Yazar "Wei, Xian" seçeneğine göre listele

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    An adaptive social-aware device-to-device communication mechanism for wireless networks
    (Elsevier, 2022) Alden, Feras Zen; Hassan, Suhaidi; Habbal, Adib; Wei, Xian
    Device-to-Device (D2D) communication is an essential element in 5G networks and beyond. It enables users to communicate either directly without network assistance or with minimum signaling through a base station. The enormous number of connected devices to the networks with high velocity of these users increases the complexity of establishing an effective and stable D2D connection when the user is moving among different available peers and modes in the network. So, the main objective for this research was to design an adaptive social-aware D2D communication mechanism to enhance the performance of D2D connection by improving procedures and efficiency of peer and mode selection. The mechanism consists of two schemes, namely the peer and mode selection schemes. The peer selection scheme includes two parts. Firstly, the peer evaluation is based on social choice theory through verifying the relationship between peers, and dividing the available peers into trusted peers and untrusted peers. The technical evaluation is based on HAW algorithm by including multi -attributes related to the connection quality to find the optimum trusted peer while excluding untrusted peers from the ranking. Secondly, the mode selection scheme evaluates the available modes based on the connection status while considering multi-attributes based on SAW algorithm to select and switch among available modes intelligently, based on the highest-ranking, to select the optimum mode. In this study, the proposed scenario considers the status of different numbers of users in the network to evaluate the proposed mechanism, and compared with two other recent approaches. The obtained results showed that the proposed mechanism out-performs other approaches in terms of delay, signal-to-noise ratio, delivery ratio, and throughput with better performance. It provides smooth switching between different modes and employs an automatic peering selection with trusted peers only.
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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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    Industrial Internet of Things: Requirements, Architecture, Challenges, and Future Research Directions
    (Ieee-Inst Electrical Electronics Engineers Inc, 2022) Alabadi, Montdher; Habbal, Adib; Wei, Xian
    Industry 4.0 relates to the digital revolution of manufacturing and other sectors, such as retail, distribution, oil and gas, and infrastructure. Meanwhile, the Industrial Internet of Things (IIoT) is a technological advancement that leads to Industry 4.0 implementation by boosting the manufacturing sector's productivity and economic impact. IIoT provides the ability to provide global connectivity between components in different locations. The manufacturing sector has had various difficulties implementing IIoT, primarily due to IIoT characteristics. This paper offers an in-depth review of Industry 4.0 and IIoT, where the primary motivation behind this is to introduce the most recent advancements related to Industry 4.0 and IIoT, as well as to address the existing limitations. Firstly, this paper presents a novel taxonomy of IIoT challenges that includes aspects of each challenge, such as the terminology and approaches utilized to solve these challenges. Besides IIoT challenges, this survey provides an in-depth demonstration of the many concepts related to IIoT, such as architecture and use cases. Secondly, this paper provides a comprehensive review of the state-of-the-art of Industry 4.0 in terms of concepts, requirements, and supporting technology. In addition, the correlation between enabling technology and technical requirements is discussed in detail. Finally, this paper highlights deep learning, edge computing, and big data as key techniques for the future directions of IIoT. Furthermore, the presented techniques are thoroughly examined to present an alternative method for future adoption. In addition to the showcased techniques, a new architecture for the future of IIoT based on these three primary techniques is also proposed.