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IEEE TRANSACTIONS ON GREEN COMMUNICATIONS AND NETWORKING 2024年第1期8卷 572-582页

作者： Yuan, Xingyu Li, He Ota, Kaoru Dong, Mianxiong Muroran Inst Technol Dept Sci & Informat Muroran 0500071 Japan

Semantic segmentation is a critical area in computer vision, which needs voluminous image data streaming from user devices. Usually, it is challenging to process semantic segmentation tasks in user devices due to the Limited computation power and battery life. intelligent edge computing effectively enhances the accuracy of semantic segmentation tasks by offloading computations to nearby devices, providing lower latency and improved responsiveness. However, inefficient offloading brings additional energy consumption due to the irregular relationship between task requirements and offloading settings. In this paper, we attempt to improve energy efficiency for processing semantic segmentation tasks in the edge environment by leveraging energy consumption and task requirements. We first investigate the power consumption with different offloading settings in a real intelligent edge environment. Based on the investigation, we formulate the offloading setting as a restricted multi-armed bandit problem and solve it by enhancing the upper confidence bound algorithm. Comprehensive simulation results show that the proposed solution significantly improves the energy efficiency for offloading semantic segmentation tasks in a given intelligent edge environment.

关键词： Task analysis edge computing Semantic segmentation Energy efficiency Semantics Power demand Graphics processing units intelligent edge computing semantic segmentation multi-armed bandit (MAB) energy efficiency

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QoS prediction in intelligent edge computing based on feature learning

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JOURNAL OF CLOUD computing-ADVANCES SYSTEMS AND APPLICATIONS 2023年第1期12卷 1-16页

作者： Zhang, Hongxia Wang, Dengyue Zhang, Wei Tan, Lizhuang Kibalya, Godfrey Zhang, Peiying Igorevich, Kostromitin Konstantin China Univ Petr East China Coll Comp Sci & Technol Qingdao Peoples R China Qilu Univ Technol Shandong Acad SciShandong Prov Key Lab Comp Netwo Shandong Comp Sci Ctr Natl Supercomp Ctr Jinan Jinan Peoples R China Busitema Univ Dept Comp Engn & Informat Tororo Uganda Xidian Univ State Key Lab Integrated Serv Networks Xian Peoples R China South Ural State Univ Dept Phys Nanoscale Syst Chelyabinsk Russia

With the development of 5G and 6G, more computing and network resources on edge nodes are deployed close to the terminal. Meanwhile, the number of smart devices and intelligent services has grown significantly, which makes it difficult for users to choose a suitable service. The rich contextual information plays an important role in the prediction of service quality. In this paper, we propose a quality of service(QoS) prediction approach based on feature learning, the contextual information represented as the explicit features and underlying relationship hidden in the implicit features are fully considered. Then, the multi-head self-attention mechanism is used in the interacting layer to determine which features should be combined to form meaningful high-order features interaction. We have implemented our proposed approach with experiments based on real-world datasets. Experimental results show that our approach achieved a better performance of service QoS prediction in an intelligent edge computing environment for future communication.

关键词： intelligent edge computing Service recommendations QoS prediction multi-head self-attention feature learning

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An Overview on intelligent edge computing for Enhancing CAEV and UAV Charging in 6G ITS

An Overview on Intelligent Edge Computing for Enhancing CAEV...

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IEEE International Conference on Smart Mobility (SM)

作者： Shaikh, Palwasha Waheed Mouftah, Hussein T. Univ Ottawa Sch Elect Engn & Comp Sci Ottawa ON Canada

ISBN: (纸本)9798350390148;9798350390131

6G networks, characterized by ultra-low latency and ubiquitous computing, herald a new era where Connected and Autonomous Electric Vehicles (CAEVs) and Unmanned Aerial Vehicles (UAVs) are redefining smart mobility. Equipped with real-time data processing, Artificial Intelligence (AI), and seamless connectivity, these vehicles seek efficient charging solutions facilitated by intelligent edge computing (IEC). IEC, through latency reduction and optimized charging processes, promises rapid charging, grid stability, enhanced security, and renewable energy integration for intelligent transportation systems (ITS). This survey comprehensively examines IEC techniques and architectures, uncovering their impact on charging efficiency, security, and reliability within smart mobility frameworks. Through a review of research papers, the survey provides insights into real-world applications and IEC advancements, revealing key challenges and emerging research directions. The survey envisions an efficient, secure, and interconnected 6G-era charging ecosystem, with future directions including ultra-fast charging, renewables integration, enhanced security, standardization, AV- V2X synergy, predictive maintenance, and blockchain transparency, fundamentally reshaping AI-driven smart mobility.

