High Accuracy Human Activity Monitoring using Neural network

This paper presents the designing of a neural network for the classification of Human activity. A Triaxial accelerometer sensor, housed in a chest worn sensor unit, has been used for capturing the acceleration of the movements associated. All the three axis acceleration data were collected at a base station PC via a CC2420 2.4GHz ISM band radio (zigbee wireless compliant), processed and classified using MATLAB. A neural network approach for classification was used with an eye on theoretical and empirical facts. The work shows a detailed description of the designing steps for the classification of human body acceleration data. A 4-layer back propagation neural network, with Levenberg-marquardt algorithm for training, showed best performance among the other neural network training algorithms.Comment: 6 pages, 4 figures, 4 Tables, International Conference on Convergence Information Technology, pp. 430-435, 2008 Third International Conference on Convergence and Hybrid Information Technology, 200

Generalized Neuron Based Secure Media Access Control Protocol for Wireless Sensor Networks

Security plays a pivotal role in most applications of wireless sensor networks. It is common to find inadequately secure networks confined only to controlled environments. The issue of security in wireless sensor networks is a hot research topic for over a decade. This paper presents a compact generalized neuron (GN) based medium access protocol that renders a CSMA/CD network secure against denial-of-service attacks launched by adversaries. The GN enhances the security by constantly monitoring multiple parameters that reflect the possibility that an attack is launched by an adversary. Particle swarm optimization, a popular bio-inspired evolutionary-like optimization algorithm is used for training the GN. The wireless sensor network is simulated using Vanderbilt Prowler, a probabilistic wireless network simulator. Simulation results show that the choice of threshold suspicion parameter impacts on the tradeoff between network effectiveness and lifetime

An experimental study on a motion sensing system for sports training

In sports science, motion data collected from athletes is used to derive key performance characteristics, such as stride length and stride frequency, that are vital coaching support information. The sensors for use must be more accurate, must capture more vigorous events, and have strict weight and size requirements, since they must not themselves affect performance. These requirements mean each wireless sensor device is necessarily resource poor and yet must be capable of communicating a considerable amount of data, contending for the bandwidth with other sensors on the body. This paper analyses the results of a set of network traffic experiments that were designed to investigate the suitability of conventional wireless motion sensing system design � which generally assumes in-network processing - as an efficient and scalable design for use in sports training

XBee wireless sensor networks for heart rate monitoring in sport training

Heart Rate Monitors (HRMs) have become widely used since the last two decades. It used as training aid for various types of sports. And the development of new HRMs has evolved rapidly. Thus, in order to determine the exercise intensity of training session or race, HRMs are mainly used. Compared to the other indication of exercise intensity, Heart rate is easy to monitor, compatible to use in most situation and relatively cheap. Thus, it is most beneficial if wireless sensor network can be implementing into the system. Other than monitored their condition by themselves, this system may allowed a number of athletes being monitored simultaneously. Arduino-Nano board was used to interface with nRF24AP1 and XBee. The wireless sensor network consists of a microcontroller on Arduino-Nano board, nRF24AP1 and the XBee wireless communication module which is based on the IEEE 802.15.4. This system will involve peer to peer communication of ANT+ and mesh networking among the XBee

A training monitoring system for cyclist based on wireless sensor networks

This paper presents a training monitoring system for cyclist that is based on the technology of wireless sensor networks (WSNs). A stable and reliable wireless cyclist monitoring system is vital to establish a smart and efficient sports management program. A training monitoring system has been developed and tested in a real cyclist training environment in a velodrome. The system is designed is such a way that the packet loss rate is minimum. Using TelG mote as the basis, customized sensor nodes that function as a forwarder node and the relay nodes are developed to form the WSN. This WSN is linked to the cloud network on the Internet. The cloud network is then established and end users application for data accessing is designed. Several experiments have been conducted in a real scenario in a velodrome to measure the reliability of the system architecture. It is shown from the experiments that the proposed system is reliable even when the cyclist is moving at a high speed. The packet loss is less than 2% which does not give a huge impact to the data transmission

FCS-MBFLEACH: Designing an Energy-Aware Fault Detection System for Mobile Wireless Sensor Networks

Wireless sensor networks (WSNs) include large-scale sensor nodes that are densely distributed over a geographical region that is completely randomized for monitoring, identifying, and analyzing physical events. The crucial challenge in wireless sensor networks is the very high dependence of the sensor nodes on limited battery power to exchange information wirelessly as well as the non-rechargeable battery of the wireless sensor nodes, which makes the management and monitoring of these nodes in terms of abnormal changes very difficult. These anomalies appear under faults, including hardware, software, anomalies, and attacks by raiders, all of which affect the comprehensiveness of the data collected by wireless sensor networks. Hence, a crucial contraption should be taken to detect the early faults in the network, despite the limitations of the sensor nodes. Machine learning methods include solutions that can be used to detect the sensor node faults in the network. The purpose of this study is to use several classification methods to compute the fault detection accuracy with different densities under two scenarios in regions of interest such as MB-FLEACH, one-class support vector machine (SVM), fuzzy one-class, or a combination of SVM and FCS-MBFLEACH methods. It should be noted that in the study so far, no super cluster head (SCH) selection has been performed to detect node faults in the network. The simulation outcomes demonstrate that the FCS-MBFLEACH method has the best performance in terms of the accuracy of fault detection, false-positive rate (FPR), average remaining energy, and network lifetime compared to other classification methods