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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.
Architecture, Protocols, and Security in IoV: Taxonomy, Analysis, Challenges, and Solutions
(Wiley-Hindawi, 2022) Karim, Sulaiman M.; Habbal, Adib; Chaudhry, Shehzad Ashraf; Irshad, Azeem
Internet of Vehicles (IoV) is a multinode network that exchanges information in an open, wireless environment. Various communication activities exist between IoV entities to share important information such as (ID, location, speed, messages, and traffic information), necessary for network operation. As part of intelligent transportation, IoV is considered a hot subject for researchers, because it is still facing many unresolved challenges, especially those concerning security and privacy. The variation of security-privacy threats that can menace the safety, privacy, and lives of vehicle occupants makes security the leading point of interest. The development of communication protocols for autonomous vehicles opens to us new issues to study and enhance the performance of IoV networks in terms of security and privacy. Several works have been reported, proposing many solutions for practical security challenges including a considerable number of survey-review papers published in respectable channels. The main motive of this review paper is to present the latest developments related to IoV security, as well as to address existing limitations. The high frequency of publication on IoV architecture, security, and new solutions leads us to write a compact, comprehensive, and up-to-date review. Inclusion criteria for selected papers include recent publications, number of citations, and impact of the research. In the present survey paper, the IoV architecture model is defined with all related communication types, and security and privacy issues are analyzed and presented with recently proposed solutions in a clear method. Clear classifications of threats, attacks, protocols, and solutions are presented. Moreover, the use of blockchain-based IoV to improve system security is discussed highlighting the most important trends and taxonomies. The paper was written to be a candidate as the first to read on the topic of the IoV security challenge, presenting problems and solutions in a clear, smooth, complete, and integrated manner.
Artificial Intelligence Trust, Risk and Security Management (AI TRiSM): Frameworks, applications, challenges and future research directions
(Pergamon-Elsevier Science Ltd, 2024) Habbal, Adib; Ali, Mohamed Khalif; Abuzaraida, Mustafa Ali
Artificial Intelligence (AI) has become pervasive, enabling transformative advancements in various industries including smart city, smart healthcare, smart manufacturing, smart virtual world and the Metaverse. However, concerns related to risk, trust, and security are emerging with the increasing reliance on AI systems. One of the most beneficial and original solutions for ensuring the reliability and trustworthiness of AI systems is AI Trust, Risk and Security Management (AI TRiSM) framework. Despite being comparatively new to the market, the framework has demonstrated already its effectiveness in various products and AI models. It has successfully contributed to fostering innovation, building trust, and creating value for businesses and society. Due to the lack of systematic investigations in AI TRiSM, we carried out a comprehensive and detailed review to bridge the existing knowledge gaps and provide a better understanding of the framework from both theoretical and technical standpoints. This paper explores various applications of the AI TRiSM framework across different domains, including finance, healthcare, and the Metaverse. Futhermore, the paper discusses the obstacles related to implementing AI TRiSM framework, including adversarial attacks, the constantly changing landscape of threats, ensuring regulatory compliance, addressing skill gaps, and acquiring expertise in the field. Finally, it explores the future directions of AI TRiSM, emphasizing the importance of continual adaptation and collaboration among stakeholders to address emerging risks and promote ethical and enhanced overall security bearing for AI systems.
An attempt for price comparison system
(2024) Özakyildiz, Emre; Menemencioglu, Oğuzhan; Habbal, Adib
Nowadays, the price of a product varies dramatically across different e-commerce retailers. This study aims to develop a user-friendly price comparison website using Jsoup, state-of-the-art web scraping techniques. The proposed platform gathers data from well-known retailers, analyzes the data to extract product information, and presents it to consumers in real time when needed. Furthermore, to assist users in determining product prices, the platform showcases trending data, economical choices, products that offer the best value per unit, and highly rated items. By meticulously analyzing various product attributes, the platform identifies meaningful patterns and correlations by using SVD algorithm. Users can find both the most affordable product and the recommended option based on other users' reviews. On the other hand, users can avoid the emotional distress of purchasing a product at a higher price than what the marketplace offers. As a result, users are empowered to make more informed purchasing decisions, benefiting from the comprehensive analysis conducted by the platform. The performance of the work was tested using real data grabbed from different marketplaces. The results show our system achieved an acceptable accuracy rate compared to the industrial solutions and relevant literature.
