Predicting battery depletion of neighboring wireless sensor nodes

Roshan Kotian; Georgios Exarchakos; Decebal Constantin Mocanu; Antonio Liotta

doi:10.1007/978-3-319-03889-6_32

Predicting battery depletion of neighboring wireless sensor nodes

Roshan Kotian, Georgios Exarchakos, Decebal Constantin Mocanu, Antonio Liotta

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

3 Citations (Scopus)

Abstract

With a view to prolong the duration of the wireless sensor network, many battery lifetime prediction algorithms run on individual nodes. If not properly designed, this approach may be detrimental and even accelerate battery depletion. Herein, we provide a comparative analysis of various machine-learning algorithms to offload the energyinference task to the most energy-rich nodes, to alleviate the nodes that are entering the critical state. Taken to its extreme, our approach may be used to divert the energy-intensive tasks to a monitoring station, enabling a cloud-based approach to sensor network management. Experiments conducted in a controlled environment with real hardware have shown that RSSI can be used to infer the state of a remote wireless node once it is approaching the cutoff point. The ADWIN algorithm was used for smoothing the input data and for helping a variety of machine learning algorithms particularly to speed up and improve their prediction accuracy

Original language	English
Title of host publication	Algorithms and Architectures for Parallel Processing
Subtitle of host publication	13th International Conference, ICA3PP 2013, Vietri sul Mare, Italy, December 18-20, 2013, Proceedings
Editors	Rocco Aversa, Joanna Kołodziej, Jun Zhang, Flora Amato, Giancarlo Fortino
Place of Publication	Cham
Publisher	Springer
Pages	276-284
Number of pages	9
Volume	Part II
ISBN (Electronic)	978-3-319-03889-6
ISBN (Print)	978-3-319-03888-9
DOIs	https://doi.org/10.1007/978-3-319-03889-6_32
Publication status	Published - 2013
Externally published	Yes
Event	13th International Conference on Algorithms and Architectures for Parallel Processing, ICA3PP 2013 - Vietri sul Mare, Italy Duration: 18 Dec 2013 → 20 Dec 2013 Conference number: 13

Publication series

Name	Lecture Notes in Computer Science
Publisher	Springer
Volume	8286
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	13th International Conference on Algorithms and Architectures for Parallel Processing, ICA3PP 2013
Abbreviated title	ICA3PP
Country/Territory	Italy
City	Vietri sul Mare
Period	18/12/13 → 20/12/13

Keywords

Sensor network
Sensor node
Wireless Sensor Network (WSN)
Sink node
Machine learning algorithm

Access to Document

10.1007/978-3-319-03889-6_32

Cite this

Kotian, R., Exarchakos, G., Mocanu, D. C., & Liotta, A. (2013). Predicting battery depletion of neighboring wireless sensor nodes. In R. Aversa, J. Kołodziej, J. Zhang, F. Amato, & G. Fortino (Eds.), Algorithms and Architectures for Parallel Processing: 13th International Conference, ICA3PP 2013, Vietri sul Mare, Italy, December 18-20, 2013, Proceedings (Vol. Part II, pp. 276-284). (Lecture Notes in Computer Science; Vol. 8286). Springer. https://doi.org/10.1007/978-3-319-03889-6_32

Kotian, Roshan ; Exarchakos, Georgios ; Mocanu, Decebal Constantin et al. / Predicting battery depletion of neighboring wireless sensor nodes. Algorithms and Architectures for Parallel Processing: 13th International Conference, ICA3PP 2013, Vietri sul Mare, Italy, December 18-20, 2013, Proceedings. editor / Rocco Aversa ; Joanna Kołodziej ; Jun Zhang ; Flora Amato ; Giancarlo Fortino. Vol. Part II Cham : Springer, 2013. pp. 276-284 (Lecture Notes in Computer Science).

