Smart IoT Data Collection
Nikos Fotiou, Vasilios A. Siris, Alexandros Mertzianis, George C. Polyzos · 2018
We present and experimentally evaluate procedures for efficient IoT data collection while achieving target requirements in terms of data accuracy and privacy protection. The procedures adjust the time period between consecutive measurements following an additive increase and multiplicative decrease (AIMD) scheme based on a target data accuracy and add noise to measurements using differential privacy techniques. The experimental evaluation involves real temperature and humidity measurements obtained from two 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, and the addition of noise to the sensor measurements using differential privacy has a negligible effect on the aggregate statistics.