Stochastic modelling of wireless energy transfer

Abstract

Abstract-This study investigates the efficiency of a new method of powering remote sensors by the means of wireless energy transfer. The increased use of sensors for data collection comes with the inherent cost of supplying power from sources such as power cables or batteries. Wireless energy transfer technology eliminates the need for power cables or periodic battery replacement. The time and cost of setting up or expanding a sensor network will be reduced while allowing sensors to be placed in areas where running power cables or battery replacement is not feasible. This paper models wireless channels for power and data separately. Smart scheduling for the data channel is proposed to avoid transmitting data on a noisy channel where the probability of data loss is high to improve power efficiency. Analytical models have been developed and verified using simulations.

A Multi-Node Energy Prediction Approach Combined with Optimum Prediction Interval for RF Powered WSNs

“…RF source with omni-directional antenna and sinusoidal impulse response was linked with the individual sensor nodes through an AWGN (Additive White Gaussian Noise) channel to mimic the effect of random process. The choice of AWGN channel model was made, as it is best suited for static (non-mobile) energy harvesting sensor nodes [18]. A mean of 0 (µ = 0) and variance of 0.1 (σ = 0.1) were assumed for channel modelling.…”