Search Results for author: Mohsen Guizani

Found 49 papers, 7 papers with code

Beyond Reality: The Pivotal Role of Generative AI in the Metaverse

no code implementations28 Jul 2023 Vinay Chamola, Gaurang Bansal, Tridib Kumar Das, Vikas Hassija, Naga Siva Sai Reddy, Jiacheng Wang, Sherali Zeadally, Amir Hussain, F. Richard Yu, Mohsen Guizani, Dusit Niyato

This paper offers a comprehensive exploration of how generative AI technologies are shaping the Metaverse, transforming it into a dynamic, immersive, and interactive virtual world.

Image Generation Text Generation

Towards Ubiquitous Semantic Metaverse: Challenges, Approaches, and Opportunities

no code implementations13 Jul 2023 Kai Li, Billy Pik Lik Lau, Xin Yuan, Wei Ni, Mohsen Guizani, Chau Yuen

In recent years, ubiquitous semantic Metaverse has been studied to revolutionize immersive cyber-virtual experiences for augmented reality (AR) and virtual reality (VR) users, which leverages advanced semantic understanding and representation to enable seamless, context-aware interactions within mixed-reality environments.

Marketing Mixed Reality

Motion Comfort Optimization for Autonomous Vehicles: Concepts, Methods, and Techniques

no code implementations15 Jun 2023 Mohammed Aledhari, Mohamed Rahouti, Junaid Qadir, Basheer Qolomany, Mohsen Guizani, Ala Al-Fuqaha

We also discuss the technical details related to the automatic driving comfort system, the response time of the AV driver, the comfort level of the AV, motion sickness, and related optimization technologies.

Autonomous Driving

The Metaverse: Survey, Trends, Novel Pipeline Ecosystem & Future Directions

no code implementations18 Apr 2023 Hani Sami, Ahmad Hammoud, Mouhamad Arafeh, Mohamad Wazzeh, Sarhad Arisdakessian, Mario Chahoud, Osama Wehbi, Mohamad Ajaj, Azzam Mourad, Hadi Otrok, Omar Abdel Wahab, Rabeb Mizouni, Jamal Bentahar, Chamseddine Talhi, Zbigniew Dziong, Ernesto Damiani, Mohsen Guizani

To address these issues, we present in this survey a novel multi-layered pipeline ecosystem composed of (1) the Metaverse computing, networking, communications and hardware infrastructure, (2) environment digitization, and (3) user interactions.

Business Ethics Cultural Vocal Bursts Intensity Prediction

Sparsity-Aware Intelligent Massive Random Access Control in Open RAN: A Reinforcement Learning Based Approach

no code implementations5 Mar 2023 Xiao Tang, Sicong Liu, Xiaojiang Du, Mohsen Guizani

Massive random access of devices in the emerging Open Radio Access Network (O-RAN) brings great challenge to the access control and management.

Management Reinforcement Learning (RL)

Semi-decentralized Inference in Heterogeneous Graph Neural Networks for Traffic Demand Forecasting: An Edge-Computing Approach

1 code implementation28 Feb 2023 Mahmoud Nazzal, Abdallah Khreishah, Joyoung Lee, Shaahin Angizi, Ala Al-Fuqaha, Mohsen Guizani

This approach minimizes inter-cloudlet communication thereby alleviating the communication overhead of the decentralized approach while promoting scalability due to cloudlet-level decentralization.


An Incremental Gray-box Physical Adversarial Attack on Neural Network Training

no code implementations20 Feb 2023 Rabiah Al-qudah, Moayad Aloqaily, Bassem Ouni, Mohsen Guizani, Thierry Lestable

Finally, the attack effectiveness property was concluded from the fact that it was able to flip the sign of the loss gradient in the conducted experiments to become positive, which indicated noisy and unstable training.

