Data fusion of multi-sensor for IOT precise measurement based on improved PSO algorithms

AbstractThis work proposes an improved particle swarm optimization (PSO) method to increase the measurement precision of multi-sensors data fusion in the Internet of Things (IOT) system. Critical IOT technologies consist of a wireless sensor network, RFID, various sensors and an embedded system. For multi-sensor data fusion computing systems, data aggregation is a main concern and can be formulated as a multiple dimensional based on particle swarm optimization approaches. The proposed improved PSO method can locate the minimizing solution to the objective cost function in multiple dimensional assignment themes, which are considered in particle swarm initiation, cross rules and mutation rules. The optimum seclusion can be searched for efficiently with respect to reducing the search range through validated candidate measures. Experimental results demonstrate that the proposed improved PSO method for multi-sensor data fusion is highly feasible for IOT system applications

Security and Power Aware IPV6 Programming in Internet of Things Using CONTIKI and COOJA

The current era is surrounded with enormous devices and gadgets connected with each other using high performance technologies. Such type of technology loaded object communication is treated under the aegis of Internet of Things (IoT). A number of applications are using IoT based communication whether it is related to defense equipments, smart cities, smart offices, highway patrolling, smart toll collections, business communications, satellite televisions, traffic systems or interconnected web cams for social security. IoT is also known and associated with other terms including Ubiquitous Computing (UbiComp), Pervasive Computing or Ambient Computing in which number of devices and objects are virtually connected for remote monitoring and decision making. This manuscript underlines the security and power aware programming in IoT for higher performance in Cooja

Research of Applying Machine Learning Methods to Outlier Detection in Wireless Sensor Networks

兵庫県立大学大学院201

Design and Implementation of System Components for Radio Frequency Based Asset Tracking Devices to Enhance Location Based Services. Study of angle of arrival techniques, effects of mutual coupling, design of an angle of arrival algorithm, design of a novel miniature reconfigurable antenna optimised for wireless communication systems

The angle of arrival estimation of multiple sources plays a vital role in the field of array signal processing as MIMO systems can be employed at both the transmitter and the receiver end and the system capacity, reliability and throughput can be significantly increased by using array signal processing. Almost all applications require accurate direction of arrival (DOA) estimation to localize the sources of the signals. Another important parameter of localization systems is the array geometry and sensor design which can be application specific and is used to estimate the DOA. In this work, various array geometries and arrival estimation algorithms are studied and then a new scheme for multiple source estimation is proposed and evaluated based on the performance of subspace and non-subspace decomposition methods. The proposed scheme has shown to outperform the conventional Multiple Signal Classification (MUSIC) estimation and Bartlett estimation techniques. The new scheme has a better performance advantage at low and high signal to noise ratio values (SNRs). The research work also studies different array geometries for both single and multiple incident sources and proposes a geometry which is cost effective and efficient for 3, 4, and 5 antenna array elements. This research also considers the shape of the ground plane and its effects on the angle of arrival estimation and in addition it shows how the mutual couplings between the elements effect the overall estimation and how this error can be minimised by using a decoupling matrix. At the end, a novel miniaturised multi element reconfigurable antenna to represent the receiver base station is designed and tested. The antenna radiation patterns in the azimuth angle are almost omni-directional with linear polarisation. The antenna geometry is uniplanar printed logspiral with striplines feeding network and biased components to improve the impedance bandwidth. The antenna provides the benefit of small size, and re-configurability and is very well suited for the asset tracking applications

An inference system framework for personal sensor devices in mobile health and internet of things networks

Future healthcare directions include individuals being monitored in real-time during day-to-day activity using wearable sensors. This thesis solves a critical requirement, that of intelligently managing when body sensors should alert doctors of changes to a person’s health status, bringing existing research closer to live health monitoring

Dual-Use Space Technology Transfer Conference and Exhibition

This document contains papers presented at the Dual-Use Space Technology Transfer Conference and Exhibition held at the Johnson Space Center February 1-3, 1994. Possible technology transfers covered during the conference were in the areas of information access; innovative microwave and optical applications; materials and structures; marketing and barriers; intelligent systems; human factors and habitation; communications and data systems; business process and technology transfer; software engineering; biotechnology and advanced bioinstrumentation; communications signal processing and analysis; new ways of doing business; medical care; applications derived from control center data systems; human performance evaluation; technology transfer methods; mathematics, modeling, and simulation; propulsion; software analysis and decision tools systems/processes in human support technology; networks, control centers, and distributed systems; power; rapid development perception and vision technologies; integrated vehicle health management; automation technologies; advanced avionics; ans robotics technologies. More than 77 papers, 20 presentations, and 20 exhibits covering various disciplines were presented b experts from NASA, universities, and industry

