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distributed artificial intelligence Empowered by End-Edge-Cloud Computing: A Survey

IEEE COMMUNICATIONS SURVEYS AND TUTORIALS 2023年第1期25卷 591-624页

作者： Duan, Sijing Wang, Dan Ren, Ju Lyu, Feng Zhang, Ye Wu, Huaqing Shen, Xuemin Cent South Univ Sch Comp Sci & Engn Changsha 410083 Peoples R China Tsinghua Univ BNRist Dept Comp Sci & Technol Beijing 100084 Peoples R China Zhongguancun Lab Beijing 100094 Peoples R China Beijing Informat Sci & Technol Univ Comp Sch Beijing 100096 Peoples R China Univ Calgary Dept Elect & Software Engn Calgary AB T2N 1N4 Canada Univ Waterloo Dept Elect & Comp Engn Waterloo ON N2L 3G1 Canada

As the computing paradigm shifts from cloud computing to end-edge-cloud computing, it also supports artificial intelligence evolving from a centralized manner to a distributed one. In this paper, we provide a comprehensive survey on the distributed artificial intelligence (DAI) empowered by end-edge-cloud computing (EECC), where the heterogeneous capabilities of on-device computing, edge computing, and cloud computing are orchestrated to satisfy the diverse requirements raised by resource-intensive and distributed AI computation. Particularly, we first introduce several mainstream computing paradigms and the benefits of the EECC paradigm in supporting distributed AI, as well as the fundamental technologies for distributed AI. We then derive a holistic taxonomy for the state-of-the-art optimization technologies that are empowered by EECC to boost distributed training and inference, respectively. After that, we point out security and privacy threats in DAI-EECC architecture and review the benefits and shortcomings of each enabling defense technology in accordance with the threats. Finally, we present some promising applications enabled by DAI-EECC and highlight several research challenges and open issues toward immersive performance acquisition.

关键词： distributed artificial intelligence end-edge-cloud computing network computing federated learning

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distributed artificial intelligence for FMS scheduling, control and design support

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JOURNAL OF INTELLIGENT MANUFACTURING 2000年第6期11卷 573-589页

作者： Trentesaux, D Pesin, P Tahon, C Lab Automat & Mecan Ind & Humaines Equipe Genie Ind & Log CNRS UMR 8530 F-59313 Valenciennes 09 France

The aim of this paper is to point out some of the abilities of distributed artificial intelligence in the domain of scheduling, control and design support of Flexible Manufacturing Systems. A distributed management system is proposed, based on distributed Problem Solving, sub-field of distributed artificial intelligence. The basic concepts are the concept of Resource Management Entity to ensure local optimization of the management of resources and the concept of cooperation to provide ability for global and local consistency. The management of resources is associated to activities such as scheduling, control or simulation. It is shown that this system computes not only practicable schedulings, but also presents, on the one hand, some abilities in supporting the design and the robust optimization of Flexible Manufacturing Systems, and, on the other hand, some abilities in supporting real-time control of such systems. This enables, in future works, to design a distributed Decision Support System for integrated scheduling, control and design support of production systems.

关键词： distributed artificial intelligence scheduling control design simulation FMS

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distributed artificial intelligence Solution for D2D Communication in 5G Networks

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IEEE SYSTEMS JOURNAL 2020年第3期14卷 4232-4241页

作者： Ioannou, Iacovos Vassiliou, Vasos Christophorou, Christophoros Pitsillides, Andreas Univ Cyprus Dept Comp Sci CY-20537 Nicosia Cyprus RISE Ctr Excellence Interact Media Smart Syst & E CY-20537 Nicosia Cyprus

Device-to-device (D2D) communication, a core technology component of the evolving fifth-generation (5G) architecture, promises improvements in energy efficiency, spectral efficiency, overall system capacity, and higher data rates. These improvements in network performance spearheaded a vast amount of research in D2D, which identified significant challenges that need to be addressed before realizing their full potential in 5G networks, and beyond. Toward this end, this article proposes the use of a distributed intelligent approach to control the generation of D2D networks. More precisely, the proposed approach uses Belief Desire Intention (BDI) intelligent agents with extended capabilities (BDIx) to manage each D2D node independently and autonomously, without the help of the base station. To illustrate the above, this article proposes the DAIS algorithm for the decision of transmission mode in D2D, which maximizes the data rate and minimizes the power consumption in the network, while taking into consideration the computational load. Simulations show the applicability of BDI agents in solving D2D challenges.

