scholarly journals Research on Cooperative Communication Strategy and Intelligent Agent Directional Source Grouping Algorithms for Internet of Things

2019 ◽  
Vol 11 (11) ◽  
pp. 233
Author(s):  
Zou ◽  
Zhang ◽  
Yi
Keyword(s):  
Internet Of Things ◽  
Cooperative Communication ◽  
Intelligent Agent ◽  
The Internet ◽  
End To End Delay ◽  
Multi Agent ◽  
End To End ◽  
Directional Source ◽  
The Internet Of Things ◽  
Cooperative Nodes

In order to improve the network layer of the Internet of things to improve transmission reliability, save time delay and energy consumption, the Internet of things cooperative communication and intelligent agent technology were studied in this paper. In cooperative communication, a new cooperative communication algorithm KCN (k-cooperative node), and a reliability prediction model are proposed. The k value is determined by the end-to-end reliability. After k cooperative nodes are selected, other nodes enter dormancy. In aggregate traffic allocation, game theory is used to model the traffic equilibrium and end-to-end delay optimization scenarios. In practice, the optimal duty cycle can be calculated, which makes some cooperative nodes enter an idle state to save energy. Under the premise of guaranteeing end-to-end delay, it is shown that the reliability of the proposed KCN algorithm is better than that of the other existing routing protocols. In the aspect of intelligent agent, a directional source grouping algorithm D-MIP is proposed. This algorithm studies the routing problem of multi-agent parallel access to multiple source nodes. A directed source packet multi-agent routing planning algorithm is proposed. The iterative algorithm of each source node is limited to a sector, and the optimal intelligent agent route is obtained by selecting an appropriate angle. Compared with other algorithms, it is shown through a lot of simulated results that energy consumption and time delay has been saved by the proposed D-MIP algorithm.

End-to-end secure group communication for the Internet of Things

2021 ◽  
Vol 58 ◽  
pp. 102772
Author(s):  
André Lizardo ◽  
Raul Barbosa ◽  
Samuel Neves ◽  
Jaime Correia ◽  
Filipe Araujo
Keyword(s):  
Internet Of Things ◽  
Group Communication ◽  
The Internet ◽  
Secure Group Communication ◽  
End To End ◽  
The Internet Of Things

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 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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