关键词： intelligent edge computing 6G Networks Artificial Intelligence Connected and Autonomous Electric Vehicles Unmanned Aerial Vehicles CAEV & UAV Charging Security and Privacy intelligent Transportation Systems

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Efficient and reliable forensics using intelligent edge computing

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FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE 2021年 118卷 230-239页

作者： Razaque, Abdul Aloqaily, Moayad Almiani, Muder Jararweh, Yaser Srivastava, Gautam Int Informat Technol Univ Dept Comp Engn & Informat Secur Alma Ata Kazakhstan Al Ain Univ Fac Engn Abu Dhabi U Arab Emirates Gulf Univ Sci & Technol Kuwait Kuwait Jordan Univ Sci & Technol Irbid Jordan Brandon Univ Dept Math & Comp Sci Brandon MB Canada China Med Univ Res Ctr Interneural Comp Taichung 40402 Taiwan

Due to the increasing awareness and use of cloud and edge computing, society and industries are beginning to understand the benefits they can provide. Cloud and edge are the future of information management, and they have transformed the Internet into an innovative and interactive computing platform. The ultimate goal of edge/cloud computing is to reduce the use of computing resources in the network, as well as support information sharing and intercommunication efforts within the network. Secure edge computing methodologies are applied in both open and heterogeneous network systems to protect them from many potential security threats. However, these approaches only provide passive protection for normal edge computing operations, and fail to address the security measures of several applications, particularly forensics in industrial settings. Forensics applications running on edge computing must be capable of support taking legal action against invaders for malicious damage or information theft. This paper proposes an efficient and reliable forensics framework (ERFF) to address industrial intelligent edge computing critical for the industry 4.0 implementation plan. The proposed ERFF consists of a detective module and validation model, with the detective module responsible for detecting the interaction between the client terminal and the edge resource, which means the investigator is capable of gathering the evidence securely. The security-validation model integrated with ERFF is far safer than sharing common key-based cryptographic approaches. The proposed conceptual framework is tested with Live Digital Forensic Framework for a Cloud (LDF2C), and results are compared with other existing industrial frameworks that fulfill fundamental ISO/IEC 17025 accreditation requirements, including Legal Reliable Forensic Framework (LRFF), Source Identification Network Forensics Framework (SINFF) and Logging Framework for Cloud computing Forensic (LFCCF)). These frameworks were des

关键词： intelligent edge computing Reliability and efficiency Cloud computing Peer-to-peer edges Security Forensics framework

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Diversified Technologies in Internet of Vehicles Under intelligent edge computing

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IEEE TRANSACTIONS ON intelligent TRANSPORTATION SYSTEMS 2021年第4期22卷 2048-2059页

作者： Lv, Zhihan Chen, Dongliang Wang, Qingjun Qingdao Univ Sch Data Sci & Software Engn Qingdao 266071 Peoples R China Shenyang Aerosp Univ Sch Econ & Management Shenyang 110136 Peoples R China Nanjing Univ Aeronaut & Astronaut Coll Automat Engn Nanjing 210016 Peoples R China

To investigate the diversified technologies in Internet of Vehicles (IoV) under intelligent edge computing, artificial intelligence, intelligent edge computing, and IoV are combined. Also, it proposes an IoV model for intelligent edge computing task offloading and migration under the SDVN (Software Defined Vehicular Networks) architecture, that is, the JDE-VCO (Joint Delay and Energy-Vehicle Computational task Offloading) optimization. And the simulation is performed. The results show that in the analysis of the impact of different offloading strategies on the IoV, it is found that the JDE-VCO algorithm is superior to other schemes in terms of transmission delay and total offloading energy consumption. In the analysis of the impact of the task unloading of the IoV, the JDE-VCO algorithm is less than RTO (Random Tasks Offloading) and UTO (Uniform Tasks Offloading) algorithm schemes in terms of the number of tasks per unit time, and the average task completion time for the same amount of uploaded data. In the analysis of the packet loss ratio and transmission delay, it can be found that the packet loss ratio and transmission delay of the JDE-VCO algorithm are less than the RTO and UTO algorithms. Moreover, the packet loss ratio of the JDE-VCO algorithm is about 0.1, and the transmission delay is stable at 0.2s, which has obvious advantages. Therefore, through research, the IoV model of task offloading and migration built by intelligent edge computing can significantly improve the load sharing rate, offloading efficiency, packet loss ratio, and transmission delay when the IoV is processing tasks and uploading data. It provides experimental basis for the improvement of the IoV system.

