Yazar "Alaidaros, Hashem" seçeneğine göre listele

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    Forwarding Strategies for Named Data Networking Based IoT: Requirements, Taxonomy, and Open Research Challenges
    (Ieee-Inst Electrical Electronics Engineers Inc, 2023) Askar, Naeem Ali; Habbal, Adib; Alden, Feras Zen; Wei, Xian; Alaidaros, Hashem; Guo, Jielong; Yu, Hui
    The Internet of Things (IoT) aims to efficiently connect various entities, including humans, machines, smart devices, physical environments, and others, so they can communicate and exchange data in real time. However, due to the massive amount of data transferred, the presence of devices with limited resources, heterogeneity, and mobility support would make it difficult to create a robust network with respect to performance in an IoT context. In order to efficiently disseminate the enormous volume of automated data, Named Data Networking (NDN), a viable networking design for the future Internet, has been proposed. NDN has shown great potential for IoT because it has built-in support for naming, caching, mobility, and security. Forwarding strategies play an important role in the successful deployment of NDN-based IoT. In this article, we introduce NDN-based IoT forwarding emphasizing on IoT characteristics and requirements. We classify NDN-based IoT forwarding strategies and then discuss in detail certain exemplary schemes. Additionally, we compare several aspects of current forwarding methods that are now in use, including the types of forwarding strategy, particular issues, type of solution, assessment metrics, and simulation platform. We wrap up our contribution by outlining the major open research issues that can guide future investigations in this area. We anticipate that this survey will help the community of NDN-based IoT researchers' understanding of forwarding strategies in IoT environments.
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    A secure multifactor-based clustering scheme for internet of vehicles
    (Elsevier, 2023) Karim, Sulaiman M.; Habbal, Adib; Hamouda, Hassen; Alaidaros, Hashem
    The development of the Internet of Vehicles (IoV) has been made possible through a variety of communication technologies and advanced AI techniques. In this context, ensuring stable and efficient communication for IoV is extremely important. It addresses several challenges related to security issues, high dynamism, constant connection outages, and the expected high traffic density. To overcome these challenges, vehicle clustering is a viable strategy for a reliable communication environment. The majority of current research focuses on solving the problem of cluster stability and efficiency by utilizing one or multiple factors, particularly vehicle location, mobility, and behavior. This article introduces an efficient Multifactor Clustering Scheme for IoV (MFCS-IoV). MFCS-IoV includes two stages: cluster formation and cluster head selection. The cluster formation is based on the improved K-means algorithm, considering both the vehicle mobility and final destination within the driving zone. While, a weighted cluster fitness function that includes mobility, behavior, dynamic location, and security is used to optimally select the Cluster Head (CH). Blockchain technology has been integrated into the model to safeguard the privacy of information like destination and other vehicle parameters. Simulation results demonstrate the success of MFCS-IoV in partitioning the vehicles into stable clusters and selecting the optimal cluster heads based on the proposed parameters. The effectiveness of MFCS-IoV is demonstrated by simulating different scenarios of 50 to 300 vehicles in the driving area. A comparison with related works shows that MFCS-IoV outperforms other schemes regarding average node-to-node delay, packet delivery rate, and throughput. Additionally, the proposed MFCS-IoV increases communication reliability by providing stable clusters while maintaining security measures.