Article
Engineering, Multidisciplinary
Baris Yamansavascilar, Ahmet Cihat Baktir, Cagatay Sonmez, Atay Ozgovde, Cem Ersoy
Summary: The improvements in edge computing technology enable diverse applications that require real-time interaction. However, it is challenging to handle task offloading in a high-performance manner due to the mobility of end-users and the dynamic edge environment. To address this, we propose DeepEdge, a deep reinforcement learning based task orchestrator that can adapt to different task requirements, even under heavily-loaded stochastic network conditions.
IEEE TRANSACTIONS ON NETWORK SCIENCE AND ENGINEERING
(2023)
Article
Computer Science, Hardware & Architecture
Mohammad Yahya Akhlaqi, Zurina Binti Mohd Hanapi
Summary: Many enterprise companies are migrating their services and applications to the cloud to take advantage of cloud computing benefits. However, the increasing number of connected devices and the massive amount of generated data using cloud services lead to congestion and delays in the centralized cloud architecture. Mobile Edge Computing (MEC) solves this problem by expanding cloud capabilities near the end devices, and new technologies like IoT, AV, 5G, and AR further enhance the potential of MEC. The offloading problem in MEC, which involves offloading delay-sensitive and computationally intensive tasks to nearby MEC nodes, is a widely studied issue but still an open problem.
JOURNAL OF NETWORK AND COMPUTER APPLICATIONS
(2023)
Article
Engineering, Electrical & Electronic
Qihang Wu, Xiaoxi Ding, Qiang Zhang, Rui Liu, Shanshan Wu, Qingbo He
Summary: This study proposes an Intelligent Edge Fault Diagnosis System (IEDS) based on a lightweight intelligent architecture called Multiplication-Convolution Sparse Network (MCSN). The system enables real-time data processing, fault identification, and fault data filtering with high accuracy and lightweight performance.
IEEE SENSORS JOURNAL
(2023)
Article
Engineering, Electrical & Electronic
Ao Ding, Yong Qin, Biao Wang, Limin Jia, Xiaoqing Cheng
Summary: Intelligent fault diagnosis of train bogie bearings based on edge computing is a promising technology to ensure the safety and reliability of train operation, which can give fault diagnosis systems better real-time performance and lower communication costs. This article proposes a new multiscale lightweight network with adaptive pruning for the intelligent diagnosis fault of train bogie bearings in edge computing scenarios. Experimental results demonstrate that the accuracy and complexity of the proposed network are superior to other state-of-the-art lightweight bearing fault diagnosis networks under varying operating conditions.
IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT
(2023)
Article
Computer Science, Artificial Intelligence
Xu Zhao, Guangqiu Huang, Jin Jiang, Lin Gao, Maozhen Li
Summary: In this paper, a collaborative intrusion detection system architecture applied to mobile edge computing is proposed, along with a task offloading scheduling algorithm based on Deep Q Network to address packet loss issues under heavy traffic. Experiments show that the proposed scheme outperforms comparative algorithms in terms of response time, energy consumption, and packet loss rate.
EXPERT SYSTEMS WITH APPLICATIONS
(2022)
Article
Computer Science, Information Systems
Fei Dai, Guozhi Liu, Qi Mo, WeiHeng Xu, Bi Huang
Summary: This paper proposes an efficient offloading scheme based on deep reinforcement learning for VEC with edge-cloud computing cooperation, aiming to meet low latency demands for computation-intensive vehicular applications. The scheme integrates the computation resources of vehicles, edge servers, and the cloud server to minimize the average processing delay of tasks.
WORLD WIDE WEB-INTERNET AND WEB INFORMATION SYSTEMS
(2022)
Article
Engineering, Electrical & Electronic
Gang Li, Jun Cai, Shuang Ni
Summary: This paper studies collaborative task offloading in edge computing, proposes a truthful mechanism to incentivize smartphone users, and introduces a new approach to tackle the high computational complexity.
IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY
(2022)
Article
Computer Science, Information Systems
Liang Chen, Jianpeng Qi, Xiao Su, Rui Wang
Summary: This paper proposes a reliability evaluation method (REMR) to study the effect of distributed and collaborative service deployment. By calculating the reliability of services supported by the solution set based on the distribution of available transmission bandwidth and computing resources, REMR provides accurate results that are nearly the same as simulation results. The experiments show lower latencies and jitters.
IEEE INTERNET OF THINGS JOURNAL
(2023)
Article
Computer Science, Information Systems
Yuzhe Xu, Thaha Mohammed, Mario Di Francesco, Carlo Fischione
Summary: This article addresses the problem of DNN inference allocation in edge computing, proposing a realistic DNN inference model and a distributed algorithm to solve it. Experimental results show that the proposed solution significantly outperforms existing techniques in terms of inference time, load balance, and convergence speed.
IEEE INTERNET OF THINGS JOURNAL
(2023)
Article
Computer Science, Information Systems
Wenhao Fan, Li Gao, Yi Su, Fan Wu, Yuan'an Liu
Summary: In this article, a collaborative optimization approach is proposed for multi-base station and multiservice edge-cloud-assisted IoT environment, aiming to minimize the processing delay of all deep learning tasks. By utilizing task offloading, DNN partition mechanism, and resource allocation, the proposed scheme achieves a notable average delay reduction of 28.3% compared to existing works, as demonstrated through extensive simulations.
IEEE INTERNET OF THINGS JOURNAL
(2023)
Article
Computer Science, Information Systems
Zongpu Zhang, Tao Song, Liwei Lin, Yang Hua, Xufeng He, Zhengui Xue, Ruhui Ma, Haibing Guan
Summary: The paper proposes a heterogeneous distributed deep neural network (HDDNN) framework for ubiquitous intelligent computing. It optimizes the utilization of hierarchical distributed systems for DNN and tailors DNN for real-world distributed systems, resulting in low response time, high performance, and better user experience.
IEEE TRANSACTIONS ON BIG DATA
(2022)
Article
Computer Science, Information Systems
Kexin Li, Xingwei Wang, Qiang He, Bo Yi, Andrea Morichetta, Min Huang
Summary: Computation offloading decisions are crucial for implementing mobile-edge computing in IoT services. This article investigates the optimization problem of multiuser delay-sensitive tasks from the perspective of mobile network operators. It proposes a new optimization model and a cooperative multiagent deep reinforcement learning algorithm to achieve higher profit and better computational performance.
IEEE INTERNET OF THINGS JOURNAL
(2022)
Article
Engineering, Electrical & Electronic
Suleman Munawar, Zaiwar Ali, Muhammad Waqas, Shanshan Tu, Syed Ali Hassan, Ghulam Abbas
Summary: This paper proposes an approach to improve service performance and reduce delay in vehicular networks using mobile edge computing (MEC) mechanisms. By intelligently dividing tasks and promoting cooperation among roadside units (RSUs), it effectively reduces computational delay and further decreases delay through parallel processing. The adoption of an efficient routing policy enhances service reliability. Simulation results demonstrate the feasibility of this method in a dynamic environment and show that it outperforms existing schemes.
IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY
(2023)
Article
Computer Science, Information Systems
Li Ma, Peng Wang, Chunlai Du, Yang Li
Summary: This paper aims to minimize the overall energy consumption of a mobile edge computing system by jointly optimizing the strategies of task deployment, offloading decisions, edge cache, and resource allocation. It proposes an energy-efficient edge caching and task deployment policy based on Deep Q-Learning. The simulation results show that the proposed algorithm can converge the model in very few iterations and significantly reduce system energy consumption and policy response delay compared to other algorithms.
Article
Engineering, Electrical & Electronic
Bartosz Kopras, Bartosz Bossy, Filip Idzikowski, Pawel Kryszkiewicz, Hanna Bogucka
Summary: Fog networks provide varying computing resources at different distances from end users. This study focuses on the task distribution between fog and cloud nodes and proposes algorithms to minimize task transmission and processing energy while satisfying delay constraints. The results show a significant decrease in the number of computational requests with unmet delay requirements and reduced energy consumption using the proposed algorithms.