BSDCE-IoV: Blockchain-Based Secure Data Collection and Exchange Scheme for IoV in 5G Environment
(Ieee-Inst Electrical Electronics Engineers Inc, 2023) Karim, Sulaiman M.; Habbal, Adib; Chaudhry, Shehzad Ashraf; Irshad, Azeem
The Internet of Vehicles (IoV) is a network that connects vehicles and their environment: in-built devices, pedestrians, and infrastructure through the Internet using heterogeneous access technologies. During communication between vehicles, roadside units, and control rooms, data confidentiality and privacy are critical issues that require effective measures. Several works have been proposed for securing IoV environments based on vehicles-to-infrastructure authentication; However, some schemes have security vulnerabilities, while others have shown efficiency issues. Due to its decentralization, stability, and transaction tracking capabilities, Blockchain as an emerging technology presents a potential solution for IoV security. This article provides an in-depth examination of the benefits of blockchain for a 5G-based IoV environment. In particular, we propose and evaluate a novel blockchain-based secure data exchange (BSDCE-IoV) scheme based on Elliptic Curve Cryptography algorithm. Our solution is designed to eliminate several potential attacks that pose a threat to the IoV environment. Deep examination using the Real-or-Random oracle model and Scyther tool, in addition to the informal security analysis, validates the scheme regarding security and privacy. The Multi-precision Integer and Rational Arithmetic Cryptographic Library (MIRACL) assesses the computational and communication overhead. Computational and communicative overheads were also evaluated using the Multi-precision Integer and Rational Arithmetic Cryptographic Library (MIRACL). BSDCE-IoV shows higher performance in terms of security, functionality, and time delay than a number of recent selective work in IoV security.
A Context-aware Radio Access Technology selection mechanism in 5G mobile network for smart city applications
(Academic Press Ltd- Elsevier Science Ltd, 2019) Habbal, Adib; Goudar, Swetha Indudhar; Hassan, Suhaidi
The Fifth Generation (5G) mobile network will revolutionize the way of communication by supporting new innovative applications that require low latency and high data rates in smart city environments. In order to meet these applications' requirements, Ultra-Dense Network (UDN) is considered as one of the promising technological enablers in 5G. 5G UDN deployments are envisaged to be heterogeneous and dense, mainly through the provisioning of small cells such as picocells and femtocells, from different Radio Access Technologies (RATs). Nevertheless, various studies have reported that the densification is not always beneficial to the network performance. As the network density increases, this will pose further requirements and complexity of determining which RAT a user should connect with at a given time. Hence, an efficient RAT selection mechanism to choose the best Radio Access Technology among multiple available ones is a must. This paper proposes a new Context-aware Radio Access Technology (CRAT) selection mechanism that examines the context of the user and the networks in choosing the appropriate RAT to serve. A simplified conceptual model of the Context-aware RAT selection is introduced. Then, a mathematical model of CRAT considering the user and network context is derived, adopting Analytical Hierarchical Process (AHP) for weighting the importance of the selection criteria and TOPSIS for ranking the available RATs. The proposed CRAT was implemented and validated in NS3 simulation environment. The performance of the proposed mechanism was tested using two different scenarios within a smart city environment, called a shopping mall and urban city scenarios. The obtained results showed that CRAT outperforms the conventional approach namely A2A4 of RAT selection in terms of the number of handovers, average network delay, throughput, and packet delivery ratio.
