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Applicability of Multi-Agent Systems and Constrained Reasoning for Sensor-Based Distributed Scenarios: A Systematic

Jose Barambones1, Ricardo Imbert1, Cristian Moral1

  • 1Madrid Human-Computer Laboratory, Universidad Politécnica de Madrid, 28660 Boadilla del Monte, Spain.

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Summary

This study maps Dynamic Distributed Constrained Optimization Problems (DCOP) in sensor-based systems. It highlights trends in achieving fast, scalable solutions for changing environments, despite real-world application gaps.

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distributed constraint optimisation problemdistributed problem solvingintelligent sensor networkssensors on dynamic environmentssystematic mapping study

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Area of Science:

  • Artificial Intelligence
  • Distributed Systems
  • Optimization

Background:

  • Sensor-based systems with intelligent agents are crucial for dynamic environments.
  • Problems are often modeled as Distributed Constrained Optimization Problems (DCOP).

Purpose of the Study:

  • To systematically map Dynamic DCOP models in changing sensor-based environments.
  • To identify trends, research gaps, and progression in this field.

Main Methods:

  • Conducted a systematic mapping study of existing literature on Dynamic DCOPs.
  • Addressed scattered literature and terminology inconsistencies.

Main Results:

  • Prioritizes sub-optimal, fast responses with low communication costs due to complexity.
  • Scalability and long-term solution guarantees are key trending aspects.

Conclusions:

  • A significant body of work applicable to dynamic sensor scenarios exists.
  • Proposals for integrating standard constraint-based algorithms are noted.
  • Further real-world analysis and experimentation are needed.