Liu, L.Q. (2012) A Parallel and Distributed Computing Platform for Neural Networks Using Wireless Sensor Networks. Ph.D. Thesis, The University of Toledo, Toledo.

has been cited by the following article:

• TITLE: Distributed Target Location in Wireless Sensors Network: An Approach Using FPGA and Artificial Neural Network

  AUTHORS: Mauro Rodrigo Larrat Frota e Silva, Glaucio Haroldo Silva de Carvalho, Dionne Cavalcante Monteiro, Leomário Silva Machado

  KEYWORDS: Wireless Sensors Network, RSSI, FPGA, Artificial Neural Network, Distributed Localization Methods

  JOURNAL NAME: Wireless Sensor Network, Vol.7 No.5, May 26, 2015

  ABSTRACT: This paper analyzes the implementation of an algorithm into a FPGA embedded and distributed target location method using the Received Signal Strength Indicator (RSSI). The objective is to show a method in which an embedded feedforward Artificial Neural Network (ANN) can estimate target location in a distributed fashion against anchor failure. We discuss the lack of FPGA implementation of equivalent methods and the benefits of using a robust platform. We introduce the description of the implementation and we explain the operation of the proposed method, followed by the calculated errors due to inherent Elliott function approximation and the discretization of decimal values used as free parameters in ANN. Furthermore, we show some target location estimation points in function of different numbers of anchor failures. Our contribution is to show that an FPGA embedded ANN implementation, with a few layers, can rapidly estimate target location in a distributed fashion and in presence of failures of anchor nodes considering accuracy, precision and execution time.