Wireless Power Transfer (WPT) is a disruptive technology that allows wireless energy provisioning for energylimited IoT devices, thus decreasing the over-reliance on batteries and wires. WPT could replace conventional energy provisioning (e.g., energy harvesting) and expand for deployment in many of our daily-life applications, including but not limited to healthcare, transportation, automation, and smart cities. As a new rising technology, WPT has attracted many researchers from academia and industry in terms of technologies and charging scheduling within a plethora of services and applications. Thus, in this paper, we review the most recent studies related to WPT, including the classifications, advantages, disadvantages, and main application domains. Furthermore, we review the recently designed wireless charging scheduling algorithms and schemesfor wireless sensor networks. Our study provides a detailed survey of wireless charging scheduling schemes covering the main scheme classifications, evaluation metrics, application domains, advantages, and disadvantages of each charging scheme. We further summarize trends and opportunities for applying WPT at some intersections.Index Terms-wireless power transfer, wireless sensor networks, smart homes, healthcare, industrial, and charging schemes.
I. INTRODUCTIONS IXTH Generation (6G) wireless networks seek to enhance the dependability, speed, and bandwidth of their forerunners, the 5G networks, in order to meet the expanding demands of the Internet of Everything (IoE) applications and accommodate cutting-edge technological trends like decentralized and pervasive artificial intelligence (AI). Energy efficiency and power provisioning are critical problems in future wireless networking, including Wireless Sensor Networks (WSN), IoE, Beyond 5G (B5G), and 6G [70], [144]. Many power-saving (energy conservation) solutions are used to improve the power usage in wireless communications, such as in [44], dynamic routing techniques [80], [146], [145], efficient construction of multi-cast trees [27], [25], mobile data collection, low power hardware architecture [61, 33], and resource allocation. Despite the fact that these solutions