Cooperative Cloud-Edge Feature Extraction Architecture for Mobile Image Retrieval by Chao He, Gang Ma

“…The existing mobile image retrieval scheme is based on mobile cloud-edge computing architecture. That is, user equipment captures images and uploads the captured image data to the edge server. …”
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Lightweight defense mechanism against adversarial attacks via adaptive pruning and robust distillation by Bin WANG, Simin LI, Yaguan QIAN, Jun ZHANG, Chaohao LI, Chenming ZHU, Hongfei ZHANG

“…Adversarial training is one of the commonly used defense methods against adversarial attacks, by incorporating adversarial samples into the training process.However, the effectiveness of adversarial training heavily relied on the size of the trained model.Specially, the size of trained models generated by the adversarial training will significantly increase for defending against adversarial attacks.This imposes constraints on the usability of adversarial training, especially in a resource-constraint environment.Thus, how to reduce the model size while ensuring the robustness of the trained model is a challenge.To address the above issues, a lightweight defense mechanism was proposed against adversarial attacks, with adaptive pruning and robust distillation.A hierarchically adaptive pruning method was applied to the model generated by adversarial training in advance.Then the trained model was further compressed by a modified robust distillation method.Experimental results on CIFAR-10 and CIFAR-100 datasets showed that our hierarchically adaptive pruning method presented stronger robustness under various FLOP than the existing pruning methods.Moreover, the fusion of pruning and robust distillation presented higher robustness than the state-of-art robust distillation methods.Therefore, the experimental results prove that the proposed method can improve the usability of the adversarial training in the IoT edge computing environment.…”
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Deep reinforcement learning based task allocation mechanism for intelligent inspection in energy Internet by Siya XU, Yifei XING, Shaoyong GUO, Chao YANG, Xuesong QIU, Luoming MENG

“…In order to reduce the cost and improve efficiency of power line inspection, UAV (unmanned aerial vehicle), which use mobile edge computing technology to access and process service data, are used to inspect power lines in the energy internet.However, due to the dynamic changes of UAV data transmission demand and geographical location, the edge server load will be unbalanced, which causes higher service processing delay and network energy consumption.Thus, an intelligent inspection task allocation mechanism for energy internet based on deep reinforcement learning was proposed.First, a two-layer edge network task offloading model was established to archive joint optimization of multi-objectives, such as delay and energy consumption.It was designed by comprehensively considering the route of UAV and edge nodes, different demands of services and limited service capabilities of edge nodes.Furthermore, based on Lyapunov optimization theory and dual-time-scaled mechanism, proximal policy optimization algorithm based deep reinforcement learning was used to solve the connection relationship and offloading strategy of edge servers between fixed edge sink layer and mobile edge access layer.The simulation results show that, the proposed mechanism can reduce the service request delay and system energy consumption while ensuring the stability of system.…”
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Energy consumption optimization scheme in UAV-assisted MEC system based on optimal SIC order by Wei JI, Xuxin YANG, Fei LI, Ting LI, Yan LIANG, Yunchao SONG

“…In uplink non-orthogonal multiple access (NOMA)-based unmanned aerial vehicle (UAV)-assisted mobile edge computing (MEC) system, the successive interference cancellation (SIC) order of NOMA became a bottleneck limiting the transmission performance of task offload in uplink link.To reduce the energy consumption of the system, the SIC order was discussed and the optimal SIC order based on channel gain and task delay constraint was proposed.The optimization problem of minimizing the system energy consumption was proposed based on the optimal SIC order while satisfying the constraints of the given task delay of the device, the maximum transmit power constraint of the device, and the UAV trajectory.Since the problem was a complex non-convex problem, an alternating optimization method was adopted to solve the optimization problem to achieve power allocation and UAV trajectory optimization.A low-complexity algorithm based on matching theory was proposed to obtain the optimal device grouping in different time slots.Simulation results show that the optimal SIC order can realize smaller system energy consumption under the same task delay constraint compared with other SIC order, the proposed low-complexity device grouping algorithm can obtain the optimal device grouping.…”
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A Comprehensive Survey of Deep Learning Approaches in Image Processing by Maria Trigka, Elias Dritsas