关键词： Device-to-device communication Interference 5G mobile communication Relays Intelligent agents Cellular networks artificial intelligence artificial intelligence (AI) BDI agents distributed artificial intelligence 5G device-to-device (D2D) D2D challenges multiagent systems

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distributed artificial-intelligence FOR GROUP DECISION-SUPPORT - INTEGRATION OF PROBLEM-SOLVING, COORDINATION, AND LEARNING

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DECISION SUPPORT SYSTEMS 1993年第4期9卷 349-367页

作者： SHAW, MJ FOX, MS UNIV TORONTO TORONTO M5S 1A1ONTARIOCANADA

Decision support systems (DSS's) for aiding group problem-solving situations have become increasingly important for supporting and coordinating complex organizations. This paper describes a framework for designing, developing, and formalizing group problem-solving systems based on distributed artificial intelligence (DAI). Among the issues, we find the coordination mechanisms and the learning schemes to be of particular importance in supporting group problem solving. Two implementation examples, one on a network of expert systems, one on a multi-agent concurrent design system, are used to illustrate the distributed artificial intelligence approach to group decision support.

关键词： GROUP DECISION SUPPORT distributed artificial intelligence MULTIAGENT LEARNING

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distributed artificial intelligence in process control

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COMPUTERS & INDUSTRIAL ENGINEERING 1996年第1-2期31卷 397-400页

作者： Chu, E Srihari, K Emerson, CR P.E. Department of Systems Science Industrial Engineering State University Of New York Binghamton New York 13902-6000 USA

A distributed artificial intelligence based system for process planning in surface mount printed circuit board assembly was designed and developed. Multiple intelligent agents work together to read a CAD drawing of an assembly and subsequently deduce the process instructions needed. The system can also review a board from a 'design for manufacturing' perspective and identify the cost of assembly. It uses fuzzy logic to deal with domain related uncertainty. Object oriented programming concepts have been used in system implementation.

关键词： distributed artificial intelligence printed wiring board assembly process planning

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distributed artificial intelligence and machine learning - from coexistence to cooperation

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JOURNAL OF EXPERIMENTAL & THEORETICAL artificial intelligence 1998年第3期10卷 287-289页

作者： Weiss, G Tech Univ Munich Inst Informat D-80290 Munchen Germany

This article does not have an abstract.

关键词： distributed artificial intelligence MACHINE learning INTELLIGENT agents (Computer software)

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distributed artificial-intelligence FOR RUNTIME FEATURE-INTERACTION RESOLUTION

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COMPUTER 1993年第8期26卷 48-55页

作者： VELTHUIJSEN, H PTT Res. Leidschendam

A feature in a telecommunications system is a package of functionality incrementally added to a service to enhance or modify it. A feature may interact with its environment in a way that interferes with the feature's desired operation. A feature's environment may include the telecommunications system. other features and services, and other instances of the same feature. The feature-interaction problem deals with the detection, prevention, and resolution of undesired feature interactions. The feature-interaction problem has many different instances. This article argues that some instances lend themselves to a distributed artificial intelligence approach. The use of DAI techniques in current telecommunications systems appears quite natural in light of two trends in the way these systems are designed: the distribution of functionality and the incorporation of ''intelligence.'' There are two basic ways to introduce agents into a telecommunications system. In certain cases, it is feasible to encapsulate services or features in agents. DAI techniques for dealing with interactions between agents can then be used to detect and deal with interactions between the encapsulated services or features. Agents can also be used to represent a user's or network provider's notion of what constitutes ''desirable'' operation of services and features. DAI negotiation techniques may help in finding an acceptable way to operate when conflicts arise over what is desirable. The author illustrates the relevance of DAI techniques to the feature-interaction problem by discussing existing work (Lodes, Team-CPS, Multistage Negotiation, and Negotiating Agents) that addresses one or more instances of the problem. He further identifies the kind of cooperation and coordination that the feature-interaction problem requires and the interesting research problems it poses to distributed artificial intelligence.

关键词： cooperative systems distributed artificial intelligence electronic switching systems feature-interaction problem lodes multistage negotiation negotiating agents runtime feature-interaction resolution team-CPS telecommunication networks telecommunications computing telecommunications systems

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distributed artificial intelligence from a Socio-Cognitive Standpoint: Looking at Reasons for Interaction

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AI & Society 1995年第4期9卷 287-287页

作者： Miceli, M. Cesta, A. Rizzo, P. IP-CNRNational Research Council of ItalyRomeItaly Center of Cognitive ScienceUniversita" di TorinoTorinoItaly

distributed artificial intelligence (DAI) deals with computational systems where several intelligent components interact in a common environment. This paper is aimed at pointing out and fostering the exchange between DAI and cognitive and social science in order to deal with the issues of interaction, and in particular with the reasons and possible strategies for social behaviour in multi-agent interaction is also described which is motivated by requirements of cognitive plausibility and grounded the notions of power, dependence and help. Connections with human-computer interaction are also suggested.