Adversarial Attack

Warmup and Transfer Knowledge-Based Federated Learning Approach for IoT Continuous Authentication

no code implementations10 Nov 2022 Mohamad Wazzeh, Hakima Ould-Slimane, Chamseddine Talhi, Azzam Mourad, Mohsen Guizani

Most of the literature focuses on training machine learning for the user by transmitting their data to an external server, subject to private user data exposure to threats.

Federated Learning Transfer Learning

ON-DEMAND-FL: A Dynamic and Efficient Multi-Criteria Federated Learning Client Deployment Scheme

no code implementations5 Nov 2022 Mario Chahoud, Hani Sami, Azzam Mourad, Safa Otoum, Hadi Otrok, Jamal Bentahar, Mohsen Guizani

In this paper, we address the aforementioned limitations by introducing an On-Demand-FL, a client deployment approach for FL, offering more volume and heterogeneity of data in the learning process.

Federated Learning

FedMint: Intelligent Bilateral Client Selection in Federated Learning with Newcomer IoT Devices

no code implementations31 Oct 2022 Osama Wehbi, Sarhad Arisdakessian, Omar Abdel Wahab, Hadi Otrok, Safa Otoum, Azzam Mourad, Mohsen Guizani

Our solution involves the design of: (1) preference functions for the client IoT devices and federated servers to allow them to rank each other according to several factors such as accuracy and price, (2) intelligent matching algorithms that take into account the preferences of both parties in their design, and (3) bootstrapping technique that capitalizes on the collaboration of multiple federated servers in order to assign initial accuracy value for the newly connected IoT devices.

Federated Learning Privacy Preserving

RL-DistPrivacy: Privacy-Aware Distributed Deep Inference for low latency IoT systems

no code implementations27 Aug 2022 Emna Baccour, Aiman Erbad, Amr Mohamed, Mounir Hamdi, Mohsen Guizani

In this paper, we present an approach that targets the security of collaborative deep inference via re-thinking the distribution strategy, without sacrificing the model performance.

Privacy Preserving Reinforcement Learning (RL)

Artificial Intelligence for 6G Networks: Technology Advancement and Standardization

no code implementations2 Apr 2022 Muhammad K. Shehzad, Luca Rose, M. Majid Butt, Istvan Z. Kovacs, Mohamad Assaad, Mohsen Guizani

With the deployment of 5G networks, standards organizations have started working on the design phase for sixth-generation (6G) networks.


PerFED-GAN: Personalized Federated Learning via Generative Adversarial Networks

no code implementations18 Feb 2022 Xingjian Cao, Gang Sun, Hongfang Yu, Mohsen Guizani

Due to the differences of clients, a single global model may not perform well on all clients, so the personalized federated learning method, which trains a personalized model for each client that better suits its individual needs, becomes a research hotspot.

Personalized Federated Learning

Cross-Silo Heterogeneous Model Federated Multitask Learning

1 code implementation17 Feb 2022 Xingjian Cao, Zonghang Li, Gang Sun, Hongfang Yu, Mohsen Guizani

CoFED is a federated learning method that is compatible with heterogeneous models, tasks, and training processes.

Federated Learning Multi-Task Learning

Exploring Deep Reinforcement Learning-Assisted Federated Learning for Online Resource Allocation in Privacy-Persevering EdgeIoT

1 code implementation15 Feb 2022 Jingjing Zheng, Kai Li, Naram Mhaisen, Wei Ni, Eduardo Tovar, Mohsen Guizani

Federated learning (FL) has been increasingly considered to preserve data training privacy from eavesdropping attacks in mobile edge computing-based Internet of Thing (EdgeIoT).

Edge-computing Federated Learning

Motivating Learners in Multi-Orchestrator Mobile Edge Learning: A Stackelberg Game Approach

no code implementations25 Sep 2021 Mhd Saria Allahham, Sameh Sorour, Amr Mohamed, Aiman Erbad, Mohsen Guizani

Therefore, it is crucial to motivate edge devices to become learners and offer their computing resources, and either offer their private data or receive the needed data from the orchestrator and participate in the training process of a learning task.