“…Additionally, it highlights the potential of combining DL with emerging technologies such as edge computing and explainable artificial intelligence (AI) to address scalability and interpretability challenges. …”
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Situation awareness via Internet of things and in-network data processing by Johannes Ehala, Jaanus Kaugerand, Raido Pahtma, Sergei Astapov, Andri Riid, Timo Tomson, Jürgo-Sören Preden, Leo Mõtus

“…The sensor nodes have the capability to perform in-sensor signal processing and distributed in-network data aggregation and fusion complying with edge computing paradigm. In-network data processing is supported by service-oriented middleware which facilitates run-time sensor discovery and tasking and ad hoc (re)configuration of the network links. …”
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A Real-Time Green and Lightweight Model for Detection of Liquefied Petroleum Gas Cylinder Surface Defects Based on YOLOv5 by Burhan Duman

“…The proposed technique offers an efficient and robust defect detection model with an eco-friendly solution compatible with edge computing devices.…”
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Quantum machine learning for Lyapunov-stabilized computation offloading in next-generation MEC networks by Vandana Rani Verma, Dinesh Kumar Nishad, Vishnu Sharma, Vinay Kumar Singh, Anshul Verma, Dharti Raj Shah

“…Abstract Quantum computing and machine learning convergence enable powerful new approaches for optimizing mobile edge computing (MEC) networks. This paper uses Lyapunov optimization theory to propose a novel quantum machine learning framework for stabilizing computation offloading in next-generation MEC systems. …”
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FedRSC: A Federated Learning Analysis for Multi-Label Road Surface Classifications by Ioannis V. Vondikakis, Ilias E. Panagiotopoulos, George J. Dimitrakopoulos

“…This research presents a federated learning analysis that brings together edge computing and cloud technology, by identifying various road conditions through a multi-label road surface classification analysis. …”
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Life Cycle Analysis in the Context of Smart Cities by Berville Charles, Croitoru Cristiana, Bode Florin

“…This can be achieved through edge computing, energy-efficient data centers, and policies that promote digital sobriety. …”
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A Spatiotemporal Graph Transformer Network for real-time ball trajectory monitoring and prediction in dynamic sports environments by Zujian Li, Dan Yu

“…Our future work aims to enhance memory efficiency and optimize multi-scenario inference speed to broaden the model’s deployment in edge computing environments.…”
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Analysis of Intelligent Transportation System Application Based on Internet of Things and Big Data Technology under the Background of Information Society by Di Wang, Weijing Xu, Xuesong Jia

“…In order to verify the feasibility of the intelligent transportation system, technologies such as big data edge computing are also used to comprehensively evaluate and study the use of the system. …”
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Smart City Traffic Flow and Signal Optimization Using STGCN-LSTM and PPO Algorithms by Tuxiang Lin, Rongliang Lin

“…Future research will explore edge computing, multi-agent reinforcement learning, and real-time data integration to further enhance scalability and adaptability.…”
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Evaluation and optimization of carbon emission for federal edge intelligence network by Peng ZHANG, Yong XIAO, Jiwei HU, Liang LIAO, Jianxin LYU, Zegang BAI

