Healthcare professionals and scholars have emphasized the need for IoT-based remote health monitoring services to track the health of the elderly. Such systems produce a large amount of data, necessitating the security and privacy of that data. On the other hand, Software Defined Networking (SDN) integration could be seen as a good solution to guarantee both flexibility and efficiency of the network which is even more important in the case of healthcare monitoring. Furthermore, Blockchain has recently been proposed as a game-changing tool that can be integrated into the Internet of Things (IoT) to have the optimal level of security and privacy. However, incorporating Blockchain into IoT networks, which rely heavily on patients’ health sensors, is extremely difficult. In this paper, a secure Healthcare Monitoring System (HMS) is proposed with a focus on trust management issues. The architecture seeks to protect multiple healthcare monitoring system components and preserves patient privacy by developing a security interface where separate security modules can be integrated to run side by side to ensure reliable HMS. The security framework architecture we propose takes advantage of the blockchain technology as a secure and timely information back-end. STHM is a proposal that uses Software-Defined Networking (SDN) as the communication medium that allows users to access SDN’s different functional and security technologies and services. Simulation results show that the use of Blockchain for the SDN-based healthcare monitoring can ensure the desired flexibility and security for a very lightweight additional overhead.

Unmanned aerial vehicles (UAVs) with artificial intelligence (AI) have ushered in a new era of mobile edge computing (MEC). Traditional AI models based on the aggregation of UAV sensing data that mostly contain private and sensitive user data, may raise serious privacy and data misuse problems. Federated learning (FL), as a potential distributed AI paradigm, has allowed UAVs to jointly train a global model without exposing their local sensing data. In the general context, flying things communicate only local model updates. The training process ends when the global model reaches a certain threshold. The centralized curator aggregation model can be subject to diverse threats such as DDoS and single point of failure. To improve the security of FL implementation and create a privacy-preserving model, we consider a new blockchain empowered AI paradigm using both wireless miners and edge computing at flying things for security requiring heterogeneous vehicular systems. In our proposal, blockchain is the backbone allowing secure and safe data exchange between them, providing decentralized FL training without the need for a central server. The proposed architecture is apt to cope with FL privacy leakage, insider/outsider attacks, malicious opponents, inference, and poisoning. It transforms intelligent MEC systems into decentralized, secure, and privacy enhanced networks.

Considered as a primordial component of Cooperative Intelligent Transportation Systems (C-ITS), Vehicular Ad Hoc Network (VANET) plays a weighty role to facilitate different on-road applications, most of which primary rely on multi hop communications. To ensure reliability and security of such communication, it is very pertinent to guarantee that the most proper and trustworthy vehicles are selected as relays. This paper takes up this challenge by introducing a weighted probabilistic and trust-aware strategy called WeiSTARS, to ensure high delivery ratios within reduced delays. Simulation results conducted using NS2 tool depicted that our technique enhances the delivery ratio by more than 8% with around 30% less delay compared to both GytAR and GPSR routing protocols even in the presence of high ratios of dishonest vehicles.

Unmanned aerial vehicles (UAVs) have gained increasing attention in boosting the performance of conventional networks due to their small size, high efficiency, low cost, and autonomously nature. The amalgamation of UAVs with both distributed/collaborative Deep Learning (DL) algorithms, such as Federated Learning (FL), and Blockchain technology have ushered in a new paradigm of Secure Multi-Access Edge Computing (S-MEC). Indeed, FL enables UAV devices to leverage their sensed data to build local DL models. The latter are then sent to a central node, e.g., S-MEC node, for aggregation, in order to generate a global DL model. Therefore, FL enables UAV devices to collaborate during several FL rounds in generating a learning model, while avoiding to share their local data, and thus ensuring UAVs’ privacy. However, UAV devices are usually limited in terms of resources such as battery, memory, and CPU. Some of the UAV devices may not be able to build a local learning models due to their resources capacity. Hence, there is a great need to select the adequate UAVs at each FL round, that are able to build a local DL model based on their resource capacities. In this paper, we design a novel and S-MEC-enabled framework that optimizes the selection of UAV participants at each FL training round, named FedSel. FedSel considers the available UAVs along with their resource capacities, in terms of energy, CPU, and memory, to determine which UAV device is able to participant in the FL process. Thus, we formulate the UAV selection problem as an Integer Linear Program, which considers the aforementioned constraints. We also prove that this problem is NP-hard, and suggest a Tabu Search (TS) metaheuristic-based approach to resolve it. Moreover, FedSel is built on top of blockchain technology, in order to ensure a secure selection of UAV participants, and hence building reliable FL-based models. Simulation results validate the efficiency of our FedSel scheme in balancing computational load among available UAVs and optimizing the UAV selection process.

In VANET (Vehicular Ad Hoc NETwork), the act of transferring important information is a main concern in every routing protocol conception. Hence, selecting next packet relay is crucial decision for the network performances. The highly dynamic topology of VANET which is result of the frequent changes in vehicles' positions, their speeds and the unpredictable drivers' directions at intersections mostly lead to wrong packet forwarding decisions. To overcome the above mentioned countermeasure, we propose in this paper a GPSR-based routing protocol incorporating weighted link quality estimation, a probabilistic reception approximation and moving direction. Based on vehicles' new coordinates and link status, our proposal called P-GPSR aims at selecting reasonable relay node to smoothly disseminate messages between vehicles. This addition allows our proposal to significantly enhance the overall GPSR performances, more specifically, the packet delivery ratio and the end-to-end delay. Simulation experiments performed using NS2 simulator have shown that our proposal outperforms both GPSR and GPSR-L in terms of throughput and packet delivery ration without negatively affecting the end-to-end delay.

In a jiffy after the outbreak of the 2019 novel coron-avirus, also called COVID-19 or SARS-CoV-2, the World Health Organization (WHO) considered it as a pandemic that threatens the demise of humanity. This quick decision was in conjunction with a real situation of biological inability to find a vaccine that can eliminate the virus or at least limit its spread. For that reason, technological intervention and cooperation are needed more than ever to face this pandemic. In this same context, we propose a novel system that deploys Vehicle-to-everything (V2X) technology and Blockchain in collaboration to face such a pandemic. Our proposal is centered on a triple-stage processing i) zone identification and classification based on vehicles' thermal cameras detection, ii) Blockchain-based information storage for enhanced patient medical information privacy, and iii) drones-based zone neutralization processes. Simulation results show that, thanks to the use of Blockchain technology, the network-related performance remain almost unchanged and hence, all Intelligent Transportation System (ITS) including inter-vehicles and inter-drones functionalities are not affected. In addition the additional overhead is very acceptable and does not exceed the 5 Kb in the worst case.