Search

Your search keyword '"Brik, Bouziane"' showing total 36 results
Start Over Author "Brik, Bouziane" Search Limiters Available in Library Collection
36 results on '"Brik, Bouziane"'

1. Federated Learning for Zero-Day Attack Detection in 5G and Beyond V2X Networks

Author

korba, Abdelaziz Amara, Boualouache, Abdelwahab, Brik, Bouziane, Rahal, Rabah, Ghamri-Doudane, Yacine, and Senouci, Sidi Mohammed

Subjects
Computer Science - Cryptography and Security, Computer Science - Artificial Intelligence
Abstract

Deploying Connected and Automated Vehicles (CAVs) on top of 5G and Beyond networks (5GB) makes them vulnerable to increasing vectors of security and privacy attacks. In this context, a wide range of advanced machine/deep learning based solutions have been designed to accurately detect security attacks. Specifically, supervised learning techniques have been widely applied to train attack detection models. However, the main limitation of such solutions is their inability to detect attacks different from those seen during the training phase, or new attacks, also called zero-day attacks. Moreover, training the detection model requires significant data collection and labeling, which increases the communication overhead, and raises privacy concerns. To address the aforementioned limits, we propose in this paper a novel detection mechanism that leverages the ability of the deep auto-encoder method to detect attacks relying only on the benign network traffic pattern. Using federated learning, the proposed intrusion detection system can be trained with large and diverse benign network traffic, while preserving the CAVs privacy, and minimizing the communication overhead. The in-depth experiment on a recent network traffic dataset shows that the proposed system achieved a high detection rate while minimizing the false positive rate, and the detection delay.

Published
2024

2. Split Federated Learning for 6G Enabled-Networks: Requirements, Challenges and Future Directions

Author

Hafi, Houda, Brik, Bouziane, Frangoudis, Pantelis A., and Ksentini, Adlen

Subjects
Computer Science - Networking and Internet Architecture, Electrical Engineering and Systems Science - Signal Processing
Abstract

Sixth-generation (6G) networks anticipate intelligently supporting a wide range of smart services and innovative applications. Such a context urges a heavy usage of Machine Learning (ML) techniques, particularly Deep Learning (DL), to foster innovation and ease the deployment of intelligent network functions/operations, which are able to fulfill the various requirements of the envisioned 6G services. Specifically, collaborative ML/DL consists of deploying a set of distributed agents that collaboratively train learning models without sharing their data, thus improving data privacy and reducing the time/communication overhead. This work provides a comprehensive study on how collaborative learning can be effectively deployed over 6G wireless networks. In particular, our study focuses on Split Federated Learning (SFL), a technique recently emerged promising better performance compared with existing collaborative learning approaches. We first provide an overview of three emerging collaborative learning paradigms, including federated learning, split learning, and split federated learning, as well as of 6G networks along with their main vision and timeline of key developments. We then highlight the need for split federated learning towards the upcoming 6G networks in every aspect, including 6G technologies (e.g., intelligent physical layer, intelligent edge computing, zero-touch network management, intelligent resource management) and 6G use cases (e.g., smart grid 2.0, Industry 5.0, connected and autonomous systems). Furthermore, we review existing datasets along with frameworks that can help in implementing SFL for 6G networks. We finally identify key technical challenges, open issues, and future research directions related to SFL-enabled 6G networks.

Published
2023

3. Explainable AI in 6G O-RAN: A Tutorial and Survey on Architecture, Use Cases, Challenges, and Future Research

Author

Brik, Bouziane, Chergui, Hatim, Zanzi, Lanfranco, Devoti, Francesco, Ksentini, Adlen, Siddiqui, Muhammad Shuaib, Costa-Pérez, Xavier, and Verikoukis, Christos

