The back propagation (BP) algorithm has been very successful in training multilayer perceptron-based equalisers; despite its success BP convergence is still too slow. Within this paper we present a new approach to enhance the training efficiency of the multilayer perceptron-based equaliser (MLPE). Our approach consists on modifying the conventional back propagation algorithm, through creating an adaptive nonlinearity in the activation function. Experiment results evaluates the performance of the MLPE trained using the conventional BP and the improved back propagation with adaptive gain (IBPAG). Due to the adaptability of the activation function gain the nonlinear capacity and flexibility of the MLP is enhanced significantly. Therefore, the convergence properties of the proposed algorithm are more improved compared to the BP. The proposed algorithm achieves the best performance in the entire simulation experiments.

WSNs consist of large number of small sensors densely deployed to monitor a phenomenon. Most of the data generated from the WSNs represent events happening at time intervals. Sometimes and according to the nature of the applications, this data stream is continuous and can reach high speeds. Therefore, adopting new techniques, platforms and tools to deal with this large amount of sensory data became necessary. Therefore, the Big Data paradigm can represent a good solution for the extraction, analysis, viewing, sharing, storage and transfer of such volume of data. This paper presents a survey on integrating Big Data tools for gathering, storing and analyzing the data generated by WSNs.

WSNs consist of large number of small sensors densely deployed to monitor a phenomenon. Most of the data generated from the WSNs represent events happening at time intervals. Sometimes and according to the nature of the applications, this data stream is continuous and can reach high speeds. Therefore, adopting new techniques, platforms and tools to deal with this large amount of sensory data became necessary. Therefore, the Big Data paradigm can represent a good solution for the extraction, analysis, viewing, sharing, storage and transfer of such volume of data. This paper presents a survey on integrating Big Data tools for gathering, storing and analyzing the data generated by WSNs.