Disasters are crisis circumstances that put human life in jeopardy. During disasters, public communication infrastructure is particularly damaged, obstructing Search And Rescue (SAR) efforts, and it takes significant time and effort to re-establish functioning communication infrastructure. SAR is a critical component of mitigating human and environmental risks in disasters and harsh environments. As a result, there is an urgent need to construct communication networks swiftly to help SAR efforts exchange emergency data. UAV technology has the potential to provide key solutions to mitigate such disaster situations. UAVs can be used to provide an adaptable and reliable emergency communication backbone and to resolve major issues in disasters for SAR operations. In this paper, we evaluate the network performance of UAV-assisted intelligent edge computing to expedite SAR missions and functionality, as this technology can be deployed within a short time and can help to rescue most people during a disaster. We have considered network parameters such as delay, throughput, and traffic sent and received, as well as path loss for the proposed network. It is also demonstrated that with the proposed parameter optimization, network performance improves significantly, eventually leading to far more efficient SAR missions in disasters and harsh environments.

Wireless Power Transfer (WPT) is a disruptive technology that allows wireless energy provisioning for energy-limited IoT devices, thus decreasing the over-reliance on batteries and wires. WPT could replace conventional energy provisioning (e.g., energy harvesting) and expand to be deployed 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 about WPT technologies and wireless charging scheduling algorithms. Therefore, in this paper, we review the most recent studies related to WPT, including classifications, advantages, disadvantages, and main domains of application. Furthermore, we review the recently designed wireless charging scheduling algorithms (schemes) for 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.

In the era of data-driven healthcare, the amalgamation of blockchain and federated learning (FL) introduces a paradigm shift toward secure, collaborative, and patient-centric data sharing. This article pioneers the exploration of the conceptual framework and technical synergy of FL and blockchain for decentralized data sharing, aiming to strike a balance between data utility and privacy. FL, a decentralized machine learning paradigm, enables collaborative AI model training across multiple healthcare institutions without sharing raw patient data. Combined with blockchain, a transparent and immutable ledger, it establishes an ecosystem fostering trust, security, and data integrity. This article elucidates the technical foundations of FL and blockchain, unravelling their roles in reshaping healthcare data sharing. This article vividly illustrates the potential impact of this fusion on patient care. The proposed approach preserves patient privacy while granting healthcare providers and researchers access to diversified data sets, ultimately leading to more accurate models and improved diagnoses. The findings underscore the potential acceleration of medical research, improved treatment outcomes, and patient empowerment through data ownership. The synergy of FL and blockchain envisions a healthcare ecosystem that prioritizes individual privacy and propels advancements in medical science.

Unmanned Aerial Vehicles (UAVs) are increasingly being used in a high-computation paradigm enabled with smart applications in the Beyond Fifth Generation (B5G) wireless communication networks. These networks have an avenue for generating a considerable amount of heterogeneous data by the expanding number of Internet of Things (IoT) devices in smart environments. However, storing and processing massive data with limited computational capability and energy availability at local nodes in the IoT network has been a significant difficulty, mainly when deploying Artificial Intelligence (AI) techniques to extract discriminatory information from the massive amount of data for different tasks.Therefore, Mobile Edge Computing (MEC) has evolved as a promising computing paradigm leveraged with efficient technology to improve the quality of services of edge devices and network performance better than cloud computing networks, addressing challenging problems of latency and computation-intensive offloading in a UAV-assisted framework. This paper provides a comprehensive review of intelligent UAV computing technology to enable 6G networks over smart environments. We highlight the utility of UAV computing and the critical role of Federated Learning (FL) in meeting the challenges related to energy, security, task offloading, and latency of IoT data in smart environments. We present the reader with an insight into UAV computing, advantages, applications, and challenges that can provide helpful guidance for future research.