English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Multi-Agent Systems (MAS) are adopted and tested with many complex andcritical industrial applications, which are required to be adaptive, scalable,context-aware, and include real-time constraints. Industrial Control Networks(ICN) are examples of these applications. An ICN is considered a system thatcontains a variety of interconnected industrial equipments, such as physicalcontrol processes, control systems, computers, and communication networks. Itis built to supervise and control industrial processes. This paper presents adevelopment case study on building a multi-layered agent-based ICN in whichagents cooperate to provide an effective supervision and control of a set ofcontrol processes, basically controlled by a set of legacy control systems withlimited computing capabilities. The proposed ICN is designed to add anintelligent layer on top of legacy control systems to compensate their limitedcapabilities using a cost-effective agent-based approach, and also to provideglobal synchronization and safety plans. It is tested and evaluated within asimulation environment. The main conclusion of this research is that agents andMAS can provide an effective, flexible, and cost-effective solution to handlethe emerged limitations of legacy control systems if they are properlyintegrated with these systems.

On the Adoption of Multi-Agent Systems for the Development of Industrial Control Networks