A LoRa-based Energy-efficient Sensing System for Urban Data Collection
<|reference_start|>A LoRa-based Energy-efficient Sensing System for Urban Data Collection: Nowadays, cities provide much more than shopping opportunities or working spaces. Individual locations such as parks and squares are used as meeting points and local recreation areas by many people. To ensure that they remain attractive in the future, the design of such squares must be regularly adapted to the needs of the public. These utilization trends can be derived using public data collection. The more diverse and rich the data sets are, the easier it is to optimize public space design through data analysis. Traditional data collection methods such as questionnaires, observations, or videos are either labor intensive or cannot guarantee to preserve the individual's privacy. This work presents a privacy-preserving, low-power, and low-cost smart sensing system that is capable of anonymously collecting data about public space utilization by analyzing the occupancy distribution of public seating. To support future urban planning the sensor nodes are capable of monitoring environmental noise, chair utilization, and their position, temperature, and humidity and provide them over a city-wide Long Range Wide Area Network (LoRaWAN). The final sensing system's robust operation is proven in a trial run at two public squares in a city with 16 sensor nodes over a duration of two months. By consuming 33.65 mWh per day with all subsystems enabled, including sitting detection based on a continuous acceleration measurement operating on a robust and simple threshold algorithm, the custom-designed sensor node achieves continuous monitoring during the 2-month trial run. The evaluation of the experimental results clearly shows how the two locations are used, which confirms the practicability of the proposed solution. All data collected during the field trial is publicly available as open data.<|reference_end|>
@article{schulthess2024a,
    title={A LoRa-based Energy-efficient Sensing System for Urban Data Collection},
    author={Lukas Schulthess, Tiago Salzmann, Christian Vogt, Michele Magno},
    journal={arXiv preprint arXiv:2406.06404},
    year={2024},
    doi={10.1109/IWASI58316.2023.10164426},
    archivePrefix={arXiv},
    eprint={2406.06404},
    primaryClass={eess.SY cs.SY}
}