Adaptive Array Processing based Wireless Energy Transmission for IoT Applications

Abstract

Rechargeable energy sources are essential for the extreme deployment of Internet-of-Things (IoT) sensors with the massive growth in smart systems. In order to meet these requirements, wireless energy transmission (WET) provides demand based power to the sensors. Temporary energy storage is done using supercapacitors. This overcomes the drawback of release of hazardous wastes released by IoT connected disposables after their working life. WET is made possible through adaptive array processing. The system consists of a transmitting side with multiple antennas and a receiving side with a programmable energy harvester. Several far-field adaptive processing schemes such as conventional beamformers, multiple sidelobe canceller (MSLC), multiple beam antenna system, regenerative hybrid array, digital beamformer, and generalized sidelobe canceller are tested and compared with the proposed modified beamforming model for superior performance. As the number of antennas increases, the gain increases. Gain and cumulative distribution function are analyzed over multiple distances for multiple iterations. The received signal strength indicator (RSSI) is also estimated to validate the performance of the proposed model.