Performance of Slotted ALOHA for LoRa-ESL Based on Adaptive Backoff and Intra Slicing

Abstract

Slotted ALOHA implemented in the internet of things (IoT) uses the LoRaWAN media access control (MAC) protocol to improve its performance. Various backoff algorithms have been proposed in LoRaWAN to evaluate the delay, throughput, and packet loss rate (PLR). However, an adaptive backoff algorithm has been implemented in this paper to examine the performance metrics for electronic shelf labels (ESLs). The use of adaptive backoff optimizes the delay and the bandwidth (BW) for more efficient and meaningful communication with a certain degree of data loss. The results illustrate that the intra-slicing model and adaptive backoff estimate the optimal delay for each slice, starting with the best slicing priority for the end device (ED) which brings mobility into the network.