Internet of Behavior (IoB) and Explainable AI Systems for Influencing IoT Behavior

no code implementations15 Sep 2021 Haya Elayan, Moayad Aloqaily, Fakhri Karray, Mohsen Guizani

The scenario results showed a decrease of 522. 2 kW of active power when compared to original consumption over a 200-hours period.

Cloud Computing Explainable Artificial Intelligence (XAI)

Energy-Efficient Multi-Orchestrator Mobile Edge Learning

no code implementations2 Sep 2021 Mhd Saria Allahham, Sameh Sorour, Amr Mohamed, Aiman Erbad, Mohsen Guizani

The heterogeneity in edge devices' capabilities will require the joint optimization of the learners-orchestrator association and task allocation.

Total Energy

Communication-Efficient Hierarchical Federated Learning for IoT Heterogeneous Systems with Imbalanced Data

1 code implementation14 Jul 2021 Alaa Awad Abdellatif, Naram Mhaisen, Amr Mohamed, Aiman Erbad, Mohsen Guizani, Zaher Dawy, Wassim Nasreddine

Federated learning (FL) is a distributed learning methodology that allows multiple nodes to cooperatively train a deep learning model, without the need to share their local data.

Federated Learning

Distributed CNN Inference on Resource-Constrained UAVs for Surveillance Systems: Design and Optimization

no code implementations23 May 2021 Mohammed Jouhari, Abdulla Al-Ali, Emna Baccour, Amr Mohamed, Aiman Erbad, Mohsen Guizani, Mounir Hamdi

Unmanned Aerial Vehicles (UAVs) have attracted great interest in the last few years owing to their ability to cover large areas and access difficult and hazardous target zones, which is not the case of traditional systems relying on direct observations obtained from fixed cameras and sensors.

Decision Making

Pervasive AI for IoT applications: A Survey on Resource-efficient Distributed Artificial Intelligence

no code implementations4 May 2021 Emna Baccour, Naram Mhaisen, Alaa Awad Abdellatif, Aiman Erbad, Amr Mohamed, Mounir Hamdi, Mohsen Guizani

The confluence of pervasive computing and artificial intelligence, Pervasive AI, expanded the role of ubiquitous IoT systems from mainly data collection to executing distributed computations with a promising alternative to centralized learning, presenting various challenges.

Recommendation Systems Scheduling

The Duo of Artificial Intelligence and Big Data for Industry 4.0: Review of Applications, Techniques, Challenges, and Future Research Directions

no code implementations6 Apr 2021 Senthil Kumar Jagatheesaperumal, Mohamed Rahouti, Kashif Ahmad, Ala Al-Fuqaha, Mohsen Guizani

In this paper, we provide a comprehensive overview of different aspects of AI and Big Data in Industry 4. 0 with a particular focus on key applications, techniques, the concepts involved, key enabling technologies, challenges, and research perspective towards deployment of Industry 5. 0.


Green IoT using UAVs in B5G Networks: A Review of Applications and Strategies

no code implementations31 Mar 2021 S. H. Alsamhi, Fatemeh Afghah, Radhya Sahal, Ammar Hawbani, A. A. Al-qaness, B. Lee, Mohsen Guizani

Due to a drone's capability to fly closer to IoT, UAV technology plays a vital role in greening IoT by transmitting collected data to achieve a sustainable, reliable, eco-friendly Industry 4. 0.


Edge Intelligence for Empowering IoT-based Healthcare Systems

no code implementations22 Mar 2021 Vahideh Hayyolalam, Moayad Aloqaily, Oznur Ozkasap, Mohsen Guizani

The objective of this article is to highlight the benefits of the adoption of edge intelligent technology, along with AI in smart healthcare systems.


