A distributed mechanism for topology discovery in ad hoc wireless networks using mobile agents
MobiHoc '00 Proceedings of the 1st ACM international symposium on Mobile ad hoc networking & computing
Dynamic information retrieval optimization using mobile agents
AAMAS '03 Proceedings of the second international joint conference on Autonomous agents and multiagent systems
Using Cooperative Mediation to Solve Distributed Constraint Satisfaction Problems
AAMAS '04 Proceedings of the Third International Joint Conference on Autonomous Agents and Multiagent Systems - Volume 1
Service discovery in agent-based pervasive computing environments
Mobile Networks and Applications
Service-based computing for agents on disruption and delay prone networks
Proceedings of the fourth international joint conference on Autonomous agents and multiagent systems
Agent transport simulation for dynamic peer-to-peer networks
MABS'05 Proceedings of the 6th international conference on Multi-Agent-Based Simulation
Service discovery on dynamic peer-to-peer networks using mobile agents
AP2PC'04 Proceedings of the Third international conference on Agents and Peer-to-Peer Computing
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The proliferation of wireless networks has underscored the need for systems capable of coping with sporadic network connectivity. The restriction of communication to neighboring hosts makes determining the global state especially difficult, if not impractical. This paper addresses the problem of coordinating the positions of an arbitrary number of services, encapsulated by mobile agents, in a dynamic peer-to-peer network. The agents' collective goal is to minimize the distance between hosts and services, even if the topology is changing constantly. We propose a distributed algorithm to efficiently calculate the stationary distribution of the network. This can be used as a hill climbing heuristic for agents to find near-optimal locations at which to provide services. Finally, we show that the agent-based hill climbing approach is temporally-stable relative to the instantaneous optimum.