Hedonic Coalition Formation for Distributed Task Allocation among Wireless Agents

Publication Type:

Journal Article

Source:

IEEE Transactions on Mobile Computing, Volume 10, Number 9, p.1327-1344 (2011)

Keywords:

ad hoc networks, game theory, hedonic coalitions, multiagent systems, task allocation, wireless networks

Abstract:

Autonomous wireless agents such as unmanned aerial vehicles, mobile base stations, or self-operating wireless nodes present a great potential for deployment in next-generation wireless networks. While current literature has been mainly focused on the use of agents within robotics or software engineering applications, we propose a novel usage model for self-organizing agents suited to wireless networks. In the proposed model, a number of agents are required to collect data from several arbitrarily located tasks. Each task represents a queue of packets that require collection and subsequent wireless transmission by the agents to a central receiver. The problem is modeled as a hedonic coalition formation game between the agents and the tasks that interact in order to form disjoint coalitions. Each formed coalition is modeled as a polling system consisting of a number of agents which move between the different tasks present in the coalition, collect and transmit the packets. Within each coalition, some agents can also take the role of a relay for improving the packet success rate of the transmission. The proposed algorithm allows the tasks and the agents to take distributed decisions to join or leave a coalition, based on the achieved benefit in terms of effective throughput, and the cost in terms of delay. As a result of these decisions, the agents and tasks structure themselves into independent disjoint coalitions which constitute a Nash-stable network partition. Moreover, the proposed algorithm allows the agents and tasks to adapt the topology to environmental changes such as the arrival/removal of tasks or the mobility of the tasks. Simulation results show how the proposed algorithm allows the agents and tasks to self-organize into independent coalitions, while improving the performance, in terms of average player (agent or task) payoff, of at least 30:26% (for a network of 5 agents with up to 25 tasks) relatively to a scheme that allocates nearby tasks equally among agents.

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