Implicit Feedback-based Group Recommender System for Internet of Thing Applications

no code implementations29 Jan 2021 Zhiwei Guo, Keping Yu, Tan Guo, Ali Kashif Bashir, Muhammad Imran, Mohsen Guizani

With the prevalence of Internet of Things (IoT)-based social media applications, the distance among people has been greatly shortened.

Recommendation Systems

Analysis and Optimal Edge Assignment For Hierarchical Federated Learning on Non-IID Data

no code implementations10 Dec 2020 Naram Mhaisen, Alaa Awad, Amr Mohamed, Aiman Erbad, Mohsen Guizani

Distributed learning algorithms aim to leverage distributed and diverse data stored at users' devices to learn a global phenomena by performing training amongst participating devices and periodically aggregating their local models' parameters into a global model.

Edge-computing Federated Learning

Budgeted Online Selection of Candidate IoT Clients to Participate in Federated Learning

no code implementations16 Nov 2020 Ihab Mohammed, Shadha Tabatabai, Ala Al-Fuqaha, Faissal El Bouanani, Junaid Qadir, Basheer Qolomany, Mohsen Guizani

In this work, we solve the problem of optimizing accuracy in stateful FL with a budgeted number of candidate clients by selecting the best candidate clients in terms of test accuracy to participate in the training process.

Federated Learning

Performance Analysis and Optimization for the MAC Protocol in UAV-based IoT Network

no code implementations22 Oct 2020 Bin Li, Xianzhen Guo, Ruonan Zhang, Xiaojiang Du, Mohsen Guizani

A modified three-dimensional Markov chain model adopting the quitting probability and cluster division is developed for the performance analysis.

Information Theory Information Theory

Optimization-driven Machine Learning for Intelligent Reflecting Surfaces Assisted Wireless Networks

no code implementations29 Aug 2020 Shimin Gong, Jiaye Lin, Jinbei Zhang, Dusit Niyato, Dong In Kim, Mohsen Guizani

Due to the large size of scattering elements, the passive beamforming is typically challenged by the high computational complexity and inexact channel information.

BIG-bench Machine Learning

Compress or Interfere?

no code implementations27 Jun 2020 Alaa Awad Abdellatif, Lutfi Samara, Amr Mohamed, Mohsen Guizani, Aiman Erbad, Abdulla Al-Ali

Rapid evolution of wireless medical devices and network technologies has fostered the growth of remote monitoring systems.

Reliable Federated Learning for Mobile Networks

no code implementations14 Oct 2019 Jiawen Kang, Zehui Xiong, Dusit Niyato, Yuze Zou, Yang Zhang, Mohsen Guizani

Based on this metric, a reliable worker selection scheme is proposed for federated learning tasks.

Cryptography and Security

QoE-Aware Resource Allocation for Crowdsourced Live Streaming: A Machine Learning Approach

no code implementations20 Jun 2019 Fatima Haouari, Emna Baccour, Aiman Erbad, Amr Mohamed, Mohsen Guizani

This can be achieved by advocating a geo-distributed cloud infrastructure to allocate the multimedia resources as close as possible to viewers, in order to minimize the access delay and video stalls.

BIG-bench Machine Learning

Self-Selective Correlation Ship Tracking Method for Smart Ocean System

no code implementations26 Feb 2019 Xu Kang, Bin Song, Jie Guo, Xiaojiang Du, Mohsen Guizani

In recent years, with the development of the marine industry, navigation environment becomes more complicated.

Management regression

Interest-Related Item Similarity Model Based on Multimodal Data for Top-N Recommendation

no code implementations13 Feb 2019 Junmei Lv, Bin Song, Jie Guo, Xiaojiang Du, Mohsen Guizani

Specifically, the Multimodal IRIS model consists of three modules, i. e., multimodal feature learning module, the Interest-Related Network (IRN) module and item similarity recommendation module.