Subjects
Computer Science - Networking and Internet Architecture
Abstract

The recent O-RAN specifications promote the evolution of RAN architecture by function disaggregation, adoption of open interfaces, and instantiation of a hierarchical closed-loop control architecture managed by RAN Intelligent Controllers (RICs) entities. This paves the road to novel data-driven network management approaches based on programmable logic. Aided by Artificial Intelligence (AI) and Machine Learning (ML), novel solutions targeting traditionally unsolved RAN management issues can be devised. Nevertheless, the adoption of such smart and autonomous systems is limited by the current inability of human operators to understand the decision process of such AI/ML solutions, affecting their trust in such novel tools. eXplainable AI (XAI) aims at solving this issue, enabling human users to better understand and effectively manage the emerging generation of artificially intelligent schemes, reducing the human-to-machine barrier. In this survey, we provide a summary of the XAI methods and metrics before studying their deployment over the O-RAN Alliance RAN architecture along with its main building blocks. We then present various use cases and discuss the automation of XAI pipelines for O-RAN as well as the underlying security aspects. We also review some projects/standards that tackle this area. Finally, we identify different challenges and research directions that may arise from the heavy adoption of AI/ML decision entities in this context, focusing on how XAI can help to interpret, understand, and improve trust in O-RAN operational networks., Comment: Accepted in IEEE Communications Surveys and Tutorials, 38 pages, 14 figures

Published
2023

7. A Privacy-Preserving Framework for Efficient Network Intrusion Detection in Consumer Network Using Quantum Federated Learning

Author

Abou El Houda, Zakaria, Moudoud, Hajar, Brik, Bouziane, and Adil, Muhammad

Abstract

The proliferation of consumer networks has increased vulnerabilities to network intrusions, emphasizing the critical need for robust intrusion detection systems (IDS). The data-driven Artificial Intelligence (AI) approach has gained attention for enhancing IDS capabilities to deal with emerging security threats. However, these AI-based IDS face challenges in scalability and privacy preservation. More importantly, they are time-consuming and may perform poorly on high-dimensional and complex data due to the lack of computational resources. To address these shortcomings, in this paper, we introduce a novel framework, called Quantum Federated Learning IDS (QFL-IDS), that merges Quantum Computing (QC) with Federated Learning (FL) to allow for an efficient, robust, and privacy-preserving approach for detecting network intrusions in consumer networks. Leveraging the decentralized nature of FL, QFL-IDS enables multiple consumer devices to collaboratively train a global intrusion detection model while preserving the privacy of individual user data. Furthermore, we leverage the computational power of quantum computing to improve the efficiency of model training and inference processes. We demonstrate the efficacy of our framework through extensive experiments. The obtained results show significant improvements in detection accuracy and computational efficiency compared to the current traditional centralized and federated learning approaches. This makes QFL-IDS a promising framework to cope with the new emerging security threats in a timely and effective manner.

Published
2024
Full Text
View/download PDF

8. Advancing Security and Trust in WSNs: A Federated Multi-Agent Deep Reinforcement Learning Approach

Author

Moudoud, Hajar, Houda, Zakaria Abou El, and Brik, Bouziane

Abstract

Wireless Sensor Networks (WSNs) show significant potential through their ability to collect and analyze real-time data, notably enhancing various sectors. The new emerging security threats present a severe risk to the security and reliability of WSNs. Data-driven Artificial Intelligence (AI) leverages WSNs data to deal with new emerging threats like zero-day attacks. However, AI-based models suffer from poor adoption due to the lack of realistic/up-to-date attack data. Recently, Multi-Agent Deep Reinforcement Learning (MARL) has gained significant attention for enhancing Intrusion Detection Systems (IDS) capabilities. MARL offers improved flexibility, efficiency, and robustness. However, this requires data sharing, leading to network bandwidth consumption and slower training. Additionally, the curse of dimensionality hampers its benefits, given the exponential expansion of the state-action space. Privacy-aware collaborative methods such as Federated Learning (FL) emerge as a new approach, enabling decentralized model training across a network of devices while preserving the privacy of each participant. In this context, we introduce a novel framework (MAF-DRL) that leverages FL and MARL to efficiently detect WSN-based attacks. MAF-DRL enables distributed learning across multiple agents with adaptive, flexible, and robust attack detection. We also introduce a trust-based scheduling mechanism that dynamically allocates resources based on agent reliability. This trust-aware approach allows FL systems to adapt to changing network conditions and device behaviors. By prioritizing reliable devices, our method improves the energy efficiency of WSNs and enhances the resilience and effectiveness of the distributed FL paradigm. Finally, we assess the robustness of our framework by testing it against real-world WSN attacks. This evaluation demonstrates its efficiency for secure and communication-efficient federated edge learning across various agents.

