Cross-domain task offloading and computing resource allocation for edge computation in industrial Internet of things by Peng ZHOU, Jincheng XU, Bo YANG

“…In the industrial Internet of things,limited by the computing capacity of field devices,the task offloading based on edge computing can effectively alleviate the computing burden of field devices and provide low-latency computing services.Moreover,because the load of edge servers are different in different areas of the network,it is necessary to reasonably arrange task offloading and allocate computing resources of edge servers,thereby reducing task completion delay and achieving load balance.Thus,the task offloading and resource allocation for edge computing in the industrial Internet of things was studied,a cross-domain offloading model for computing tasks in the industrial Internet of things was proposed,and a mixed integer nonlinear optimization problem that minimizes task completion time was formulated.The problem was decomposed it into two sub-problems of resource allocation and task offloading,based on the characteristics of the two sub-problems,the optimal solution of resource allocation and task offloading strategy were obtained through iterative and alternating solution.The experimental results show that compared with the non-cross-domain strategy,the load imbalance of the edge server and the task completion delay are reduced effectively by the proposed strategy.…”
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A plunger lifting optimization control method based on APSO-MPC for edge computing applications by Zhi Qiu, Lei Zhang, He Zhang, Haibo Liang, Yinxian Li

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Analisis Kinerja Jaringan Sensor Nirkabel untuk Edge Computing Menggunakan LORA SX1278 by Mochammad Hannats Hanafi Ichsan

“…Proses sensing dapat dilakukan dengan menggunakan berbagai sensor sesuai kebutuhan, sedangkan teknologi untuk pemrosesan pada node sensing disebut dengan teknologi Edge Computing. Konsep dari Edge Computing adalah bagaimana sebuah node bisa berpikir untuk menyelesaikan masalah atau mengambil keputusan. …”
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Research on deep reinforcement learning in Internet of vehicles edge computing based on Quasi-Newton method by ZHANG Jianwu, LU Zetao, ZHANG Qianhua, ZHAN Ming

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Cybersecurity Solutions for Industrial Internet of Things–Edge Computing Integration: Challenges, Threats, and Future Directions by Tamara Zhukabayeva, Lazzat Zholshiyeva, Nurdaulet Karabayev, Shafiullah Khan, Noha Alnazzawi

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Building intelligent integrated management system based on Internet of things and edge computing and its application by Haiying SONG, Zhiwen CHEN, Baiping QIU, Fuhua KUANG, Shijun DENG

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Computer Vision-Based Drowsiness Detection Using Handcrafted Feature Extraction for Edge Computing Devices by Valerius Owen, Nico Surantha

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Client grouping and time-sharing scheduling for asynchronous federated learning in heterogeneous edge computing environment by Qianpiao MA, Qingmin JIA, Jianchun LIU, Hongli XU, Renchao XIE, Tao HUANG

Subjects: “…edge computing…”
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Verification of an artificial intelligence vision chip design for edge computing based on hardware simulation system by Xuanzhe XU, Ke NING, Xuemin ZHENG, Mingxin ZHAO, Mengmeng XU, Nanjian WU, Liyuan LIU

“…The rise of visual deep learning algorithms based on convolutional neural network (CNN) has promoted the rapid development of the artificial intelligence (AI) vision chip design research.The step of chip verification is a bottleneck in the development of AI vision chips.A software and hardware verification method for AI vision chip design based on hardware simulation system was introduced.Taking AI vision chip design for edge computing as an example, the chip was run on the hardware simulation system (ZeBu) and the simulation verification work of typical deep learning network MobileNet was completed.The results show that the network model implemented on the hardware chip architecture keeps accuracy while the detection time of a single frame is only 18.51 ms under a 200 MHz clock frequency.The spread of the hardware simulation is 7 times faster than than of the software simulation.…”
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Joint Task Offloading and Resource Allocation in Mobile Edge Computing-Enabled Medical Vehicular Networks by Chuangchuang Zhang, Siquan Liu, Hongyong Yang, Guanghai Cui, Fuliang Li, Xingwei Wang

“…The delay requirement is very vital for medical services to guarantee service quality and save the lives of patients. Mobile Edge Computing (MEC), as an emerging network paradigm, enables the computation extensive tasks to be offloaded to edge servers, efficiently reducing the delay and bandwidth demands. …”
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Research on task offloading algorithm of mobile edge computing based on deep reinforcement learning in SDCN by Shouhua JIANG, Yiwu WANG

