Cognitive Radio-Based Smart Grid Traffic Scheduling With Binary Exponential Backoff

Abstract

This paper develops the traffic models of smart grid electronic data ( ${E}$ data) and multimedia video over cognitive radio (CR). Unlike the traditional “Poisson” arrival model, each arrival monitoring stream follows fixed time triggered Gaussian distribution which approximates to the reality, and the video data is classified as key frame data with higher priority than nonkey frame data to reduce communication burden. To enhance the delivery probability of ${E}$ data and multimedia data, we adopt a buffer mechanism to store the “sending fail” data and try to resend them together with new coming data by using the new data’s sending opportunity. To avoid buffer overflow, the unsent data should be compressed and some should be removed, and the new coming data rate should be reduced to alleviate the congestion of the CR communication network. In this paper, we propose a new binary exponential backoff (NBEB) algorithm to “compress” the unsent data which can keep key information but recover the electronic tendency as much as possible. With NBEB, the new coming data can be temporally selected and thrown into the buffer and more new data can be put in the buffer. The algorithm can reduce the arrival traffic rate exponentially related with the sending failure times. The results show that NBEB can significantly decrease the blocking/dropping probability, increase the communication success probability, and improve the communication performance.