Adaptive Small Cell position algorithm (ASPA) for green farming using NB-IoT

摘要

5G-NB-oT, cellular assisted low power wide area loT technology, has become an emerging technology for smart farming applications. As NB-loT offers a significant link budget improvement by utilizing repeated transmissions and varies the repetition order based on the path loss in communications. Hence can cater to the farming geographical requirements effectively. However, these repetitions inevitably increase the energy consumption of the whole NB-loT system (including BS and sensor networks). Therefore, in this paper, the energy efficiency maximization problem focused on improving NB-loT DL performance using a small cell access point (SCA) was studied. For this two SCA positioning algorithms were considered i.e. edge-based uniform SCA positioning algorithm (EUSA) & proposed adaptive SCA positioning algorithm (ASPA). Where EUSA deploys SCA's irrespective of device density and ASPA deploys the SCA optimally based on the density of the device and throughput requirement. The simulation results reveal that ASPA significantly increased the energy efficiency of NB-oT enabled sensor network by 39.8% and EUSA improved the energy efficiency by 34.3% in comparison of reference model (i.e. with BS only). However, in comparison to EUSA and D2D approach, ASPA improved energy efficiency by 5% and 20.1% respectively by utilizing significantly less number of SCA's. Furthermore, ASPA has a constructive impact on sensor battery life too, it augments the battery life by two days. In addition to this HMM training model was also used that reduced BS transmission power consumption by 25mw. The proposed model has promising potential in communication. terms of saving infrastructure requirements and ensuring green farming communication.y