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Öğe Privacy as a Lifestyle: Empowering assistive technologies for people with disabilities, challenges and future directions(Elsevier, 2024) Habbal, Adib; Hamouda, Hassen; Alnajim, Abdullah M.; Khan, Sheroz; Alrifaie, Mohammed F.Between the changing Industry 4.0 landscape and the rise of Industry 5.0, where human intelligence and intelligent machines work together, vast amounts of privacy-sensitive data are generated, processed, and exchanged, making them attractive targets of various attacks. Hence, privacy protection has become a major human concern. Due to its importance, this paper investigates the state-of-the-art research efforts directed toward privacy preservation. Firstly, this paper examines the main privacy requirements and identifies the key privacy-related threats and attacks for systems integrating blockchain and AI. Secondly, we study Blockchainbased privacy preservation solutions, and we devise a taxonomy to classify them based on privacy of data and privacy of network. Thirdly, AI-based privacy-preserving methods are discussed and categorized into privacypreserving data, privacy-preserving model, and privacy-preserving service. Moreover, this paper adds value by analyzing the impact of privacy from technical aspects, human and social-economic aspects, and ethical and legal considerations. Additionally, it sheds light on the role of privacy as a lifestyle, which is crucial not only in mainstream sectors but also for people with disabilities who depend on technological advancements and privacy safeguards for their empowerment. The paper concludes by addressing open challenges in privacy preservation, paving the way for future research directions vital for fostering a privacy-centric evolution in the dynamic Industry 4.0 and beyond.Öğe A secure multifactor-based clustering scheme for internet of vehicles(Elsevier, 2023) Karim, Sulaiman M.; Habbal, Adib; Hamouda, Hassen; Alaidaros, HashemThe 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.Öğe SEF: A smart and energy-aware forwarding strategy for NDN-based internet of healthcare(Tech Science Press, 2024) Askar, Naeem Ali; Habbal, Adib; Hamouda, Hassen; Alnajim, Abdullah Mohammad; Khan, SherozNamed Data Networking (NDN) has emerged as a promising communication paradigm, emphasizing content-centric access rather than location-based access. This model offers several advantages for Internet of Healthcare Things (IoHT) environments, including efficient content distribution, built-in security, and natural support for mobility and scalability. However, existing NDN-based IoHT systems face inefficiencies in their forwarding strategy, where identical Interest packets are forwarded across multiple nodes, causing broadcast storms, increased collisions, higher energy consumption, and delays. These issues negatively impact healthcare system performance, particularly for individuals with disabilities and chronic diseases requiring continuous monitoring. To address these challenges, we propose a Smart and Energy-Aware Forwarding (SEF) strategy based on reinforcement learning for NDN-based IoHT. The SEF strategy leverages the geographical distance and energy levels of neighboring nodes, enabling devices to make more informed forwarding decisions and optimize next-hop selection. This approach reduces broadcast storms, optimizes overall energy consumption, and extends network lifetime. The system model, which targets smart hospitals and monitoring systems for individuals with disabilities, was examined in relation to the proposed strategy. The SEF strategy was then implemented in the NS-3 simulation environment to assess its performance in healthcare scenarios. Results demonstrated that SEF significantly enhanced NDN-based IoHT performance. Specifically, it reduced energy consumption by up to 27.11%, 82.23%, and 84.44%, decreased retrieval time by 20.23%, 48.12%, and 51.65%, and achieved satisfaction rates that were approximately 0.69 higher than those of other strategies, even in more densely populated areas. This forwarding strategy is anticipated to substantially improve the quality and efficiency of NDN-based IoHT systems. Copyright
