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Incremental Distributed Learning with JavaScript Agents for Earthquake and Disaster Monitoring

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  • Stefan Bosse

    (University of Bremen, Department of Mathematics and Computer Science, Bremen, Germany)

Abstract

Ubiquitous computing and The Internet-of-Things (IoT) grow rapidly in today's life and evolving to Self-organizing systems (SoS). A unified and scalable information processing and communication methodology is required. In this work, mobile agents are used to merge the IoT with Mobile and Cloud environments seamless. A portable and scalable Agent Processing Platform (APP) provides an enabling technology that is central for the deployment of Multi-Agent Systems (MAS) in strong heterogeneous networks including the Internet. A large-scale use-case deploying Multi-agent systems in a distributed heterogeneous seismic sensor and geodetic network is used to demonstrate the suitability of the MAS and platform approach. The MAS is used for earthquake monitoring based on a new incremental distributed learning algorithm applied to seismic station data, which can be extended by ubiquitous sensing devices like smart phones. Different (mobile) agents perform sensor sensing, aggregation, local learning and prediction, global voting and decision making, and the application.

Suggested Citation

  • Stefan Bosse, 2017. "Incremental Distributed Learning with JavaScript Agents for Earthquake and Disaster Monitoring," International Journal of Distributed Systems and Technologies (IJDST), IGI Global, vol. 8(4), pages 34-53, October.
    • Handle: RePEc:igg:jdst00:v:8:y:2017:i:4:p:34-53
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    Cited by:

    1. Farnaz Derakhshan & Shamim Yousefi, 2019. "A review on the applications of multiagent systems in wireless sensor networks," International Journal of Distributed Sensor Networks, , vol. 15(5), pages 15501477198, May.

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