Engineering and Technology Journal

One of the major weakness of Wireless Sensor Networks is the energy consumption because of limited battery resource and battery replacement or recharging difficulty. The research presented in this paper aims to reduce the energy consumption in the physical layer because most the energy consumption occurred in this layer. This reduction will be achieved via the use of ZigBee transceiver standard at the physical layer under 2.4GHz frequency band with the reduced complexity and lower power consumption than other techniques used in Wireless Sensor Network. Furthermore, such use will also enhance energy efficiency, bit error rate, and throughput of the wireless sensor network. In this paper, three different systems have been proposed according to the type of transceiver technique used in wireless sensor network. System1 uses ZigBee transceiver standard over the wireless sensor network without any additional technique whereas system2 uses ZigBee transceiver with Convolutional Coding over the WSNs model. On the other hand, system3 uses the ZigBee transceiver with both diversity techniquesVirtual Multi-Input Multi-Output and with Convolutional Coding over the wireless sensor network model. The simulation of matlab results show that system3 achieves the best energy efficiency performance compared with the other two systems at lower quality of channel. System2 achieves the best energy efficiency performance at medium quality of channel, and system1 achieves better energy efficiency at high quality of channel.

Wireless sensor networks(WSN) are an exiting emerging technology that
scientists believe to become a part of every day life in the next few years. However, at this time many issues in wireless sensor networks remain unresolved. This paper studies the architecture of a neural wireless sensor network designed to identify technical condition of the base station of wireless sensor networks ,and this work presents an on-chip artificial neural networks(ANN) in a Field Programmable Gate Arrays (FPGA) system. In order to take maximum advantage of the distributed architecture of multiple NN systems is to providing a high degree of parallelism between NNs and, hence, a higher speed-up in relation to a sequential implementation. The goal of this work is to realize the hardware implementation of the base station of neural wireless sensor network using FPGAs to measure hamidi ty, temperature and light for the security system of an office