scholarly journals A Unified Distributed Computing Framework with Mobile Multi-Agent Systems and Virtual Machines for Large-Scale Applications: From the Internet-of-Things to Sensor Clouds

2015 ◽  
Author(s):  
Stefan Bosse
Keyword(s):  
Internet Of Things ◽  
Distributed Computing ◽  
Large Scale ◽  
Virtual Machines ◽  
The Internet ◽  
Multi Agent Systems ◽  
Agent Systems ◽  
Multi Agent ◽  
Computing Framework ◽  
The Internet Of Things

scholarly journals Trust Model of Information Security of the Environment of Multi-Agent Robotic Systems

2020 ◽  
pp. 23-31
Author(s):  
Andrey Stepenkin ◽  
Keyword(s):  
Internet Of Things ◽  
Trust Model ◽  
Cyber Physical Systems ◽  
Robotic Systems ◽  
The Internet ◽  
Multi Agent Systems ◽  
Physical Systems ◽  
Agent Systems ◽  
Multi Agent ◽  
The Internet Of Things

The capabilities of robotic systems are growing rapidly. The ways of operating such systems are increasing every day. Sensor networks, the Internet of Things, cyber-physical systems have similar properties as multi-agent robotic systems, which allows us to consider the approaches and methods of ensuring security used in them. Purpose of the article: improving the security of multi-agent systems in an uncontrolled environment, developing methods for assessing trust of the environment. Research methods: analysis of existing threat models for multi-agent robotic systems, as well as systems with similar properties: cyber-physical systems and the Internet of things. Analysis of the research results of existing approaches to ensuring the security of multi-agent systems. The results: an analysis of security threats and existing methods of ensuring security for robotic multi-agent systems, as well as systems with similar properties, was carried out. An extension for security methods based on trust and reputation has been developed, taking into account the operating environment of the system as part of information interaction. A method for localizing the subject of an external intruder in the environment is proposed.

scholarly journals COBRA: Context-Aware Bernoulli Neural Networks for Reputation Assessment

2020 ◽  
Vol 34 (05) ◽  
pp. 7317-7324
Author(s):  
Leonit Zeynalvand ◽  
Tie Luo ◽  
Jie Zhang
Keyword(s):  
Large Scale ◽  
Service Providers ◽  
Reputation Management ◽  
The Internet ◽  
Context Aware ◽  
Multi Agent Systems ◽  
Agent Systems ◽  
Trust And Reputation ◽  
Multi Agent ◽  
The Internet Of Things

Trust and reputation management (TRM) plays an increasingly important role in large-scale online environments such as multi-agent systems (MAS) and the Internet of Things (IoT). One main objective of TRM is to achieve accurate trust assessment of entities such as agents or IoT service providers. However, this encounters an accuracy-privacy dilemma as we identify in this paper, and we propose a framework called Context-aware Bernoulli Neural Network based

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.

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