Energy efficient WSN: a cross-layer graph signal processing solution to information redundancy

Alessandro Chiumento; Nicola Marchetti; Irene Macaluso

doi:10.1109/ISWCS.2019.8877243

Energy efficient WSN: a cross-layer graph signal processing solution to information redundancy

Alessandro Chiumento, Nicola Marchetti, Irene Macaluso

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

8 Citations (Scopus)

71 Downloads (Pure)

Abstract

In this work an iterative solution to build a network lifetime-preserving sampling strategy for WSNs is presented. The paper describes the necessary steps to reconstruct a graph from application data. Once the graph structure is obtained, a sampling strategy aimed at finding the smallest number of concurrent sensors needed to reconstruct the data in the unsampled nodes within a specific error bound, is presented. An iterative method then divides the sensor nodes into sets to be sampled sequentially to increase lifetime. Results on a real-life dataset show that the reconstruction RMSE can be easily traded off for a larger number of disjoint sampling sets which improve the network lifetime linearly.

Original language	English
Title of host publication	2019 16th International Symposium on Wireless Communication Systems (ISWCS)
Pages	645-650
Number of pages	6
ISBN (Electronic)	978-1-7281-2527-5
DOIs	https://doi.org/10.1109/ISWCS.2019.8877243
Publication status	Published - 2019
Externally published	Yes
Event	16th International Symposium on Wireless Communication Systems ISWCS 2019 - Oulu, Finland Duration: 27 Aug 2019 → 30 Aug 2019 Conference number: 16

Conference

Conference	16th International Symposium on Wireless Communication Systems ISWCS 2019
Abbreviated title	ISWCS 2019
Country/Territory	Finland
City	Oulu
Period	27/08/19 → 30/08/19

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10.1109/ISWCS.2019.8877243

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title = "Energy efficient WSN: a cross-layer graph signal processing solution to information redundancy",

abstract = "In this work an iterative solution to build a network lifetime-preserving sampling strategy for WSNs is presented. The paper describes the necessary steps to reconstruct a graph from application data. Once the graph structure is obtained, a sampling strategy aimed at finding the smallest number of concurrent sensors needed to reconstruct the data in the unsampled nodes within a specific error bound, is presented. An iterative method then divides the sensor nodes into sets to be sampled sequentially to increase lifetime. Results on a real-life dataset show that the reconstruction RMSE can be easily traded off for a larger number of disjoint sampling sets which improve the network lifetime linearly.",

author = "Alessandro Chiumento and Nicola Marchetti and Irene Macaluso",

year = "2019",

doi = "10.1109/ISWCS.2019.8877243",

language = "English",

pages = "645--650",

booktitle = "2019 16th International Symposium on Wireless Communication Systems (ISWCS)",

note = "16th International Symposium on Wireless Communication Systems ISWCS 2019, ISWCS 2019 ; Conference date: 27-08-2019 Through 30-08-2019",

}

Chiumento, A, Marchetti, N & Macaluso, I 2019, Energy efficient WSN: a cross-layer graph signal processing solution to information redundancy. in 2019 16th International Symposium on Wireless Communication Systems (ISWCS). pp. 645-650, 16th International Symposium on Wireless Communication Systems ISWCS 2019, Oulu, Finland, 27/08/19. https://doi.org/10.1109/ISWCS.2019.8877243

TY - GEN

T1 - Energy efficient WSN: a cross-layer graph signal processing solution to information redundancy

AU - Chiumento, Alessandro

AU - Marchetti, Nicola

AU - Macaluso, Irene

N1 - Conference code: 16

PY - 2019

Y1 - 2019

N2 - In this work an iterative solution to build a network lifetime-preserving sampling strategy for WSNs is presented. The paper describes the necessary steps to reconstruct a graph from application data. Once the graph structure is obtained, a sampling strategy aimed at finding the smallest number of concurrent sensors needed to reconstruct the data in the unsampled nodes within a specific error bound, is presented. An iterative method then divides the sensor nodes into sets to be sampled sequentially to increase lifetime. Results on a real-life dataset show that the reconstruction RMSE can be easily traded off for a larger number of disjoint sampling sets which improve the network lifetime linearly.

AB - In this work an iterative solution to build a network lifetime-preserving sampling strategy for WSNs is presented. The paper describes the necessary steps to reconstruct a graph from application data. Once the graph structure is obtained, a sampling strategy aimed at finding the smallest number of concurrent sensors needed to reconstruct the data in the unsampled nodes within a specific error bound, is presented. An iterative method then divides the sensor nodes into sets to be sampled sequentially to increase lifetime. Results on a real-life dataset show that the reconstruction RMSE can be easily traded off for a larger number of disjoint sampling sets which improve the network lifetime linearly.

U2 - 10.1109/ISWCS.2019.8877243

DO - 10.1109/ISWCS.2019.8877243

M3 - Conference contribution

SP - 645

EP - 650

BT - 2019 16th International Symposium on Wireless Communication Systems (ISWCS)

T2 - 16th International Symposium on Wireless Communication Systems ISWCS 2019

Y2 - 27 August 2019 through 30 August 2019

ER -

Energy efficient WSN: a cross-layer graph signal processing solution to information redundancy

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