Multi-Armed Bandit Channel Access Scheme With Cognitive Radio Technology in Wireless Sensor Networks for the Internet of Things

Abstract

The wireless sensor network (WSN) is one of the key enablers for the Internet of Things (IoT), where WSNs will play an important role in future internet by several application scenarios, such as healthcare, agriculture, environment monitoring, and smart metering. However, today's radio spectrum is very crowded for the rapid increasing popularities of various wireless applications. Hence, WSN utilizing the advantages of cognitive radio technology, namely, cognitive radio-based WSN (CR-WSN), is a promising solution for spectrum scarcity problem of IoT applications. A major challenge in CR-WSN is utilizing spectrum more efficiently. Therefore, a novel channel access scheme is proposed for the problem that how to access the multiple channels with the unknown environment information for cognitive users, so as to maximize system throughput. The problem is modeled as I.I.D. multi-armed bandit model with M cognitive users and N arms (M<;N). In order to solve the competition and the fairness between cognitive users of WSNs, a fair channel-grouping scheme is proposed. The proposed scheme divides these channels into M groups according to the water-filling principle based on the learning algorithm UCB-K index, the number of channels not less than one in each group and then allocate channel group for each cognitive user by using distributed learning algorithm fairly. Finally, the experimental results demonstrate that the proposed scheme cannot only effectively solve the problem of collision between the cognitive users, improve the utilization rate of the idle spectrum, and at the same time reflect the fairness of selecting channels between cognitive users.