IEEE TRANSACTIONS ON COMMUNICATIONS
(2022)
Article
Computer Science, Artificial Intelligence
Heena Rathore, Amr Mohamed, Mohsen Guizani, Shailendra Rathore
Summary: This paper introduces a machine learning approach called NueroFATH for the physical assessment of athletes. It uses neural networks and fuzzy c-means techniques to predict the potential of athletes winning medals. The study also identifies important physical characteristics related to the assessment results.
NEURAL COMPUTING & APPLICATIONS
(2023)
Article
Computer Science, Theory & Methods
Honnesh Rohmetra, Navaneeth Raghunath, Pratik Narang, Vinay Chamola, Mohsen Guizani, Naga Rajiv Lakkaniga
Summary: The COVID-19 pandemic has overwhelmed healthcare systems and posed risks to healthcare professionals. Remote monitoring of patient symptoms using machine learning and deep learning techniques offers a promising solution, utilizing common devices like smartphones.
Article
Computer Science, Artificial Intelligence
Yuwen Chen, Bin Song, Yuan Zeng, Xiaojiang Du, Mohsen Guizani
Summary: This study proposes a fault diagnosis method for the current-carrying ring based on an improved CenterNet model in the Industrial Internet of Things. By using attention modules and weighted loss, the accuracy of fault diagnosis for the current-carrying rings is improved.
NEURAL COMPUTING & APPLICATIONS
(2023)
Article
Computer Science, Artificial Intelligence
Mangena Venu Madhavan, Aditya Khamparia, Deepak Gupta, Sagar Pande, Prayag Tiwari, M. Shamim Hossain
Summary: Major countries are facing difficulties due to the COVID-19 pandemic. Existing medical practices, such as PCR and RT-PCR, may result in false positives and false negatives when identifying COVID-19 symptoms. CT imaging or X-rays of the lungs can provide more accurate identification of patients with COVID-19 symptoms. Automating the identification of COVID-19 using feasible technology can improve facilities. The Res-CovNet framework is a hybrid methodology that integrates different platforms into a single platform to identify and classify pneumonia and COVID-19 cases.
NEURAL COMPUTING & APPLICATIONS
(2023)
Article
Engineering, Civil
Ghulam Muhammad, M. Shamim Hossain
Summary: This paper proposes light convolutional neural network (CNN) models for cognitive networking in an intelligent transportation system (ITS). The models include a 1D CNN for processing 1D temporal data and a deep tree CNN for processing image data from car camera sensors. By processing data independently on edge devices, the load and time of model execution are reduced. The proposed method achieves an accuracy of approximately 94-96% and an information density of 4.4 when tested on a publicly available facial emotion database.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2023)
Editorial Material
Computer Science, Theory & Methods
M. Shamim Hossain, Josu Bilbao, Diana P. P. Tobon, Abdulmotaleb El Saddik
Article
Chemistry, Multidisciplinary
Thamer Alanazi, Khalid Babutain, Ghulam Muhammad
Summary: Unintentional falls, especially among older adults, can lead to severe injuries and negative impact on quality of life. To address this issue, a vision-based fall detection system is proposed to reduce fall frequency and associated healthcare and productivity costs. The system utilizes a human segmentation model and image fusion technique for preprocessing, and a 3D multi-stream CNN model for classification, achieving impressive accuracy, sensitivity, specificity, and precision.
APPLIED SCIENCES-BASEL
(2023)
Article
Engineering, Chemical
Jaspreet Singh, Gurpreet Singh, Deepali Gupta, Ghulam Muhammad, Ali Nauman
Summary: This article introduces an improved OCI-OLSR routing protocol that aims to enhance the performance of the regular OLSR protocol in wireless ad hoc networks. By optimizing control interval management, an advanced MPR selection process, reducing neighbor hold time, and decreasing flooding, the suggested protocol shows promise in terms of performance metrics under diverse conditions.
