Worst-Case Performance Guarantees of Scheduling Algorithms Maximizing Weighted Throughput in Energy-Harvesting Networks

Energy harvesting has recently emerged as a technique to enable longer operating time of sensor networks. However, due to harvesting energy’s not-completely-predictable stochastic nature, some packets may still fail to be transmitted due to insufficient energy supply. Also, packets in sensor networks are usually associated with sensitive time-critical information. Based on these observations, we theoretically study algorithms scheduling weighted packets with deadlines in energy-harvesting networks. In our model, packets arrive in an online manner, each packet has a value representing its priority and a value representing its deadline. Harvesting energy is gathered over time and transmitting one packet takes a unit of energy. The objective is to maximize the total value of the packets sent, subject to energy and deadline constraints. In this paper, we design both offline and online algorithms maximizing weighted throughput. We analyze these algorithms’ performance guarantees against their worst-case scenarios and empirically compare them with the conventional and classic scheduling algorithms. The simulation results show that our online algorithms have far better performance than conventional ones.