Eco-Friendly 0G Networks: Unlocking the Power of Backscatter Communications for a Greener Future

Backscatter Communication (BackCom) technology has emerged as a promising paradigm for the Green Internet of Things (IoT) ecosystem, offering advantages such as low power consumption, cost-effectiveness, and ease of deployment. While traditional BackCom systems, such as RFID technology, have found widespread applications, the advent of ambient backscatter presents new opportunities for expanding applications and enhancing capabilities. Moreover, ongoing standardization efforts are actively focusing on BackCom technologies, positioning them as a potential solution to meet the near-zero power consumption and massive connectivity requirements of next-generation wireless systems. 0G networks have the potential to provide advanced solutions by leveraging BackCom technology to deliver ultra-low-power, ubiquitous connectivity for the expanding IoT ecosystem, supporting billions of devices with minimal energy consumption. This paper investigates the integration of BackCom and 0G networks to enhance the capabilities of traditional BackCom systems and enable Green IoT. We conduct an in-depth analysis of BackCom-enabled 0G networks, exploring their architecture and operational objectives, and also explore the Waste Factor (WF) metric for evaluating energy efficiency and minimizing energy waste within integrated systems. By examining both structural and operational aspects, we demonstrate how this synergy enhances the performance, scalability, and sustainability of next-generation wireless networks. Moreover, we highlight possible applications, open challenges, and future directions, offering valuable insights for guiding future research and practical implementations aimed at achieving large-scale, sustainable IoT deployments.

Communication and Control in Collaborative UAVs: Recent Advances and Future Trends

The recent progress in unmanned aerial vehicles (UAV) technology has significantly advanced UAV-based applications for military, civil, and commercial domains. Nevertheless, the challenges of establishing high-speed communication links, flexible control strategies, and developing efficient collaborative decision-making algorithms for a swarm of UAVs limit their autonomy, robustness, and reliability. Thus, a growing focus has been witnessed on collaborative communication to allow a swarm of UAVs to coordinate and communicate autonomously for the cooperative completion of tasks in a short time with improved efficiency and reliability. This work presents a comprehensive review of collaborative communication in a multi-UAV system. We thoroughly discuss the characteristics of intelligent UAVs and their communication and control requirements for autonomous collaboration and coordination. Moreover, we review various UAV collaboration tasks, summarize the applications of UAV swarm networks for dense urban environments and present the use case scenarios to highlight the current developments of UAV-based applications in various domains. Finally, we identify several exciting future research direction that needs attention for advancing the research in collaborative UAVs.

Neural Networks for Infectious Diseases Detection: Prospects and Challenges

Artificial neural network (ANN) ability to learn, correct errors, and transform a large amount of raw data into useful medical decisions for treatment and care have increased its popularity for enhanced patient safety and quality of care. Therefore, this paper reviews the critical role of ANNs in providing valuable insights for patients' healthcare decisions and efficient disease diagnosis. We thoroughly review different types of ANNs presented in the existing literature that advanced ANNs adaptation for complex applications. Moreover, we also investigate ANN's advances for various disease diagnoses and treatments such as viral, skin, cancer, and COVID-19. Furthermore, we propose a novel deep Convolutional Neural Network (CNN) model called ConXNet for improving the detection accuracy of COVID-19 disease. ConXNet is trained and tested using different datasets, and it achieves more than 97% detection accuracy and precision, which is significantly better than existing models. Finally, we highlight future research directions and challenges such as complexity of the algorithms, insufficient available data, privacy and security, and integration of biosensing with ANNs. These research directions require considerable attention for improving the scope of ANNs for medical diagnostic and treatment applications.