A comprehensive analysis of LoRa/LoRaWAN effectiveness using WaterGrid-Sense in a real system deployment

Abstract

LoRa also known as Long Range is a leading Low Powered Wide Area Network because it operates in unlicensed bands and has attracted widespread research. However, to continually improve this technology and for it to remain attractive, identification of gaps and future directions for LoRa network deployments via comprehensive analysis and evaluations are essential. This is critical to improving its performance in real-deployments in order to realize it for Machine to Machine communications. Existing works lack thorough investigation of LoRa as a new promising technology for Internet of Things deployments. Therefore, this work carried out an investigation of LoRa effectiveness for real world deployments. The constructive research design is employed, which composes of both qualitative and quantitative research methodology. We conducted this study on an operating smart water management system that uses LoRa for its communication among network nodes and on a network environment characterized as harsh. The network employed 34 nodes and the data from the nodes was collected for almost a year and was used to discover insights that could not be possible with short time experiments using fewer nodes or simulation works investigating LoRa. To visualize the data from the nodes and do interpretations, a web based dashboard data visualization tool was developed. The results of data collected and visualized using the developed tool revealed that LoRa was effective and reliable at the initial stage but as time goes on, the network started experiencing difficulties, which in turn affected the reliability of the network and potentially its effectiveness. The findings indicate that although Low Powered Wide Area Network aimed to alleviate network maintenance and costs, overtime this network's demands could be costly. Moreover, large number of nodes start to disconnect from the network due to their nature of link profiles and the harsh environments caused the LoRa adaptive data rates to start using link-demanding parameters, which consequently exhausted the battery life of the devices. The obstacles involved between the links play a bigger role in attenuating the performance of the network. The battery is charged through solar energy as an external energy harvester and we noted that the nodes charged through a solar source should be exposed to sunlight otherwise they end up in a reset state, where they are sometimes offline from the network. Improvements to this network rest on improving the LoRa adaptive data rate to be fair in allocating the network resources for communication. The powering of the network nodes also needs attention to prove that LoRa nodes can last for at least a decade without the need for battery change. Moreover, the visualization tool developed is not limited to this work. This work was successfully carried out and future works were identified.