| This thesis presents a study on implementing the multilayer perceptron neural network on the wireless sensor network in a parallel and distributed way. We take advantage of the topological resemblance between the multilayer perceptron and wireless sensor network. A single neuron in the multilayer perceptron neural network is implemented on a wireless sensor node, and the connections between neurons are achieved by the wireless links between nodes. While the computation of the multilayer perceptron benefits from the massive parallelism and the fully distribution when the wireless sensor network is serving as the hardware platform, it is still unknown whether the delay and drop phenomena for message packets carrying neuron outputs would prohibit the multilayer perceptron from getting a decent performance. A simulation-based empirical study is conducted to assess the performance profile of the multilayer perceptron on a number of different problems. Simulation study is performed using a simulator which is developed in-house for the unique requirements of the study proposed herein. The simulator only simulates the major effects of wireless sensor network operation which influence the running of multilayer perceptron. A model for delay and drop in wireless sensor network is proposed for creating the simulator. The setting of the simulation is well defined. Back-Propagation with Momentum learning is employed as the learning algorithms for the neural network. A formula for the number of neurons in the hidden layer neuron is chosen by empirical study. The simulation is done under different network topology and condition of delay and drop for the wireless sensor network. Seven data sets, namely Iris, Wine, Ionosphere, Dermatology, Handwritten Numerical, Isolet and Gisette, with the attributes counts up to 5000 and instances counts up to 7797 are employed to profile the performance. The simulation results are compared with those from the literature and through the non-distributed multilayer perceptron. Comparative performance evaluation suggests that the performance of multilayer perceptron using wireless sensor network as the hardware platform is comparable with other machine learning algorithms and as good as the non-distributed multilayer perceptron. The time and message complexity have been analyzed and it shows the scalability of the proposed method is promising. |
