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Efficient agile sink selection in wireless sensor networks based on compressed sensing

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Efficient agile sink selection in wireless sensor networks based on compressed sensing. / Mahmudimanesh, M.; Naseri, A.; Suri, Neeraj.

2014 IEEE International Conference on Distributed Computing in Sensor Systems. IEEE, 2014. p. 193-200.

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSNConference contribution/Paper

Harvard

Mahmudimanesh, M, Naseri, A & Suri, N 2014, Efficient agile sink selection in wireless sensor networks based on compressed sensing. in 2014 IEEE International Conference on Distributed Computing in Sensor Systems. IEEE, pp. 193-200. https://doi.org/10.1109/DCOSS.2014.21

APA

Mahmudimanesh, M., Naseri, A., & Suri, N. (2014). Efficient agile sink selection in wireless sensor networks based on compressed sensing. In 2014 IEEE International Conference on Distributed Computing in Sensor Systems (pp. 193-200). IEEE. https://doi.org/10.1109/DCOSS.2014.21

Vancouver

Mahmudimanesh M, Naseri A, Suri N. Efficient agile sink selection in wireless sensor networks based on compressed sensing. In 2014 IEEE International Conference on Distributed Computing in Sensor Systems. IEEE. 2014. p. 193-200 https://doi.org/10.1109/DCOSS.2014.21

Author

Mahmudimanesh, M. ; Naseri, A. ; Suri, Neeraj. / Efficient agile sink selection in wireless sensor networks based on compressed sensing. 2014 IEEE International Conference on Distributed Computing in Sensor Systems. IEEE, 2014. pp. 193-200

Bibtex

@inproceedings{9f9556f2d3b74eea8f7c10e4ab12d8b8,
title = "Efficient agile sink selection in wireless sensor networks based on compressed sensing",
abstract = "Collection of the sensed data in a wireless sensor network at one or more sink (s) is a well studied problem and there are a lot of efficient solutions for a variety of wireless sensor network configurations and application requirements. These methods are often optimized towards collection of the sensed data at a predetermined base station or sink. This inherently reduces the agility of the wireless sensor network as the flow of information is not easily changeable after the establishment of the routing and data collection algorithms. This paper presents an efficient data dissemination method based on the compressed sensing theory that allows each sensor node to take the role of a sink. Agile sink selection is especially advantageous in scenarios where the sink or the end user of the wireless sensor network is mobile. The proposed method allows availing the global state of the environment by fetching a small set of data from any arbitrary node. Our evaluations prove the better performance of our technique over existing methods. Also a comparison with an oracle-based approach gives sufficient experimental evidences of a nearly optimal performance of our method. {\textcopyright} 2014 IEEE.",
keywords = "Agile manufacturing systems, Sensors, Signal reconstruction, Application requirements, Better performance, Data collection, Efficient data disseminations, End users, Experimental evidence, Global state, Optimal performance, Sensor nodes",
author = "M. Mahmudimanesh and A. Naseri and Neeraj Suri",
year = "2014",
month = may,
day = "26",
doi = "10.1109/DCOSS.2014.21",
language = "English",
pages = "193--200",
booktitle = "2014 IEEE International Conference on Distributed Computing in Sensor Systems",
publisher = "IEEE",

}

RIS

TY - GEN

T1 - Efficient agile sink selection in wireless sensor networks based on compressed sensing

AU - Mahmudimanesh, M.

AU - Naseri, A.

AU - Suri, Neeraj

PY - 2014/5/26

Y1 - 2014/5/26

N2 - Collection of the sensed data in a wireless sensor network at one or more sink (s) is a well studied problem and there are a lot of efficient solutions for a variety of wireless sensor network configurations and application requirements. These methods are often optimized towards collection of the sensed data at a predetermined base station or sink. This inherently reduces the agility of the wireless sensor network as the flow of information is not easily changeable after the establishment of the routing and data collection algorithms. This paper presents an efficient data dissemination method based on the compressed sensing theory that allows each sensor node to take the role of a sink. Agile sink selection is especially advantageous in scenarios where the sink or the end user of the wireless sensor network is mobile. The proposed method allows availing the global state of the environment by fetching a small set of data from any arbitrary node. Our evaluations prove the better performance of our technique over existing methods. Also a comparison with an oracle-based approach gives sufficient experimental evidences of a nearly optimal performance of our method. © 2014 IEEE.

AB - Collection of the sensed data in a wireless sensor network at one or more sink (s) is a well studied problem and there are a lot of efficient solutions for a variety of wireless sensor network configurations and application requirements. These methods are often optimized towards collection of the sensed data at a predetermined base station or sink. This inherently reduces the agility of the wireless sensor network as the flow of information is not easily changeable after the establishment of the routing and data collection algorithms. This paper presents an efficient data dissemination method based on the compressed sensing theory that allows each sensor node to take the role of a sink. Agile sink selection is especially advantageous in scenarios where the sink or the end user of the wireless sensor network is mobile. The proposed method allows availing the global state of the environment by fetching a small set of data from any arbitrary node. Our evaluations prove the better performance of our technique over existing methods. Also a comparison with an oracle-based approach gives sufficient experimental evidences of a nearly optimal performance of our method. © 2014 IEEE.

KW - Agile manufacturing systems

KW - Sensors

KW - Signal reconstruction

KW - Application requirements

KW - Better performance

KW - Data collection

KW - Efficient data disseminations

KW - End users

KW - Experimental evidence

KW - Global state

KW - Optimal performance

KW - Sensor nodes

U2 - 10.1109/DCOSS.2014.21

DO - 10.1109/DCOSS.2014.21

M3 - Conference contribution/Paper

SP - 193

EP - 200

BT - 2014 IEEE International Conference on Distributed Computing in Sensor Systems

PB - IEEE

ER -