The integration of Multi Agent System within the Internet of Things

2018 ◽  
Author(s):  
Fatima Zahra Chafi ◽  
Youssef Fakhri
Keyword(s):  
Internet Of Things ◽  
The Internet ◽  
Multi Agent System ◽  
Agent System ◽  
Multi Agent ◽  
The Internet Of Things

scholarly journals A Multi-Agent System for Data Fusion Techniques Applied to the Internet of Things Enabling Physical Rehabilitation Monitoring

2020 ◽  
Vol 11 (1) ◽  
pp. 331
Author(s):  
Héctor Sánchez San Blas ◽  
André Sales Mendes ◽  
Francisco García Encinas ◽  
Luís Augusto Silva ◽  
Gabriel Villarubia González
Keyword(s):  
Internet Of Things ◽  
Recovery Process ◽  
Feedback System ◽  
Easy Access ◽  
The Internet ◽  
Multi Agent System ◽  
Agent System ◽  
Healthcare Facilities ◽  
Multi Agent ◽  
The Internet Of Things

There are more than 800 million people in the world with chronic diseases. Many of these people do not have easy access to healthcare facilities for recovery. Telerehabilitation seeks to provide a solution to this problem. According to the researchers, the topic has been treated as medical aid, making an exchange between technological issues such as the Internet of Things and virtual reality. The main objective of this work is to design a distributed platform to monitor the patient’s movements and status during rehabilitation exercises. Later, this information can be processed and analyzed remotely by the doctor assigned to the patient. In this way, the doctor can follow the patient’s progress, enhancing the improvement and recovery process. To achieve this, a case study has been made using a PANGEA-based multi-agent system that coordinates different parts of the architecture using ubiquitous computing techniques. In addition, the system uses real-time feedback from the patient. This feedback system makes the patients aware of their errors so that they can improve their performance in later executions. An evaluation was carried out with real patients, achieving promising results.

An Architecture to Implement the Internet-of-Things using the Prometheus Methodology

2020 ◽  
pp. 609-629
Author(s):  
Bogdan Manațe ◽  
Florin Fortiş ◽  
Philip Moore
Keyword(s):  
Internet Of Things ◽  
Rapid Expansion ◽  
The Internet ◽  
Data Types ◽  
Diverse Range ◽  
Complex Agent ◽  
Multi Agent ◽  
Complex Scenario ◽  
The Internet Of Things ◽  
Prometheus Methodology

The rapid expansion of the Internet of Things (IoT) will generate a diverse range of data types that needs to be handled, processed and stored. This paper aims to create a multi-agent system that suits the needs introduced by the IoT expansion, thus being able to oversee the Big Data collection and processing and also to maintain the semantic links between the data sources and data consumers. In order to build a complex agent oriented architecture, we have assessed the existing agent oriented methodologies searching for the best solution that is not bound to a specific programming language of framework, and it is flexible enough to be applied in such a divers domain like IoT. As complex scenario, the proposed approach has been applied to medical diagnosis and motoring of mental disorders.

scholarly journals Implementasi Sistem Load Balancing Menggunakan Metode Least Time First Byte Dan Multi-Agent System Pada Lingkungan Virtual

2019 ◽  
Vol 4 (2) ◽  
pp. 63-70
Author(s):  
Dyah Ayu Wiranti ◽  
Kurnia Siwi Kinasih ◽  
Shinta Rizki Firdina Sugiono
Keyword(s):  
Information System ◽  
Load Balancing ◽  
Response Time ◽  
Service Provider ◽  
The Internet ◽  
Multi Agent System ◽  
Agent System ◽  
Time Value ◽  
Multi Agent ◽  
Modern Era

In this modern era, the technology is growing rapidly, the Internet is misled. This condition will be related to the service provider or commonly referred to as a server. Increasing the number of clients, the server also has to work heavier so that it often occurs overload. The Load Balancing mechanism uses the Least Time First Byte and Multi Agent system methods. This mechanism allows the server to overcome the number of users who perform service requests so that the load from the server can be resolved. This solution is considered efficient and effective because the request process on the information system will be shared evenly on multiple server back ends. The results of the research that can be proved if using this mechanism the server can work well when the request is from a user or client dating, this method successfully distributes the balancer evenly through the server backend. So the server is no longer experiencing overload. This can be proved when a system that has used the load balancing method with 300 connections generates a throughput of 123.1 KB/s as well as response time value of 4.72 MS and a system that does not use the load balancing method has a throughput of 108.4 KB/s as well as a response time value of 120.3 Ms. Therefore by implementing load balancing the performance of the system can always be improved.

scholarly journals Dynamic Co-Simulation of Internet-of-Things-Components using a Multi-Agent-System

Procedia CIRP ◽  
2018 ◽  
Vol 72 ◽  
pp. 874-879 ◽  
Author(s):  
Tobias Jung ◽  
Payal Shah ◽  
Michael Weyrich
Keyword(s):  
Internet Of Things ◽  
Multi Agent System ◽  
Agent System ◽  
Multi Agent

scholarly journals Formation Generation for Multiple Unmanned Vehicles Using Multi-Agent Hybrid Social Cognitive Optimization Based on the Internet of Things

Sensors ◽  
2019 ◽  
Vol 19 (7) ◽  
pp. 1600 ◽  
Author(s):  
Zheng Yao ◽  
Sentang Wu ◽  
Yongming Wen
Keyword(s):  
Internet Of Things ◽  
Social Cognitive ◽  
Simulation Experiment ◽  
Unmanned Vehicles ◽  
The Internet ◽  
Unmanned Vehicle ◽  
Aerial Vehicle ◽  
Multi Agent ◽  
The Internet Of Things ◽  
Cognitive Optimization

Multi-agent hybrid social cognitive optimization (MAHSCO) based on the Internet of Things (IoT) is suggested to solve the problem of the generation of formations of unmanned vehicles. Through the analysis of the unmanned vehicle formation problem, formation principles, formation scale, unmanned vehicle formation safety distance, and formation evaluation indicators are taken into consideration. The application of the IoT enables the optimization of distributed computing. To ensure the reliability of the formation algorithm, the convergence of MAHSCO has been proved. Finally, computer simulation and actual unmanned aerial vehicle (UAV) formation generation flight generating four typical formations are carried out. The result of the actual UAV formation generation flight is consistent with the simulation experiment, and the algorithm performs well. The MAHSCO algorithm based on the IoT is proved to be able to generate formations that meet the mission requirements quickly and accurately.

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