DynamIoT - Geomatics Synthesis Project on IoT

Using a dynamic sensor network to obtain spatiotemporal data in an urban environment

Student report (2017)

Authors

L.G. Angelova Architecture and the Built Environment
P.L.M. Flikweert Architecture and the Built Environment
P. Karydakis Architecture and the Built Environment
D. Kersbergen Architecture and the Built Environment
R.F.L. Teeuwen Architecture and the Built Environment
K. Valečkaitė Architecture and the Built Environment

Contributors

E. Verbree (supervisor 1)
B.M. Meijers (supervisor 1)
S.C. van der Spek (supervisor 1)
Faculty
Architecture and the Built Environment
Copyright: © 2017 Lilia Angelova, Puck Flikweert, Panagiotis Karydakis, Daniël Kersbergen, Roos Teeuwen, Kotryna Valečkaitė
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Published Date

23-06-2017

Language

English

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Faculty

Architecture and the Built Environment

Abstract

Along with the rise of the smart city movement, Internet of Things is an upcoming phenomenon. Objects and devices are becoming more and more wirelessly interconnected, communicating information between themselves and to human beings. As an extension on static sensor networks that gather real-time environmental data, the feasibility of implementing a dynamic sensor network based on LoRa
communication is researched. To achieve such a dynamic system, a self-developed sensor platform was constructed, based on the microcontroller LoPy. Sensors attached to it include a hygrometer, thermometer and microphone.
The emphasis of the research was on localisation of the sensors, to put the gathered sensor data into geographical context. A WiFi fingerprinting radiomap was constructed based on available MAC-addresses, their signal strengths, and GPS coordinates. The GPS module was only used for composing the radiomap. When the radiomap is completed, the module can be switched off, only to be switched on for periodical updates of the radiomap. The quality of the radiomap methodology was evaluated by constructing it of measurements gathered in four days, and testing it for the remaining three days. This test gave a correctness of 50% while another 38% of measurements were localised in a neighbouring cell. The correctness can be improved by having a longer training period.
The quality of the collected sensor data turned out to be dependent on the weather conditions and the placement location on the carrier vehicle. Vehicle requirements were specified as driving through the city centre and having a schedule and route producing as little noise, heat and air pollution as possible. Another topic of research was LoRa communication, which was deemed as very limited for dynamic implementations, as the sending of location-related data takes up a large part of the already limited message size. To decrypt the sent message and store it in a meaningful database, Node-RED was used. Despite visualisation of measurements showed promising results, there is margin for improvement as far as data capturing is concerned.