CONTROL APPROACH OF A GRID CONNECTED DFIG BASED WIND TURBINE USING MPPT AND PI CONTROLLER
(Vsb-Technical Univ Ostrava, 2023) Yonis, Samatar A. B. D., I; Yusupov, Ziyodulla; Habbal, Adib; Toirov, Olimjon
A double-fed induction generator (DFIG) has been frequently utilized in wind turbines due to its ability to handle variable-speed operations. This study investigates the real parameters of the Mitsubishi MWT 92/2.4 MW wind turbine model. It performs and implements grid-connected variable-speed turbines to control the active and reactive powers. Moreover, it presents a vector control strategy for DFIG for controlling the generated stator power. The unique feature of the approach proposed in the study is the comparison between two control techniques -the Maximum Power Point Tracking (MPPT) algorithm and the Proportional-Integral (PI) controller -for regulating DFIG based wind turbine systems. Thus, the result demonstrates that the performance of the MPPT technique provides strong robustness and reaches steadystate much faster than the PI controller with variable parameters. To the contrary, a typical PI controller gives a fast response when tracking the references of DFIG magnitudes. The effectiveness of the overall system is tested by MATLAB simulation.
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.
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.
An Innovative Decentralized and Distributed Deep Learning Framework for Predictive Maintenance in the Industrial Internet of Things
(Ieee-Inst Electrical Electronics Engineers Inc, 2024) Alabadi, Montdher; Habbal, Adib; Guizani, Mohsen
The integration of predictive maintenance (PdM) with the Industrial Internet of Things (IIoT) represents a pivotal shift in equipment management, particularly with the incorporation of deep learning (DL) for processing time series data from IIoT devices. This combination offers a sophisticated approach to predictive analysis, harnessing DL's prowess in analyzing complex patterns in large data sets. However, it also presents notable challenges, including significant security risks associated with centralized organizations and the immense volume of time series data generated by IIoT. To address these issues, our study introduces an innovative decentralized framework thoughtfully segmented into device and edge levels. This framework leverages the strengths of blockchain technology and the interplanetary file system (IPFS). IPFS effectively manages the large-scale storage needs of time series data for DL applications in a decentralized manner, while blockchain provides a robust foundation for ensuring data security and maintaining consistent transactions. Furthermore, we conducted thorough performance analyses, examining aspects, such as accuracy, execution time, and computational cost, which validated the efficacy of our approach. Security considerations were also rigorously evaluated, focusing on potential attacker scenarios, the strengths of a decentralized architecture, and the immutable nature of smart contracts. The results highlight our framework's exceptional ability to ensure the highest level of security in DL, maintain data integrity, and preserve model accuracy. In conclusion, the seamless integration of DL, PdM, blockchain, and IPFS in our framework marks a significant advancement in contemporary industrial maintenance strategies. It successfully bridges the gap between advanced security needs and the handling of vast quantities of data, positioning our approach at the forefront of modern industrial maintenance solutions.
Next-generation predictive maintenance: leveraging blockchain and dynamic deep learning in a domain-independent system
(Peerj Inc, 2023) Alabadi, Montdher; Habbal, Adib
The fourth industrial revolution, often referred to as Industry 4.0, has revolutionized the manufacturing sector by integrating emerging technologies such as artificial intelligence (AI), machine and deep learning, Industrial Internet of Things (IIoT), cloud computing, cyber physical systems (CPSs) and cognitive computing, throughout the production life cycle. Predictive maintenance (PdM) emerges as a critical component, utilizing data analytic to track machine health and proactively detect machinery failures. Deep learning (DL), is pivotal in this context, offering superior accuracy in prediction through neural networks' data processing capabilities. However, DL adoption in PdM faces challenges, including continuous model updates and domain dependence. Meanwhile, centralized DL models, prevalent in PdM, pose security risks such as central points of failure and unauthorized access. To address these issues, this study presents an innovative decentralized PdM system integrating DL, blockchain, and decentralized storage based on the InterPlanetary File System (IPFS) for accurately predicting Remaining Useful Lifetime (RUL). DL handles predictive tasks, while blockchain secures data orchestration. Decentralized storage safeguards model metadata and training data for dynamic models. The system features synchronized two DL pipelines for time series data, encompassing prediction and training mechanisms. The detailed material and methods of this research shed light on the system's development and validation processes. Rigorous validation confirms the system's accuracy, performance, and security through an experimental testbed. The results demonstrate the system's dynamic updating and domain independence. Prediction model surpass state-of -the-art models in terms of the root mean squared error (RMSE) score. Blockchain-based scalability performance was tested based on smart contract gas usage, and the analysis shows efficient performance across varying input and output data scales. A comprehensive CIA analysis highlights the system's robust security features, addressing confidentiality, integrity, and availability aspects. The proposed decentralized predictive maintenance (PdM) system, which incorporates deep learning (DL), blockchain technology, and decentralized storage, has the potential to improve predictive accuracy and overcome significant security and scalability obstacles. Consequently, this system holds promising implications for the advancement of predictive maintenance in the context of Industry 4.0.
