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An Ensemble-Based Hybrid Model for the Detection of Attacks in the Internet of Vehicular Things
Inam Ullah;Irshad Khalil;Xiaoshan Bai;Sahil Garg;Georges Kaddoum;M. Shamim
IEEE Transactions on Intelligent Transportation Systems
Year: 2025 | Early Access Article |
The Internet of Vehicles (IoV) enables technology that allows IoV and vehicles to connect everything. IoV has become an essential component of modern life. This exponential growth of IoV technology has introduced significant security and privacy issues, which pose potential threats to different types of attacks and cause different threats to the normal operation of vehicles. To prevent intelligent...Show More
GAI based Resource and QoE Aware Service Placement in Next-Generation Multi-domain IoT Networks
Chuangchuang Zhang;Qiang He;Fuliang Li;Xingwei Wang;Sahil Garg;M. Shamim Hossain Zhu Han;Wei Yuan
IEEE Transactions on Cognitive Communications and Networking
Year: 2025 | Early Access Article |
Network Function Virtualization (NFV) has recently emerged as a highly cost-effective paradigm for flexibly provisioning services in next-generation Internet of Things (IoT) networks, by introducing Service Function Chain (SFC) technology. However, the rapid expansion of network scales and increasing diversification of service requirements in recent years pose significant challenges to ensuring th...Show More
An Improved Nonlinear Precoding Scheme in Multicarrier Signaling Optimization for Transportation Networks Applications
Cheng Dai;Sha Xiang;Lipeng Xie;Sahil Garg;Georges Kaddoum;Mohammad Mehedi Hassan
IEEE Transactions on Intelligent Transportation Systems
Year: 2025 | Early Access Article |
The digitalization of traffic networks has spurred the development of intelligent transportation systems. By utilizing reinforcement learning for dynamic traffic optimization, it efficiently handles real-world traffic complexities. However, as the demand for real-time, high-efficiency tasks increases, relying solely on reinforcement learning struggles to meet both goals. Integrating reinforcement ...Show More
Split Learning-Based Robust Resource Allocation for Consumer Electronics in Smart Cities
IEEE Transactions on Consumer Electronics
Year: 2025 | Early Access Article |
In the smart city, high-density deployment of consumer electronics (CE) may lead to mutual interference, resulting in imperfect estimation of the channel state information (CSI). To tackle the problem, this paper proposes a split learning-based robust resource allocation for CEs in smart cities. We constructed an interference hypergraph model and divided resource allocation conflicts in overlappin...Show More
Density-Clustering Aggregation for Personalized Federated Learning with AI-Enabled Aerial and Edge Computing in UAVs
Wei-Che Chien;Chih-Hsun Lin;Tianli Zhu;Cheng Dai;Sahil Garg;Amrit Mukherjee
IEEE Internet of Things Journal
Year: 2025 | Early Access Article |
This research introduces the Density-Clustering based Aggregation for Personalized Federated Learning (DCPFL) algorithm, which utilizes DBSCAN clustering to enhance model accuracy in AI-enabled aerial and edge computing contexts, particularly for UAVs. The DCPFL framework promotes model sharing among clients, fostering the development of personalized and optimized models. DBSCAN is beneficial in a...Show More
An Improved Reconstruction-Based Multiattribute Contrastive Learning for Digital-Twin-Enabled Industrial System
Digital twin (DT) is a promising technology for responding to Industry 4.0 and realizing comprehensive automation and virtualization. In the Web3.0-powered 5G/6G era, the expansion of the industrial data and closer interaction among cross-industrial entities pose new security challenges for DT industrial systems. As a prevalent computing paradigm, Graph Anomaly Detection provides an effective solu...Show More
Federated Self-Supervised Learning Based on Prototypes Clustering Contrastive Learning for Internet of Vehicles Applications
Cheng Dai;Shuai Wei;Shengxin Dai;Sahil Garg;Georges Kaddoum;M. Shamim Hossain
Federated learning (FL) is a novel paradigm for distribute edge intelligence for the Internet-of-Vehicles (IoV) application, which can enable superior performance in model training without the need to share local data. However, in the actual architecture of FL, the existence of nonindependent and identically distributed (non-IID) data at the edge device, along with the involvement of randomly part...Show More
A Data Completion Algorithm Based on Low-Rank Prior Knowledge for Data-Driven Applications
Jun Tang;Bing Guo;Yan Shen;Sahil Garg;Georges Kaddoum;M. Shamim Hossain
IEEE Transactions on Network and Service Management
Year: 2024 | Early Access Article |
Low rank tensor ring based data recovery algorithms have been widely used in data-driven consumer electronics to recover missing data entries in the collecting data pre-processing stage for providing stable and reliable service. However, traditional recovery methods often fail to utilize the abundant prior knowledge of data and the non-local self-similarity of the data, thus leading to the failure...Show More