“…In recent years, the continuous evolution of communication technology has led to a significant increase in energy consumption.With the widespread application and deep deployment of artificial intelligence (AI) technology and algorithms in telecommunication networks, the network architecture and technological evolution of network intelligent will pose even more severe challenges to the energy efficiency and emission reduction of future 6G.Federated edge intelligence (FEI), based on edge computing and distributed federated machine learning, has been widely acknowledged as one of the key pathway for implementing network native intelligence.However, evaluating and optimizing the comprehensive carbon emissions of federated edge intelligence networks remains a significant challenge.To address this issue, a framework and a method for assessing the carbon emissions of federated edge intelligence networks were proposed.Subsequently, three carbon emission optimization schemes for FEI networks were presented, including dynamic energy trading (DET), dynamic task allocation (DTA), and dynamic energy trading and task allocation (DETA).Finally, by utilizing a simulation network built on real hardware and employing real-world carbon intensity datasets, FEI networks lifecycle carbon emission experiments were conducted.The experimental results demonstrate that all three optimization schemes significantly reduce the carbon emissions of FEI networks under different scenarios and constraints.This provides a basis for the sustainable development of next-generation intelligent communication networks and the realization of low-carbon 6G networks.…”
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A Review on Software-Defined Networking for Internet of Things Inclusive of Distributed Computing, Blockchain, and Mobile Network Technology: Basics, Trends, Challenges, and Future... by Shakila Shafiq, Md. Sazzadur Rahman, Shamim Ahmed Shaon, Imtiaz Mahmud, A. S. M. Sanwar Hosen

“…This study is aimed at reviewing the literature on SDN for IoT (SDN-IoT) published from 2014 to 2022 and presenting insights and directions for future research, with a particular focus on cloud, fog, and edge computing. The study collects data from Science Direct, IEEE Explore, and Google Scholar and objectively selects 126 papers and conducts metadata analysis. …”
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Decentralized Federated Learning with Prototype Exchange by Lu Qi, Haoze Chen, Hongliang Zou, Shaohua Chen, Xiaoying Zhang, Hongyan Chen

“…As AI applications become increasingly integrated into daily life, protecting user privacy while enabling collaborative model training has become a crucial challenge, especially in decentralized edge computing environments. Traditional federated learning (FL) approaches, which rely on centralized model aggregation, struggle in such settings due to bandwidth limitations, data heterogeneity, and varying device capabilities among edge nodes. …”
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Low-Complexity Timing Correction Methods for Heart Rate Estimation Using Remote Photoplethysmography by Chun-Chi Chen, Song-Xian Lin, Hyundoo Jeong

“…Through a comparative analysis, this study offers insights into efficient timing correction techniques for enhancing HR estimation from rPPG, particularly suitable for edge-computing applications where low computational complexity is essential. …”
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Design and Implementation of a Deep Learning-Based Hand Gesture Recognition System for Rehabilitation Internet-of-Things (RIoT) Environments Using MediaPipe by Nurul Hanis Mohd Dhuzuki, Ahmad Anwar Zainuddin, Nur Anis Sofea Kamarul Zaman, Alin Nur Maisarah Ahmad Razmi, Wonderful Shammah Kaitane, Asmarani Ahmad Puzi, Mohd Naqiuddin Johar, Maslina Yazid, Nor Azlin Mohd Nordin, Shahrul Naim Sidek, Hasan Firdaus Mohd Zaki

“…Several limitations, including latency and distance sensitivity, are addressed in this system with edge computing alongside adaptive algorithms. The key contributions of this research are as follows: First, developing a real-time and cost-effective solution for remote stroke rehabilitation. …”
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Using Internet of things technology to construct an integrated intelligent sensing environment for long-term care service by Lun-Ping Hung, Nan-Chen Hsieh, Li-Ju Lin, Zong-Jie Wu

“…This study uses a medical-grade Internet of Things module that can calculate the environmental values with edge computing to generate different levels of alarms by combining the index-weighted moving average method to dynamically calculate the optimal threshold value for the environment. …”
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Security-enhanced three-factor remote user authentication scheme based on Chebyshev chaotic maps by Yan Zhao, Shiming Li, Liehui Jiang, Tieming Liu

“…With the wide deployment of new computing paradigms, such as cloud computing and edge computing, the people can access services provided by remote servers more conveniently via the Internet. …”
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