Energy-Collision Aware Data Aggregation Scheduling for Energy Harvesting Sensor Networks

The emerging energy harvesting technology enables charging sensor batteries with renewable energy sources, which has been effectively integrated into Wireless Sensor Networks (EH-WSNs). Meanwhile, data aggregation is an essential operation in a WSN. The problem of Minimum Latency Aggregation Scheduling (MLAS) which seeks a fast and collision-free aggregation schedule has been well studied when nodes are energy-abundant. However, due to the limited energy harvesting capacities of tiny sensors, the captured energy remains scarce and differs greatly among nodes. Thus, all of the previous algorithms for MLAS are not suitable in EH-WSNs. In this paper, we investigate the MLAS problem in EH-WSNs. To make use of the harvested energy smartly, we construct an aggregation tree adaptively according to the residual battery level at each node. Furthermore, we identify a new kind of collision, named as energy -collision, and design a special structure to assist in avoiding it. By considering transmitting time, residual energy, and energy-collision, we propose three scheduling algorithms for MLAS problem in EH-WSNs. The theoretical analysis and simulation results verify that the proposed algorithms have high performance in terms of aggregation latency compared with the baseline methods.