Published
2024
Full Text
View/download PDF

11. A Lightweight 5G-V2X Intra-slice Intrusion Detection System Using Knowledge Distillation

Author

Hossain, Shajjad, Boualouache, Abdelwahab, Brik, Bouziane, Senouci, Sidi-Mohammed, Hossain, Shajjad, Boualouache, Abdelwahab, Brik, Bouziane, and Senouci, Sidi-Mohammed

Abstract

As the automotive industry grows, modern vehicles will be connected to 5G networks, creating a new Vehicular-to-Everything (V2X) ecosystem. Network Slicing (NS) supports this 5G-V2X ecosystem by enabling network operators to flexibly provide dedicated logical networks addressing use case specific-requirements on top of a shared physical infrastructure. Despite its benefits, NS is highly vulnerable to privacy and security threats, which can put Connected and Automated Vehicles (CAVs) in dangerous situations. Deep Learning-based Intrusion Detection Systems (DL-based IDSs) have been proposed as the first defense line to detect and report these attacks. However, current DL-based IDSs are processing and memory-consuming, increasing security costs and jeopardizing 5G-V2X acceptance. To this end, this paper proposes a lightweight intrusion detection scheme for 5G-V2X sliced networks. Our scheme leverages DL and Knowledge Distillation (KD) for training in the cloud and offloading knowledge to slice-tailored lightweight DL models running on CAVs. Our results show that our scheme provides an optimal trade-off between detection accuracy and security overhead. Specifically, it can reduce security overhead in computation and memory complexity to more than 50% while keeping almost the same performance as heavy DL-based IDSs.

Published
2023

12. On-Demand Security Framework for 5GB Vehicular Networks

Author

Boualouache, Abdelwahab, Brik, Bouziane, Senouci, Sidi-Mohammed, Engel, Thomas, Boualouache, Abdelwahab, Brik, Bouziane, Senouci, Sidi-Mohammed, and Engel, Thomas

Abstract

Building accurate Machine Learning (ML) attack detection models for 5G and Beyond (5GB) vehicular networks requires collaboration between Vehicle-to-Everything (V2X) nodes. However, while operating collaboratively, ensuring the ML model's security and data privacy is challenging. To this end, this article proposes a secure and privacy-preservation on-demand framework for building attack-detection ML models for 5GB vehicular networks. The proposed framework emerged from combining 5GB technologies, namely, Federated Learning (FL), blockchain, and smart contracts to ensure fair and trusted interactions between FL servers (edge nodes) with FL workers (vehicles). Moreover, it also provides an efficient consensus algorithm with an intelligent incentive mechanism to select the best FL workers that deliver highly accurate local ML models. Our experiments demonstrate that the framework achieves higher accuracy on a well-known vehicular dataset with a lower blockchain consensus time than related solutions. Specifically, our framework enhances the accuracy by 14% and decreases the consensus time, at least by 50%, compared to related works. Finally, this article discusses the framework's key challenges and potential solutions.

