Applications of game theory to mobile ad hoc networks: node spreading potential game
SARNOFF'09 Proceedings of the 32nd international conference on Sarnoff symposium
The price of ignorance: distributed topology control in cognitive networks
IEEE Transactions on Wireless Communications
Efficient node distribution techniques in mobile ad hoc networks using game theory
MILCOM'09 Proceedings of the 28th IEEE conference on Military communications
A combinational perspective in stimulating cooperation in mobile ad hoc networks
Journal of Computer Science and Technology
Energy-efficient topology control in wireless ad hoc networks with selfish nodes
Computer Networks: The International Journal of Computer and Telecommunications Networking
Virtual Game-Based Energy Balanced Topology Control Algorithm for Wireless Sensor Networks
Wireless Personal Communications: An International Journal
Neighbor Selection Game in Wireless Ad Hoc Networks
Wireless Personal Communications: An International Journal
Wireless Personal Communications: An International Journal
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The problem of topology control is to assign per-node transmission power such that the resulting topology is energy-efficient and satisfies certain global properties such as connectivity. The conventional approach to achieve these objectives is based on the fundamental assumption that nodes are socially responsible. We examine the following question: if nodes behave in a selfish manner, how does it impact the overall connectivity and energy consumption in the resulting topologies? We pose the above problem as a non-cooperative game and use game-theoretic analysis to address it. We study Nash equilibrium properties of the topology control game and evaluate the efficiency of the induced topology when nodes employ a greedy best response algorithm. We show that even when the nodes have complete information about the network, the steady state topologies are suboptimal. We propose a modified algorithm based on a better response dynamic and show that this algorithm is guaranteed to converge to energy-efficient and connected topologies. Moreover, the node transmit power levels are more evenly distributed and the network performance is comparable to that obtained from centralized algorithms.