Radio frequency (RF) based wireless power transfer provides an attractive solution to extend the lifetime of power-constrained wireless sensor networks. Through harvesting RF energy from surrounding environments or dedicated energy sources, low-power wireless devices can be self-sustaining and environment-friendly. These features make the RF energy harvesting wireless communication (RF-EHWC) technique attractive to a wide range of applications. The objective of this article is to investigate the latest research activities on the practical RF-EHWC design. The distribution of RF energy in the real environment, the hardware design of RF-EHWC devices and the practical issues in the implementation of RF-EHWC networks are discussed. At the end of this article, we introduce several interesting applications that exploit the RF-EHWC technology to provide smart healthcare services for animals, wirelessly charge the wearable devices, and implement 5G-assisted RF-EHWC.

Cognitive radio networks (CRNs) have been widely studied to improve spectrum utilization through a dynamic spectrum allocation scheme. However, many challenges remain and one big issue is how to provide an efficient spectrum management in a mobile environment. In this paper, we propose a new prediction-based spectrum management strategy to tackle this challenge. Both spectrum prediction and users’ mobility prediction are fully considered in the proposed strategy. Combining the prediction information and cooperative sensing, a CR base station can obtain the sensing information for future location of each secondary user, so as to prearrange high-quality channels for secondary users ahead of time. In addition, we propose a new channel selection scheme when multiple channels are available simultaneously. Besides traditional decision factors, the new channel selection algorithm takes the channel’s future availability obtained from spectrum prediction into account. By properly integrating the spectrum prediction, user mobility prediction, and channel selection, the new spectrum management strategy is capable of allocating the spectrum resource more efficiently. Extensive simulations are conducted and results verify that the proposed spectrum management strategy significantly improves the system performance in terms of reducing handoff times and improving user satisfactory, connection reliability, and channel utilization.

This article introduces a practical approach to wirelessly charge underground Internet of Things (IoT) (UIoT) for soil monitoring using ultra-high-frequency (UHF) radio energy. In the proposed system, UIoT nodes do not require batteries or any aboveground attachments (e.g., solar panel). Instead, they harvest 915 MHz radio energy emitted from a UAV for semiperpetual operation. The UIoT nodes utilize the harvested energy to measure soil parameters and transmit data back to the UAV using ZigBee protocol. After collecting the data from UIoTs, the UAV uploads it to a cloud server for online soil quality analysis. The system has been optimized for efficient operation, with a UAV transmit power as low as 2 W. Startup, drive, and power management circuits have been designed to ensure reliable operation of UIoT nodes, even with low incident energy. According to experimental results, the developed radio frequency charging-enabled underground IoT system can successfully power up and transmit more than 1 kB of data within 10 s of wireless charging, followed by an additional 1.7 kB of data every 1.6 s thereafter.

Sustainable edge computing (SEC) is a promising technology that can reduce energy consumption and computing latency for the mobile Internet of things (IoT). By collecting renewable energy such as solar or wind energy from the environment, a sustainable cloudlet outside the electric grid can provide powerful computing capabilities for resource-constrained mobile IoT devices. In the real world, the density of sustainable energy can vary significantly over time. Therefore, the SEC cloudlet needs to dynamically adjust the clock frequency to balance energy consumption and computing latency. In this paper, we consider the limited energy storage of the cloudlet and the dynamic intensity of renewable energy, and then develop offline optimal CPU frequency scaling policies that (a) maximize the computing power of the cloudlet within a certain period of time, and (b) minimize the execution time given tasks offloaded to the cloudlet. An optimal tightest string policy is proposed to solve the optimization problem. In addition, a dynamic programming (DP) based suboptimal solution is introduced to simplify the practical implementation. How to design an online CPU frequency management strategy is also briefly discussed.

Energy-efficient federated learning (FL) is important for decentralized learning-based edge computing. The energy consumption of FL is largely affected by the efficiency of local training in edge devices and their communication efficiency to the central server. The majority of studies have focused on improving the latter while continuing to use traditional neural networks for local training. This can be computationally heavy for edge devices, especially given their limited energy supply and computation capabilities. In this paper, we propose a joint lightweight design and energy-saving algorithm (LDES) for efficient FL training and parameter uploading in the wireless edge. In the lightweight design of LDES, we investigate lightweight models in local training by deploying sparse or binary neural networks (SNN or BNN) at the edge, in order to reduce the uploaded data volume and the energy consumption. We develop an enhanced stochastic gradient descent algorithm with guaranteed convergence to handle the issues of non-smoothness and weight constraints (NSC-SGD) in training sparse models. In the energy-optimization part of LDES, we optimize power, bandwidth, and learning parameters to jointly minimize computing, uploading, and broadcasting energy. The numerical results show that LDES can reduce 56.21% energy consumption compared to benchmark FL schemes.