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Task Offloading with LLM-Enhanced Multi-Agent Reinforcement Learning in UAV-Assisted Edge Computing by Feifan Zhu, Fei Huang, Yantao Yu, Guojin Liu, Tiancong Huang

“…Unmanned aerial vehicles (UAVs) furnished with computational servers enable user equipment (UE) to offload complex computational tasks, thereby addressing the limitations of edge computing in remote or resource-constrained environments. …”
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Research on intrusion response strategy based on static Bayesian game in mobile edge computing network by Wei FAN, Cheng PENG, Dali ZHU, Yuqing WANG

Subjects: “…mobile edge computing…”
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Service Architecture and Control Configuration Technology of Distribution-Network Edge Computing Based on Hybrid Control by GUO Ning, JI Tuo, LU Xiaoxing, DONG Shufeng, XIAO Maoran, JIAO Hao, XIAO Xiaolong, WU Fan

Subjects: “…edge computing|distribution network|hybrid control|event-driven…”
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Edge computing for detection of ship and ship port from remote sensing images using YOLO by Vasavi Sanikommu, Sai Pravallika Marripudi, Harini Reddy Yekkanti, Revanth Divi, R. Chandrakanth, P. Mahindra

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Task security scheduling method for 5G+MEC based grid edge computing platform by Shimulin XIE, Ijie BA, Xiang ZHANG, Zeyi TANG, Weifan NIAN, Xujie LIU

“…In order to ensure the security of task scheduling of grid edge computing platform and the data quality required by task scheduling, a task security scheduling method of grid edge computing platform based on 5G + MEC was proposed.Combined with confidentiality service and integrity service, the security level model of task scheduling was constructed to restrict the risk in the process of scheduling and transmission of scheduling task queue, so as to realize the secure transmission of 5G core network.The priority queue type was confirmd, the minimum queue and the maximum queue was selected, the maximization of data resources and the task scheduling of MEC equipment was supported, and a distributed task scheduling model was built.Using Lyapunov candidate function to improve the stability of task scheduling, and the model was solved by alternating direction multiplier method to obtain the optimal solution of task security scheduling.The test results show that after the application of this method, the risk probability results fluctuate in the range of 0.15～0.35, and the fitting degree between the relevant data provided by MEC equipment and the scheduling task of core server is higher than 0.92, the quality score of task scheduling data is also higher than 0.94.…”
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An UAV-Assisted Edge Computing Resource Allocation Strategy for 5G Communication in IoT Environment by Hao Liu

“…As the computing capacity of existing mobile devices cannot fully meet the needs of users for communication quality, a computing resource allocation strategy for 5G communication in the Internet of Things (IoT) environment is proposed by applying UAV-assisted edge computing. First, a system model is constructed with the UAV deployed with mobile edge computing (MEC) servers to provide assisted computing services for multiple users on the ground. …”
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Every Second Counts: Integrating Edge Computing and Service Oriented Architecture for Automatic Emergency Management by Lei Chen, Cristofer Englund

“…Applying the concept of multiaccess edge computing architecture, as well as choreography of the service oriented architecture, the system allows seamless coordination between multiple organizations in a distributed way through standard web services. …”
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Joint Stochastic Computation Offloading and Trajectory Optimization for Unmanned-Aerial-Vehicle-Assisted Mobile Edge Computing by Yi Zhou

Subjects: “…Mobile edge computing…”
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Multi-scale aware dual path network for face detection in resource-constrained edge computing environment by Qi QI, Yingxin MA, Jingyu WANG, Haifeng SUN, Jianxin LIAO

“…Aiming at the problem that face detectors with complex deep neural structures are difficult to deploy in the resource-constrained edge computing environment,to reduce the resource consumption while maintain the accuracy in complex scenes such as multi-scale face changes,occlusion,blur,and illumination,SDPN(multi-scale aware dual path network) for face detection was proposed.The Face-ResNet (face residual neural network) was improved,and a dual path shallow feature extractor was used to understand the multi-scale information of the image through parallel branches.Then the deep and shallow feature fusion module,a combination of the underlying image information and the high-level semantic feature,was used in conjunction with the multi-scale awareness training strategy to supervise the multi-branch learning discriminating features.The experimental results show that SDPN can extract more diversified features,which effectively improve the accuracy and robustness of face detection while maintaining the efficiency of the model and low inference delay.…”
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