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title = "Predicting battery depletion of neighboring wireless sensor nodes",

abstract = "With a view to prolong the duration of the wireless sensor network, many battery lifetime prediction algorithms run on individual nodes. If not properly designed, this approach may be detrimental and even accelerate battery depletion. Herein, we provide a comparative analysis of various machine-learning algorithms to offload the energyinference task to the most energy-rich nodes, to alleviate the nodes that are entering the critical state. Taken to its extreme, our approach may be used to divert the energy-intensive tasks to a monitoring station, enabling a cloud-based approach to sensor network management. Experiments conducted in a controlled environment with real hardware have shown that RSSI can be used to infer the state of a remote wireless node once it is approaching the cutoff point. The ADWIN algorithm was used for smoothing the input data and for helping a variety of machine learning algorithms particularly to speed up and improve their prediction accuracy",

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author = "Roshan Kotian and Georgios Exarchakos and Mocanu, {Decebal Constantin} and Antonio Liotta",

year = "2013",

doi = "10.1007/978-3-319-03889-6_32",

language = "English",

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series = "Lecture Notes in Computer Science",

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pages = "276--284",

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Kotian, R, Exarchakos, G, Mocanu, DC & Liotta, A 2013, Predicting battery depletion of neighboring wireless sensor nodes. in R Aversa, J Kołodziej, J Zhang, F Amato & G Fortino (eds), Algorithms and Architectures for Parallel Processing: 13th International Conference, ICA3PP 2013, Vietri sul Mare, Italy, December 18-20, 2013, Proceedings. vol. Part II, Lecture Notes in Computer Science, vol. 8286, Springer, Cham, pp. 276-284, 13th International Conference on Algorithms and Architectures for Parallel Processing, ICA3PP 2013, Vietri sul Mare, Italy, 18/12/13. https://doi.org/10.1007/978-3-319-03889-6_32

Predicting battery depletion of neighboring wireless sensor nodes. / Kotian, Roshan; Exarchakos, Georgios; Mocanu, Decebal Constantin et al.
Algorithms and Architectures for Parallel Processing: 13th International Conference, ICA3PP 2013, Vietri sul Mare, Italy, December 18-20, 2013, Proceedings. ed. / Rocco Aversa; Joanna Kołodziej; Jun Zhang; Flora Amato; Giancarlo Fortino. Vol. Part II Cham: Springer, 2013. p. 276-284 (Lecture Notes in Computer Science; Vol. 8286).

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

TY - GEN

T1 - Predicting battery depletion of neighboring wireless sensor nodes

AU - Kotian, Roshan

AU - Exarchakos, Georgios

AU - Mocanu, Decebal Constantin

AU - Liotta, Antonio

N1 - Conference code: 13

PY - 2013

Y1 - 2013

N2 - With a view to prolong the duration of the wireless sensor network, many battery lifetime prediction algorithms run on individual nodes. If not properly designed, this approach may be detrimental and even accelerate battery depletion. Herein, we provide a comparative analysis of various machine-learning algorithms to offload the energyinference task to the most energy-rich nodes, to alleviate the nodes that are entering the critical state. Taken to its extreme, our approach may be used to divert the energy-intensive tasks to a monitoring station, enabling a cloud-based approach to sensor network management. Experiments conducted in a controlled environment with real hardware have shown that RSSI can be used to infer the state of a remote wireless node once it is approaching the cutoff point. The ADWIN algorithm was used for smoothing the input data and for helping a variety of machine learning algorithms particularly to speed up and improve their prediction accuracy

AB - With a view to prolong the duration of the wireless sensor network, many battery lifetime prediction algorithms run on individual nodes. If not properly designed, this approach may be detrimental and even accelerate battery depletion. Herein, we provide a comparative analysis of various machine-learning algorithms to offload the energyinference task to the most energy-rich nodes, to alleviate the nodes that are entering the critical state. Taken to its extreme, our approach may be used to divert the energy-intensive tasks to a monitoring station, enabling a cloud-based approach to sensor network management. Experiments conducted in a controlled environment with real hardware have shown that RSSI can be used to infer the state of a remote wireless node once it is approaching the cutoff point. The ADWIN algorithm was used for smoothing the input data and for helping a variety of machine learning algorithms particularly to speed up and improve their prediction accuracy

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BT - Algorithms and Architectures for Parallel Processing

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ER -

Kotian R, Exarchakos G, Mocanu DC, Liotta A. Predicting battery depletion of neighboring wireless sensor nodes. In Aversa R, Kołodziej J, Zhang J, Amato F, Fortino G, editors, Algorithms and Architectures for Parallel Processing: 13th International Conference, ICA3PP 2013, Vietri sul Mare, Italy, December 18-20, 2013, Proceedings. Vol. Part II. Cham: Springer. 2013. p. 276-284. (Lecture Notes in Computer Science). doi: 10.1007/978-3-319-03889-6_32

Predicting battery depletion of neighboring wireless sensor nodes

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