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Abstract
Harvested energy in Energy Harvesting-Wireless Sensor Network is never uniform by nature that causes unpredictable energy scavenging by harvesting systems. The energy load of WSN varying in nature and depends on many factors. In order to ensure uninterrupted power supply to the sensors the crucial aspect is to adjust unpredictable energy by using multiple resources. In the proposed study, we bring forth the effect of harvesting energy unpredictability on the network load and proposed a model for minimizing the gap between energy scavenged and energy requirement of the whole architecture.
To design a robust sensor network, in this paper, we use mobility to circumvent communication bottlenecks caused by spatial energy variations. We employ a mobile collector, called SenCar to collect data from designated sensors and balance energy consumptions in the network. To show spatial-temporal energy variations, we first conduct a case study in a solar-powered network and analyze possible impact on network performance. Next, we present a two-step approach for mobile data collection. First, we adaptively select a subset of sensor locations where the SenCar stops to collect data packets in a multi-hop fashion. We develop an adaptive algorithm to search for nodes based on their energy and guarantee data collection tour length is bounded. Second, we focus on designing distributed algorithms to achieve maximum network utility by adjusting data rates, link scheduling and flow routing that adapts to the spatial-temporal environmental energy fluctuations. Finally, our numerical results indicate the distributed algorithms can converge to optimality very fast and validate its convergence in case of node failure.