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We formulate the wireless security problem as an indirect reciprocity game, and propose a security mechanism that applies the indirect reciprocity principle to suppress attacks in wireless networks. In this system, a large number of nodes cooperate to reject the network access requests from attackers during the punishment periods. If the punishment time is so long that the cost due to the loss of network services exceeds the illegal security gains of the attack, rational nodes do not have incentive to attack, and hence our system can reduce the attacking probability in the network. We develop a social norm and reputation updating process to build such an indirect reciprocity mechanism for the network. We evaluate the evolutionarily stable strategy (ESS) in the game, and provide the optimal action strategy and its corresponding stationary reputation distribution. Our system is robust against collusion attacks, and can significantly reduce the attacking rate for a wide range of attacks. Simulation results show that our system has much better security performance than the direct reciprocity mechanism, especially in the large-scale wireless network with terminal mobility. Our system can be applied to many wireless networks including cognitive radio networks to improve their security performance.

The rising cost of energy and increased environmental awareness have sparked a keen interest in the development and deployment of energy-efficient communication technologies. The energy efficiency of cellular networks can be increased significantly by selectively switching off some of the base stations (BSs) during periods of low traffic load. In this paper, we propose a scalable BS switching strategy and use cooperative communications and power control to extend network coverage to the service areas of the switched-off BSs. The outage probability and the achievable power savings of the proposed scheme are analyzed, both analytically and numerically, and a potential of up to 50% power saving is observed in the numerical results.

While demand response has achieved promising results on making the power grid more efficient and reliable, the additional dynamics and flexibility brought by demand response also increase the uncertainty and complexity of the centralized load forecast. In this paper, we propose a game theoretic demand response scheme that can transform the traditional centralized load prediction structure into a distributed load prediction system by the participation of customers. Moreover, since customers are generally rational and thus naturally selfish, they may cheat if cheating can improve their payoff. Therefore, enforcing truth-telling is crucial. We prove analytically and demonstrate with simulations that the proposed game theoretic scheme is cheat-proof, i.e., all customers are motivated to report and consume their true optimal demands and any deviation will lead to a utility loss. We also prove theoretically that the proposed demand response scheme can lead to the solution that maximizes social welfare and is proportionally fair in terms of utility function. Moreover, we propose a simple dynamic pricing algorithm for the power substation to control the total demand of all customers to meet the target demand curve. Finally, simulations are shown to demonstrate the efficiency and effectiveness of the proposed game theoretic algorithm.