We present and experimentally evaluate procedures for efficient IoT data collection while achieving target requirements in terms of data accuracy, response time, energy, and privacy protection. Different strategies are considered because different IoT applications can have different requirements. Specifically, the accuracy-driven strategy adjusts the time period between consecutive measurements following an additive increase and multiplicative decrease (AIMD) scheme based on a target data accuracy, while the time-driven strategy adjusts the time period between measurement requests to achieve delay less than a given maximum delay between consecutive measurements. The energy-driven strategy considers both the data accuracy and the energy costs for the corresponding measurements. Finally, the privacy-driven strategy adds noise to measurements using differential privacy techniques. The experimental evaluation involves real temperature, humidity, and ozone (O3) measurements obtained from three testbeds through the FIESTA-IoT platform. Our results show that the AIMD adaptation of the measurement period is robust to different types of measurements from different testbeds, without having any tuning parameters. Also, the experimental results show the trade-offs between the target data accuracy and the number of measurements and between the target data accuracy and the corresponding energy costs. For the privacy-driven strategy, the results show that the addition of noise to the sensor measurements using differential privacy has a negligible effect on the aggregate statistics.
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FIESTA-IoT: Federated Interoperable Semantic IoT Testbeds and Applications, http://fiesta-iot.eu/.
In this paper we include a subset of our results. Interested users are encouraged to use our live demo located at https://mm.aueb.gr/fiesta/privacy.php.
The graphs for all other hours follow a similar pattern.
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This work was supported in part by the EU FIESTA-IoT project (Grant Agreement number: 643943 / H2020-ICT-2014-1) through the Open Call 3 Experiment “BeSmart: Smart IoT Data Collection” and by the Athens University of Economics and Business Research Center.
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Siris, V.A., Fotiou, N., Mertzianis, A. et al. Smart application-aware IoT data collection. J Reliable Intell Environ 5, 17–28 (2019). https://doi.org/10.1007/s40860-019-00077-y
- Data accuracy
- Adaptive data collection
- Differential privacy
- Testbed experiments