LoRa Meets Artificial Intelligence to Optimize Indoor Networks for Static EDs

Abstract

The architectural design of the Indoor Internet of Things (IIoT) network targeting static end devices (EDs) and gateways (GWs) has been innovatively formulated in this paper, integrating LoRa technology to mitigate losses and ensure seamless information reception through meticulous ED allocation. The arrangement of simultaneously transmitted data within the network server (NS) employs a deep neural network (DNN) with distributed machine learning (DML) to adjust transmission parameters, ensuring frequent uninterrupted bidirectional communication. This augmentation is obtained by strategically deploying EDs within distinct clusters determined by K-means and density-based spatial clustering with noise (DBSCAN), thus optimizing spreading factor (SF) and data rate (DR) allocation to prevent data congestion and improve signal-to-interference noise ratio (SINR). The proposed hybrid model (DR|SF) for pure and slotted ALOHA amplifies the network's performance metrics for indoor scenarios. A unified framework utilizing a one-slope model estimates path losses (PL) while exploring various bandwidths (BW), bidirectional interrogations, and duty cycles (DC) to lower the saturation and prolong the active lifespan of the EDs. The results manifest a packet rejection rate (PRR) of 0% for the DBSCAN, contrasting a 4.7% estimate for the K-means. The network saturation is minimized to 9.5% and 10.1%, correspondingly, significantly increasing the efficiency of slotted ALOHA (91%) and pure ALOHA (90.6%), thereby prolonging the longevity of EDs.