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.

Action assessment and feedback can effectively assist fitness practitioners in improving exercise benefits. In this paper, we address key challenges in human action recognition and assessment by proposing innovative methods that enhance performance while reducing computational complexity. Firstly, we present Oct-MobileNet, a lightweight backbone network, to overcome the limitations of the traditional OpenPose algorithm’s VGG19 network, which exhibits a large parameter size and high device requirements. Oct-MobileNet employs octave convolution and attention mechanisms to improve the extraction of high-frequency features from the human body contour, resulting in enhanced accuracy with reduced model computational burden. Furthermore, we introduce a novel approach for action recognition that combines skeleton-based information and multiple feature fusion. By extracting spatial geometric and temporal characteristics from actions, we employ a sliding window algorithm to integrate these features. Experimental results show the effectiveness of our approach, demonstrating its ability to accurately recognize and classify various human actions. Additionally, we address the evaluation of traditional fitness exercises, specifically focusing on the BaDunJin movements. We propose a multimodal information-based assessment method that combines pose detection and keypoint analysis. Label sequences are obtained through a pose detector and each frame’s keypoint coordinates are represented as pose vectors. Leveraging multimodal information, including label sequences and pose vectors, we explore action similarity and perform quantitative evaluations to help exercisers assess the quality of their exercise performance.

We investigate how to provide Quality-of-Service (QoS) for diversified video flows. We design a fine-grained video traffic control mechanism that integrates traffic classification with path selection for video flows within the framework of SDN. For the design, we present a category-theoretic ontology log (olog) diagram model, which provides a novel perspective on the interdependency among various system components. For the video traffic classification, we first evaluate various machine learning classifiers in terms of their performance and then chose the most effective one to be the first module. For the path selection, we devise a multi-constrained QoS routing strategy by restructuring a state-of-the-art graph algorithm, combine it with the k-shortest path algorithm, and deploy this strategy as another video traffic control module. We implemented a prototype of the proposed mechanism on the SDN emulator Mininet, and we evaluate its effectiveness using the performance results obtained.