Mobile Multi-Agent Systems for the Internet-of-Things and Clouds using the Java']JavaScript Agent Machine Platform and Machine Learning as a Service

被引:14
|
作者
Bosse, Stefan [1 ]
机构
[1] Univ Bremen, Dept Math & Comp Sci, Bremen, Germany
关键词
Agents; IoT; Cloud Computing; Agent Platforms;
D O I
10.1109/FiCloud.2016.43
中图分类号
TP301 [理论、方法];
学科分类号
081202 ;
摘要
The Internet-of-Things (IoT) gets real and is becoming part of pervasive and ubiquitous computing networks offering distributed and transparent services. A unified and common data processing and communication methodology is shown to merge the IoT, sensor networks, and Cloud based environments seamless, which is fulfilled by the mobile agent-based computing paradigm. Currently, portability, resource constraints, security, and scalability of Agent Processing Platforms (APP) are essential issues for the deployment of Multi-agent Systems (MAS) in strong heterogeneous networks including the Internet, addressed in this work. Agents are directly implemented in JavaScript, which is a well known and public widespread used programming language, and JS VMs are available on all host platforms including WEB browsers. The novel proposed JS Agent Machine (JAM) is capable to execute AgentJS agents in a sandbox environment with full run-time protection and Machine learning as a service. Agents can migrate between different JAM nodes seamless preserving their data and control state by using a on-the-fly code-to-text transformation in an extended JSON+ format. A Distributed Organization System (DOS) layer provides JAM node connectivity and security in the Internet, completed by a Directory-Name Service offering an organizational graph structure. Agent authorization and platform security is ensured with capability-based access and different agent privilege levels.
引用
收藏
页码:246 / 255
页数:10
相关论文
共 50 条
  • [21] A multi-Agent supported adaptive mobile collaborative service platform
    Cao, Yuhui
    Wang, Weihong
    Qin, Zheng
    [J]. ICNC 2007: THIRD INTERNATIONAL CONFERENCE ON NATURAL COMPUTATION, VOL 5, PROCEEDINGS, 2007, : 331 - +
  • [22] Multi-machine scheduling - A multi-agent learning approach
    Brauer, W
    Weiss, G
    [J]. INTERNATIONAL CONFERENCE ON MULTI-AGENT SYSTEMS, PROCEEDINGS, 1998, : 42 - 48
  • [23] Extended multi-agent system based service composition in the Internet of things
    Berrani, Samir
    Yachir, Ali
    Djemaa, Badis
    Aissani, Mohamed
    [J]. 2018 3RD INTERNATIONAL CONFERENCE ON PATTERN ANALYSIS AND INTELLIGENT SYSTEMS (PAIS), 2018, : 176 - 183
  • [24] Improving Efficiency of Hybrid HPC Systems Using a Multi-agent Scheduler and Machine Learning Methods
    Zaborovsky, Vladimir S.
    Utkin, Lev V.
    Muliukha, Vladimir A.
    Lukashin, Alexey A.
    [J]. Supercomputing Frontiers and Innovations, 2023, 10 (02) : 104 - 126
  • [25] Multi-agent Environment for Decision-Support in Production Systems Using Machine Learning Methods
    Koźlak, Jaroslaw
    Sniezynski, Bartlomiej
    Wilk-Kolodziejczyk, Dorota
    Leśniak, Albert
    Jaśkowiec, Krzysztof
    [J]. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2019, 11537 LNCS : 517 - 529
  • [26] Multi-agent Environment for Decision-Support in Production Systems Using Machine Learning Methods
    Kozlak, Jaroslaw
    Sniezynski, Bartlomiej
    Wilk-Kolodziejczyk, Dorota
    Lesniak, Albert
    Jaskowiec, Krzysztof
    [J]. COMPUTATIONAL SCIENCE - ICCS 2019, PT II, 2019, 11537 : 517 - 529
  • [27] Scalability in Modeling and Simulation Systems for Multi-Agent, AI, and Machine Learning Applications
    Newton, Charles
    Singleton, John
    Copland, Cameron
    Kitchen, Sarah
    Hudack, Jeffrey
    [J]. ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING FOR MULTI-DOMAIN OPERATIONS APPLICATIONS III, 2021, 11746
  • [28] Machine learning and multi-agent systems in oil and gas industry applications: A survey
    Hanga, Khadijah M.
    Kovalchuk, Yevgeniya
    [J]. COMPUTER SCIENCE REVIEW, 2019, 34
  • [29] Machine Learning Data Markets: Trading Data using a Multi-Agent System
    Baghcheband, Hajar
    Soares, Carlos
    Reis, Luis Paulo
    [J]. 2022 IEEE/WIC/ACM INTERNATIONAL JOINT CONFERENCE ON WEB INTELLIGENCE AND INTELLIGENT AGENT TECHNOLOGY, WI-IAT, 2022, : 450 - 457
  • [30] Study on Multi-Agent Based Simulation of Team Machine Learning
    Li, Tie
    Peng, Yi
    Shi, Yong
    Kou, Gang
    [J]. PROMOTING BUSINESS ANALYTICS AND QUANTITATIVE MANAGEMENT OF TECHNOLOGY: 4TH INTERNATIONAL CONFERENCE ON INFORMATION TECHNOLOGY AND QUANTITATIVE MANAGEMENT (ITQM 2016), 2016, 91 : 847 - 854