Trust Aware Opportunistic Data Transmission for Cognitive IoT: An Edge Learning Perspective

Abstract

Abstract As the rapid development of communication technology, more and more smart devices will be connected in Industrial Internet of Things (IIoT) using an opportunistic manner, in which will bring spectrum scarcity with a false positive. The fifth generation (5G) cognitive radio (CR) network is a promising way to enhance the performance of IIoT, called cognitive IIoT (CIIoT), providing a better quality of user experience. However, the dynamicity and heterogeneity of spectrum will make it more difficult in data transmission strategy design for unreliable IIoT. Opportunistic routing exploits the broadcast property of wireless medium to improve network performance in an opportunistic communication manner. Inspired by this, in this paper, we propose a Nash-learning and Transfer Learning based Opportunistic Data Transmission scheme, NTLODT, considering efficiency and reliability. In NTLODT, a new learning based opportunistic data transmission for differentiated services CIIoT was presented, which exploits Nash-learning for candidate set optimization and transfer learning for data transmission decision from a trust mechanism perspective. In addition, we prove that the convergence and complexity of the proposed algorithm. The simulation results demonstrate that the effectiveness of NTLODT in terms of average energy cost per bit, average delay, expected cost of routing and throughput.