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We study the dynamics of network selection in heterogeneous wireless networks based on client-side control. Clients in such networks selfishly select the best radio access technology (RAT) that maximizes their own throughputs. We study two general classes of throughput models that capture the basic properties of random access (e.g., Wi-Fi) and scheduled access (e.g., WiMAX, LTE, and 3G) networks. Formulating the problem as a non-cooperative game, we study its existence of equilibria, convergence time, efficiency, and practicality. Our results reveal that: 1) single-class RAT selection games converge to Nash equilibria, while an improvement path can be repeated infinitely with a mixture of classes; 2) we provide tight bounds on the convergence time of these games; 3) we analyze the Pareto-efficiency of the Nash equilibria of these games, deriving the conditions under which Nash equilibria are Pareto-optimal, and quantifying the distance of equilibria with respect to the set of Pareto-dominant points when the conditions are not satisfied; and 4) with extensive measurement-driven simulations, we show that RAT selection games converge to Nash equilibria in a small number of steps, and are amenable to practical implementation. We also investigate the impact of noisy throughput estimates, and propose solutions to handle them. [ABSTRACT FROM PUBLISHER]

Cognitive Radio (CR) is an adaptive, intelligent radio technology that can automatically detects available channels in a wireless spectrum and change transmission parameters to allow more concurrent wireless communications in a given spectrum band at a location. Physical layer attack is a common attack in these networks which can degrade the performance of cognitive radio networks severely. In this attack, the attacker mimics the signals of the Primary Users (PU) to mislead the cognitive users about the existence of the primary users. Two physical layer attack approaches are studied in this paper. First, we have considered the case that the cognitive users are united against the attacker to maximize the network total throughput. Second, we have studied the case that the cognitive users are selfish but have a common attacker. In both cases, we have assumed that the cognitive network employs carrier sense multiple access with collision avoidance (CSMA/CA) to prevent collisions. We use the game theory approach to model the optimization problem and then try to solve it. Some simulations are conducted to evaluate the cognitive network performances. [ABSTRACT FROM AUTHOR]