AEFL: Anonymous and Efficient Federated Learning in Vehicle–Road Cooperation Systems With Augmented Intelligence of Things
Xiaoding Wang;Jiadong Li;Hui Lin;Cheng Dai;Sahil Garg;Georges Kaddoum
As the Augmented Intelligence of Things (AIoT) advances within vehicle-road coordination systems, challenges related to road traffic data transmission and processing are being increasingly addressed. However, this progress also brings significant risks of privacy data leakage. Federated learning (FL), a distributed machine learning paradigm, effectively safeguards client data privacy by allowing m...Show More
Energy-Efficient Joint Optimization of Sensing and Computation in MEC-Assisted IoT Using Mean-Field Game
Runchen Xu;Zheng Chang;Zhu Han;Sahil Garg;Georges Kaddoum;Joel J. P. C. Rodrigues
Integrating multiaccess edge computing (MEC) with the Internet of Things (IoT) is able to provide IoT sufficient computational resources in addition to its capabilities of sensing and communication. In this article, given the limited computational and energy resources, IoT devices (IDs) are allowed to offload computational tasks to MEC servers for execution. However, as the number of IDs increases...Show More
Acoustic Sensing for Fitness Activities Recognition: A Deep Learning Approach
Chen Dai;Weihao Zhou;Linqing Gui;Haotong Cao;Sahil Garg;Fu Xiao
IEEE INFOCOM 2024 - IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS)
Year: 2024 | Conference Paper |
Currently, the demand for home fitness has increased as more people choose to work remotely. Acoustic sensing technology has gained popularity due to its practicality on fitness activity monitoring. Due to the fact that sound waves do not propagate uniformly in all directions, identifying fitness activities can be challenging when sound waves do not directly face the device. To address it, we prop...Show More
GCN-Based Multi-Agent Deep Reinforcement Learning for Dynamic Service Function Chain Deployment in IoT
Shuyi Wang;Haotong Cao;Longxiang Yang;Sahil Garg;Georges Kaddoum;Mubarak Alrashoud
The rapid development of technologies such as the Internet of Things, SDN/NFV, and 6G is driving up the demand for dynamic deployment of service function chains (SFC). These technologies are making network architectures more complex and service deployments more dynamic and adaptable. More than ever, there are situations that call for multi-objective SFC dynamic deployment, which necessitates resou...Show More
A Stochastic Geometry Model and Analysis Scheme for SCMA Aided Mobile Edge Computing
Pengtao Liu;Jing Lei;Haotong Cao;Sahil Garg;Kuljeet Kaur;Georges Kaddoum
2024 IEEE Wireless Communications and Networking Conference (WCNC)
Year: 2024 | Conference Paper |
Cited by: Papers (1)
Sparse code multiple access (SCMA) and mobile edge computing (MEC) can greatly enhance the capabilities of IoT networks by providing massive connectivity and timely computation. The paper presents a model and analysis of the performance for a large-scale grant-free (GF) SCMA aided MEC network. Firstly, stochastic geometry is used to derive closed-form solutions for offloading probability and SCMA ...Show More
Lightweight Infrared and Visible Image Fusion Technique: Guided Gradient Optimization Driven
Yuhang Song;Ruijin Wang;Zengpeng Li;Sahil Garg;Georges Kaddoum;Mubarak Alrashoud
Infrared and visible image fusion technology aims to combine data from several spectral bands in order to increase target identification, processing capabilities, and image quality. With the rapid development of consumer electronic products for imaging, there is an urgent need for a lightweight and efficient fusion technology that ensures efficient information extraction and fusion while maintaini...Show More
Smart Contract-Based Access Control Scheme for Blockchain Assisted 6G-Enabled IoT-Based Big Data Driven Healthcare Cyber Physical Systems
Sourav Saha;Ashok Kumar Das;Mohammad Wazid;Youngho Park;Sahil Garg;Mubarak Alrashoud
6G (sixth-generation wireless), the successor to 5G cellular technology, operates at higher frequencies than its predecessor and supports significantly greater capacity and markedly reduced latency. Healthcare is treated as a complex system with various stakeholders, like doctors, patients, hospitals, pharmaceutical companies as well as healthcare decision-makers. The innovations in the Internet o...Show More
Softwarized Resource Allocation of Tailored Services with Zero Security Trust in 6G Networks
Haotong Cao;Longxiang Yang;Sahil Garg;Mubarak Alrashoud;Mohsen Guizani
IEEE Wireless Communications
Cited by: Papers (4)
Comparing with previous five generation networks consisting of dedicated appliance, 6G network appliances are designed to be softwarized and isolated into software (resource and function) blocks and general-purpose hardware. Tailored services, having customized resources and function demands, can be executed by chained software blocks and co-exist in the underlying 6G general-purpose hardware. How...Show More
Tensor-Based Secure Truthful Incentive Mechanism for Mobile Crowdsourcing in IoT-Enabled Maritime Transportation Systems
