A Multi-tier Data Reduction Mechanism for IoT Sensors

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

Standard

A Multi-tier Data Reduction Mechanism for IoT Sensors. / Feng, Liang; Kortoci, Pranvera; Liu, Yong.

Proceedings of the Seventh International Conference on the Internet of Things . IEEE, 2017. p. 1-8 6.

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

Harvard

Feng, L, Kortoci, P & Liu, Y 2017, A Multi-tier Data Reduction Mechanism for IoT Sensors. in Proceedings of the Seventh International Conference on the Internet of Things ., 6, IEEE, pp. 1-8, International Conference on the Internet of Things, Linz, Austria, 22/10/2017. https://doi.org/10.1145/3131542.3131557

APA

Feng, L., Kortoci, P., & Liu, Y. (2017). A Multi-tier Data Reduction Mechanism for IoT Sensors. In Proceedings of the Seventh International Conference on the Internet of Things (pp. 1-8). [6] IEEE. https://doi.org/10.1145/3131542.3131557

Vancouver

Feng L, Kortoci P, Liu Y. A Multi-tier Data Reduction Mechanism for IoT Sensors. In Proceedings of the Seventh International Conference on the Internet of Things . IEEE. 2017. p. 1-8. 6 https://doi.org/10.1145/3131542.3131557

Author

Feng, Liang ; Kortoci, Pranvera ; Liu, Yong. / A Multi-tier Data Reduction Mechanism for IoT Sensors. Proceedings of the Seventh International Conference on the Internet of Things . IEEE, 2017. pp. 1-8

Bibtex - Download

@inproceedings{73a3a2e4976a4d5b9836f84f0c9c68b0,
title = "A Multi-tier Data Reduction Mechanism for IoT Sensors",
abstract = "The increasing number and variety of IoT (Internet of Things) devices produce a huge amount of diverse data upon which applications are built. Depending on the specific use case, the sampling rate of IoT sensors may be high, thus leadingthe devices to fast energy and storage depletion. One option to address these issues is to perform data reduction at the source nodes so as to decrease both energy consumption and used storage. Most of current available solutions perform data reduction only at a single tier of the IoT architecture (e.g., at gateways), or simply operate a-posteriori once the data transmission has already taken place (i.e., at the cloud data center). This paper proposes a multi-tier data reduction mechanism deployed at both gateways and the edge tier. At the gateways, we apply the PIP (Perceptually Important Point) method to represent the features of a time series by using a finite amount of data. We extend such an algorithm by introducing several techniques, namely interval restriction, dynamic caching and weighted sequence selection. At the edge tier, we propose a data fusion method based on an optimal set selection. Such a method employs a simple strategy to fuse the data in the same time domain for a specific location.Finally, we evaluate the performance of the proposed filtering and the fusion technique. The obtained results demonstrate the efficiency of the proposed mechanism in terms of time and accuracy.",
keywords = "data filtering, data fusion, Internet of Things, sensors, time series",
author = "Liang Feng and Pranvera Kortoci and Yong Liu",
year = "2017",
doi = "10.1145/3131542.3131557",
language = "English",
pages = "1--8",
booktitle = "Proceedings of the Seventh International Conference on the Internet of Things",
publisher = "IEEE",

}

RIS - Download

TY - GEN

T1 - A Multi-tier Data Reduction Mechanism for IoT Sensors

AU - Feng, Liang

AU - Kortoci, Pranvera

AU - Liu, Yong

PY - 2017

Y1 - 2017

N2 - The increasing number and variety of IoT (Internet of Things) devices produce a huge amount of diverse data upon which applications are built. Depending on the specific use case, the sampling rate of IoT sensors may be high, thus leadingthe devices to fast energy and storage depletion. One option to address these issues is to perform data reduction at the source nodes so as to decrease both energy consumption and used storage. Most of current available solutions perform data reduction only at a single tier of the IoT architecture (e.g., at gateways), or simply operate a-posteriori once the data transmission has already taken place (i.e., at the cloud data center). This paper proposes a multi-tier data reduction mechanism deployed at both gateways and the edge tier. At the gateways, we apply the PIP (Perceptually Important Point) method to represent the features of a time series by using a finite amount of data. We extend such an algorithm by introducing several techniques, namely interval restriction, dynamic caching and weighted sequence selection. At the edge tier, we propose a data fusion method based on an optimal set selection. Such a method employs a simple strategy to fuse the data in the same time domain for a specific location.Finally, we evaluate the performance of the proposed filtering and the fusion technique. The obtained results demonstrate the efficiency of the proposed mechanism in terms of time and accuracy.

AB - The increasing number and variety of IoT (Internet of Things) devices produce a huge amount of diverse data upon which applications are built. Depending on the specific use case, the sampling rate of IoT sensors may be high, thus leadingthe devices to fast energy and storage depletion. One option to address these issues is to perform data reduction at the source nodes so as to decrease both energy consumption and used storage. Most of current available solutions perform data reduction only at a single tier of the IoT architecture (e.g., at gateways), or simply operate a-posteriori once the data transmission has already taken place (i.e., at the cloud data center). This paper proposes a multi-tier data reduction mechanism deployed at both gateways and the edge tier. At the gateways, we apply the PIP (Perceptually Important Point) method to represent the features of a time series by using a finite amount of data. We extend such an algorithm by introducing several techniques, namely interval restriction, dynamic caching and weighted sequence selection. At the edge tier, we propose a data fusion method based on an optimal set selection. Such a method employs a simple strategy to fuse the data in the same time domain for a specific location.Finally, we evaluate the performance of the proposed filtering and the fusion technique. The obtained results demonstrate the efficiency of the proposed mechanism in terms of time and accuracy.

KW - data filtering

KW - data fusion

KW - Internet of Things

KW - sensors

KW - time series

U2 - 10.1145/3131542.3131557

DO - 10.1145/3131542.3131557

M3 - Conference contribution

SP - 1

EP - 8

BT - Proceedings of the Seventh International Conference on the Internet of Things

PB - IEEE

ER -

ID: 17000834