Efficient charging for batteryless solutions in energy harvesting Energy Harvesting Systems seek to remove the batteries from electronic devices and replace them with devices that generate directly from the environment around them. This paper presents an intelligent algorithm to manage the charging and discharging of a supercapacitor for use in sensor nodes which has considerable efficiency advantages. The ultralow power microcontrollers used make intelligent decisions whether to sleep or wake according to previously received and stored energy. Using an adaptive strategy of this kind the amount of work can be precisely matched to the resources available to achieve maximum utilization allowing the satisfactory completion of a stated set of tasks.

Spatiotemporal charging scheduling in wireless rechargeable sensor networks Abstract   Wireless sensor networks have a wide range of applications. However, the batteryconstrained energy limits the scope of network applications and timeliness. In this paper, we study how to schedule charging and allocate charging time simultaneously for a wireless sensor network for the purpose of prolonging network lifetime and improve charging efficiency. To this end, a mixed integer optimization model for simultaneous charging scheduling and charging time allocation is established through the maximization of the charging efficiency. Then, an offline algorithm is developed to solve the problem. Further, an online charging node insertion algorithm is developed for realtime service. The simulation results show that the proposed algorithm can achieve near 100% charging success rate under periodic and hybrid services, which is significantly superior to the results obtained by the compared algorithms.