Clustering Optimization of LoRa Networks for Perturbed Ultra-Dense IoT Networks

Long Range (LoRa) communication is widely adapted in long-range Internet of Things (IoT) applications. LoRa one the powerful technologies Low Power Wide Area Networking (LPWAN) standards designed for IoT Enormous applications lead to massive traffic results, which affect entire network’s operation by decreasing quality service (QoS) and minimizing throughput capacity network. To this end, paper proposes a novel cluster model distribution function estimate expected value capacity. This develops two main clustering algorithms using dense LoRa-based networks that allow end devices according criterion maximum served traffic. The are built based on two-common methods, K-means FOREL. In contrast existing developed method provides cluster. Results reveal our proposed obtained higher average normal than uniform distribution.