Abstract
Wireless sensor networks have enabled smart infrastructures and novel applications. With the recent roll-out of Narrowband IoT (NB-IoT) cellular radio technology, wireless sensors can be widely deployed for data collection in cities around the world. However, empirical evidence regarding the coverage and connectivity of NB-IoT in dense urban areas is limited. This article presents an empirical study that focuses on evaluating the coverage and connectivity of NB-IoT in a dense urban environment. We have designed an NB-IoT sensor node and deployed over 100 of them in high-rise apartment buildings in Hong Kong. These sensor nodes utilize a commercial NB-IoT network to collect high-resolution water flow data for machine learning model training and provide timely feedback to users. We collect and analyze the empirical NB-IoT signal measurements from the sensor nodes deployed in various challenging outdoor and indoor environments for over three months. These empirical measurements reveal correlations between NB-IoT connectivity and sensor installation environments. We also observe that inter-cell interference, as a result of coverage by multiple neighboring NB-IoT cells in a dense urban environment, is a source of connectivity degradation. We discuss potential issues that IoT application designers and system integrators might encounter in practical NB-IoT devices deployment, and we propose a transmission decision algorithm based on signal measurements for mitigating energy wasted due to transmission failures. Finally, we demonstrate the results and the benefits of using high-resolution water flow data collected by our purpose-built NB-IoT sensor nodes for studying the patterns of domestic water consumption in Hong Kong.
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