Making greed work in networks: a game-theoretic analysis of switch service disciplines
IEEE/ACM Transactions on Networking (TON)
Mitigating routing misbehavior in mobile ad hoc networks
MobiCom '00 Proceedings of the 6th annual international conference on Mobile computing and networking
Algorithms, games, and the internet
STOC '01 Proceedings of the thirty-third annual ACM symposium on Theory of computing
Handbook of wireless networks and mobile computing
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
A game-theoretic approach towards congestion control in communication networks
ACM SIGCOMM Computer Communication Review
Selfish routing
Coping with inaccurate reputation sources: experimental analysis of a probabilistic trust model
Proceedings of the fourth international joint conference on Autonomous agents and multiagent systems
A Bayesian framework for online reputation systems
AICT-ICIW '06 Proceedings of the Advanced Int'l Conference on Telecommunications and Int'l Conference on Internet and Web Applications and Services
Non-cooperative forwarding in ad-hoc networks
NETWORKING'05 Proceedings of the 4th IFIP-TC6 international conference on Networking Technologies, Services, and Protocols; Performance of Computer and Communication Networks; Mobile and Wireless Communication Systems
Bayesian-based game theoretic model to guarantee cooperativeness in hybrid RF/FSO mesh networks
GLOBECOM'09 Proceedings of the 28th IEEE conference on Global telecommunications
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In static networks, game theory has long been used to model the routing decisions of network nodes. However, once we move to dynamic and resource constrained settings, such as ad hoc or sensor networks, traditional models are no longer sufficient. Instead, new models that capture the dynamic nature of the decisions and the resource constraints of the devices are needed. To date, several models that attempt to capture the dynamic nature of routing decisions have been proposed. However, the resource constraints of the devices and the uncertainty about the resources of other devices have been thus far ignored. To this end, we present a game theoretic model that formalizes the resources of the nodes and the beliefs the nodes have about the resources of other nodes. We also discuss the structure of strategies in the proposed model and make explicit the role the resources and beliefs of the nodes play in routing decisions. In addition to presenting a game theoretic model, we propose a method that allows the nodes to learn equilibrium strategies over time, and prove that the strategies suggested by the mechanism converge to a sequential equilibrium. Finally, we present simulations that give insights into the expected behavior of the devices under the proposed model.