A Metadata and Z Score-based Load-Shedding Technique in IoT-based Data Collection Systems

Abstract

The Internet-of-Things (IoT) has emerged as an alternative to communicate different pieces of technology to foster the distributed data collection. The measurement projects and the Real-time data processing are articulated to take advantage of this environment, fostering a sustainable data-driven decision making. The Data Stream Processing Strategy (DSPS) is a Stream Processing Engine focused on measurement projects, where each concept is previously agreed through a measurement framework. The Measurement Adapter (MA) is a component whose responsibility is to pair each metric’s definition from the measurement project with data sensors to transmit data (i.e., measures) with metadata (i.e., tags indicating the data meaning) together. The Gathering Function (GF) receives and derivates data for its processing from each MA, while it implements load-shedding (LS) techniques based on Metadata to avoid a processing collapse when all MAs informs jointly and frequently. Here, a Metadata and Z-score based load-shedding technique implemented locally in the MA is proposed. Thus, the load-shedding is located at the same data source to avoid data transmission and saving resources. Also, an incremental estimation of average, deviations, covariance, and correlations are implemented and employed to calculate the Z-scores and to data retain/discard selectively. Four simulations discrete were designed and performed to analyze the proposal. Results indicate that the local LS required only 24% of the original data transmissions, a minimum of 18.61 ms as the data lifespan, while it consumes 890.26 KB. As future work, other kinds of dependencies analysis will be analyzed to provide local alternatives to LS.

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