Adaptive Segmentation of Streaming Sensor Data on Edge Devices

Abstract

Sensor data streams often represent signals/trajectories which are twice differentiable (e.g., to give a continuous velocity and acceleration), and this property must be reflected in their segmentation. An adaptive streaming algorithm for this problem is presented. It is based on the greedy look-ahead strategy and is built on the concept of a cubic splinelet. A characteristic feature of the proposed algorithm is the real-time simultaneous segmentation, smoothing, and compression of data streams. The segmentation quality is measured in terms of the signal approximation accuracy and the corresponding compression ratio. The numerical results show the relatively high compression ratios (from 135 to 208, i.e., compressed stream sizes up to 208 times smaller) combined with the approximation errors comparable to those obtained from the state-of-the-art global reference algorithm. The proposed algorithm can be applied to various domains, including online compression and/or smoothing of data streams coming from sensors, real-time IoT analytics, and embedded time-series databases.

Time Series Compression for IoT: A Systematic Literature Review

“…The number of articles excluded at this stage for not reaching the cutoff point is also stated in Table 1 (column QA Cut). Those studies evaluated above the cutoff point (40) were then selected for the data extraction step, as well as for detailed review and full reading, as listed in Table 2.…”