TY - GEN
T1 - Exploring sensor networks using mobile agents
AU - Massaguert, Daniel
AU - Fok, Chien Liang
AU - Venkatasubramanian, Nalini
AU - Roman, Gruia Catalin
AU - Lu, Chenyang
PY - 2006
Y1 - 2006
N2 - Wireless sensor networks are often difficult to program and unable to adapt to a changing environment. Mobile agent middleware promises to address both concerns by providing higher-level programming abstractions and the ability to inject new agents into a preexisting network. The unique characteristics of wireless sensor networks like resource scarcity and emphasis on spatial locality require new algorithms for controlling agent behavior. This paper presents a procedure for one specific behavior: network exploration. Network exploration is needed by many tasks ranging from simple data collection to network health monitoring. Our proposed procedure uses a genetic algorithm to determine the number of agents and their itineraries, followed by techniques for in-network adaptation to unpredictable situations like node failure. This paper presents a genetic algorithm and its adaptation strategies. The procedure is evaluated using a wireless sensor network consisting of 25 Mica2 motes running Agilla, a, mobile agent middleware for wireless sensor networks.
AB - Wireless sensor networks are often difficult to program and unable to adapt to a changing environment. Mobile agent middleware promises to address both concerns by providing higher-level programming abstractions and the ability to inject new agents into a preexisting network. The unique characteristics of wireless sensor networks like resource scarcity and emphasis on spatial locality require new algorithms for controlling agent behavior. This paper presents a procedure for one specific behavior: network exploration. Network exploration is needed by many tasks ranging from simple data collection to network health monitoring. Our proposed procedure uses a genetic algorithm to determine the number of agents and their itineraries, followed by techniques for in-network adaptation to unpredictable situations like node failure. This paper presents a genetic algorithm and its adaptation strategies. The procedure is evaluated using a wireless sensor network consisting of 25 Mica2 motes running Agilla, a, mobile agent middleware for wireless sensor networks.
KW - (multi-)agent planning
KW - Applications of autonomous agents and multi-agent systems
KW - Mobile agents
KW - Performance evaluation of agent systems
UR - https://www.scopus.com/pages/publications/34247220431
U2 - 10.1145/1160633.1160688
DO - 10.1145/1160633.1160688
M3 - Conference contribution
AN - SCOPUS:34247220431
SN - 1595933034
SN - 9781595933034
T3 - Proceedings of the International Conference on Autonomous Agents
SP - 323
EP - 325
BT - Proceedings of the Fifth International Joint Conference on Autonomous Agents and Multiagent Systems
T2 - Fifth International Joint Conference on Autonomous Agents and Multiagent Systems, AAMAS
Y2 - 8 May 2006 through 12 May 2006
ER -