Parallel and distributed neurocomputing with wireless sensor networks

This paper proposes a novel hardware computing platform for fully parallel and distributed computation of artificial neural network (ANN) algorithms. The proposed idea entails leveraging the existing wireless sensor networks (WSN) technology to serve as a parallel and distributed hardware platform to implement computations for artificial neural network algorithms. Feasibility of the proposed neurocomputing architecture is demonstrated through a simulation-based case study, which uses Kohonen׳s self-organizing map as the neural network algorithm. MATLAB-based PROWLER, which is a protocol and application level simulator for wireless sensor networks, is employed for the simulation study. Findings demonstrated that the proposed neurocomputing architecture was able to train the SOM neural network with competitive accuracy values for the unsupervised clustering task. Conclusions of the simulation study suggest that the WSN-based neurocomputing architecture is a feasible alternative for realizing parallel and distributed computation of artificial neural network algorithms.