Browsing by Author "Hemam Sofiane Mounine"

Now showing 1 - 8 of 8
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    A Method based on Multi-agent Systems and Passive Replication Technique for Predicting Failures in Cloud Computing
    (Bentham Science Publishers, 2023-11-01) Hioual Ouided; Hioual Ouassila; Hemam Sofiane Mounine; Maifi Lyes
    Cloud computing refers to the computing capacities of remote computers, where the user has considerable computing power without having to own power units. The probability of failures, which can occur during execution, increases in the number of nodes. Since failures cannot be completely avoided, one solution is to use failure tolerance mechanisms. Predicting failures has become a major task for engineers and software developers, as failure increases resource usage costs. Objectives and Methods: This paper presents a hybrid method of predicting failures in a cloudcomputing environment based on the passive replication technique and multi-agent systems. It detects failures, improves the average response time and minimizes lost time. This method makes it possible to efficiently and transparently guarantee the continuity of cloud computing services in the presence of failures. Results: The results show that the proposed method performs well in the presence of failures, improves the response time and minimizes the additional costs caused by the failures. Conclusion: This paper proposes a hybrid method of predicting failures in cloud-computing based on the passive replication technique and multi-agent systems to detect failures and minimize lost time. The replication technique works by duplicating some system components, which are deployed simultaneously across different resources. This technique aims to make the system robust, increase availability and guarantee the execution of jobs. In addition, it is suitable for long-running tasks.
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    A robust and efficient vector-based key management scheme for IoT networks
    (Elsevier, 2023-10-01) Bettayeb Sami; Messai Mohamed Lamine; Hemam Sofiane Mounine
    Information and communication security is a critical concern in the rapid growth of Internet of Things (IoT) networks that need to exchange sensitive data. Therefore, key management is essential to address such networks’ security problems. In this context, several existing research work focuses on key management solutions, which consider the resource-limitation of IoT devices. However, the weaknesses of these solutions are (1) the lack of protection for sensitive parameters during transmission, (2) security and performance metrics’ misbalancing, and (3) the vulnerability to node-compromising attacks. This paper presents a new key management scheme based on pre-distributed vectors to overcome these limits to secure key establishment, refresh, and revocation. Moreover, the network area is divided into subareas, which makes our solution lightweight, flexible, scalable, and resilient to multiple attacks while minimizing communications, computation, and storage overheads on IoT devices. We assess the proposed scheme by using (1) the BAN (Burrows Abadi Needham) formal verification logic, (2) the informal security to demonstrate its resilience against some known attacks, and (3) the performance analysis to prove its correctness. The obtained results show that the proposed scheme is more efficient than other schemes in terms of storage by saving more than 81.84%, communications by 100% during the group-wise key establishment, computation overheads by reducing the number of multiplication operations to 50%, and energy consumption by saving up to 99.99% during the group-wise key establishment phase. Moreover, the results show that our scheme is more resilient against node capture attacks by up to 96.43% during the initialization phase and by more than 9.89% after, making it suitable for use in resource-limited IoT networks.
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    An approach based on genetic algorithms and neural networks for QoS-aware IoT services composition
    (Elsevier, 2022-09-13) Boucetti Rabeh; Hioual Ouassila; Hemam Sofiane Mounine
    In the Internet of Things (IoT) environment, the services provided by the connected objects are published as services through the web. This allows to machines to interact between them and, makes the IoT services composition possible. However, the vast proliferation of smart object generates services with the same functionalities but different in terms of quality of services (QoS) proprieties. This makes the satisfaction of the user requirements often complex and a NP-hard problem. Indeed, respecting the QoS constraints (user preferences in terms of QoS) is a challenge, due to the high number of candidate services for the composition. This challenge consists of selecting the most appropriate services so that the composite service must meet both the functional and the non-functional requirements of the user. To deal with this challenge, we propose an approach based on Genetic Algorithms (GA) and Neural Networks (NN) for QoS-aware IoT Services Composition in the context of large-scale environments. The combination between GA and NN allows finding the quasi-optimal IoT service composition. This latter is based on global QoS optimization. To reach this objective, the QoS intervals are decomposed into M QoS-levels to engage them into the theoretical composition. Then, the proposed first GA is used to obtain the ideal theoretical composition with an overall QoS optimization. Afterward, the proposed NN is used to eliminate the inappropriate concrete IoT services, and retain only the services having the same categories of the atomic theoretical services composing the ideal theoretical composition. This allows us to optimize the search space as well as the execution time. Finally, we apply the second GA on the concrete services of the retained categories, in order to obtain the IoT service concrete composition with an overall QoS optimization. The simulation results show that the proposed approach has the best composition time, the best Hypervolume indicator and the best compositional optimality compared to SC-FLA, Improved GA and MGA approaches. On another side, it has almost the same performances compared to TS-QCA and, it finds the near-optimal composition in a very short time compared to PSA, which is an optimal approach. Thus, the obtained results show the effectiveness of our approach.
