ECOR: An Energy Aware Coded Opportunistic Routing for Cognitive Radio Social Internet of Things
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In recent years, the social internet of things (SIoT) has become a research hot topic in the field of wireless networks, which are inseparable relationships between human and devices for internet of things. As a huge numbers of mobile devices will be connected, it needs more frequency spectrum. The Cognitive radio (CR) technology can improve spectrum utilization in an opportunistic communication manner for SIoT, which is called CR-SIoT. However, dynamic spectrum availability and mobile devices make it more difficult for routing design in CR-SIoT. Opportunistic routing (OR) can mitigate drawbacks from CR-SIoT, which leverages the broadcast nature of wireless channels, and then can enhance network performance. In this work, we propose an energy aware coded OR in CR-SIoT from a different types of flows perspective, which jointly considers energy efficiency and social feature for designing coded OR. In the proposed scheme, we exploit a new routing metric and an auction model for selecting forwarding candidates and use network coding for the data transmission between selected nodes in CR-SIoT. In addition, we prove the candidate selection problem is NP-hard and propose a game-theoretic approach to allocate channels which is based on interference graph. Extensive simulation results show that the proposed coded opportunistic routing performs better compared with existing routing schemes in terms of packet delivery ratio, delay and hop count.
KeywordsSocial internet of things Cognitive radio Opportunistic routing
This work was supported by the National Natural Science Foundation of China (Grant Nos. 61802221, 61802220, 61602125), the Natural Science Foundation of Guangxi Province under grant 2017GXNSFAA198192, the Innovation Project of Guangxi Graduate Education under grant YCSW2019141, and the Key Research and Development Program for Guangdong Province 2019B010136001, the Peng Cheng Laboratory Project of Guangdong Province PCL2018KP005 and PCL2018KP004. We would like to acknowledge the editor and the reviewers whose comments and suggestions significantly improved this paper.
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