Published
2023

13. A Survey on Explainable AI for 6G O-RAN: Architecture, Use Cases, Challenges and Research Directions

Author

Brik, Bouziane, Chergui, Hatim, Zanzi, Lanfranco, Devoti, Francesco, Ksentini, Adlen, Siddiqui, Muhammad Shuaib, Costa-Pérez, Xavier, Verikoukis, Christos, Brik, Bouziane, Chergui, Hatim, Zanzi, Lanfranco, Devoti, Francesco, Ksentini, Adlen, Siddiqui, Muhammad Shuaib, Costa-Pérez, Xavier, and Verikoukis, Christos

Abstract

The recent O-RAN specifications promote the evolution of RAN architecture by function disaggregation, adoption of open interfaces, and instantiation of a hierarchical closed-loop control architecture managed by RAN Intelligent Controllers (RICs) entities. This paves the road to novel data-driven network management approaches based on programmable logic. Aided by Artificial Intelligence (AI) and Machine Learning (ML), novel solutions targeting traditionally unsolved RAN management issues can be devised. Nevertheless, the adoption of such smart and autonomous systems is limited by the current inability of human operators to understand the decision process of such AI/ML solutions, affecting their trust in such novel tools. eXplainable AI (XAI) aims at solving this issue, enabling human users to better understand and effectively manage the emerging generation of artificially intelligent schemes, reducing the human-to-machine barrier. In this survey, we provide a summary of the XAI methods and metrics before studying their deployment over the O-RAN Alliance RAN architecture along with its main building blocks. We then present various use-cases and discuss the automation of XAI pipelines for O-RAN as well as the underlying security aspects. We also review some projects/standards that tackle this area. Finally, we identify different challenges and research directions that may arise from the heavy adoption of AI/ML decision entities in this context, focusing on how XAI can help to interpret, understand, and improve trust in O-RAN operational networks., Comment: 33 pages, 13 figures

Published
2023

15. Federated Learning for Zero-Day Attack Detection in 5G and Beyond V2X Networks

Author

Amara Korba, Abdelaziz, Boualouache, Abdelwahab, Brik, Bouziane, Rahal, Rabah, Ghamri-Doudane, Yacine, Senouci, Sidi, and Amara-Korba, Abdelaziz

Subjects
[INFO.INFO-AI] Computer Science [cs]/Artificial Intelligence [cs.AI], 5GB, IoV, [INFO.INFO-NI] Computer Science [cs]/Networking and Internet Architecture [cs.NI], Zero-day attacks, Sécurité, 5G V2X IoV Sécurité Attaques Détection Apprentissage Fédéré, [INFO] Computer Science [cs], Intrusion Detection, Détection, Attaques, Security, V2X, Apprentissage Fédéré, Federated Learning, 5G, Connected and Automated Vehicles, [INFO.INFO-CR] Computer Science [cs]/Cryptography and Security [cs.CR]
Abstract

Deploying Connected and Automated Vehicles (CAVs) on top of 5G and Beyond networks (5GB) makes them vulnerable to increasing vectors of security and privacy attacks. In this context, a wide range of advanced machine/deep learning-based solutions have been designed to accurately detect security attacks. Specifically, supervised learning techniques have been widely applied to train attack detection models. However, the main limitation of such solutions is their inability to detect attacks different from those seen during the training phase, or new attacks, also called zero-day attacks. Moreover, training the detection model requires significant data collection and labeling, which increases the communication overhead, and raises privacy concerns. To address the aforementioned limits, we propose in this paper a novel detection mechanism that leverages the ability of the deep auto-encoder method to detect attacks relying only on the benign network traffic pattern. Using federated learning, the proposed intrusion detection system can be trained with large and diverse benign network traffic, while preserving the CAVs’ privacy, and minimizing the communication overhead. The in-depth experiment on a recent network traffic dataset shows that the proposed system achieved a high detection rate while minimizing the false positive rate, and the detection delay., Le déploiement de véhicules connectés et automatisés (CAV) utilisant les réseaux 5G et 5G-B les rend vulnérables à de nouveaux vecteurs d'attaques. Afin de détecter et de mitiger ces attaques, plusieurs systèmes de détection d'intrusions (IDS) basés sur l'apprentissage automatique ont été proposés, dont la grande majorité utilise l'apprentissage profond supervisé. Cependant, la principale limite de ce type de solution est son incapacité à détecter des attaques différentes de celles vues lors de l'apprentissage de l'IDS, ou de nouvelles attaques, également appelées attaques Zero-day. De plus, l'apprentissage du modèle de détection nécessite une collecte et un étiquetage importants des données, ce qui augmente le coût de communication et soulève des problèmes de confidentialité. Afin de pallier ces problèmes, nous proposons dans cet article un nouveau IDS basé sur l'apprentissage fédéré non supervisé préservant la confidentialité, et dont l'apprentissage du système ne nécessite que le trafic réseau légitime. L'expérimentation de la solution proposée sur un jeu de données récent montre que le système proposé présente un taux de détection élevé tout en minimisant le taux de faux positifs et le délai de détection.

