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.

Although the concept of digital twin technology has been in existence for nearly half a century, its application in healthcare is a relatively recent development. In healthcare, the utilization of digital twin and data-driven models has proven to enhance clinical decision support, particularly in the treatment and assessment of chronic wounds, leading to improved clinical outcomes. This paper proposes the implementation of a digital twin in the domain of healthcare, specifically in the management of chronic wounds, by leveraging artificial intelligence techniques. The digital twin is composed of data collection, data processing, and AI models dedicated to wound healing. A novel AI pipeline is utilized to track the healing of chronic wounds. The digital twin, serving as a virtual representation of the actual wound, simulates and replicates the healing process. Furthermore, the proposed wound-healing prediction model effectively guides the treatment of chronic wounds. Additionally, by comparing the actual wound with its digital twin, the system enables early identification of non-healing wounds, facilitating timely adjustments and modifications to the treatment plan. By incorporating a digital twin in healthcare, the proposed system enables personalized and tailored treatments, potentially playing a crucial role in proactive problem identification.

Occupational health and safety hazards in the extreme work environment of underground mines remained a serious concern for both mine management and regulatory agency. Miners are often employed to perform different mining activities across the mine and are always exposed to health risks such as lung cancer. With the technology advancements, it is now possible to keep track of mine individuals and their health parameters. The current practice of health analysis is periodic in nature and is highly dependent on voluntary participation. Wearable health sensing system is an alternative solution to overcome these challenges and is able to provide insights on miners’ health conditions. Timely analysis of physiological parameters of the miners is immensely helpful to minimize the injuries and can also provide preventive measures for potential health hazards. In this paper, we propose a wireless health monitoring system, especially for underground mines. The contributions of this paper are twofold. First, it presents and discusses our proposed system architecture and solution followed by challenges of such system in the context of underground mines. Second, as a preliminary analysis, detailed discussion on the wireless link behavior for reliable data transmission and communication are presented. We performed real-world experimental measurements in an operational underground coal mine considering several deployment settings in straight, near face and curved mine galleries. The communication metrics (e.g., received signal strength and packet reception rate) are extensively evaluated.

The coronavirus disease (COVID-19) has continued to cause severe challenges during this unprecedented time, affecting every part of daily life in terms of health, economics, and social development. There is an increasing demand for chest X-ray (CXR) scans, as pneumonia is the primary and vital complication of COVID-19. CXR is widely used as a screening tool for lung-related diseases due to its simple and relatively inexpensive application. However, these scans require expert radiologists to interpret the results for clinical decisions, i.e., diagnosis, treatment, and prognosis. The digitalization of various sectors, including healthcare, has accelerated during the pandemic, with the use and importance of Artificial Intelligence (AI) dramatically increasing. This paper proposes a model using an Explainable Artificial Intelligence (XAI) technique to detect and interpret COVID-19 positive CXR images. We further analyze the impact of COVID-19 positive CXR images using heatmaps. The proposed model leverages transfer learning and data augmentation techniques for faster and more adequate model training. Lung segmentation is applied to enhance the model performance further. We conducted a pre-trained network comparison with the highest classification performance (F1-Score: 98%) using the ResNet model.

The transmission scheduling is a critical problem in radio frequency (RF) energy harvesting communications. Existing transmission strategies are mainly based on a conventional model, in which the amount of harvested energy is modeled as predetermined random variables and the data transmission is arranged in a fixed feasible energy tunnel. In this paper, we show through the theoretical analysis and experimental results that due to the nonlinear battery charging characteristics, the harvested energy will largely depend on the transmission strategy. The bounds of feasible energy tunnel become dynamic. To describe a practical ambient energy harvesting process more accurately, a new nonlinear recursive model is proposed by adding a feedback loop that reflects the real-time influence of the data transmission on the energy harvesting process. In addition, to improve communication performance, we redesign the optimal transmission scheduling strategy based on the new model. In order to cope with the challenge of the endless loop in the new model, a recursive algorithm is developed. The simulation results reveal that the new transmission scheduling strategy can balance the efficiency of energy harvest and energy utilization regardless of the length of energy packets, thus improving the throughput performance of RF energy harvesting wireless communications.

Intelligent Reflecting Surfaces (IRS) have been perceived as a promising and innovative technology for 5G and next-generation communications, which offers the flexibility of re-configuring a wireless communication environment as desired. Their attractive benefits include low cost, energy-saving, and no interference to others, and hence yield high-performance communications. On the other hand, malicious users may take advantage of IRS to conduct attacks such as eavesdropping and jamming. The integration of IRS into 5G and beyond networks will open up new opportunities from the security perspective, which has not been thoroughly studied. This paper presents the recent accomplishment of the use of IRS-based systems for secure wireless communication by grouping into two categories: secure communication utilizing IRS and security threats using IRS. Representative use cases are proposed, e.g., using IRS to enhance Signal-to-Noise Ratio (SNR) for legitimate users, reducing malicious signals via IRS, and increasing SNR for malicious users. Extensive simulations are conducted for these use cases. The results validate the effectiveness of using IRS to increase or decrease SNR and enhance to improve secure communication or threaten the communication.