关键词： Cognitive modelling distributed artificial intelligence Human-computer interaction Multi-agent systems Social behaviour

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distributed artificial intelligence and Adaptive Fish Swarm Optimization based Resource Allocation in Wireless Sensor Network

Distributed Artificial Intelligence and Adaptive Fish Swarm ...

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2023 International Conference on Integrated intelligence and Communication Systems, ICIICS 2023

作者： Reddy, Y. Geetha Priyanka, K. Kumar, K. Sudheer Habelalmateen, Mohammed I. Hussein, A.H.A. Malla Reddy Engineering College for Women Hyderabad India Nitte Meenakshi Institute of Technology Department of Information Science and Engineering Bengaluru India SR University School of Computer Science & Artificial Intelligence Warangal India The Islamic University Najaf Iraq Ahl Al Bayt University College of Pharmacy Karbala Iraq

ISBN: (纸本)9798350315455

The Wireless Sensor Network (WSN) is a decentralized and distributed ad-hoc network that comprises the powerful computing, sensing and processing nodes. In WSN, the sensor nodes are limited in terms of computational latency, communication range, bandwidth, storage and battery power. The efficient utilization of WSN resource is a difficult task for improve network lifetime, throughput, minimize the computational delay and overhead. Various intelligent strategies are developed through adopting intelligent resource management approaches. Due to the sensor heterogeneity, the inter and intra-cluster cooperative communication among sensor node in terms of response time and energy consumption. In this paper, a distributed artificial Intel ligence (DAI) with a hierarchical resource allocation for address the resource allocation issues. For inter-cluster power allocation, this paper considers the Quality of Service (QoS) and energy consumption with DAI. Additionally, for intra-cluster resource allocation, the Adaptive Fish Swarm Optimization (AFSO) is utilized which utilizes its objective functions as node distance and corresponding energy loads. The proposed DAI and AFSO less energy consumption of 7.86%, packet delivery ratio of 99.4% which is better than other existing methods. © 2023 IEEE.

关键词： Adaptive Fish Swarm Optimization distributed artificial intelligence Energy Consumption Quality of Service Resource Allocation Wireless Sensor Network

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distributed artificial intelligence Empowered Sustainable Cognitive Radio Sensor Networks: A Smart City on-demand Perspective

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SUSTAINABLE CITIES AND SOCIETY 2021年 75卷 103265-103265页

作者： Manman, Li Goswami, Pratik Mukherjee, Proshikshya Mukherjee, Amrit Yang, Lixia Ghosh, Uttam Menon, Varun G. Qi, Yinan Nkenyereye, Lewis Jiangsu Univ Sch Comp Sci Commun Engn Zhenjiang Jiangsu Peoples R China KIIT DU Sch Elect Engn Bhubaneswar India Anhui Univ Sch Elect & Informat Engn Hefei Peoples R China Vanderbilt Univ Elect Engn & Comp Sci 221 Kirkland Hall Nashville TN 37235 USA SCMS Sch Engn & Technol Dept Comp Sci & Engn Karukutty India Sejong Univ Dept Comp & Informat Secur Seoul South Korea Zhejiang Univ Zhejiang Lab Hangzhou Peoples R China

Smart cities are claimed to be smart if the new technologies are capable of providing desired sustainable outcome. The sustainable properties of smart city applications require less energy consumption and efficient resource allocation. The Internet-of-Things (IoT), 5G, and fog networks have emerged as the most crucial researched areas due to their numerous applications for smart cities to provide the desired sustainable outcome. The sustainable properties of Wireless Sensor Networks (WSNs) play a vital role in the deployment of these technologies into the physical world and efficient utilization of the available spectrum is a major problem faced here. As a potential solution of this, Cognitive Radio (CR) merged with WSN as Cognitive Radio Sensor Networks (CRSNs) make the smart perspective with high resource management through cooperative communication. The proposed work establishes a dynamic correlation between Secondary Users/nodes (SUs) in a single cluster according to their statistical behavior at the time of performing smart cooperative communication in CRSNs to improve sustainability of smart world IoT applications. distributed artificial intelligence (DAI) is used to calculate the real-time resource allocation to these clusters using their respective Coordinator Agent (CoA) based on the dynamic behaviors. To improve sustainability in the smart city applications, the time delay in the prediction of vacant channels is reduced which results in making these applications become more energy efficient. The effectiveness of the proposed work is illustrated with mathematical analysis and simulation results confirm its better sustainable performance compared to the existing techniques.

关键词： Cooperative Communication Cognitive Radio Sensor Network distributed artificial intelligence Resource allocation sustainable Smart World Applications

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