Optimizing Data Transmission from IoT Devices Through Weighted Online Data Changing Detectors

The real-time data analysis requires an integrated approach to know the last known state of variables of a concept under monitoring. Thereby, the Internet-of-Thing (IoT) devices have provided alternatives to address distributed data collection strategies. However, the autonomy of IoT devices represents one of the main challenges to implement the collecting strategy. Battery autonomy is affected directly by the energy consumption derived from data transmissions. The Data Stream Processing Strategy (DSPS) is an architecture oriented to the implementation of measurement projects based on a measurement and evaluation framework. Its online processing is guided by the measurement metadata informed from IoT devices associated with a component named Measurement Adapter (MA). This paper presents a new data buffer organization based on measurement metadata articulated with online data filtering to optimize the data transmissions from MA. As contributions, a weighted data change detection approach is incorporated, while a new local buffer based on logical windows is proposed for MA. Also, an articulation among the data buffer, a temporal barrier, and data change detectors is introduced. The proposal was implemented and released on the pabmmCommons library. A discrete simulation on the library is here described to provide initial applicability patterns. The data buffer consumed 568 Kb for monitoring 100 simultaneous metrics. The online estimation of the mean and variance based on the Statistical Process Control consumed 238 ns. However, as a limitation, other scenarios need to be addressed before generalizing results. As future work, new alternatives to filter noise online will be addressed.