The proliferation of the Internet of Things (IoT) demands a diverse and wide range of requirements in terms of latency, reliability, energy efficiency, etc. Future IoT systems must have the ability to deal with the challenging requirements of both users and applications. Cognitive 5G network is envisioned to play a key role in leveraging the performance of IoT systems. IoT systems in cognitive 5G network are expected to provide flexible delivery of broad services and robust operations under highly dynamic conditions. In this paper, we present multi-band cooperative spectrum sensing and resource allocation framework for IoT in cognitive 5G networks. Multi-band approach can significantly reduce energy consumption for spectrum sensing compared to the traditional single-band scheme. We formulate an optimization problem to determine a minimum number of channels to be sensed by each IoT node in multi-band approach to minimize the energy consumption for spectrum sensing while satisfying probabilities of detection and false alarm requirements. We then propose a cross-layer reconfiguration scheme (CLRS) for dynamic resource allocation in IoT applications with different quality of service (QoS) requirements including data rate, latency, reliability, economic price, and environment cost. The potential game is employed for cross-layer reconfiguration, in which IoT nodes are considered as the players. The proposed CLRS efficiently allocate resources to satisfy QoS requirements through opportunistic spectrum access. Finally, extensive simulation results are presented to demonstrate the benefits offered by the proposed framework for IoT systems.

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Keywords 5G mobile communication, Cognitive 5G networks, Energy consumption, Internet of Things, Long Term Evolution, multi-band spectrum sensing, Quality of service, Reliability, resource allocation., Resource management, Sensors
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Journal IEEE Internet of Things Journal
Ejaz, W. (Waleed), & Ibnkahla, M. (2017). Multi-band Spectrum Sensing and Resource Allocation for IoT in Cognitive 5G Networks. IEEE Internet of Things Journal. doi:10.1109/JIOT.2017.2775959