Abdelkarim Ben Sada

Abdelkarim Ben Sada Information

University

University of Science and Technology Beijing

Position

___

Citations(all)

54

Citations(since 2020)

54

Cited By

14

hIndex(all)

3

hIndex(since 2020)

3

i10Index(all)

2

i10Index(since 2020)

2

Email

University Profile Page

University of Science and Technology Beijing

Top articles of Abdelkarim Ben Sada

Maximizing UAV Fog Deployment Efficiency for Critical Rescue Operations

In disaster scenarios and high-stakes rescue operations, integrating Unmanned Aerial Vehicles (UAVs) as fog nodes has become crucial. This integration ensures a smooth connection between affected populations and essential health monitoring devices, supported by the Internet of Things (IoT). Integrating UAVs in such environments is inherently challenging, where the primary objectives involve maximizing network connectivity and coverage while extending the network's lifetime through energy-efficient strategies to serve the maximum number of affected individuals. In this paper, We propose a novel model centred around dynamic UAV-based fog deployment that optimizes the system's adaptability and operational efficacy within the afflicted areas. First, we decomposed the problem into two subproblems. Connectivity and coverage subproblem, and network lifespan optimization subproblem. We shape our UAV fog deployment problem as a uni-objective optimization and introduce a specialized UAV fog deployment algorithm tailored specifically for UAV fog nodes deployed in rescue missions. While the network lifespan optimization subproblem is efficiently solved via a one-dimensional swapping method. Following that, We introduce a novel optimization strategy for UAV fog node placement in dynamic networks during evacuation scenarios, with a primary focus on ensuring robust connectivity and maximal coverage for mobile users, while extending the network's lifespan. Finally, we introduce Adaptive Whale Optimization Algorithm (WOA) for fog node deployment in a dynamic network. Its agility, rapid convergence, and low computational …

Authors

Abdenacer Naouri,Huansheng Ning,Nabil Abdelkader Nouri,Amar Khelloufi,Abdelkarim Ben Sada,Salim Naouri,Attia Qammar,Sahraoui Dhelim

Journal

arXiv preprint arXiv:2402.16052

Published Date

2024/2/25

Selective Task offloading for Maximum Inference Accuracy and Energy efficient Real-Time IoT Sensing Systems

The recent advancements in small-size inference models facilitated AI deployment on the edge. However, the limited resource nature of edge devices poses new challenges especially for real-time applications. Deploying multiple inference models (or a single tunable model) varying in size and therefore accuracy and power consumption, in addition to an edge server inference model, can offer a dynamic system in which the allocation of inference models to inference jobs is performed according to the current resource conditions. Therefore, in this work, we tackle the problem of selectively allocating inference models to jobs or offloading them to the edge server to maximize inference accuracy under time and energy constraints. This problem is shown to be an instance of the unbounded multidimensional knapsack problem which is considered a strongly NP-hard problem. We propose a lightweight hybrid genetic algorithm (LGSTO) to solve this problem. We introduce a termination condition and neighborhood exploration techniques for faster evolution of populations. We compare LGSTO with the Naive and Dynamic programming solutions. In addition to classic genetic algorithms using different reproduction methods including NSGA-II, and finally we compare to other evolutionary methods such as Particle swarm optimization (PSO) and Ant colony optimization (ACO). Experiment results show that LGSTO performed 3 times faster than the fastest comparable schemes while producing schedules with higher average accuracy.

Authors

Abdelkarim Ben Sada,Amar Khelloufi,Abdenacer Naouri,Huansheng Ning,Sahraoui Dhelim

Journal

arXiv preprint arXiv:2402.16904

Published Date

2024/2/24

A Multimodal Latent-Features-Based Service Recommendation System for the Social Internet of Things

The Social Internet of Things (SIoT) is revolutionizing how we interact with our everyday lives. By adding the social dimension to connecting devices, the SIoT has the potential to drastically change the way we interact with smart devices. This connected infrastructure allows for unprecedented levels of convenience, automation, and access to information, allowing us to do more with less effort. However, this revolutionary new technology also brings an eager need for service recommendation systems. As the SIoT grows in scope and complexity, it becomes increasingly important for businesses and individuals, and SIoT objects alike to have reliable sources for products, services, and information that are tailored to their specific needs. Few works have been proposed to provide service recommendations for SIoT environments. However, these efforts have been confined to only focusing on modeling user-item …

Authors

Amar Khelloufi,Huansheng Ning,Abdenacer Naouri,Abdelkarim Ben Sada,Attia Qammar,Abdelkader Khalil,Lingfeng Mao,Sahraoui Dhelim

Journal

IEEE Transactions on Computational Social Systems

Published Date

2024/2/21

Efficient fog node placement using nature-inspired metaheuristic for IoT applications

Managing the explosion of data from the edge to the cloud requires intelligent supervision, such as fog node deployments, which is an essential task to assess network operability. To ensure network operability, the deployment process must be carried out effectively regarding two main factors: connectivity and coverage. The network connectivity is based on fog node deployment, which determines the network’s physical topology, while the coverage determines the network accessibility. Both have a significant impact on network performance and guarantee the network quality of service. Determining an optimum fog node deployment method that minimizes cost, reduces computation and communication overhead, and provides a high degree of network connection coverage is extremely hard. Therefore, maximizing coverage and preserving network connectivity is a non-trivial problem. In this paper, we propose a fog …

