Research output per year
Research output per year
Description
SYNERGIA, funded by Innovate UK, aimed to improve privacy and reduce latency in IoT platforms by enhancing the security and resilience of industrial IoT devices, enabling the development of future networks. It moves some computation to the Edge to address privacy and scalability issues of the current cloud-based IoT platforms.
During the project, to collect real-world data, we deployed an end-to-end IoT network in an office used by research staff at the University of Bristol. The office is actively used by a significant number of academic personnel and students (max occupancy of 28 people). It gets exposed to environmental changes such as seasonal temperature, humidity, and light fluctuations. The endpoints are located in different locations in the lab to collect varying data due to differentiation between the areas. The network consists of eight stationary, severely resource-constrained IoT endpoints, an additional device called Umbrella acting as the “edge”, and a server for data collection and controlling the experiment.
Each IoT endpoint hosts sensors providing temperature, humidity, pressure, gas, accelerometer, and light readings. We collected two additional pieces of information: the measurements’ accuracy value, calculated by the environmental sensors, and the received signal strength indicator(RSSI). The data was acquired using several sensors in a smart building/office environment. The sensors were integrated into an IoT Nordic nRF52840 DK board endpoint as follows:
(1) "ISL29125" Light Sensors: Collects intensity of the light.
(2) "MMA8452Q" Accelerometer Sensors.
(3) "BME680" Environmental Digital Sensors: Comprise of gas (VOC/ CO₂), pressure, temperature, and humidity sensors.
The experiment started in February 2022. We stored over six months (February - September 2022) of sensor readings for experimental reasons, in CSV file format.
This repository provides the following.
Raw environmental sensor data from a deployment in an indoor office area.
During the project, to collect real-world data, we deployed an end-to-end IoT network in an office used by research staff at the University of Bristol. The office is actively used by a significant number of academic personnel and students (max occupancy of 28 people). It gets exposed to environmental changes such as seasonal temperature, humidity, and light fluctuations. The endpoints are located in different locations in the lab to collect varying data due to differentiation between the areas. The network consists of eight stationary, severely resource-constrained IoT endpoints, an additional device called Umbrella acting as the “edge”, and a server for data collection and controlling the experiment.
Each IoT endpoint hosts sensors providing temperature, humidity, pressure, gas, accelerometer, and light readings. We collected two additional pieces of information: the measurements’ accuracy value, calculated by the environmental sensors, and the received signal strength indicator(RSSI). The data was acquired using several sensors in a smart building/office environment. The sensors were integrated into an IoT Nordic nRF52840 DK board endpoint as follows:
(1) "ISL29125" Light Sensors: Collects intensity of the light.
(2) "MMA8452Q" Accelerometer Sensors.
(3) "BME680" Environmental Digital Sensors: Comprise of gas (VOC/ CO₂), pressure, temperature, and humidity sensors.
The experiment started in February 2022. We stored over six months (February - September 2022) of sensor readings for experimental reasons, in CSV file format.
This repository provides the following.
Raw environmental sensor data from a deployment in an indoor office area.
| Date made available | 22 Mar 2023 |
|---|---|
| Publisher | University of Bristol |
| Temporal coverage | 11 Feb 2022 - 20 Jul 2022 |
| Date of data production | 11 Feb 2022 - 20 Jul 2022 |
| Geospatial point | 51.455643, -2.602358Show on map |
Keywords
- Time series dataset
- Internet of Things
- Environmental
- Smart building
- Data drift
- Sensor
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Evaluating Concept Drift Detectors on Real-World Data
Erol, U., Raimondo, F., Pope, J., Gunner, S. D., Kumar, V., Mavromatis, I., Carnelli, P., Spyridopoulos, T., Khan, A. & Oikonomou, G., 29 Jun 2023, (Accepted/In press).Research output: Contribution to conference › Conference Paper › peer-review
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Multi-sensor, Multi-device Smart Building Indoor Environmental Dataset
Erol, U., Raimondo, F., Pope, J., Gunner, S. D., Kumar, V., Mavromatis, I., Carnelli, P., Spyridopoulos, T., Khan, A. & Oikonomou, G., 14 Jul 2023, In: Data in Brief. 49, 109392.Research output: Contribution to journal › Article (Academic Journal) › peer-review
Open Access3 Citations (Scopus)
Projects
- 1 Finished
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SYNERGIA: 8031 SYNERGIA 53707
Oikonomou, G. (Principal Investigator), Piechocki, R. J. (Co-Investigator), Paquier, T. (Co-Investigator), McConville, R. (Co-Investigator), Pope, J. (Co-Investigator), Raimondo, F. (Researcher), Erol, U. (Researcher), Paschou, C. (Researcher), Zakrzewski, R. (Researcher) & Gunner, S. D. (Researcher)
1/11/20 → 31/10/22
Project: Research