Optimization of Large-scaled Random Access Congestion Control Oriented to Narrow Band Internet of Things

Abstract

Narrow band internet of things (NB-IoT), as an important low-power wide-area coverage of 5G with independent intellectual property rights, has great potential in small data monitoring of urban power transmission and distribution. However, it is extremely easy to cause congestion and overloading of the system upon large-scaled terminal requests for random access, which will decrease network access performance and bring great challenges to promote NB-IoT in urban power Internet of Things. On this basis, a mathematical model of network load was constructed to estimate and predict load changes. Then, a dynamic adjustment functional model was set up according to dynamic adjustment of access parameter optimization based on loads. Based on above idea, a random access algorithm based on dynamic parameter adjustment in accordance to dynamic priority was developed. It increases the success rate of devices access to NB-IoT system, decreases access delay of devices, and relieves network congestion. According to simulation results, the proposed algorithm has higher access success rate, lower total delay. And it guarantees delay for important node users and more optimized network performances compared with traditional network access algorithms.