Optimal LoRaWAN Gateway Placement for Efficient Large-Scale IoT Deployments

Abstract

LoRaWAN (Long-Range Wide Area Network) is a key protocol for large-scale IoT applications, but optimizing the placement of gateways remains a significant challenge, balancing network coverage, resource efficiency, and computational cost. The placement of the gateway directly impacts the performance, scalability, and reliability of the network, which makes it essential for efficient IoT network design. Although clustering algorithms show promise in optimizing gateway placement, each face limitations related to scalability, computational demands, and adaptability to changing network conditions. This paper evaluates three clustering algorithms, that is, K-Means, MeanShift, and DBSCAN, to optimize the placement of the LoRaWAN gateway. We examine their effects on network coverage, gateway usage, and computational efficiency in networks of varying sizes. Our results demonstrate that DBSCAN is highly scalable and efficient but struggles with coverage optimization. MeanShift offers the best coverage, but is computationally expensive, while K-Means provides a balanced solution for medium-sized networks.