WSN-ANN: Parallel and distributed neurocomputing with wireless sensor networks

This paper proposes wireless sensor networks as a parallel and distributed computing platform for neurocomputing. The proposal entails leveraging the existing wireless sensor networks technology to serve as a hardware-software platform to implement and realize artificial neural network algorithms in fully parallel and distributed computation mode. The study describes the proposed parallel and distributed neurocomputing architecture, which is named as WSN-ANN, and its use as a hardware platform on a case study. A Hopfield neural network, which is configured to solve the minimum weakly connected dominating set problem, is embedded into a wireless sensor network. Simulation study results indicate that the proposed computing platform based on wireless sensor networks, WSN-ANN, is feasible and promising to serve as a parallel and distributed neurocomputer.

[1]  J. J. Hopfield,et al.  “Neural” computation of decisions in optimization problems , 1985, Biological Cybernetics.

[2]  David E. Culler,et al.  TOSSIM: accurate and scalable simulation of entire TinyOS applications , 2003, SenSys '03.

[3]  Eric Goles Ch.,et al.  Transient length in sequential iteration of threshold functions , 1983, Discret. Appl. Math..

[4]  Linqian Liu,et al.  A Parallel and Distributed Computing Platform for Neural Networks Using Wireless Sensor Networks , 2012 .

[5]  Ganesh K. Venayagamoorthy,et al.  Computational Intelligence in Wireless Sensor Networks: A Survey , 2011, IEEE Communications Surveys & Tutorials.

[6]  Andreas Willig,et al.  Protocols and Architectures for Wireless Sensor Networks , 2005 .

[7]  M. Geike,et al.  Emulation engine for spiking neurons and adaptive synaptic weights , 2005, Proceedings. 2005 IEEE International Joint Conference on Neural Networks, 2005..

[8]  H. Markram.  The Blue Brain Project , 2006, Nature Reviews Neuroscience.

[9]  Dharmendra S. Modha,et al.  The cat is out of the bag: cortical simulations with 109 neurons, 1013 synapses , 2009, Proceedings of the Conference on High Performance Computing Networking, Storage and Analysis.

[10]  Pekka Orponen,et al.  General-Purpose Computation with Neural Networks: A Survey of Complexity Theoretic Results , 2003, Neural Computation.

[11]  Yong Liu,et al.  A 45nm CMOS neuromorphic chip with a scalable architecture for learning in networks of spiking neurons , 2011, 2011 IEEE Custom Integrated Circuits Conference (CICC).

[12]  Andrzej Cichocki,et al.  Neural networks for optimization and signal processing , 1993 .

[13]  Deborah Estrin,et al.  Geography-informed energy conservation for Ad Hoc routing , 2001, MobiCom '01.

[14]  S. L. Pinjare,et al.  Design and Analog VLSI Implementation of Neural Network Architecture for Signal Processing , 2009 .

[15]  Fernando Morgado Dias,et al.  Artificial neural networks: a review of commercial hardware , 2004, Eng. Appl. Artif. Intell..

[16]  Erkki Mäkinen,et al.  A Neural Network Model to Minimize the Connected Dominating Set for Self-Configuration of Wireless Sensor Networks , 2009, IEEE Transactions on Neural Networks.

[17]  Andreas Grübl.  VLSI implementation of a spiking neural network , 2007 .

[18]  Gürsel Serpen,et al.  Determination of weights for relaxation recurrent neural networks , 2000, Neurocomputing.

[19]  Gürsel Serpen.  Managing spatio-temporal complexity in Hopfield neural network simulations for large-scale static optimization , 2004, Math. Comput. Simul..

[20]  Steve Furber,et al.  High-performance computing for systems of spiking neurons , 2006 .

[21]  David E. Culler,et al.  Versatile low power media access for wireless sensor networks , 2004, SenSys '04.

[22]  Maurizio Valle,et al.  Analog VLSI Implementation of Artificial Neural Networks with Supervised On-Chip Learning , 2002 .

[23]  Xiuzhen Cheng,et al.  Connected Dominating Set in Sensor Networks and MANETs , 2004 .

[24]  Antonio Puliafito,et al.  Artificial Intelligence and Synchronization in Wireless Sensor Networks , 2009, J. Networks.

[25]  Heik Heinrich Hellmich,et al.  Synaptic plasticity in spiking neural networks (SP2INN): a system approach , 2003, IEEE Trans. Neural Networks.

[26]  Gyula Simon,et al.  The flooding time synchronization protocol , 2004, SenSys '04.

[27]  A QoS network architecture to interconnect large-scale VLSI neural networks , 2009, 2009 International Joint Conference on Neural Networks.