A language for programming Internet wide multi-agent systems and mobile agents

1996 ◽  
Author(s):  
K.L. Clark
Keyword(s):  
Mobile Agents ◽  
Multi Agent Systems ◽  
Agent Systems ◽  
Multi Agent

Embeddable Mobile-C for Runtime Support of Code Mobility in Multi-Agent Systems

2007 ◽  
Author(s):  
Yu-Cheng Chou ◽  
David Ko ◽  
Harry H. Cheng
Keyword(s):  
Operating Systems ◽  
Mobile Agent ◽  
Mobile Agents ◽  
Distributed Applications ◽  
Multi Agent Systems ◽  
Agent Platform ◽  
Agent Systems ◽  
Code Mobility ◽  
Mobile Agent Systems ◽  
Multi Agent

Agent technology is emerging as an important concept for the development of distributed complex systems. A number of mobile agent systems have been developed in the last decade. However, most of them were developed to support only Java mobile agents. Furthermore, many of them are standalone platforms. In other words, they were not designed to be embedded in a user application to support the code mobility. In order to provide distributed applications with the code mobility, this article presents a mobile agent library, the Mobile-C library. The Mobile-C library is supported by various operating systems including Windows, Unix, and real-time operating systems. It has a small footprint to meet the stringent memory capacity for a variety of mechatronic and embedded systems. This library allows a Mobile-C agency, a mobile agent platform, to be embedded in a program to support C/C++ mobile agents. Functions in this library facilitate the development of a multi-agent system that can easily interface with a variety of hardware devices.

Incremental Distributed Learning with JavaScript Agents for Earthquake and Disaster Monitoring

2017 ◽  
Vol 8 (4) ◽  
pp. 34-53 ◽  
Author(s):  
Stefan Bosse
Keyword(s):  
Mobile Agents ◽  
Large Scale ◽  
Learning Algorithm ◽  
Seismic Station ◽  
Geodetic Network ◽  
Distributed Learning ◽  
The Internet ◽  
Multi Agent Systems ◽  
Agent Systems ◽  
Multi Agent

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.

Incremental Distributed Learning With JavaScript Agents for Earthquake and Disaster Monitoring

2019 ◽  
pp. 813-833
Author(s):  
Stefan Bosse
Keyword(s):  
Mobile Agents ◽  
Large Scale ◽  
Learning Algorithm ◽  
Seismic Station ◽  
Geodetic Network ◽  
Distributed Learning ◽  
The Internet ◽  
Multi Agent Systems ◽  
Agent Systems ◽  
Multi Agent

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.

Consensus-Based Cooperative Source Localization of Multi-Agent Systems with Sampled Range Measurements

2014 ◽  
Vol 02 (03) ◽  
pp. 231-241 ◽  
Author(s):  
Guofei Chai ◽  
Che Lin ◽  
Zhiyun Lin ◽  
Weidong Zhang
Keyword(s):  
Source Localization ◽  
Mobile Agents ◽  
Multi Agent Systems ◽  
Globally Asymptotically Stable ◽  
Range Measurements ◽  
Agent Systems ◽  
Mild Conditions ◽  
Relative Coordinate ◽  
Multi Agent ◽  
Fusion Scheme

This paper deals with the cooperative source localization problem for a cluster of mobile agents. The goal of each agent is to estimate the relative coordinate of a stationary source in its local frame via a cooperative manner. It is assumed that each agent may or may not have direct range measurements about the source or some neighbors. Collaboration among agents is desired so that every agent is able to estimate the relative coordinate of the source in real time though some agents may not have direct range measurements about the source. A novel discrete-time estimator and a consensus-like fusion scheme are developed for the problem. It is shown that the estimator together with the fusion scheme are globally asymptotically stable under very mild conditions. Numerical simulations are presented to illustrate the effectiveness of the proposed algorithms.

A THRESHOLD-BASED CONTROLLER FOR MULTI-AGENT SYSTEMS

2015 ◽  
Vol 77 (18) ◽  
Author(s):  
Olumide Simeon Ogunnusi ◽  
Shukor Abd Razak ◽  
Abdul Hanan Abdullah
Keyword(s):  
Mobile Agents ◽  
Multi Agent Systems ◽  
Available Bandwidth ◽  
Agent Systems ◽  
Network Bandwidth ◽  
Proposed Model ◽  
Basic Parameters ◽  
Multi Agent

Monitoring and regulating the deployment of mobile agents to a network based on its available bandwidth is crucial to forestall the possibility of congestion and consequent network degradation. Our study has shown that only one experimental modelhas addressed the issue. Investigation into this model revealed its failure to honour some basic parameters necessary to yield efficient result. These parameters and network bandwidth determine the maximum deployable number of agents to a network. To achieve the set objective, a threshold-based controller is proposed to regulate the injection of mobile agents into the network relative to the available bandwidth, agent size and router traffic size. The result obtained shows that the proposed model is more accurate and reliable than the existing one.

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