Published
2023

17. Edge Computing enabled Intrusion Detection for C-V2X Networks using Federated Learning

Author

Selamnia, Aymene, Brik, Bouziane, Senouci, Sidi-Mohammed, and Boualouache, Abdelwahab

Subjects
Computer science [C05] [Engineering, computing & technology], Federated deep learning, Intrusion detection system, Edge computing, Sciences informatiques [C05] [Ingénierie, informatique & technologie], C-V2X
Abstract

Intrusion detection systems (IDS) have already demonstrated their effectiveness in detecting various attacks in cellular vehicle-to-everything (C-V2X) networks, especially when using machine learning (ML) techniques. However, it has been shown that generating ML-based models in a centralized way consumes a massive quantity of network resources, such as CPU/memory and bandwidth, which may represent a critical issue in such networks. To avoid this problem, the new concept of Federated Learning (FL) emerged to build ML-based models in a distributed and collaborative way. In such an approach, the set of nodes, e.g., vehicles or gNodeB, collaborate to create a global ML model trained across these multiple decentralized nodes, each one with its respective data samples that are not shared with any other nodes. In this way, FL enables, on the one hand, data privacy since sharing data with a central location is not always feasible and, on the other hand, network overhead reduction. This paper designs a new IDS for C-V2X networks based on FL. It leverages edge computing to not only build a prediction model in a distributed way but also to enable low-latency intrusion detection. Moreover, we build our FL-based IDS on top of the well-known CIC-IDS2018 dataset, which includes the main network attacks. Noting that, we first perform feature engineering on the dataset using the ANOVA method to consider only the most informative features. Simulation results show the efficiency of our system compared to the existing solutions in terms of attack detection accuracy while reducing network resource consumption.

Published
2022

18. Federated Deep Reinforcement Learning for Efficient Jamming Attack Mitigation in O-RAN

Author

Houda, Zakaria Abou El, Moudoud, Hajar, and Brik, Bouziane

Abstract

Open RAN (ORAN or O-RAN) revolutionizesRadio Access Networks (RAN) by offering flexibility and cost-efficiency through inter-vendor equipment interoperability.More importantly, it addresses emerging security threats, such as jamming attacks, by incorporating network softwarization and leveraging Artificial Intelligence (AI) techniques. However, AI-based systems face challenges such as limited training data, slow convergence, and vulnerability to dynamic attack patterns like Zero-day attacks. To enhance jamming attack mitigation in O-RAN, Multi-Agent Reinforcement Learning (MARL) has been introduced for improved flexibility and robustness. However, MARL requires data sharing, which consumes network bandwidth and slows down training, and the curse of dimensionality limits its benefits due to the exponential growth of the state-action space. To overcome these limitations, we provide a novel framework that combines federated learning (FL) and deep reinforcement learning (DRL) for efficient jamming attack detection in O-RAN. FL allows decentralized agents to train local models using their data sources, and the models are aggregated into a global model at a Non-real-time RAN Intelligent Controller (RIC) to guide decision-making. The federated learning process enables distributed intelligence, while deep reinforcement learning ensures adaptive and robust jamming attack detection. Our proposed framework improves security, privacy, and resilience in ORAN through collaborative FL and adaptive DRL. Extensive simulations demonstrate its superiority in detection accuracy, resource efficiency, and scalability.

