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Top1. Introduction
The overwhelming demand of wireless applications as well as new devices coming to the market from different vendors are raising more challenges for the existing networks. Internet-of-Things (IoT) is emerging as a promising future 5G paradigm, which aims to enable communication between the physical things or objects and the digital world. It is a global network of heterogeneous things ranging from indoor short range devices to smart cities; equipped with sensing, computing and communication capabilities to connect and exchange data over the existing Internet (Nikoukar et al., 2018). IoT allows Anything to connect with Anything at Any-time and Anyplace through Any available network service (Khan, Rehmani & Rachedi, 2017). Cisco Systems, a worldwide leader in Information Technology (IT) and networking, predicted in 2011 that IoT is expected to connect about 50 billion heterogeneous objects by 2020 (Kawabata et al., 2017). In order to support broad range of diverse applications, IoT is facing challenges with respect to flexible infrastructures, heterogeneity and energy efficiency.
It is endangered that the expected huge deployment of IoT devices will overburden the precious radio spectrum and the static spectrum allocation policy will not be able to meet the demands of future wireless networks (Hu, Chen & Zhu, 2018). Meanwhile in contrast to this belief, recent extensive studies revealed that while certain portions of radio spectrum are over-crowded, significant portions are heavily under-utilized where the spectrum occupancy percentage is below 10% (Chiang, Rowe & Sowerby, 2007). Joseph Mitola’s Cognitive Radio (CR) addresses this unbalanced spectrum utilization issue by allowing dynamic coexistence of the unlicensed or secondary users (SUs) with the licensed or primary users (PUs) in the licensed spectrum bands (Cichon, Kliks & Bogucka, 2016). There are primarily three Dynamic Spectrum Access (DSA) modes to enable such spectrum sharing in CR paradigm, Interweave, Overlay and Underlay (Tanab & Hamouda, 2017). Among these modes, Underlay mode allows the coexistence of primary and secondary users without any prior spectrum sensing performed at the CR network. However, SUs must satisfy the interference constraint towards the PUs in order to keep their communication undisturbed (Das & Mehta, 2017).
In this era of cognitive computing, a combination of CR and IoT answers all the above challenges facing IoT and stands as the potential candidate for autonomous operation of future IoT. The idea is to infuse the cognitive abilities of self-learning and self-adaptation in IoT devices giving rise to a new paradigm, Cognitive-IoT (CIoT) (Ploennigs, Ba & Barry, 2018). Although research in CIoT is still in its infancy, however the prime motivation behind this combination is based on the following reasons (Nitti et. al. 2016). First, the growing number of PUs and the IoT users will generate more bandwidth demand, thus making spectrum allocation even more complicated. Second, CRs provide solid solutions to mitigate mutual interference between SUs and PUs as well as self-interference between SUs. Third, cognitive ability enables mobile IoT devices to achieve smooth spectrum handoff and seamless connectivity. Finally, spectrum sensing capability enables CIoT devices to perform autonomous searching of available storage in the cloud servers for data storage.
The design, management and resource allocation in IoT networks for smooth and uninterrupted communication under different operating conditions and constraints is a hot topic in research communities. Ejaz & Ibnkahla (2017) proposed a cooperative spectrum sensing scheme incorporated with resource allocation for cognitive 5G networks. Lowrance & Lauf (2015) designed a lightweight Fuzzy Logic based controller for IoT-based resource-constrained ad hoc networks. However, IoT devices would be designed for deployment in heterogeneous environments such as factories, free and urban spaces, and restricted areas such as for structural monitoring. The combined effects of diverse operating environments are confined as power loss exponent and it opens up various challenges for CIoT.