nesC-TinyOS model for parallel and distributed computation of max independent set by Hopfield network on wireless sensor network

This paper, the second one in a three-paper sequence, presents the nesC model of a Hopfield neural network configured for a static optimization problem, the maximum independent set, in fully parallel and distributed mode for TinyOS-based wireless sensor networks. Actual nesC code that implements the...

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Vydané v:Procedia computer science Ročník 6; s. 396 - 401
Hlavní autori: Li, Jiakai, Serpen, Gursel
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Elsevier B.V 2011
Predmet:
Hopfield neural network
maximum independent set problem
nesC
parallel and distributed computation
static optimization
TinyOS
wireless sensor network
nesC
TinyOS
parallel and distributed computation
wireless sensor network
static optimization
Hopfield neural network
maximum independent set problem
ISSN:1877-0509, 1877-0509
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Abstract This paper, the second one in a three-paper sequence, presents the nesC model of a Hopfield neural network configured for a static optimization problem, the maximum independent set, in fully parallel and distributed mode for TinyOS-based wireless sensor networks. Actual nesC code that implements the required neural computing functionality is presented. The graph representation of the maximum independent set problem is used as the basis for the topology of the Hopfield network as well as the wireless sensor network since each mote is conceived to house one neuron in order to facilitate fully parallel and distributed computation. The nesC implementation of a multitude of phases of computation is detailed including initialization of the neural network, relaxation, convergence detection, and solution detection all while the neural computations are performed on the wireless sensor network. Simulation of the presented nesC-TinyOS model is deferred to the third paper in the sequence.
AbstractList This paper, the second one in a three-paper sequence, presents the nesC model of a Hopfield neural network configured for a static optimization problem, the maximum independent set, in fully parallel and distributed mode for TinyOS-based wireless sensor networks. Actual nesC code that implements the required neural computing functionality is presented. The graph representation of the maximum independent set problem is used as the basis for the topology of the Hopfield network as well as the wireless sensor network since each mote is conceived to house one neuron in order to facilitate fully parallel and distributed computation. The nesC implementation of a multitude of phases of computation is detailed including initialization of the neural network, relaxation, convergence detection, and solution detection all while the neural computations are performed on the wireless sensor network. Simulation of the presented nesC-TinyOS model is deferred to the third paper in the sequence.
Author Li, Jiakai
Serpen, Gursel
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CitedBy_id crossref_primary_10_1016_j_procs_2012_09_089
crossref_primary_10_1109_JIOT_2021_3064176
crossref_primary_10_1016_j_procs_2011_08_076
crossref_primary_10_1016_j_procs_2011_08_073
crossref_primary_10_1109_JSEN_2016_2519924
Cites_doi 10.1007/BF00339943
10.1016/j.procs.2011.08.073
10.1016/j.procs.2011.08.076
10.1145/1031495.1031508
10.1145/781131.781133
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Keywords nesC
TinyOS
parallel and distributed computation
wireless sensor network
static optimization
Hopfield neural network
maximum independent set problem
Language English
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Gay,D., Levis, P. Behren, R., Welsh, M., Brewer, E. and Culler. D “The nesC language: A holistic approach to networked embedded systems”
New York, NY, USA 2004.
Serpen, G. and Li, J., “Parallel and distributed computation of maximum independent set by Hopfield neural net embedded into a wireless sensor network”, Proc. of Complex and Adaptive Systems Conference, Chicago, 2011.
Hopfield, Tank (bib0010) 1985; 52
NY, USA 2003.
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10.1016/j.procs.2011.08.074_bib0015
10.1016/j.procs.2011.08.074_bib0020
Hopfield (10.1016/j.procs.2011.08.074_bib0010) 1985; 52
References_xml – reference: Serpen, G. and Li, J., “Parallel and distributed computation of maximum independent set by Hopfield neural net embedded into a wireless sensor network”, Proc. of Complex and Adaptive Systems Conference, Chicago, 2011.
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  start-page: 141
  year: 1985
  end-page: 152
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  article-title: Neural Computation of Decision in Optimization Problems
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– reference: NY, USA 2003.
– reference: Gay,D., Levis, P. Behren, R., Welsh, M., Brewer, E. and Culler. D “The nesC language: A holistic approach to networked embedded systems”
– reference: Li, J. and Serpen, G., “TOSSIM simulation of wireless sensor network serving as hardware platform for Hopfield neural net configured for max independent set,” Proc. of Complex and Adaptive Systems Conference, Chicago, 2011.
– reference: Polastre, J., Hill, J. and Culler, D. “Versatile Low Power Media Access for Wireless Sensor Networks”
– volume: 52
  start-page: 141
  year: 1985
  ident: 10.1016/j.procs.2011.08.074_bib0010
  article-title: Neural Computation of Decision in Optimization Problems
  publication-title: Biological Cybernetics
  doi: 10.1007/BF00339943
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  doi: 10.1145/1031495.1031508
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SubjectTerms Hopfield neural network
maximum independent set problem
nesC
parallel and distributed computation
static optimization
TinyOS
wireless sensor network
Title nesC-TinyOS model for parallel and distributed computation of max independent set by Hopfield network on wireless sensor network
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