An evolutionary game based security decision algorithm for wireless sensor networks

When using traditional game methods to study information security of the wireless sensor networks,players are mostly based on the assumption of completely rational.Based on bounded rationality,the evolutionary game theory is used to establish the attack-defense model,analyze the strategy selection process of players,solve the evolutionarily stable strategy and design the optimal strategy selection algorithm.Then,considering the strategy dependence,the incentive and punishment mechanism is introduced to improve the replicator dynamic equation.The simulation results show that the model is reasonable and the algorithm is effective,which provides new theoretical support for the security of wireless sensor networks.

The Game Model Analysis between the Individual and the Group

This paper analyses the game model between the individual and the group that has the characteristics of Stackelberg model in traditional game theory and replicator dynamic model in evolutionary game theory. In the first phase of game, the bounded rationality group players adopt the replicator dynamic behavior. Secondly, the full rationality individual player decides the own response function by the strategies distribution of group players. The shortsighted individual player will take the risk-dominant strategy. This model has some unique characteristics.

Markov decision evolutionary game theoretic learning for cooperative sensing of unmanned aerial vehicles

As one of the major contributions of biology to competitive decision making, evolutionary game theory provides a useful tool for studying the evolution of cooperation. To achieve the optimal solution for unmanned aerial vehicles （UAVs） that are car- rying out a sensing task, this paper presents a Markov decision evolutionary game （MDEG） based learning algorithm. Each in- dividual in the algorithm follows a Markov decision strategy to maximize its payoff against the well known Tit-for-Tat strate- gy. Simulation results demonstrate that the MDEG theory based approach effectively improves the collective payoff of the roam. The proposed algorithm can not only obtain the best action sequence but also a sub-optimal Markov policy that is inde- pendent of the game duration. Furthermore, the paper also studies the emergence of cooperation in the evolution of self-regarded UAVs. The results show that it is the adaptive ability of the MDEG based approach as well as the perfect balance between revenge and forgiveness of the Tit-for-Tat strategy that the emergence of cooperation should be attributed to.

IDRMS: an improved dynamic replication management scheme for cloud storage

Cloud storage is getting more and more popular as a new trend of data management. Data replication has been widely used to increase the data availability in cloud storage systems. However,most data replication schemes do not fully consider cost and latency issues when users need large amounts of remote replicas. We present an improved dynamic replication management scheme( IDRMS). By adding a prediction model,the optimal allocation of replicas among the cloud storage nodes is determined that the total communication cost and network delay are minimal. When the local data block is frequently requested,the data replicas can be moved to a closer or cheaper node for cost reduction and increased efficiency. Moreover,we replace the B+tree with the B*tree to speed up the search and reduce workload with the lowest blocking probability. We define the value of popularity to adjust the placement of replicas dynamically. We divide the data nodes in the network into hot nodes and cool nodes. By changing to visit cool nodes instead of hot nodes,we can balance the workload in the network. Finally,we implement IDRMS in Matlab simulation platform and simulation results demonstrate that IDRMS outperforms other replication management schemes in terms of communication cost and load balancing for large-scale cloud storage.