Cloud-based setups are intertwined with the Internet of Things and advanced, and technologies such as blockchain revolutionize conventional healthcare infrastructure. This digitization has major advantages, mainly enhancing the security barriers of the green tree infrastructure. In this study, we conducted a systematic review of over 150 articles that focused exclusively on blockchain-based healthcare systems, security vulnerabilities, cyberattacks, and system limitations. In addition, we considered several solutions proposed by thousands of researchers worldwide. Our results mostly delineate sustained threats and security concerns in blockchain-based medical health infrastructures for data management, transmission, and processing. Here, we describe 17 security threats that violate the privacy and data integrity of a system, over 21 cyber-attacks on security and QoS, and some system implementation problems such as node compromise, scalability, efficiency, regulatory issues, computation speed, and power consumption. We propose a multi-layered architecture for the future healthcare infrastructure. Second, we classify all threats and security concerns based on these layers and assess suggested solutions in terms of these contingencies. Our thorough theoretical examination of several performance criteria—including confidentiality, access control, interoperability problems, and energy efficiency—as well as mathematical verifications establishes the superiority of security, privacy maintenance, reliability, and efficiency over conventional systems. We conducted in-depth comparative studies on different interoperability parameters in the blockchain models. Our research justifies the use of various positive protocols and optimization methods to improve the quality of services in e-healthcare and overcome problems arising from laws and ethics. Determining the theoretical aspects, their scope, and future expectations encourages us to design reliable, secure, and privacy-preserving systems.

“Flying Ad Hoc Networks (FANETs)”, which use “Unmanned Aerial Vehicles (UAVs)”, are developing as a critical mechanism for numerous applications, such as military operations and civilian services. The dynamic nature of FANETs, with high mobility, quick node migration, and frequent topology changes, presents substantial hurdles for routing protocol development. Over the preceding few years, researchers have found that machine learning gives productive solutions in routing while preserving the nature of FANET, which is topology change and high mobility. This paper reviews current research on routing protocols and Machine Learning (ML) approaches applied to FANETs, emphasizing developments between 2021 and 2023. The research uses the PRISMA approach to sift through the literature, filtering results from the SCOPUS database to find 82 relevant publications. The research study uses machine learning-based routing algorithms to beat the issues of high mobility, dynamic topologies, and intermittent connection in FANETs. When compared with conventional routing, it gives an energy-efficient and fast decision-making solution in a real-time environment, with greater fault tolerance capabilities. These protocols aim to increase routing efficiency, flexibility, and network stability using ML’s predictive and adaptive capabilities. This comprehensive review seeks to integrate existing information, offer novel integration approaches, and recommend future research topics for improving routing efficiency and flexibility in FANETs. Moreover, the study highlights emerging trends in ML integration, discusses challenges faced during the review, and discusses overcoming these hurdles in future research.

Mobile adhoc networks have grown in prominence in recent years, and they are now utilized in a broader range of applications. The main challenges are related to routing techniques that are generally employed in them. Mobile Adhoc system management, on the other hand, requires further testing and improvements in terms of security. Traditional routing protocols, such as Adhoc On-Demand Distance Vector (AODV) and Dynamic Source Routing (DSR), employ the hop count to calculate the distance between two nodes. The main aim of this research work is to determine the optimum method for sending packets while also extending life time of the network. It is achieved by changing the residual energy of each network node. Also, in this paper, various algorithms for optimal routing based on parameters like energy, distance, mobility, and the pheromone value are proposed. Moreover, an approach based on a reward and penalty system is given in this paper to evaluate the efficiency of the proposed algorithms under the impact of parameters. The simulation results unveil that the reward penalty-based approach is quite effective for the selection of an optimal path for routing when the algorithms are implemented under the parameters of interest, which helps in achieving less packet drop and energy consumption of the nodes along with enhancing the network efficiency.