Published
2024
Full Text
View/download PDF

20. DRIVE-B5G: A Flexible and Scalable Platform Testbed for B5G-V2X Networks

Author

Djaidja, Taki Eddine Toufik, Brik, Bouziane, Boualouache, Abdelwahab, Senouci, Sidi Mohammed, Djaidja, Taki Eddine Toufik, Brik, Bouziane, Boualouache, Abdelwahab, and Senouci, Sidi Mohammed

Abstract

Unlike previous mobile networks, 5G and beyond (B5G) networks are expected to be the key enabler of various vertical industries such as eHealth, intelligent transportation, and Industrial IoT verticals. To support that, B5G networks enable to sharing of common physical resources (radio, computation, network) among different tenants, thanks to network slicing concept and network softwarization technologies, including Software Defined Networking (SDN) and Network Function Virtualization (NFV). Therefore, new research challenges related to B5G networks have emerged, such as resources management and orchestration, service chaining, security, and QoS management. However, there is a lack of a realistic platform enabling researchers to design and validate their solutions effectively, since B5G networks are still in their early stages. In this paper, we first discuss the different methods for deploying realistic B5G platforms for the V2X vertical, including the key B5G technologies. Then, we describe DRIVE-B5G, a novel platform that serves as an end-to-end test-bed to emulate a vehicular network environment, allowing researchers to provide proof of concept, validate, and evaluate their research approaches.

Published
2022

21. 5G Vehicle-to-Everything at the Cross-Borders: Security Challenges and Opportunities

Author

Boualouache, Abdelwahab, Brik, Bouziane, Tang, Qiang, Abdelaziz, Cherrier, Sylvain, Senouci, Sidi-Mohammed, Pardo, Enric, Ghamri-Doudane, Langar, Rami, Engel, Thomas, Boualouache, Abdelwahab, Brik, Bouziane, Tang, Qiang, Abdelaziz, Cherrier, Sylvain, Senouci, Sidi-Mohammed, Pardo, Enric, Ghamri-Doudane, Langar, Rami, and Engel, Thomas

Abstract

5G Vehicle-to-Everything (5G-V2X) communications will play a vital role in the development of the automotive industry. Indeed and thanks to the Network Slicing (NS) concept of 5G and beyond networks (B5G), unprecedented new vehicular use–cases can be supported on top of the same physical network. NS promises to enable the sharing of common network infrastructure and resources while ensuring strict traffic isolation and providing necessary network resources to each NS. However, enabling NS in vehicular networks brings new security challenges and requirements that automotive or 5G standards have not yet addressed. Attackers can exploit the weakest link in the slicing chain, connected and automated vehicles, to violate the slice isolation and degrade its performance. Furthermore, these attacks can be more powerful, especially if they are produced in cross-border areas of two countries, which require an optimal network transition from one operator to another. Therefore, this article aims to provide an overview of newly enabled 5G-V2X slicing use cases and their security issues while focusing on cross-border slicing attacks. It also presents the open security issues of 5G-V2X slicing and identifies some opportunities.

Published
2022

22. Towards an Optimal MEC Resources Dimensioning for Vehicle Collision Avoidance System: A Deep Learning Approach

Author

Brik, Bouziane, Ksentini, Adlen, Eurecom [Sophia Antipolis], European Project: 287654,EC:FP7:ICT,FP7-ICT-2011-7,CHIST-ERA II(2011), and European Project: 871780,H2020-EU.2.1.1. - INDUSTRIAL LEADERSHIP - Leadership in enabling and industrial technologies - Information and Communication Technologies (ICT),MonB5G(2019)

Subjects
Collision Avoidance, Internet of Vehicles, Deep Learning, MEC, [INFO.INFO-IA]Computer Science [cs]/Computer Aided Engineering, LSTM, 5G
Abstract

