Transmission Scheduling for Wireless Energy Transfer with Dual Data-Energy Channel Models

Wireless sensing in hard to reach areas or extreme environments faces a major hurdle when it comes to battery replacement. Passive sensor technology is an alternative solution eliminating the need for battery, but it suffers from short communication range. Wireless energy transfer has received a great deal of attention recently, since it can alleviate the need for battery replacement through remote charging of sensing circuit. Current research in this area assumes continuous or periodic pulsed energy transmission and periodic data sampling and retrieval. This paper proposes a different paradigm that includes random energy transmission and random data reception based on two separate stochastic models for data and energy channels. It is demonstrated that this approach renders a more robust wireless sensing system with extended lifetime and decreased outage probability. A decision threshold is obtained for data transmission based on current battery level, predicted future incoming energy, and data channel status.