Abstract
Systems within IoT domains such as ITS, Smart City, Smart Grid and other, often rely on real-time information and communication. These types of systems often include geographically distributed nodes which are connected via cellular or other wireless networks. This means great variability and uncertainty in network connection performance, effectively increasing the expected minimum system response time. Having information about network connection performance means that it is possible to predict the performance of the system in terms of sensor access delay or application response time. We obtain the performance information, in terms of signal strength and transport layer round trip time, using crowd sourcing and consumer devices which causes the measurements to be heterogeneously distributed. From these measurements we want to create a network performance map but in areas with sparse measurements the reliability of the map values will be low. To solve this problem we include neighboring measurements and evaluate the impact of doing so. We show that generally there is a benefit from including neighboring measurements, and that transport layer round trip times are less sensitive to bias when increasing the size of the extended area to include measurements from.
Highlights
Internet of Things (IoT) is rapidly being developed and starting to being deployed
We describe our approach to evaluate the impact on the map values when interpolating measurements to sparse measurement cells
In the previous sections we evaluated the impact on the mean estimate from cells with sparse measurements when including neighboring measurements, by comparing to ground truth (GT) mean of the cell
Summary
Internet of Things (IoT) is rapidly being developed and starting to being deployed. More and more IoT devices, systems and services are emerging [8]. Air quality, number of users, device state and other This information can both be used as historical or live information, i.e. from a database with previous values or directly from the sensor as the source of the information. In this work we use crowd sourcing to measure the connection performance from the end user devices, realized using the NetMap system [6]. The measurements are influenced from factors such as signal disturbances and interferences, network load, device load, different device and antenna characteristics, different networks and network technologies, etc., all of which cause measurement values to vary This has been explored and documented in works such as [12] that shows that movement highly influences the measured connection performance, while [5] studies the impact of.
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