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DIPPM: A Deep Learning Inference Performance Predictive Model Using Graph Neural Networks Read chapter Read first chapter

2023 | OriginalPaper | Chapter

SymED: Adaptive and Online Symbolic Representation of Data on the Edge

Authors : Daniel Hofstätter, Shashikant Ilager, Ivan Lujic, Ivona Brandic

Published in: Euro-Par 2023: Parallel Processing

Publisher: Springer Nature Switzerland

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Abstract

The edge computing paradigm helps handle the Internet of Things (IoT) generated data in proximity to its source. Challenges occur in transferring, storing, and processing this rapidly growing amount of data on resource-constrained edge devices. Symbolic Representation (SR) algorithms are promising solutions to reduce the data size by converting actual raw data into symbols. Also, they allow data analytics (e.g., anomaly detection and trend prediction) directly on symbols, benefiting large classes of edge applications. However, existing SR algorithms are centralized in design and work offline with batch data, which is infeasible for real-time cases. We propose SymED - Symbolic Edge Data representation method, i.e., an online, adaptive, and distributed approach for symbolic representation of data on edge. SymED is based on the Adaptive Brownian Bridge-based Aggregation (ABBA), where we assume low-powered IoT devices do initial data compression (senders) and the more robust edge devices do the symbolic conversion (receivers). We evaluate SymED by measuring compression performance, reconstruction accuracy through Dynamic Time Warping (DTW) distance, and computational latency. The results show that SymED is able to (i) reduce the raw data with an average compression rate of \(9.5\%\); (ii) keep a low reconstruction error of 13.25 in the DTW space; (iii) simultaneously provide real-time adaptability for online streaming IoT data at typical latencies of 42 ms per symbol, reducing the overall network traffic.

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Metadata
Title
SymED: Adaptive and Online Symbolic Representation of Data on the Edge
Authors
Daniel Hofstätter
Shashikant Ilager
Ivan Lujic
Ivona Brandic
Copyright Year
2023
Book
Euro-Par 2023: Parallel Processing

Print ISBN: 978-3-031-39697-7

Electronic ISBN: 978-3-031-39698-4

Copyright Year: 2023

DOI
https://doi.org/10.1007/978-3-031-39698-4_28

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