Abstract
The Narrowband Internet of Things (NB-IoT) is a very promising licensed Internet of things (IoT) technology for accommodating massive device connections in 5G and beyond. To enable network scalability, this study proposes a two-layers novel mixed approach that aims not only to create an efficient spectrum sharing among the many NB-IoT devices but also provides an energy-efficient network. On one layer, the approach uses an Adaptive Frequency Hopping Spread Spectrum (AFHSS) technique that uses a lightweight and secure pseudo-random sequence to exploit the channel diversity, to mitigate inter-link and cross-technology interference. On the second layer, the approach consists of a clustering and network coding (data aggregation) approach based on an energy-signal strength mixed gradient. The second layer contributes to offload the BS, allows for energy-efficient network scalability, helps balance the energy consumption of the network, and enhances the overall network lifetime. The proposed mixed strategy algorithm is modelled and simulated using the Matrix Laboratory (MATLAB) Long Term Evolution (LTE) toolbox. The obtained results reveal that the proposed mixed approach enhances network scalability while improving energy efficiency, transmission reliability, and network lifetime when compared to the existing spread spectrum only, nodes clustering only, and mixed approach with no network coding approaches.
Highlights
The Internet of Things (IoT) has changed the way we view and use technology
The performance evaluation of the proposed N-MANC approach versus the N-Adaptive Frequency Hopping Spread Spectrum (AFHSS), the N-Low-Energy Adaptive Clustering Hierarchy (LEACH) and the NB-IoT Mixed approach with no network coding (N-MANNC) is performed under the network considerations as summarized in
After establishing through literature survey that this enhanced scalability of the Narrowband Internet of Things (NB-IoT) network is a cross-layers effort, the present research work has proposed a mixed approach which at the Physical layer (PHY) proposes an adaptive frequency hopping spread spectrum technique using a lightweight pseudo-random sequence generator coupled with the clustering and network coding techniques at the network layer
Summary
The Internet of Things (IoT) has changed the way we view and use technology. As consumers, we have come to expect connectivity and access to information wherever we go. The above three reasons coupled with the fact that most NB-IoT technology designs, as currently proposed, mainly use simple ALOHA or CSMA based MAC protocols which do not scale well with a large number of connected devices; have been considered by the present study as a trigger for a need for a novel spread spectrum and clustering approach for the NB-IoT. This is expected to enhance NB-IoT system designs as a way to enable network scalability, data rate enhancement, and maintain energy efficiency and network reliability.
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