Article
Computer Science, Theory & Methods
Liguo Dong, Zhenmou Liu, Kejia Zhang, Abdulsalam Yassine, M. Shamim Hossain
Summary: Federated Learning (FL) is a promising privacy computing framework for complex network systems. To incentivize data owners, it is important to fairly evaluate and compensate their contributions to the FL training process. The collaboration of FL and Shapley value, namely Federated Shapley Value (FedSV), provides an effective solution but faces challenges in computational overhead, privacy, and fairness in the FL setting.
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
(2023)
Article
Engineering, Civil
Yazhou Zhang, Prayag Tiwari, Qian Zheng, Abdulmotaleb El Saddik, M. Shamim Hossain
Summary: Traffic events are a major cause of traffic accidents, and detecting these events poses a challenge in traffic management and intelligent transportation systems (ITSs). This paper proposes a multimodal coupled graph attention network (MCGAT) that extracts valuable information from various traffic data sources and represents it in a graphical structure. The proposed model outperforms state-of-the-art baselines in terms of F1 and accuracy, with significant improvements.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2023)
Article
Green & Sustainable Science & Technology
Arif Hussain Magsi, Leanna Vidya Yovita, Ali Ghulam, Ghulam Muhammad, Zulfiqar Ali
Summary: A threshold-based content caching mechanism is proposed to detect and prevent content poisoning attacks, along with the integration of a blockchain system for privacy protection and network extension. Experimental results show that the mechanism achieves a 100% accuracy in identifying and preventing attackers, while effectively filtering out malicious blocks.
Article
Telecommunications
Anichur Rahman, Md Jahidul Islam, Shahab S. Band, Ghulam Muhammad, Kamrul Hasan, Prayag Tiwari
Summary: Recent studies have highlighted the importance of new technologies such as Blockchain (BC), Software Defined Networking (SDN), and Smart Industrial Internet of Things (IIoT). These technologies offer data integrity, confidentiality, and integrity, particularly in industrial applications. Cloud computing, a well-established technology, is used to exchange sensitive information and provide remote access to computing and storage resources in the IIoT. However, cloud computing also presents significant security risks and challenges. To tackle these issues, this paper proposes a cloud computing platform for the IIoT that combines BC and SDN. The proposed architecture, named DistB-SDCloud, utilizes distributed BC for enhanced security, privacy, and integrity while maintaining flexibility and scalability. Furthermore, an SDN method is introduced to improve the durability, stability, and load balancing of the cloud infrastructure. The effectiveness of this implementation is experimentally tested using various parameters and monitoring attacks on the system.
DIGITAL COMMUNICATIONS AND NETWORKS
(2023)
Article
Chemistry, Multidisciplinary
Md. Ariful Islam, Vidhya Selvanathan, Puvaneswaran Chelvanathan, M. Mottakin, Mohammod Aminuzzaman, Mohd Adib Ibrahim, Ghulam Muhammad, Md. Akhtaruzzaman
Summary: NiOx NPs with different properties were successfully synthesized using four different nickel based-metal organic frameworks as precursors. Ni-TPA MOF derived NiOx NPs calcined at 600 degrees C were identified as the most suitable for hole transport layer application. The fabricated thin film exhibited a band energy gap of 3.25 eV and had a carrier concentration, hole mobility, and resistivity of 6.8 x 10(14) cm(-3), 4.7 x 10(14) ? cm, and 2.0 cm(2) V-1 s(-1), respectively. The device configuration of FTO/TiO2/CsPbBr3/NiOx/C achieved an efficiency of 13.9% with V-oc of 1.89 V, J(sc) of 11.07 mA cm(-2), and FF of 66.6%.
Article
Engineering, Biomedical
Esraa Hassan, M. Shamim Hossain, Abeer Saber, Samir Elmougy, Ahmed Ghoneim, Ghulam Muhammad
Summary: Biomedical image classification is crucial for computer vision tasks and clinical care. This paper proposes an architecture called MQCNN, based on the QCNN model and modified ResNet (50) pre-trained model, to enhance biomedical image classification in the MNIST medical dataset. Results show that MQCNN model outperforms other models in terms of accuracy, precision, recall, and F1 score.
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
(2024)
