FANET (flying ad-hoc networks) is currently a trending research topic. Unmanned aerial vehicles (UAVs) have two significant challenges: short flight times and inefficient routing due to low battery power and high mobility. Due to these topological restrictions, FANETS routing is considered more complicated than MANETs or VANETs. Clustering approaches based on artificial intelligence (AI) approaches can be used to solve complex routing issues when static and dynamic routings fail. Evolutionary algorithm-based clustering techniques, such as moth flame optimization, and ant colony optimization, can be used to solve these kinds of problems with routes. Moth flame optimization gives excellent coverage while consuming little energy and requiring a minimum number of cluster heads (CHs) for routing. This paper employs a moth flame optimization algorithm for network building and node deployment. Then, we employ a variation of the K-Means Density clustering approach to choosing the cluster head. Choosing the right cluster heads increases the cluster’s lifespan and reduces routing traffic. Moreover, it lowers the number of routing overheads. This step is followed by MRCQ image-based compression techniques to reduce the amount of data that must be transmitted. Finally, the reference point group mobility model is used to send data by the most optimal path. Particle swarm optimization (PSO), ant colony optimization (ACO), and grey wolf optimization (GWO) were put to the test against our proposed EECP-MFO. Several metrics are used to gauge the efficiency of our proposed method, including the number of clusters, cluster construction time, cluster lifespan, consistency of cluster heads, and energy consumption. This paper demonstrates that our proposed algorithm performance is superior to the current state-of-the-art approaches using experimental results.

Blockchain-based framework for secure and reliable land registry system

The Internet of Things (IoT) has radically transformed how patient information and healthcare monitoring are monitored and recorded and has revolutionized the area by ensuring regular 24 × 7 tracking without costly and restricted human resources and with a low mistake probability. The Internet of Medical Things (IoMT) is a subsection of the Internet of things (IoT) that uses medical equipment as things or nodes to enable cost-effective and efficient patient monitoring and recording. The IoMT can cope with a wide range of problems, including observing patients in hospitals, monitoring patients in their homes, and assisting consulting physicians and nurses in monitoring health conditions at regular intervals and issuing warning signals if emergency care is necessary. EEG signals, electrocardiograms (ECGs), blood sugar levels, blood pressure levels, and other conditions can be examined. In crucial situations, quick and real-time analysis is essential, and failure to provide careful attention can be fatal. A cloud-based IoT platform cannot handle these latency-sensitive conditions. Fog computing (FC) is a novel paradigm for assigning, processing, and storing resources to IoT devices with limited resources. Where substantial processing power or storage is required, all nodes in a fog computing scheme can delegate their jobs to local fog nodes rather than forwarding them to the cloud module at a greater distance. Identifying potential security risks and putting in place adequate security measures are critical. This work aims to examine a blockchain (BC) as a potential tool for mitigating the impact of these difficulties in conjunction with fog computing. This research shows that blockchain can overcome fog computing’s privacy and security concerns. It also discusses blockchain’s issues and limitations from the perspective of fog computing (FC) and the IoMT.

The internet of medical things (IoMT) is a smart medical device structure that includes apps, health services, and systems. These medical equipment and applications are linked to healthcare systems via the internet. Because IoT devices lack computational power, the collected data can be processed and analyzed in the cloud by more computationally intensive tools. Cloud computing in IoMT is also used to store IoT data as part of a collaborative effort. Cloud computing has provided new avenues for providing services to users with better user experience, scalability, and proper resource utilization compared to traditional platforms. However, these cloud platforms are susceptible to several security breaches evident from recent and past incidents. Trust management is a crucial feature required for providing secure and reliable service to users. The traditional trust management protocols in the cloud computing situation are centralized and result in single-point failure. Blockchain has emerged as the possible use case for the domain that requires trust and reliability in several aspects. Different researchers have presented various blockchain-based trust management approaches. This study reviews the trust challenges in cloud computing and analyzes how blockchain technology addresses these challenges using blockchain-based trust management frameworks. There are ten (10) solutions under two broad categories of decentralization and security. These challenges are centralization, huge overhead, trust evidence, less adaptive, and inaccuracy. This systematic review has been performed in six stages: identifying the research question, research methods, screening the related articles, abstract and keyword examination, data retrieval, and mapping processing. Atlas.ti software is used to analyze the relevant articles based on keywords. A total of 70 codes and 262 quotations are compiled, and furthermore, these quotations are categorized using manual coding. Finally, 20 solutions under two main categories of decentralization and security were retrieved. Out of these ten (10) solutions, three (03) fell in the security category, and the rest seven (07) came under the decentralization category.

The Internet of Things (IoT) and blockchain (BC) are reliable technologies widely employed in various contexts. IoT devices have a lot of potential for data sensing and recording without human intervention, but they also have processing and security issues. Due to their limited computing power, IoT devices cannot use specialized cryptographic security mechanisms. There are various challenges when using traditional cryptographic techniques to transport and store medical records securely. The general public’s health depends on having an electronic health record (EHR) system that is current. In the era of e-health and m-health, problems with integrating data from various EHRs, preserving data interoperability, and ensuring that all data access is in the patient’s hands are all obstacles to creating a dependable EHR system. If health records get into the wrong hands, they could endanger the lives of patients and their right to privacy. BC technology has become a potent tool for ensuring recorded data’s immutability, validity, and confidentiality while enabling decentralized storage. This study focuses on EHR and other types of e-healthcare, evaluating the advantages of complementary technologies and the underlying functional principles. The major BC consensus mechanisms for BC-based EHR systems are analyzed in this study. It also examines several IoT-EHR frameworks’ current infrastructures. A breakdown of BC integration’s benefits with the IoT-EHR framework is also offered. A BC-based IoT-EHR architecture has been developed to enable the automated sensing of patient records and to store and retrieve these records in a secure and reliable environment. Finally, we conduct a security study to demonstrate the security of our suggested EHR framework.