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Cooperative management of a net of intelligent surveillance agent sensors
Author(s) -
López J. M. Molina,
Herrero Jesús García,
Rodríguez F. J. Jiménez,
Corredera J. R. Casar
Publication year - 2003
Publication title -
international journal of intelligent systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.291
H-Index - 87
eISSN - 1098-111X
pISSN - 0884-8173
DOI - 10.1002/int.10089
Subject(s) - computer science , task (project management) , sensor fusion , process (computing) , wireless sensor network , intelligent agent , multi agent system , real time computing , artificial intelligence , fuzzy logic , distributed computing , systems engineering , computer network , engineering , operating system
The use of distributed artificial intelligence (DAI) techniques, particularly the multiagent systems theory, in a decentralized architecture, is proposed to manage cooperatively, all sensor tasks in a network of (air) surveillance radars with capabilities for autonomous operation. At the multisensor data fusion (DF) center, the fusion agent will periodically deliver to sensor agents a list with the system‐level tasks that need to be fulfilled. For each system task, indications about its system‐level priority are included (inferred global necessity of fulfilling the task) as well as the performance objectives that are required, expressed in different terms depending on the type of task (sector surveillance, target tracking, target identification, etc.). Periodically, the local manager at each sensor (the sensor agent) will decide on the list of sensor‐level tasks to be executed by its sensor, providing also the sensor‐level priority and performance objectives for each task. The problem of sensor(s)‐to‐task(s) assignment (including decomposition of system‐level tasks into sensor‐level tasks and translation of system‐level performance requirements to sensor‐level performance objectives) is the result of a negotiation process performed among sensor agents, initiated with the information sent to them by the fusion agent. With types of agents, a symbolic bottom‐up fuzzy reasoning process is performed that considers the available fused or local target tracks, surveillance sectors data, and (external) intelligence information. As a result of these reasoning processes, performed at each agent planning level, the priorities of system‐level and sensor‐level tasks will be inferred and applied during the negation process. © 2003 Wiley Periodicals, Inc.