Mobile Cooperative Sensing Based Secure Communication Strategy of Edge Computational Networks for Smart Cities

With the development of smart cities, lots of mobile cooperative sensing based nodes have emerged. However, due to the open nature of wireless transmission, attackers in the networks can use some intelligent radio devices to deteriorate the secure transmission, which imposes a severe issue of information leakage. In this paper, we consider the transmitter has some computational tasks to be computed, under the environments of intelligent attacker. Due to the limited computational capability, the sender needs to offload some tasks to the receiver. To address this problem, we propose a power allocation algorithm based on combining the technology of reinforcement learning and game theory, in order to achieve an optimal secure data rate and meanwhile reduce the whole task latency of the transmission and computation with Q learning and Nash equilibrium. Then, the Nash equilibrium and its existence conditions are derived and proven mathematically. Finally, we perform some simulations under Matlab platform, and the results show that the proposed algorithm can effectively improve the secrecy data rate and reduce the whole system latency.