International audience; Collision detection and avoidance between vehicles is one of the key services envisioned in the Internet of Vehicles (IoV). Such services are usually deployed at the Multi-access Edge Computing (MEC) to ensure low latency communication and thus guarantee real-time reactions to avoid collisions between vehicles. In order to maximize the coverage of the road and ensure that all vehicles are connected to an optimal MEC host (in terms of geographical location), the collision avoidance application needs to be instantiated on all the MEC hosts. This may add a burden on the computing resources available at the latter. In this paper, we propose an AI-empowered framework that aims to optimize the computing resources at the MEC hosts. Our framework uses deep learning to (1) predict the vehicle density to be served by a MEC host and (2) derive the exact computing resources required by the collision detection application to run optimally. We evaluate the proposed framework using a real dataset representing vehicle mobility in a big city. Obtained results show the accuracy of our prediction model and hence, the efficiency of our resources assignment framework to exactly deduce the optimal computing resources needed by each instance of the application.

Published
2022

33. Towards Predicting System Disruption in Industry 4.0: Machine Learning-Based Approach.

Author

Brik, Bouziane, Bettayeb, Belgacem, Sahnoun, M'hammed, and Duval, Fabrice

Subjects
INDUSTRY 4.0, MACHINERY industry, COMPUTER scheduling, MACHINE learning, INTERNET of things
Abstract

Industry 4.0 is the most recent industrial revolution that aims to improve not only the productivity in the 21st century, but also the flexibility, adaptability, and resilience of the industrial systems. It enables the collection of real-time data from industrial systems, Thanks to the development of Internet of Things (IoT) technology. Hence, analyzing online collected data enables to deal with several industrial issues in real-time such as machines' break- or slow-downs, quality crisis, flows disruptions, etc. In traditional industrial systems, previous works focused on both scheduling and rescheduling schemes in order to improve the system performance. However, few works dealt with system disruption monitoring due to the lack of real-time data about the system running. Furthermore, the remote and constant monitoring amenities were not established yet, properly. In this paper, we propose a system disruption monitoring tool in Industry 4.0 system. Our tool focuses on system disruption related to resources localization, or when a resource is in an unexpected location. Thus, a machine learning algorithm is used to generate a prediction model of resources localization by considering the real tasks scheduling in terms of resources localization. Therefore, as real resources localization can be collected from the industrial system through the IoT network, our tool enables to detect system disruption, risk, in real-time when comparing predicted localization to the real one. Moreover, our tool is executed in a Fog computing architecture which is emerging as an extension of cloud computing to provide local processing support with good latency. The experimental results show the efficiency of our tool in terms of prediction accuracy and time complexity when compared to other machine learning algorithms, in addition to its ability to control and detect system disruption in real-time. [ABSTRACT FROM AUTHOR]

Published
2020
Full Text
View/download PDF

35. DDGP : Distributed Data Gathering Protocol for vehicular networks

Author

Brik, Bouziane, Lagraa, Nasreddine, Abderrahmane, Lakas, Cheddad, Abbas, Brik, Bouziane, Lagraa, Nasreddine, Abderrahmane, Lakas, and Cheddad, Abbas

Abstract

Vehicular Ad-Hoc Network (VANet) is an emerging research area, it offers a wide range of applications including safety, road traffic efficiency, and infotainment applications. Recently researchers are studying the possibility of making use of deployed VANet applications for data collection. In this case, vehicles are considered as mobile collectors that gather both real time and delay tolerant data and deliver them to interested entities. In this paper, we propose a novel Distributed Data Gathering Protocol (DDGP) for the collection of delay tolerant as well as real time data in both urban and highway environments. The main contribution of DDGP is a new medium access technique that enables vehicles to access the channel in a distributed way based on their location information. In addition, DDGP implements a new aggregation scheme, which deletes redundant, expired, and undesired data. We provide an analytical proof of correctness of DDGP, in addition to the performance evaluation through an extensive set of simulation experiments. Our results indicate that DDGP enhances the efficiency and the reliability of the data collection process by outperforming existing schemes in terms of several criteria such as delay and message overhead, aggregation ratio, and data retransmission rate. (C) 2016 Elsevier Inc. All rights reserved.

Published
2016
Full Text
View/download PDF

Catalog

Books, media, physical & digital resources
See catalog results