Efficient Flash Indexing for Time Series Data on Memory-constrained Embedded Sensor Devices

Scott Fazackerley, Nadir Ould-Khessal, Ramon Lawrence

2021

Abstract

Embedded sensor devices with limited hardware resources must efficiently collect environmental and industrial time series data for analysis. Performing data analysis on the device requires data storage and indexing that minimizes memory, I/O, and energy usage. This paper presents an index structure that is optimized for the constrained use cases associated with sensor time series collection and analysis. By supporting only planned queries and analysis patterns, the storage and indexing implementation is simplified, and outperforms general techniques based on hashing and trees. The indexing technique is analyzed and compared with other indexing approaches and is adapted to all flash memory types including memory that supports overwriting.

Paper Citation

in Harvard Style

Fazackerley S., Ould-Khessal N. and Lawrence R. (2021). Efficient Flash Indexing for Time Series Data on Memory-constrained Embedded Sensor Devices.In Proceedings of the 10th International Conference on Sensor Networks - Volume 1: SENSORNETS, ISBN 978-989-758-489-3, pages 92-99. DOI: 10.5220/0010318800920099

in Bibtex Style

@conference{sensornets21,
author={Scott Fazackerley and Nadir Ould-Khessal and Ramon Lawrence},
title={Efficient Flash Indexing for Time Series Data on Memory-constrained Embedded Sensor Devices},
booktitle={Proceedings of the 10th International Conference on Sensor Networks - Volume 1: SENSORNETS,},
year={2021},
pages={92-99},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010318800920099},
isbn={978-989-758-489-3},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 10th International Conference on Sensor Networks - Volume 1: SENSORNETS,
TI - Efficient Flash Indexing for Time Series Data on Memory-constrained Embedded Sensor Devices
SN - 978-989-758-489-3
AU - Fazackerley S.
AU - Ould-Khessal N.
AU - Lawrence R.
PY - 2021
SP - 92
EP - 99
DO - 10.5220/0010318800920099