Madsen, Tatiana Kozlova

Mikkelsen, Lars Møller

Schwefel, Hans-Peter

English

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.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

On the Benefits and Challenges of Crowd-Sourced Network Performance Measurements for IoT Scenarios

https://vbn.aau.dk/ws/files/329631928/Mikkelsen2020_Article_OnTheBenefitsAndChallengesOfCr.pdf

On the Benefits and Challenges of Crowd-Sourced Network Performance Measurements for IoT Scenarios

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