DOI: 10.5281/zenodo.18245140
title: Ubiwhere Smart Lamppost Dataset

description: About the Dataset

This dataset was collected by Ubiwhere from a smart lamp post installed in the city of Aveiro, Portugal. The smart lamp post is equipped with video and sound sensors (camera and microphone) and captures environmental and traffic-related data.

Time period: Data covers from 2025-08-22 13:10:00 to 2025-08-29 12:00:00.

About the Smart Lamppost

This dataset was generated from a Smart Lamppost IoT installation by Ubiwhere in the city of Aveiro, Portugal.

The Smart Lamppost is a modular urban infrastructure solution that supports:



intelligent LED lighting with remote management and analytics,

optional electric vehicle (EV) charging capability,

edge computing and telemetry,

neutral hosting for 4G/5G telecom services,

environmental sensing (video, sound, noise level, temperature).


For more details, see the product page: https://www.ubiwhere.com/en/products/smart-cities/smart-lamppost/

Dataset Description



timestamp: Date and time of the observation (ISO 8601 format)

GPU Usage: Percentage of GPU utilization of the device

CPU Usage: Percentage of CPU utilization of the device

Memory Used: Amount of RAM used by the device (in bytes)

Jetson Energy: Energy consumption of the Jetson device (in joules or watt-seconds)

Switch Energy: Energy consumption of the network switch (in joules or watt-seconds)

Inference Time: Time taken for AI inference processing (in seconds)

Tracking Time: Time taken for object tracking processing (in seconds)

Noise Level: Measured environmental noise level (in decibels, dB)

Temperature: Ambient temperature recorded by the sensor (in degrees Celsius)

Instantaneous Person Detections: Number of persons detected in the current instant/frame

Instantaneous Car Detections: Number of cars detected in the current instant/frame

Instantaneous Motorcycle Detections: Number of motorcycles detected in the current instant/frame


Intended Use



Environmental monitoring

Traffic analysis

Energy consumption profiling

AI and ML model training for object detection and inference optimization

Smart city infrastructure research


Column Summary and Data Types




Descriptor
Count
Mean
Std
Min
25%
50% (Median)
75%
Max




GPU Usage (i64)
597,002
96.2
15.0
0.0
100.0
100.0
100.0
100.0


CPU Usage (f64)
597,002
11.2
0.7
9.6
10.8
11.1
11.4
36.5


Memory Used (i64)
597,002
7.21e9
1.07e8
5.75e9
7.11e9
7.21e9
7.30e9
7.51e9


Jetson Energy (f64)
597,002
16.1
1.3
10.4
15.0
15.9
17.0
22.1


Switch Energy (f64)
597,002
13.0
2.0
9.5
11.3
11.4
15.4
16.3


Inference Time (f64)
597,002
0.2
0.0
0.1
0.2
0.2
0.2
0.3


Tracking Time (f64)
597,002
0.0
0.0
0.0
0.0
0.0
0.0
0.0


Noise Level (f64)
597,002
12.2
7.2
-3.5
8.7
10.4
12.9
55.4


Temperature (f64)
596,972
33.8
9.7
22.1
24.7
27.3
44.7
46.6


Inst. Person Detections (i64)
597,002
0.0
1.6
0.0
0.0
0.0
0.0
777.0


Inst. Car Detections (i64)
597,002
0.0
1.3
0.0
0.0
0.0
0.0
580.0


Inst. Motorcycle Detections (i64)
597,002
0.0
0.0
0.0
0.0
0.0
0.0
7.0




📜 Citation

If you use this dataset in your research, please use the citation generated by Zenodo (located in the right sidebar of this record).

🤝 Acknowledgements & Funding

This work is part of the MLSysOps project, funded by the European Union’s Horizon Europe research and innovation programme under grant agreement No. 101092912.

More information about the project is available at https://mlsysops.eu/