BIG IoT - Interconnecting IoT Platforms from Different Domains - Final Results

Jell, Thomas; Baumgartner, Claudia; Bröring, Arne; Mitić, Jelena

doi:10.1007/978-3-030-25629-6_124

Cited by 1 publication

(1 citation statement)

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“…The data flow in the architecture is designed and developed to be a demonstration use case of the BIG IoT project [21], where data and services are offered, using a common BIG IoT API. The goal of the BIG IoT project is to enable interoperability among IoT platforms, services, and applications by streamlining interfaces.…”

Section: Wlan Probe Collection Systemmentioning

confidence: 99%

Comparison of WLAN Probe and Light Sensor-Based Estimators of Bus Occupancy Using Live Deployment Data

Mikkelsen

Schwefel

Madsen³

et al. 2022

Sensors

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Bus company operators are interested in obtaining knowledge about the number of passengers on their buses—preferably doing so at low deployment costs and in an automated manner, while keeping accuracy high. One solution, widely used in practice, involves deploying a light sensor-based system, counting the people entering and leaving the bus. The light sensor system is simple, but errors accumulate over time, because it is not capable of error correcting. For this reason, the light sensor-based system is compared to a WLAN probe-based system, which has entirely different characteristics. Inaccuracy with the WLAN estimator comes from a need to filter out mobile devices outside the bus and to map the number of detected devices to a number of people. The comparison is performed based on data collected from a real-life deployment in a medium sized German city. The comparison shows the trade-off in selecting either of the two methods. Furthermore, a novel approach for fusion of the light sensor and WLAN estimators is proposed which has a big potential in improving accuracy of both estimators. A fusion approach is proposed that utilizes the different error characteristics for error compensation by calculating compensation terms. The knowledge of Ground Truth is not required as part of this fusion approach for calibration; results show that the approach can find the optimal parameter settings and that it makes this occupancy estimation approach scalable and automated.

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Section: Wlan Probe Collection Systemmentioning

confidence: 99%