Authors

Abdenacer Naouri,Nabil Abdelkader Nouri,Amar Khelloufi,Abdelkarim Ben Sada,Huansheng Ning,Sahraoui Dhelim

Journal

Cluster Computing

Published Date

2024/4/8

BusCache: V2V-based infrastructure-free content dissemination system for Internet of Vehicles

Internet of Vehicles (IoV) offers many services aiming to enhance the safety and comfort of drivers and passengers such as accident alarms, congestion avoidance, and multimedia, entertainment applications. Cooperatively sharing and retrieving large-scale files in IoV is a challenging task due extremely volatile nature of IoV. Therefore, it is paramount to develop a vehicle-to-vehicle (V2V) content delivery mechanism that can adapt to IoV communication requirements. One of the challenging tasks in this regard is to locate content pieces and gather information about peers. Previous proposed systems broadcast beacon messages to the whole network to locate certain content pieces, which consume the bandwidth and limit the network resources. To overcome these issues, in this paper, we propose BusCache, a traffic-aware content delivery system for IoV. BusCache uses buses as trackers for the content distribution …

Authors

Abdenacer Naouri,Nabil Abdelkader Nouri,Amar Khelloufi,Abdelkarim Ben Sada,Salim Naouri,Huansheng Ning,Sahraoui Dhelim

Journal

IEEE Access

Published Date

2024/3/7

Context-Aware Service Recommendation System for the Social Internet of Things

The Social Internet of Things (SIoT) enables interconnected smart devices to share data and services, opening up opportunities for personalized service recommendations. However, existing research often overlooks crucial aspects that can enhance the accuracy and relevance of recommendations in the SIoT context. Specifically, existing techniques tend to consider the extraction of social relationships between devices and neglect the contextual presentation of service reviews. This study aims to address these gaps by exploring the contextual representation of each device-service pair. Firstly, we propose a latent features combination technique that can capture latent feature interactions, by aggregating the device-device relationships within the SIoT. Then, we leverage Factorization Machines to model higher-order feature interactions specific to each SIoT device-service pair to accomplish accurate rating prediction. Finally, we propose a service recommendation framework for SIoT based on review aggregation and feature learning processes. The experimental evaluation demonstrates the framework's effectiveness in improving service recommendation accuracy and relevance.

Authors

Amar Khelloufi,Huansheng Ning,Abdelkarim Ben Sada,Abdenacer Naouri,Sahraoui Dhelim

Journal

arXiv preprint arXiv:2308.08499

Published Date

2023/8/14

A context-aware edge computing framework for smart internet of things

The data explosion caused by the rapid and widespread use of IoT devices is placing tremendous pressure on current communication, computing and storage resources. In an ambient ubiquitous computing environment, taking advantage of the context of the application scenario can significantly improve the system performance of IoT networks. Therefore, in this paper, we propose CONTESS, a context-aware edge computing framework with selective sensing that leverages the context information of the sensed environment to improve its applicability to smart IoT systems. CONTESS is composed of two parts: context management, where context acquisition, modeling and reasoning happens; and selective sensing, where data selection happens. We demonstrate the capabilities of CONTESS in the scenario of a parking management system for a smart city environment. We implement CONTESS using linked data and semantic web technologies. We start by designing an OWL-based ontology and then simulating the proposed scenario using the OMNET++ network simulator along with the Veins framework and SUMO traffic simulator. The results show an improvement compared to traditional sensing methods in both communication overhead and the application results.

Authors

Abdelkarim Ben Sada,Abdenacer Naouri,Amar Khelloufi,Sahraoui Dhelim,Huansheng Ning

Journal

Future Internet

Published Date

2023/4/22

Abdelkarim Ben Sada FAQs

What is Abdelkarim Ben Sada's h-index at University of Science and Technology Beijing?

The h-index of Abdelkarim Ben Sada has been 3 since 2020 and 3 in total.

What are Abdelkarim Ben Sada's top articles?

The articles with the titles of

Maximizing UAV Fog Deployment Efficiency for Critical Rescue Operations

Selective Task offloading for Maximum Inference Accuracy and Energy efficient Real-Time IoT Sensing Systems

A Multimodal Latent-Features-Based Service Recommendation System for the Social Internet of Things

Efficient fog node placement using nature-inspired metaheuristic for IoT applications

BusCache: V2V-based infrastructure-free content dissemination system for Internet of Vehicles

Context-Aware Service Recommendation System for the Social Internet of Things

A context-aware edge computing framework for smart internet of things

are the top articles of Abdelkarim Ben Sada at University of Science and Technology Beijing.

What is Abdelkarim Ben Sada's total number of citations?

Abdelkarim Ben Sada has 54 citations in total.

What are the co-authors of Abdelkarim Ben Sada?

The co-authors of Abdelkarim Ben Sada are Jianhua Ma, Huansheng Ning, Mahmoud Daneshmand, Ph.D, Mohammed Amine BOURAS.

    Co-Authors

    H-index: 59
    Jianhua Ma

    Jianhua Ma

    Hosei University

    H-index: 58
    Huansheng Ning

    Huansheng Ning

    University of Science and Technology Beijing

    H-index: 32
    Mahmoud Daneshmand, Ph.D

    Mahmoud Daneshmand, Ph.D

    Stevens Institute of Technology

    H-index: 9
    Mohammed  Amine BOURAS

    Mohammed Amine BOURAS

    University of Science and Technology Beijing

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