Organizing Named Data Objects in Distributed Name Resolution System for Information-Centric Networks
(Springer International Publishing Ag, 2020) Elbrieki, Walid; Hassan, Suhaidi; Arlimatti, Shivaleela; Habbal, Adib
The Information-centric Networks (ICN), an important research direction of the future Internet architecture, has gained lot of attention from the research community. The aim is to improve the current Internet with a new architecture where the design is completely based on information instead of the host. The information is named and is called as Named Data Object (NDO), utilized for data registration and name resolution, and the system that translates the object identifiers to network address is known as Name Resolution System (NRS). The random NRS distribution and network segregation are important and challenging issues in increasing NDO registration and storage. It is challenging for a single NRS to handle more than 10(15) expected NDO's with low latency and good throughput, along with the issues interest flooding and interest congestion. To overcome these problems NRS is distributed based on the location of routers. The mechanism is called Distributed Name Resolution Mechanism (DNRM). The NDO storage needs to be organized to increase the scalability of NRS. Distributed hash table and Bloom Filter have their own problems such as high latency in individual element searching and member deletion respectively. To overcome the above-mentioned issues the Balance Binary Tree (BBT) data structure is introduced to manage large NDO storage. The implementation results reduce the end-to-end delay by increasing network throughput. The contribution of this study is significant in promoting the use of NRS in ICN for handling the heterogeneity of the future Internet.
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.
Resource Scheduling in Edge Computing: Architecture, Taxonomy, Open Issues and Future Research Directions
(Ieee-Inst Electrical Electronics Engineers Inc, 2023) Raeisi-Varzaneh, Mostafa; Dakkak, Omar; Habbal, Adib; Kim, Byung-Seo
An inflection point in the computing industry is occurring with the implementation of the Internet of Things and 5G communications, which has pushed centralized cloud computing toward edge computing resulting in a paradigm shift in computing. The purpose of edge computing is to provide computing, network control, and storage to the network edge to accommodate computationally intense and latency-critical applications at resource-limited endpoints. Edge computing allows edge devices to offload their overflowing computing tasks to edge servers. This procedure may completely exploit the edge server's computational and storage capabilities and efficiently execute computing operations. However, transferring all the overflowing computing tasks to an edge server leads to long processing delays and surprisingly high energy consumption for numerous computing tasks. Aside from this, unused edge devices and powerful cloud centers may lead to resource waste. Thus, hiring a collaborative scheduling approach based on task properties, optimization targets, and system status with edge servers, cloud centers, and edge devices is critical for the successful operation of edge computing. This paper briefly summarizes the edge computing architecture for information and task processing. Meanwhile, the collaborative scheduling scenarios are examined. Resource scheduling techniques are then discussed and compared based on four collaboration modes. As part of our survey, we present a thorough overview of the various task offloading schemes proposed by researchers for edge computing. Additionally, according to the literature surveyed, we briefly looked at the fairness and load balancing indicators in scheduling. Finally, edge computing resource scheduling issues, challenges, and future directions have discussed.
