Agent-based artificial immune system approach for adaptive damage detection in monitoring networks
This paper presents an agent-based artificial immune system approach for adaptive damage detection in distributed monitoring networks. The presented approach establishes a new monitoring paradigm by embodying desirable immune attributes, such as adaptation, immune pattern recognition, and self-organization, into monitoring networks. In the artificial immune system-based paradigm, a group of autonomous mobile monitoring agents mimic immune cells (such as B-cells) in the natural immune system, interact locally with monitoring environment, and respond to emerging problems through simulated immune responses. The presented immune-inspired monitoring paradigm has been applied to structural health monitoring. The "antibody" of a mobile monitoring agent is a pattern recognition algorithm tuned to a certain type of structural damage pattern. The mobile monitoring agent performs damage diagnosis based on structural dynamic response data. Mobile monitoring agents communicate with each other and collaborate with network components based on agent interaction protocols defined in agent standards, the Foundation for Intelligent Physical Agents standards. A mobile agent system embedded in sensor nodes supports the selective generation, migration, communication, and management of mobile monitoring agents automatically. The active structural health monitoring is achieved by distributing mobile monitoring agents to the sites where they are needed. The structural damage diagnosis using mobile monitoring agents and artificial immune pattern recognition method has been tested using a scaled steel bridge structure. The test result shows the feasibility of using this approach for real-time structural damage diagnosis. © 2010 Elsevier Ltd. All rights reserved.
Journal of Network and Computer Applications
Agent-based artificial immune system approach for adaptive damage detection in monitoring networks.
Journal of Network and Computer Applications,
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