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    An Approach Based on Genetic and Grasshopper Optimization Algorithms for Dynamic Load Balancing in CloudIoT
    (Computing and Informatics, 2023-05-30) Benabbes Sofiane; Hemam Sofiane Mounine
    CloudIoT is a new paradigm, which has emerged as a result of the combination of Cloud Computing (CC) and the Internet of Things (IoT). It has experienced a growing and rapid development, and it has become more popular in information and technology (IT) environments because of the advantages it offers. However, due to a strong use of this paradigm, especially in smart cities, the problem of imbalance load has emerged. Indeed, to satisfy the needs of the user, the intelligent objects send the collected data to the virtual machines (VMs) of the cloud in order to be processed. So, it is necessary to have an idea about the load of its VM. Thus, the problem of load balancing between VMs is strongly related to the technique used for the VMs selection. To tackle this problem, we propose in this paper a task scheduler called Scheduler Genetic Grasshopper Algorithm (SGGA). It allows to ensure a dynamic load balancing, as well as the optimization of the makespan and the resource usage. Our proposed SGGA is based on the combination of Genetic Algorithm (GA) and Grasshopper Optimization Algorithm (GOA). First, the tasks sent by the IoTs are mapped to the VMs in order to build the initial population, then SGGA performs the genetic algorithm, which has expressed a considerable performance. However, the weakness of the GA is marked by its heaviness caused by the mutation operator, especially when the number of tasks increases. Because of this insufficiency, we have replaced the mutation operator with the grasshopper optimization algorithm. The results of the experiments show that our approach (SGGA) is the most efficient, compared to the recent approaches, in terms of the response time to obtain the optimal solution, makespan, throughput, an average resource utilization rate and the hypervolume indicator.
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    BKRSC-IoT: Blockchain-Based Key Revocation Using Smart Contracts for IoT Networks
    (Springer, Cham, 2024-02-02) Bettayeb Sami; Messai Mohamed Lamine; Hemam Sofiane Mounine
    The growth of Internet of Things (IoT) networks has brought the crucial need for information and communication security to the fore. Since IoT devices exchange sensitive data, secure key management with its different phases and the key revocation phase especially is essential for IoT network security. However, existing IoT key management solutions need improvements because of the resource limitations of IoT devices. Despite considering these limitations, current key revocation solutions have several areas for improvement, including the high communication overhead of the limited-resource devices. Therefore, there is always a need for a decentralized and efficient solution to address these issues in IoT networks. This paper presents a new solution for key revocation based on Blockchain using smart contracts to minimize the communication overhead and the energy consumption in IoT networks. Furthermore, we assess the correctness of the proposed solution through security and performance analysis. The results show that our solution has reduced the communication overhead by 93,55%, 91,87%, and 99,75% compared to other solutions during the compromising, leaving, and draining cases, respectively, which proves that our solution is efficient and appropriate for IoT networks.
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    Dynamic load balancing upon the replication and deletion of cloud services
    (IOS Press, 2023-01-05) Hemam Sofiane Mounine; Hioual Ouided; Hioual Ouassila
    In the last decade, the considerable increase of the cloud services use has led to the need to have search and selection techniques that match both the requirements of end users and those of the system. Indeed, to select a cloud service that meet the needs of both system and user is a challenge, due to the several conflicting criteria problem for the user on one hand, and for the system, i.e., the load balancing between Virtual Machines (VMs), on the second hand. Therefore, the main challenge, in this context, is how to ensure the user requirements by maintaining the system performance constraint. To deal with this challenge, we present in this paper an approach based on the cloud service replication on one or more VMs when the number of the user requests will be important at a given moment. This allows better load balancing between VMs by distrusting the users’ requests over them. In addition, it allows to select the best cloud service according to the users need. However, the cloud services replication introduces the problem of the storage space saturation. Thus, our second contribution is to select and delete the cloud service replicas without degradation of the load balancing. The two proposed contributions are based on the MCDM techniques in order to select the VMs that can receive the replica of the cloud service and to select those, which their storage space is overloaded in order to delete the replica cloud service. The experimental results, based on Cloudsim simulator, show that our proposal can effectively achieve good performance (load balancing) and improve the response time.
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    Real-Time Human Action Representation Based on 2D Skeleton Joints
    (IEEE, 2023-12-04) Bouguerra Adil; Hemam Sofiane Mounine; Hioual Ouassila
    We present a new approach to capture human actions by using 2D skeletal joints as the foundation for the real-time representation. Our approach combines three distinct types of information, namely: (1) motion detection to identify salient regions within the action. This allows us to compute joint contribution ratios and save processing time by excluding still joints and focusing on the main joints involved in the action; (2) utilization of a predefined map for joints trajectory shapes, which encodes the temporal information and avoids noisy data using the map; and (3) incorporation of a direction map that captures the movement of the joints, in the spatial space. By integrating these elements, we have devised a comprehensive representation capable of discerning even highly similar actions. To evaluate the effectiveness of our proposed representation, we conducted experiments on the UTD-MHAD dataset [25], which encompasses a diverse range of 27 actions performed by 8 subjects (4 females and 4 males), each with 4 repetitions. The evaluation results exhibit a notable dissimilarity within the same action category (intra-class) and a significant similarity across different action categories (inter-class). Specifically, our approach achieved a commendable score of 94.81 on the UTD-MHAD dataset, thus demonstrating its efficacy and robustness.
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    Semantic Interoperability in Electronic Health Record System Based on Blockchain Technology: a Survey
    (IEEE, 2023-11-22) Chemini Nawal; Hioual Ouassila; Hemam Sofiane Mounine
    The Electronic Health Record (EHR) represents a patient's medical information and treatment history in a digital format. Patients’ data are heterogeneous, divided, and distributed among healthcare organizations. Thus, these data help enhance the quality of healthcare services and improve diagnosis efficiency. Designing an EHR system requires many considerations, such as security, privacy, scalability, integrity, information confidentiality, and interoperability. Healthcare parties involved can internally exchange patient data, but there is an issue in sharing, interpreting, and processing it semantically with other healthcare providers. In this context, blockchain enables the sharing of clinic data in a robust, transparent, and interoperable way between different healthcare providers. It provides secure and scalable sharing of the patient’s health data, expediting coordinated care. This paper provides a systematic review focused on semantic interoperability in EHR systems based on blockchain technology. The objective is to identify the current challenges, solutions, and limitations related to this topic.