The Role of Management Techniques for High-Performance Pending Interest Table: A Survey
(Springer International Publishing Ag, 2020) Alubady, Raaid; Hassan, Suhaidi; Habbal, Adib
Most of the services used by Internet consumers such as social network platforms, video-on-demand, on-line gaming, web Media, and IP Television which are content-centric in nature; meaning they focus on named content objects instead of being focused on the host-location. In this context, many projects around named data propose redesigning and developing the communication of Internet-based on named data. NDN (Named Data Networking) is an ideal solution to achieve efficient data sharing and retrieval since NDN focuses on the contents themselves regardless of their sources. The focus of this survey is a unique characteristic presented by NDN; PIT (Pending Interest table). PIT is part of three fundamental data structures newly introduced in the NDN router to enable full functionality of NDN. NDN router depends on reverse paths in PIT to return back Data packets to consumers. Accordingly, the PIT may present stringent restrictions in terms of scalability, for-warding, and management. The challenging task is the design of a scalable and manageable PIT because it requires per-packet updating and controlling the impact of increasing Interest packets with the highest Interest lifetime of PIT. Therefore, this survey describes into greater detail the background and several important previous researches related to issues of PIT which is PIT management based on PIT placement, and replacement, PIT implementation as a data structure, and Adaptive Interest Life-time. Thus, would assist in defining the general framework of this survey.
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.
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, Sheroz
Named 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
Software Defined Network Partitioning with Graph Partitioning Algorithms
(Springer International Publishing Ag, 2020) Arlimatti, Shivaleela; Elbrieki, Walid; Hassan, Suhaidi; Habbal, Adib
Software Defined Networks is an emerging paradigm in Internet communication world that increases the flexibility of today's networks by decoupling control plane and data plane of the network devices. The fundamental aim is to centralize the control and reduce the complexity of the networks. The communication medium between control and data plane is through OpenFlow protocol, an open standard network protocol designed to manage the network traffic by software programs. To increase the scalability and flexibility of controllers the OpenFlow controllers are distributed based on location and network types. However, most critical issue is minimizing the communication cost between the controller domains. In this paper, two graph partitioning algorithms Fiduccia-Matthyses algorithm and Kernighan-Lin algorithm are used to minimize the communication cost between distributed OpenFlow controller domains. The implementation of the algorithms is under Matlab simulation environment. The methodology used for the proposed algorithms is to interchange the elements from one domain to other domain to calculate the gain. The simulated results show that Kernighan-Lin algorithm minimizes more communication cost rather than the Fiduccia-Matthyses algorithm.
A User-Preference-Based Charging Station Recommendation for Electric Vehicles
(Ieee-Inst Electrical Electronics Engineers Inc, 2024) Habbal, Adib; Alrifaie, Mohammed F.
The popularity of electric vehicles (EVs) is increasing, leading to higher demand for electric vehicle charging stations (EVCS). It is crucial to select an appropriate charging station based on user preferences; however, current selection solutions are limited and primarily focus on proximity or price. Such an approach neglects other significant factors of interest to EV users, namely charging time, waiting time, charging cost, and available facilities near the EVCS. To address this issue, this paper proposes a novel recommendation scheme, the User-Preferences based Charging Station Recommendation Scheme (UPCSRS), which integrates user preferences with Multiple Attribute Decision Making (MADM) theory to suggest the best available charging stations for EV users. UPCSRS consists of two parts: adopting Analytical Hierarchical Process (AHP) for weighting the importance of each selection criterion and the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) for ranking available charging stations. A mathematical model of the proposed scheme was developed, and then the effectiveness and accuracy were evaluated using MATLAB and a real dataset from the US Department of Energy website. Results showed that this proposed scheme provides more precise and personalized recommendations for users compared to current solutions that only consider the nearest or cheapest option. By enhancing the overall user experience through a more customized and efficient charging station selection process, this proposed scheme has the potential to contribute to more EVs adoption.

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