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Engineering Approaches for Programming Agent-Based IoT Objects Using the Resource Management Architecture.

Fabian Cesar Brandão1, Maria Alice Trinta Lima1, Carlos Eduardo Pantoja1,2

  • 1Federal Center for Technological Education (CEFET-RJ), Rio de Janeiro 20271-110, Brazil.

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Summary

This study introduces three engineering approaches for embedded Multi-agent Systems (MAS) in the Internet of Things (IoT), enhancing autonomous device capabilities. The re-engineered IoT architecture offers scalable resource sharing and diverse design choices for cognitive edge systems.

Keywords:
IoTedge computingembedded multi-agent systems

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Internet of Things (IoT)

Background:

  • Current Internet of Things (IoT) systems often lack pro-active and autonomous capabilities due to limitations in agent architectures for embedded systems.
  • Existing agent approaches can be domain-specific and may compromise system performance based on perception access.

Purpose of the Study:

  • To present three novel engineering approaches for developing IoT Objects using Embedded Multi-agent Systems (MAS) as cognitive systems at the edge.
  • To introduce a re-engineered IoT architecture based on the Sensor as a Service model for enhanced connectivity, action, and information sharing.
  • To diversify design choices for implementing embedded MAS within IoT systems.

Main Methods:

  • Development and application of three engineering approaches utilizing Belief-Desire-Intention (BDI) agents and the JaCaMo framework.
  • Re-engineering of the IoT architecture to integrate embedded MAS and adopt the Sensor as a Service model.
  • Validation through a case study, performance tests, and comparative analysis of the proposed approaches and architecture.

Main Results:

  • The case study demonstrated the varying suitability of each approach depending on the specific domain.
  • Performance tests confirmed the scalability of the re-engineered IoT architecture.
  • Trade-offs were identified in adopting different engineering approaches, offering designers valuable insights.

Conclusions:

  • The proposed architecture facilitates resource sharing in IoT networks through embedded MAS on IoT Objects.
  • The three engineering approaches provide flexible options for designing cognitive edge systems in IoT.
  • The research contributes a scalable architecture and practical methodologies for advanced IoT system development.