HEED: A Hybrid, Energy-Efficient, Distributed Clustering Approach for Ad Hoc Sensor Networks
IEEE Transactions on Mobile Computing
Regret based dynamics: convergence in weakly acyclic games
Proceedings of the 6th international joint conference on Autonomous agents and multiagent systems
Markov Chains and Stochastic Stability
Markov Chains and Stochastic Stability
Payoff-Based Dynamics for Multiplayer Weakly Acyclic Games
SIAM Journal on Control and Optimization
Cooperative control and potential games
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Coalition Formation Games for Distributed Cooperation Among Roadside Units in Vehicular Networks
IEEE Journal on Selected Areas in Communications
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This paper presents a distributed algorithmic solution, termed Coalition formation and deployment algorithm, to achieve network configurations where agents cluster into coincident groups that are distributed optimally over the environment. The motivation for this problem comes from spatial estimation tasks executed with unreliable sensors. We propose a probabilistic strategy that combines a repeated game governing the formation of coalitions with a spatial motion component governing their location. For a class of probabilistic coalition switching laws, we establish the convergence of the agents to coincident groups of a desired size in finite time and the asymptotic convergence of the overall network to the optimal deployment, both with probability 1. We also investigate the algorithm's time and communication complexity. Specifically, we upper bound the expected completion time of executions that use the proportional-to-number-of-unmatched-agentscoalition switching law under arbitrary and complete communication topologies. We also upper bound the number of messages required per timestep to execute our strategy. The proposed algorithm is robust to agent addition and subtraction. From a coalitional game perspective, the algorithm is novel in that the players' information is limited to the neighboring clusters. From a motion coordination perspective, the algorithm is novel because it brings together the basic tasks of rendezvous (individual agents into clusters) and deployment (clusters in the environment). Simulations illustrate the correctness, robustness, and complexity results.