关键词： edge computing Task analysis Internet Delays intelligent vehicles Protocols Internet of Vehicles intelligent edge computing artificial intelligence technology transmission delay diversified technologies

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intelligent edge computing: State-of-the-art Techniques and Applications 28

Intelligent Edge Computing: State-of-the-art Techniques and ...

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28th Signal Processing and Communications Applications Conference (SIU)

作者： Gill, Waris Ozkasap, Oznur Gursoy, Attila Koc Univ Dept Comp Engn Istanbul Turkey

ISBN: (纸本)9781728172064

To enable intelligent decisions at the network edge, supervised and unsupervised machine learning techniques and their variations are highly utilized in recent research studies. These include techniques and the corresponding applications such as detecting manufacturing faults in a smart factory setting, monitoring patient activities and health problems in smart health systems, detecting security attacks on the Internet of Things devices, and finding the rare events in the audio signals. In this paper, we present an extensive review of state-of-the-art techniques and applications of intelligent edge computing and provide classification and discussion of various approaches in this field.

关键词： intelligent edge computing distributed systems supervised learning unsupervised learning

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Social learning discrete Particle Swarm Optimization based two-stage X-routing for IC design under intelligent edge computing architecture

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APPLIED SOFT computing 2021年 104卷 107215-107215页

作者： Liu, Genggeng Chen, Xiaohua Zhou, Ruping Xu, Saijuan Chen, Yeh-Cheng Chen, Guolong Fuzhou Univ Coll Math & Comp Sci Fuzhou Peoples R China Fujian Business Univ Dept Informat Engn Fuzhou Peoples R China Univ Calif Davis Dept Comp Sci Davis CA 95616 USA

One of the core features of intelligent edge computing (IEC) is real-time decision making, therefore low delay is more important for IC design under IEC architecture. And in very large scale integration routing, wirelength is one of the most important indexes affecting the final delay of the IC design. Therefore, this paper introduces the X-routing with more potential for wirelength optimization and the Steiner Minimum Tree (SMT), which is the best routing model in multi-terminal nets. Then, based on Particle Swarm Optimization (PSO) technique which has the strong global optimization ability in Soft computing, an effective Two-Stage X-routing Steiner minimum tree construction algorithm is proposed. The proposed algorithm is divided into two stages: social learning discrete PSO searching and wirelength reduction. In the first stage, two excellent strategies are proposed to maintain a good balance between exploration and exploitation capabilities of the PSO technique: (1) Chaotic decreasing inertia weight combined with mutation operator is set to enhance the exploration capability. (2) A new social learning approach combined with crossover operator is designed to ensure the diverse evolution of the swarm while maintaining the exploitation capability. In the second stage, a strategy based on local topology optimization is proposed to further reduce the length of X-routing Steiner tree. Experiments show that the proposed algorithm can achieve the best wirelength optimization and has a strong stability, especially for large-scale SMT problem, so as to better satisfy the demand of low delay of IC design under IEC architecture. (c) 2021 Elsevier B.V. All rights reserved.

关键词： intelligent edge computing Social learning discrete particle swarm optimization Soft computing X-routing Steiner minimum tree Wirelength

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A fine-grained task scheduling mechanism for digital economy services based on intelligent edge and cloud computing

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JOURNAL OF CLOUD computing-ADVANCES SYSTEMS AND APPLICATIONS 2023年第1期12卷 30-30页

作者： Zhang, Xiaoming Shanxi Univ Sch Econ & Management Taiyuan 030000 Peoples R China

