Achieving Processing Balance in LoRaWAN Using Multiple Edge Gateways

Abstract

The Internet of Things (IoT) has emerged as a revolutionary force, with its devices and applications being widely adopted across various sectors. The exponential growth of IoT devices is projected to generate a huge volume of data, commonly referred to as IoT big data. To handle this data, analysis across the Cloud-Edge Computing Continuum becomes necessary. At the same time, LoRaWAN (Long-Range Wide Area Network) technology has emerged as a solution for efficient communication between a large number of IoT devices over long distances with minimal energy consumption. Unfortunately, it presents a strong centralized architecture where processing across the edge is not allowed. However, the integration of edge computing has become crucial in reducing network traffic and enabling real-time processing and response. This paper proposes the integration of a processing module into a LoRaWAN network using the principles of edge computing. Our contribution, Edge4LoRa, incorporates a distinct computing module capable of processing data streams at the network edge. The module utilizes a Map/Reduce engine based on Apache Spark, enabling the execution of various processing applications, including anomaly detection and data reduction techniques. Additionally, Edge4LoRa enables traffic to move across LoRaWAN gateways, we face the nature of the IoT data traffic mining and mobility of the source devices. The proposed architecture ensures modularity, reliability, scalability, and robustness. We evaluated its effectiveness under different configuration settings of the testbed environment. The evaluation is conducted using a hardware setup in our laboratory and we assess the performance of the architecture in three scenarios: data reduction, scaling activation of edge gateways, and mobility-aware scenarios.