Search

The Internet of Things (IoT) is revolutionising our everyday life by connecting a massive number of smart objects. IoT is considered a key enabler of multiple emerging applications in several areas such as manufacturing, agriculture, healthcare, automotive, smart city, etc. However, as it becomes increasingly prevalent in our life, IoT brings a number of challenging new security risks. The first issue is related to device authentication, especially with the massive number of connected devices with different types (sensors, vehicles, drones, etc.). This issue can be exploited by attackers to break into IoT networks. In addition, IoT devices generate a huge amount of confidential data that must be processed and transmitted independently without human intervention, which cannot be performed by traditional centralized security systems. Besides, the isolation imposed between different networks and the number of messages used for device's association are considered the main limitations in the two recent works. To overcome these limits, we design a novel decentralized scheme leveraging a hybridization of two main types of blockchain, local and public, which ensures authentication of things before establishing communication and exchanging information between devices. Our solution removes the isolation between different networks, contributes in reducing the number of messages required for the association phase, and solves the time delay problem imposed by the PoW consensus mechanism that is used in public blockchain as well. We adopt the Ethereum blockchain in its two versions local and public to implement our solution, and evaluate its performance as compared to the existing literature. The obtained results proved its effectiveness in terms of execution time and energy consumption which are the same metrics used in the most recent works. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

The rapid emergence of the Metaverse as a leading virtual digital technology is driving innovation in power generation. The increasing demand for sustainable energy and the rise of the Metaverse has provided an opportunity to develop a secure and sustainable energy trading system using Smart Grid (SG), Virtual Power Plants (VPP), Digital Twins (DT), and Blockchain. Integrating VPP with SG can optimize the utilization of distributed energy resources and improve the stability and reliability of the energy supply in the metaverse. Meanwhile, the use of DT can facilitate the efficient management of VPPs by simulating their behavior and performance to identify potential issues and test different scenarios for improvement. Blockchain is a rising technology that enables decentralized and trustworthy applications, making it a promising tool for creating decentralized energy trading systems in the Metaverse. In this context, we propose Next-Power, a next-generation framework for secure and sustainable energy trading in the Metaverse. Next-Power consists of: (1) an automated blockchain-based fair payment of energy traded between consumers and prosumers in the Metaverse, it uses Ethereum's smart contracts to determine the price at which energy is traded using a cooperative game theory (i. e. , reverse auction); (2) an access token-based blockchain system that ensures a secure mutual authentication between consumers and prosumers; and (3) a lightweight on-chain collaborative reputation scheme that keeps track of prosumers reputation as well as consumers credibility in the Metaverse in a fully decentralized manner. The system of Next-Power is implemented, tested, and deployed on the Ethereum official test network. Next-Power is a promising framework for the future of energy trading in the Metaverse, as demonstrated by its successful experimental results in terms of flexibility, efficiency, security, and cost-effectiveness. [ABSTRACT FROM AUTHOR]