Digital economy is regarded countries as an inevitable choice to promote economic growth and provides new opportunities and new paths for the high-quality development of economy. For the Chinese market, the strong base behind cloud computing is the unstoppable development trend of the digital economy. In digital economy, the cloud as infrastructure becomes the base of the pyramid to build the digital economy. To relieve the pressure on the servers of the digital economy and develop a reasonable scheduling scheme, this paper proposes a fine-grained task scheduling method for cloud and edge computing based on a hybrid ant colony optimization algorithm. The edge computing task scheduling problem is described, and assumptions are set to simplify the difficulty of a scheduling solution. The multi-objective function is solved by using a hybrid ant colony optimization algorithm which solves computational problems by finding the optimal solution with the help of graphs. Ant colony optimization algorithm is easy to use and effective in scheduling problems. The proposed scheduling model includes an end-device layer and an edge layer. A terminal device layer consists of devices used by the clients that may generate computationally intensive tasks and are sometime uncapable to complete the tasks. The proposed scheduling policy migrates these tasks to a suitable place where they can be completed while meeting the latency requirements. The CPUs of the idle users on the end-device layer are used for other CPU-overloaded terminals. The simulation results, in terms of energy consumptions, and task scheduling delays, show that the task scheduling performance is better under the application of this method and the obtained scheduling scheme is more reasonable.

关键词： intelligent edge computing Cloud computing Digital economy development Task scheduling

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Smart Traffic Navigation System for Fault-Tolerant edge computing of Internet of Vehicle in intelligent Transportation Gateway

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IEEE TRANSACTIONS ON intelligent TRANSPORTATION SYSTEMS 2023年第11期24卷 13011-13022页

作者： Yang, Shuangming Tan, Jiangtong Lei, Tao Linares-Barranco, Bernabe Tianjin Univ Sch Elect & Informat Engn Tianjin 300072 Peoples R China Shaanxi Univ Sci & Technol Sch Elect Informat & Artificial Intelligence Xian 710021 Peoples R China Microelect Inst Seville Seville 41092 Spain

To investigate the diversified technologies in Internet of Vehicles (IoVs) under intelligent edge computing, brain-inspired computing techniques are proposed in this study, which is a promising biologically inspired method by using brain cognition mechanism for various applications. A neuromorphic approach in a scalable and fault-tolerant framework is presented, targeting to realize the navigation function for the edge computing in IoV applications. A novel fault-tolerant address event representation approach is proposed for the spike information routing, which makes the presented model both scalable and fault-tolerant. Experimental results reveal that the proposed approaches can enhance the communication distance, the load balancing and the maximum throughput of the neuromorphic system accordingly. Based on the proposed neuromorphic model, the effects of the dopamine level are investigated. Besides, the results show that the proposed work can realize the accurate obstacle avoidance for the edge IoV computing, and the performance of the proposed network is superior to the network without the proposed scalable and fault-tolerant design. Therefore, the proposed IoV model provides an experimental basis for the improvement of the IoV system.

关键词： Navigation edge computing Neuromorphics Fault tolerant systems Fault tolerance Computational modeling Real-time systems Internet of Vehicles neuromorphic intelligent edge computing brain-inspired navigation

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intelligent Traffic-Service Mapping of Network for Advanced Industrial IoT edge computing 20

Intelligent Traffic-Service Mapping of Network for Advanced ...

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20th International Conference on Factory Communication Systems (WFCS)

作者： Liu, Bowen Zheng, Tao Thar, Kyi Gidlund, Mikael Ma, Xiaoting Lei, Bo Zhang, Hongke Guizani, Mohsen Beijng Jiaotong Univ Sch Elect & Informat Engn Beijing Peoples R China Mid Sweden Univ Dept Comp & Elect Engn Ostersund Sweden China Telecom Corp Ltd Res Inst Beijing Peoples R China Mohamed Bin Zayed Univ Artificial Intelligence Machine Learning Dept Abu Dhabi U Arab Emirates

ISBN: (纸本)9798350319347;9798350319354

The increasing number of IoT devices in the network brings new challenges to the network carrying capacity of intelligent edge computing, and the complicated network services make the demand for network resources in industrial production scenarios or ordinary network users often exceed the carrying capacity of the edge computing network. To alleviate this problem, this paper proposes an intelligent edge computing architecture that introduces network service identification, extracts and analyses the data characteristics of network traffic, and designs appropriate algorithms to classify network traffic into six different service types. This enables real-time and computing-requiring tasks to be prioritised in the network. Using two machine learning algorithms, KNN and MLP, a model validation is carried out on the constructed dataset, and the results show the effectiveness of the method, with the correct rate of data validation reaching 85%, which is more than 5% higher than the correct rate of direct classification of the specified applications, and the accuracy can be as high as 97% in certain scenarios.

关键词： intelligent edge computing Network service perception Machine learning Explainable AI

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