Recommendation Systems

Adversarial Samples on Android Malware Detection Systems for IoT Systems

no code implementations12 Feb 2019 Xiaolei Liu, Xiaojiang Du, Xiaosong Zhang, Qingxin Zhu, Mohsen Guizani

An automated testing framework is needed to help these learning-based malware detection systems for IoT devices perform security analysis.

Android Malware Detection Malware Detection

Design Challenges of Multi-UAV Systems in Cyber-Physical Applications: A Comprehensive Survey, and Future Directions

no code implementations23 Oct 2018 Reza Shakeri, Mohammed Ali Al-Garadi, Ahmed Badawy, Amr Mohamed, Tamer Khattab, Abdulla Al-Ali, Khaled A. Harras, Mohsen Guizani

We present and propose state-of-the-art algorithms to address design challenges with both quantitative and qualitative methods and map these challenges with important CPS applications to draw insightful conclusions on the challenges of each application.

Semi-supervised Deep Reinforcement Learning in Support of IoT and Smart City Services

1 code implementation9 Oct 2018 Mehdi Mohammadi, Ala Al-Fuqaha, Mohsen Guizani, Jun-Seok Oh

In this paper, we propose a semi-supervised deep reinforcement learning model that fits smart city applications as it consumes both labeled and unlabeled data to improve the performance and accuracy of the learning agent.

Indoor Localization reinforcement-learning +1

A Survey of Machine and Deep Learning Methods for Internet of Things (IoT) Security

no code implementations29 Jul 2018 Mohammed Ali Al-Garadi, Amr Mohamed, Abdulla Al-Ali, Xiaojiang Du, Mohsen Guizani

Consequently, ML/DL methods are important in transforming the security of IoT systems from merely facilitating secure communication between devices to security-based intelligence systems.

Reinforcement Learning based QoS/QoE-aware Service Function Chaining in Software-Driven 5G Slices

no code implementations6 Apr 2018 Xi Chen, Zonghang Li, Yupeng Zhang, Ruiming Long, Hongfang Yu, Xiaojiang Du, Mohsen Guizani

With the ever growing diversity of devices and applications that will be connected to 5G networks, flexible and agile service orchestration with acknowledged QoE that satisfies end-user's functional and QoS requirements is necessary.

FPAN: Fine-grained and Progressive Attention Localization Network for Data Retrieval

no code implementations5 Apr 2018 Sijia Chen, Bin Song, Jie Guo, Xiaojiang Du, Mohsen Guizani

The Localization of the target object for data retrieval is a key issue in the Intelligent and Connected Transportation Systems (ICTS).

Multi-Task Learning Object Localization +2

Deep Learning for IoT Big Data and Streaming Analytics: A Survey

1 code implementation9 Dec 2017 Mehdi Mohammadi, Ala Al-Fuqaha, Sameh Sorour, Mohsen Guizani

The potential of using emerging DL techniques for IoT data analytics are then discussed, and its promises and challenges are introduced.

When Traffic Flow Prediction Meets Wireless Big Data Analytics

no code implementations23 Sep 2017 Yuanfang Chen, Mohsen Guizani, Yan Zhang, Lei Wang, Noel Crespi, Gyu Myoung Lee

Traffic congestion is one of the most serious problems in a city, which can be predicted in advance by analyzing traffic flow patterns.

Time Series Time Series Prediction

VANETs Meet Autonomous Vehicles: A Multimodal 3D Environment Learning Approach

no code implementations24 May 2017 Yassine Maalej, Sameh Sorour, Ahmed Abdel-Rahim, Mohsen Guizani

In this paper, we design a multimodal framework for object detection, recognition and mapping based on the fusion of stereo camera frames, point cloud Velodyne Lidar scans, and Vehicle-to-Vehicle (V2V) Basic Safety Messages (BSMs) exchanged using Dedicated Short Range Communication (DSRC).

Autonomous Vehicles object-detection +1

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