An agent-based intelligent environmental monitoring system

TL;DR

This paper introduces a multi-agent system that continuously monitors air quality using sensor data, employs data mining for intelligent decision-making, and is demonstrated through a real-world deployment.

Contribution

It presents a novel multi-agent architecture for environmental monitoring that integrates data mining and real-time decision-making in a practical setting.

Findings

Effective air-quality assessment through multi-agent collaboration

Successful deployment in a real-world environment

Enhanced decision-making with data-driven agent intelligence

Abstract

Fairly rapid environmental changes call for continuous surveillance and on-line decision making. There are two main areas where IT technologies can be valuable. In this paper we present a multi-agent system for monitoring and assessing air-quality attributes, which uses data coming from a meteorological station. A community of software agents is assigned to monitor and validate measurements coming from several sensors, to assess air-quality, and, finally, to fire alarms to appropriate recipients, when needed. Data mining techniques have been used for adding data-driven, customized intelligence into agents. The architecture of the developed system, its domain ontology, and typical agent interactions are presented. Finally, the deployment of a real-world test case is demonstrated.