressure routing mechanisms for stochastic energy harvesting wireless sensor networks

Abstract

In energy harvesting wireless sensor networks, energy imbalance among sensor nodes is detrimental to network performance and battery life. Particularly, nodes that are closer to a data sink or have less energy replenishment tend to exhaust the energy earlier, leading to some sub-regions of the environment being left unmonitored. Existing research efforts focus on the energy management based on the assumption that the energy harvesting process is predictable. Unfortunately, such an assumption is not practicable in real-world energy harvesting systems. With the consideration of the unpredictability of the harvestable energy, in this article, we adopt the stochastic Lyapunov optimization framework to jointly manage energy and make routing decision, which could help mitigate the energy imbalance problem. We develop two online policies: (1) Energy-balanced Backpressure Routing Algorithm for lossless networks and (2) Enhanced Energy-balanced Backpressure Routing Algorithm for time varying wireless networks with lossy links. Both Energy-balanced Backpressure Routing Algorithm and Enhanced Energy-balanced Backpressure Routing Algorithm are distributed, queuing stable, and do not require the explicit knowledge of the statistics of the energy harvesting. The simulation data show that our developed algorithms can achieve significantly higher performance in terms of energy balance than existing schemes such as Original Backpressure Algorithm and the Backpressure Collection Protocol.