Application of Neurocomputing for Data Approximation and Classification in Wireless Sensor Networks

A new application of neurocomputing for data approximation and classification is introduced to process data in a wireless sensor network. For this purpose, a simplified dynamic sliding backpropagation algorithm is implemented on a wireless sensor network for transportation applications. It is able to approximate temperature and humidity in sensor nodes. In addition, two architectures of “radial basis function” (RBF) classifiers are introduced with probabilistic features for data classification in sensor nodes. The applied approximation and classification algorithms could be used in similar applications for data processing in embedded systems

An Overview of Own Tracking Wireless Sensors with GSM-GPS Features

Wireless Sensors (WS) mobility and pause time have a major impact directly influencing the energy consumption. Lifetime of a WS Network (WSN) depends directly on the energy consumption, thus, the hardware and software components must be optimized for energy management. This study aims to combine a compact hardware architecture with a smart energy management efficiency in order to increase ratio Lifetime/Energy Consumption, to improve the operating time on a portable tracking system with GPS/GSM/GPRS features and own power. In this paper we present the evolution of own WS tracking architecture with GPS/GSM/GPRS features, basic criterion being the lifetime combined with low power consumption. Concern was focused on hardware and software areas: Large number of physical components led to reconsideration of hardware architecture, while for software, we focused on algorithms able to reduce the number of bits in transmitted data packets, which help to reduce energy consumption. The results and conclusions show that the goal was achieved