Distributed Sensor Networks: A Multiagent Perspective
Distributed Sensor Networks: A Multiagent Perspective
Combinatorial Auctions
An Application of Automated Negotiation to Distributed Task Allocation
IAT '07 Proceedings of the 2007 IEEE/WIC/ACM International Conference on Intelligent Agent Technology
Agent Technologies for Sensor Networks
IEEE Intelligent Systems
Networked distributed POMDPs: a synthesis of distributed constraint optimization and POMDPs
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 1
Reflective negotiating agents for real-time multisensor target tracking
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 2
Artificial Intelligence - Special issue: Distributed constraint satisfaction
Adopt: asynchronous distributed constraint optimization with quality guarantees
Artificial Intelligence - Special issue: Distributed constraint satisfaction
Decentralised coordination of mobile sensors using the max-sum algorithm
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
International Journal of Sensor Networks
An implementation of the contract net protocol based on marginal cost calculations
AAAI'93 Proceedings of the eleventh national conference on Artificial intelligence
Self-management of ambient intelligence systems: a pure agent-based approach
Proceedings of the 11th International Conference on Autonomous Agents and Multiagent Systems - Volume 3
Distributed, multi-user, multi-application, and multi-sensor data fusion over named data networks
Computer Networks: The International Journal of Computer and Telecommunications Networking
Multiagent meta-level control for radar coordination
Web Intelligence and Agent Systems
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Distributed collaborative adaptive sensing (DCAS) of the atmosphere is a new paradigm for detecting and predicting hazardous weather using a large dense network of short-range, low-powered radars to sense the lowest few kilometers of the earths atmosphere. In DCAS, radars are controlled by a collection of Meteorological Command and Control (MC&C) agents that instruct where to scan based on emerging weather conditions. Within this context, this work concentrates on designing efficient approaches for allocating sensing resources to cope with restricted real-time requirements and limited computational resources. We have developed a new approach based on explicit goals that can span multiple system heartbeats. This allows us to reason ahead about sensor allocations based on expected requirements of goals as they project forward in time. Each goal explicitly specifies end-users' preferences as well as a prediction of how a phenomena will move. We use a genetic algorithm to generate scanning strategies of each single MC&C and a distributed negotiation model to coordinate multiple MC&Cs' scanning strategies over multiple heartbeats. Simulation results show that as compared to simpler variants of our approach, the proposed distributed model achieved the highest social welfare. Our approach also has exhibited similarly very good performance in an operational radar testbed that is deployed in Oklahoma to observe severe weather events.