## 3 Applications of Game Theory in WSN
### Other Uses of Game Theory in WSN
#### 3.5.2 Others  
[PERSON] _et al._[207] introduced the concept of multi-domain sensor networks to a WSN, it contains a game-theoretic model to investigate the impact of cooperation, and shows conditions for which cooperation is the best strategy. [PERSON] and [PERSON] [26] used Gur Game in order to achieve the optimal assignment of active sensors while maximizing the number of regions covered by sensor nodes. They used a dynamic clustering algorithm that employs the concept of connected dominating sets. The proposed algorithm addresses this problem by playing the Gur Game among the cluster nodes. They also further developed the earlier ants algorithm and genetic algorithm to take into consideration node addition and deletion. [PERSON] _et al._[208] formulated two approaches for optimization of computing rates. The first is a team problem for maximizing the minimum communication throughput of sensors, and the second is a game problem in which cost for each sensor is a measure of its communication time with its neighbors. The game formulation not only leads to an explicit characterization of the NE but also to a simple iterative scheme by which sensors can learn the equilibrium attempt probabilities using only the estimates of transmission and reception times from their local measurements. [PERSON] _et al._[209] presented decentralized adaptive filtering algorithms for sensor activation control in an unattended ground sensor network (UGSN) comprised of ZigBee-enabled nodes. [PERSON] and [PERSON] [210] considered finite-size slotted ALOHA sensor networks with multiple packet reception capability and selfish sensors. The problem is formulated as a finite player finite action, non-cooperative stochastic game where each sensor is a selfish but rational player. Instead of the Blackwell approachability method used in previous works, [PERSON] _et al._[211] gave an ordinary differential equation formulation with a Lyapunov function to prove convergence to a correlated equilibrium. [PERSON] _et al._[129] dealt with a scheduling algorithm to fulfill a qualified end-to-end video service in terms of fairness and energy efficiency over multi-hop wireless sensor networks. In [212], on the basis of comprehensive consideration of game theory and the IEEE 802.11 Distributed Coordination Function (DCF), for the characteristics of the WSN, the authors put the channel competitive process between nodes molded into an incompletely information dynamic game. Then improve DCF by the NE game strategy. The simulation results show that the proposed model and strategy can obtain higher efficiency than the 802.11 DCF and improve many performances of the WSN. [PERSON] and [PERSON] [213; 214; 215] described a decentralized game theoretic adaptive algorithm and learning-based activation algorithm for a ZigBee-enabled unattended ground sensor network. [PERSON] and [PERSON] [216] applied GT to achieve collusive networking behavior in WSNs. [PERSON] and [PERSON] [60] proposed, COllective INtelligence (COIN), a macro-learning paradigm that aims to specifically address the problem of designing utility functions for individual agents in order to achieve higher system wide utility. They extended the method of distributed independent reinforcement learning, by combining it with COIN based macro-learning paradigm to steer the system towards global optimization, and improve performance with minimal communication overheads. [PERSON] _et al_. [32] presented a two-encoder scheme which imitates the ping-pong game and has a successive approximation structure. This ping-pong distributed coding idea can be extended to the multiple encoder case and provides the theoretical foundation for a new class of distributed image coding method in wireless scenarios. [PERSON] _et al_. [217; 218; 219] employed the game-theoretic analysis in wireless visual sensor network (WVSN) and wireless sensor actuator networks (WSANs) which are concerned about security.