Interference Sources Localization and Communication Relationship Inference With Cognitive Radio IoT Networks

Abstract

With the widespread application of Internet of things (IoT), the interference problem becomes more and more serious, which results in not only the poor network performance but also the increased energy consumption of IoT nodes. Therefore, in this paper, we investigate the problem of how to locate the interference sources and infer their communication relationships in the cognitive radio IoT (CR-IoT) deployment scenario. First, we utilize MDS-MAP(P) algorithm with dynamic power control to realize cooperative self-localization of the CR-IoT nodes, which is more energy-efficient than all the IoT nodes equipped with global positioning system (GPS) receivers. Then, we propose a non-cooperative localization method to determine the inference sources with the angle of arrivals (AoAs) measured by the CR-IoT nodes. Finally, the communication relationship between interference sources can be inferred based on the association rule of signals. The network simulation results validate that the proposed methods can locate the interference sources accurately with low energy consumption and correctly infer their communication relationships, which is helpful for the interrupted CR-IoT nodes to take a specific opportunistic transmission policy to reduce their energy consumption. INDEX TERMS Internet of Things (IoT), green, localization, communication relationship inference.

Comparative Analysis of Diversity Combining Schemes in Power Limited Cognitive Radio under the impact of Co-Channel Interference

“…The implementation of CR systems becomes challenging in order to mitigate the CCI among competing CR users. With the widespread application of IoTs, the interference caused by numerous connected devices or 'things', increases [12].…”