Incremental Distributed Learning with JavaScript Agents for Earthquake and Disaster Monitoring

Incremental Distributed Learning with JavaScript Agents for Earthquake and Disaster Monitoring

Stefan Bosse (University of Bremen, Department of Mathematics and Computer Science, Bremen, Germany)
Copyright: © 2017 |Pages: 20
DOI: 10.4018/IJDST.2017100103
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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.
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Introduction

The IoT and mobile networks emerge in today’s life and are becoming part of pervasive and ubiquitous computing networks with distributed and transparent services. One major goal is the integration of sensor networks in the Internet and Cloud environments, with emerging robustness and scalability requirements. Robustness and scalability can be achieved by self-organizing and self-adaptive systems (self-*). Agents are already deployed successfully in sensing, production, and manufacturing processes, proposed by Caridi (2000), and newer trends poses the suitability of distributed agent-based systems for the control of manufacturing processes, shown by Pechoucek (2008), facing manufacturing, maintenance, evolvable assembly systems, quality control, and energy management aspects, finally introducing the paradigm of industrial agents meeting the requirements of modern industrial applications by integrating sensor networks. Mobile Multi-agent systems can fulfill the self-organizing and adaptive (self-*) paradigm, shown in Bosse (2015A). Distributed data mining and Map-Reduce algorithms are well suited for self-organizing MAS. Cloud-based computing with MAS means the virtualization of resources, i.e., storage, processing platforms, sensing data or generic information, discussed by Lehmhus (2015). Mobile Agents reflect a mobile service architecture. Commonly, distributed perceptive systems are composed of sensing, aggregation, and application layers, shown in Figure 1, merging mobile and embedded devices with the Cloud paradigm as in Lecce (2013). But generic Internet, IoT, and Cloud environments differ significantly in terms of resources: The IoT consists of a large number of low-resource or mobile devices interacting with the real world. The devices have strictly limited storage capacities and computing power, and the Cloud consists of large-scale computers with arbitrary and extensible computing power and storage capacities in a basically virtual world. A unified and common data processing and communication methodology is required to merge the IoT with Cloud environments seamless, which can be fulfilled by the mobile agent-based computing paradigm, discussed in this work.

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