Jamming resilient multi-channel transmission for cognitive radio IoT-based medical networks

Abstract

The era of the Internet-of-things (IoT) comes with tremendous burdens on pre-existing network infrastructures and protocols due to spectrum scarcity and reliability concerns. Cognitive radio (CR) technology is proposed for IoT applications to alleviate the spectrum scarcity paradigm. In CR-IoT-based networks, the IoT devices/nodes share the spectrum with primary users (PUs). However, in order not to interfere with PUs communications and to conform with the elevating throughput requirements, efficient multi-radio/multi-channel assignment algorithms are required. Additionally, in order to ensure reliable transmission, algorithms need to be resilient to jamming attacks, which have detrimental impacts on network performance. In this paper, parallel-channel security-aware medium access control (PCS-MAC) is proposed as a probabilistic-based jamming resilient multi-channel assignment algorithm proposed for medical networks. PCS-MAC considers primary user activity, channel conditions, jamming attack levels, and data-rate requirements to provide spectrally efficient data transmission between CR-IoT nodes subject to delay constraints under jamming attacks to assure the delivery of time-critical patient data. The performance of PCS-MAC is practically validated using the open large-scale future Internet-of-things (FIT) IoT-LAB testbed. Practical results show that our proposed algorithm significantly enhances network performance, yielding throughput rates that supersedes the state-of-the-art algorithms presented in literature.