Publications

The need to deploy wireless sensor networks (WSNs) for real-world applications, such as mobile multimedia for healthcare organizations, is increasing spectacularly. However, the energy problem remains one of the core barriers preventing an increase in investment in this technology. In this paper, we propose a new technique to resolve the problems due to limited energy sources. Using a quaternary transceiver (in the architecture on a sensor node), instead of a binary one, which will use the amplitude/phase, modulator/demodulator units to increase the number of bits transmitted per symbol. The system will reduce the consumption of energy in the transmission phase due to the increased bits transmitted per symbol. Moreover, neural network static random access memory (NN-SRAM) implementation in a clustering- based system for energy-constrained WSNs is proposed. The scheme reduces the total amount of energy consumption in storage and transmissions during the data dissemination process. Through simulation results based on MATLAB and Spice software tools, it is shown that the neural network static random access memory implementation in a clustering-based system reduces the energy consumption of the entire system by about 76.99%.