Our client is the owner of a broad-coverage low power IoT sensor network. They own a high number of IoT gateways that their customers can use to connect sensors to the internet. In order to further expand their coverage and bandwidth availability, they needed a planning and measuring tool.
Using the real-time MQTT data, Data Kernel first analyzed their current network utilization. The MQTT feed contained geotemporal information that we used for visualizing field strengths across the city.
Then, we used this real-time data stream to start simulating hypothetical scenarios, such as adding new gateways to the network. We used a Monte Carlo simulation to estimate the impact of adding more subscribers to the network.
Furthermore, we created a predictive model that estimates operating cost with rising network demand.
The client was able to reliably estimate the cost impact of growing their network. Real-time feedback allows them to monitor and expand gateway coverage without any guesswork necessary. The client was able to surpass the expected amount of bandwidth available. This meant more customer value for the same operating cost.