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Sensor-field modeling based on in-network data prediction: an efficient strategy for answering complex queries in wireless sensor networks

Authors:

Jose Everardo Bessa Maia,

Angelo BraynerAuthors Info & Claims

SAC '13: Proceedings of the 28th Annual ACM Symposium on Applied Computing

Pages 554 - 559

https://doi.org/10.1145/2480362.2480468

Published: 18 March 2013 Publication History

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Abstract

In this work, we present a mechanism, denoted ADAGA -- P, for managing sensor-field regression models. ADAGA -- P implements an in-network data prediction mechanism in order to only transmit data which are novelties for a regression model applied by ADAGA -- P. Experiments using real data have been executed to validate our approach. The results show that ADAGA -- P is quite efficient regarding communication cost and the number of executed float-point operations. In fact, the energy consumption rate to run ADAGA -- P is 15 times lower than the energy consumed by kernel distributed regression for an RMSE difference of 0.003.

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Cited By

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Sharma NGupta V(2022)A Framework for Wireless Sensor Network Optimization Using Fuzzy-Based Fractal Clustering to Enhance Energy EfficiencyJournal of Circuits, Systems and Computers10.1142/S021812662250223131:13Online publication date: 13-May-2022
https://doi.org/10.1142/S0218126622502231
Almeida FBrayner ARodrigues JMaia J(2017)Improving Multidimensional Wireless Sensor Network Lifetime Using Pearson Correlation and Fractal ClusteringSensors10.3390/s1706131717:6(1317)Online publication date: 7-Jun-2017
https://doi.org/10.3390/s17061317
Shakibian HMoghadam Charkari N(2014)In-cluster vector evaluated particle swarm optimization for distributed regression in WSNsJournal of Network and Computer Applications10.1016/j.jnca.2014.02.01142(80-91)Online publication date: Jun-2014
https://doi.org/10.1016/j.jnca.2014.02.011

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SAC '13: Proceedings of the 28th Annual ACM Symposium on Applied Computing

March 2013

2124 pages

ISBN:9781450316569

DOI:10.1145/2480362

Conference Chairs:
Sung Y. Shin
South Dakota State University, United States
,
José Carlos Maldonado
ICMC - University of São Paulo, Brazil

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 18 March 2013

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March 18 - 22, 2013

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SAC '13 Paper Acceptance Rate 255 of 1,063 submissions, 24%;

Overall Acceptance Rate 1,650 of 6,669 submissions, 25%

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Sharma NGupta V(2022)A Framework for Wireless Sensor Network Optimization Using Fuzzy-Based Fractal Clustering to Enhance Energy EfficiencyJournal of Circuits, Systems and Computers10.1142/S021812662250223131:13Online publication date: 13-May-2022
https://doi.org/10.1142/S0218126622502231
Almeida FBrayner ARodrigues JMaia J(2017)Improving Multidimensional Wireless Sensor Network Lifetime Using Pearson Correlation and Fractal ClusteringSensors10.3390/s1706131717:6(1317)Online publication date: 7-Jun-2017
https://doi.org/10.3390/s17061317
Shakibian HMoghadam Charkari N(2014)In-cluster vector evaluated particle swarm optimization for distributed regression in WSNsJournal of Network and Computer Applications10.1016/j.jnca.2014.02.01142(80-91)Online publication date: Jun-2014
https://doi.org/10.1016/j.jnca.2014.02.011

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Towards in-network data prediction in wireless sensor networks

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