The Narrowband Internet of Things (NB-IoT) Resources Management Performance State of Art, Challenges, and Opportunities

The Narrowband Internet of Things (NB-IoT) has been introduced in the 3rd Generation Partnership Project (3GPP) Rel-13 with the aim to provide low-cost, low-power, wide-area cellular connectivity for the Internet of Things. With the exponentially increasing number of connected wireless devices in the order of 100 billion, it has become crucial that researchers develop efficient resource management techniques to meet the 5
^th
Generation (5G) quality of service (QoS) requirements. Recently, several research challenges including the low modulation data rates, energy-expensive channel coding techniques, and the fast-growing number of connected devices have been identified as some of the main issues encountered in the design and deployment of NB-IoT systems. In addition, several techniques have emerged in the literature to resolve some of these challenges of NB-IoT systems. However, the research activities towards the enhancement of the NB-IoT resource management are yet to continue in the next half-decade before, high data rates, energy-efficient, and scalable NB-IoT specifications can be released for the standardisation and commercialisation. Considering the limited number of existing surveys of such technical enhancement approaches in a broader perspective (i.e. energy efficiency, data rate performance and scalability) and also the non-existence, to the best of our knowledge, of a comparative survey, this paper seeks to elaborate, describe and compare the performances of such resources management approaches. Of the multiple NB-IoT resources, the focus of this paper is on the data rate, energy efficiency, and scalability enhancement schemes that have been proposed for the last three years. The contribution of the paper lies in the analysis, synthesis, comparison and summarised alignments of some of the major existing schemes towards identifying challenges faced by the NB-IoT development. Finally, this work seeks to identify research challenges, open questions, and opportunities that could arise from the existing techniques analysed.