Ruonan Zhao;Laurence T. Yang;Debin Liu;Xianjun Deng;Xueming Tang;Sahil Garg
The evolution of the Internet of Things-enabled Maritime Transportation Systems (IoT-MTS) provides a sturdy data cornerstone for efficient maritime traffic scheduling and management. However, due to task heterogeneity and the limited computing power of individual vessels or stakeholders, although third-party clouds could provide powerful computing support for MTS, directly aggregating data from ve...Show More
Softwarized Resource Allocation in Digital Twins-Empowered Networks for Future Quantum-Enabled Consumer Applications
Haotong Cao;Sahil Garg;Shahid Mumtaz;Mubarak Alrashoud;Longxiang Yang;Georges Kaddoum
Network softwarization (NetSoft), recognized as crucial attribute of 6G networks, promises to provide enhanced and advanced services, including future quantum-enabled consumer applications. Softwarized resource allocation is the core issue in NetSoft concept. Digital twins (DT) guarantees to generate the corresponding digital world that reflects and interacts with the original physical world seaml...Show More
AI-Enabled Trajectory Optimization of Logistics UAVs With Wind Impacts in Smart Cities
Pengfei Du;Yueqiang Shi;Haotong Cao;Sahil Garg;Mubarak Alrashoud;Piyush Kumar Shukla
AI-enabled logistics unmanned aerial vehicles (UAVs) are progressively revealing their unique advantages for future smart cities. Nevertheless, the existing research on logistics UAV path planning lacks to simultaneously consider the UAV energy consumption constraints, the customer time windows, the impacts of wind speed and direction. This omission renders the existing models inappropriate for re...Show More
An Adaptive Rank-Based Tensor Ring Completion Model for Intelligent Transportation Systems
Low rank tensor ring based data recovery algorithms have been widely used in vehicle road cooperation intelligent transportation system to recover missing data entries in the sensing data pre-processing stage. However, the existing tensor ring decomposition based methods often resolve the low rank optimization with predefined ranks, which often leads over-fitting when the selected rank is large. T...Show More
Long-Term Energy Management Empowered Hierarchical Federated Learning for Smart Consumer Electronics
Managing for local data among the increasingly popular consumer smart electronics in a secure manner while increasing the user experience is still a hard work. Federated learning (FL), can develop intelligent electronics-related applications while protecting the privacy of local data. However, considering the traditional cloud-based FL training process, consumer electronics with a limited energy b...Show More
Federated Multimodal Learning for Privacy-Preserving Driver Break Recommendations in Consumer Electronics
Peng Liu;Longfei Jiang;Huaming Lin;Jia Hu;Sahil Garg;Mubarak Alrashoud
In recent years, driver distraction behaviors, such as eating, drinking, and making phone calls, have become more and more frequent, especially during continuous driving. This results in a significant increase in traffic accidents caused by those behaviors. Therefore, it is crucial to recommend driver breaks when frequent instances of driver distraction are detected. Despite numerous single modal-...Show More
Community Detection-Empowered Self-Adaptive Network Slicing in Multi-Tier Edge-Cloud System
Chenjing Tian;Haotong Cao;Jun Xie;Sahil Garg;Mubarak Alrashoud;Prayag Tiwari
Network slicing (NS) is a highly promising paradigm in 5G and forthcoming 6G communication networks. NS allows for the customization of multiple logically independent network slices to provide tailored service for vertical applications with diverse quality of service (QoS) requirements. However, current research on NS primarily relies on the traditional modeling methods such as service function ch...Show More
Efficient Resource Allocation of Slicing Services in Softwarized Space-Aerial-Ground Integrated Networks for Seamless and Open Access Services
Haotong Cao;Sahil Garg;Georges Kaddoum;Mubarak Alrashoud;Longxiang Yang
In current 6G study, space-aerial-ground integrated networks (SAGIN) are attracting the eye-catching attention. Traditional ground networks and facilities (e.g., open random access networks, Open-RAN) simply provide two-dimensional (2D) and limited-coverage services at low altitudes. SAGIN are designed to overcome these limitations and promise to provide 3D and seamless services and connectivity o...Show More
3-D Trajectory Optimization and Communication Resources Allocation in UAV-Assisted IoT Networks for Sustainable Industry 5.0
Pengfei Du;Yueqiang Shi;Haotong Cao;Sahil Garg;Georges Kaddoum;Mubarak Alrashoud
Unmanned aerial vehicle (UAV) has been utilized as an efficient data collector for Internet of Things (IoT) networks in sustainable industry 5.0. Whereas, how to sustain a stable power for the energy-constrained IoT devices (IoTDs) and to enhance the data gathering throughput of UAV-aided IoT networks via the wireless power transfer (WPT) or non-orthogonal multiple access (NOMA) is a twofold chall...Show More
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