Learning automaton based distributed caching for mobile social networks
Chuan Ma, Zihuai Lin, Loris Marini, Jun Li, Branka S. Vucetic · 2016
In this paper, a novel distributed caching strategy in mobile social networks based on device-to-device communications is proposed. The proposed approach combines the characters of social networks to handle some practical issues, e.g., the selfishness of users. In order to maximize the throughput of the whole system, a fast convergence learning automaton, called the discrete generalized pursuit algorithm is utilized. Incorporating with social characters, the algorithm not only optimizes the content placement problems in caching theory, but also satisfies the physical and social constraints appropriately. Simulation results show that, compared with other investigated caching strategies, the proposed algorithm has higher convergence speed and at the same time, it can reduce the transmission delay and improve the system throughput. Moreover, the proposed algorithm can get a better performance in higher density district.