FAULT DETECTION AND PREDICTION IN ELECTROMECHANICAL SYSTEMS VIA THE DISCRETIZED STATE VECTOR-BASED PATTERN ANALYSIS OF MULTI-SENSOR SIGNALS

Department of System Design and Control EngineeringIn recent decades, operation and maintenance strategies for industrial applications have evolved from corrective maintenance and preventive maintenance, to condition-based monitoring and eventually predictive maintenance. High performance sensors and data logging technologies have enabled us to monitor the operational states of systems and predict fault occurrences. Several time series analysis methods have been proposed in the literature to classify system states via multi-sensor signals. Since the time series of sensor signals is often characterized as very-short, intermittent, transient, highly nonlinear, and non-stationary random signals, they make time series analyses more complex. Therefore, time series discretization has been popularly applied to extract meaningful features from original complex signals. There are several important issues to be addressed in discretization for fault detection and prediction: (i) What is the fault pattern that represents a system???s faulty states, (ii) How can we effectively search for fault patterns, (iii) What is a symptom pattern to predict fault occurrences, and (iv) What is a systematic procedure for online fault detection and prediction. In this regard, this study proposes a fault detection and prediction framework that consists of (i) definition of system???s operational states, (ii) definitions of fault and symptom patterns, (iii) multivariate discretization, (iv) severity and criticality analyses, and (v) online detection and prediction procedures. Given the time markers of fault occurrences, we can divide a system???s operational states into fault and no-fault states. We postulate that a symptom state precedes the occurrence of a fault within a certain time period and hence a no-fault state consists of normal and symptom states. Fault patterns are therefore found only in fault states, whereas symptom patterns are either only found in the system???s symptom states (being absent in the normal states) or not found in the given time series, but similar to fault patterns. To determine the length of a symptom state, we present a symptom pattern-based iterative search method. In order to identify the distinctive behaviors of multi-sensor signals, we propose a multivariate discretization approach that consists mainly of label definition, label specification, and event codification. Discretization parameters are delicately controlled by considering the key characteristics of multi-sensor signals. We discuss how to measure the severity degrees of fault and symptom patterns, and how to assess the criticalities of fault states. We apply the fault and symptom pattern extraction and severity assessment methods to online fault detection and prediction. Finally, we demonstrate the performance of the proposed framework through the following six case studies: abnormal cylinder temperature in a marine diesel engine, automotive gasoline engine knockings, laser weld defects, buzz, squeak, and rattle (BSR) noises from a car door trim (using a typical acoustic sensor array and using acoustic emission sensors respectively), and visual stimuli cognition tests by the P300 experiment.ope

Review of advanced road materials, structures, equipment, and detection technologies

As a vital and integral component of transportation infrastructure, pavement has a direct and tangible impact on socio-economic sustainability. In recent years, an influx of groundbreaking and state-of-the-art materials, structures, equipment, and detection technologies related to road engineering have continually and progressively emerged, reshaping the landscape of pavement systems. There is a pressing and growing need for a timely summarization of the current research status and a clear identification of future research directions in these advanced and evolving technologies. Therefore, Journal of Road Engineering has undertaken the significant initiative of introducing a comprehensive review paper with the overarching theme of “advanced road materials, structures, equipment, and detection technologies”. This extensive and insightful review meticulously gathers and synthesizes research findings from 39 distinguished scholars, all of whom are affiliated with 19 renowned universities or research institutions specializing in the diverse and multidimensional field of highway engineering. It covers the current state and anticipates future development directions in the four major and interconnected domains of road engineering: advanced road materials, advanced road structures and performance evaluation, advanced road construction equipment and technology, and advanced road detection and assessment technologies

Eleventh International Conference on the Bearing Capacity of Roads, Railways and Airfields

Innovations in Road, Railway and Airfield Bearing Capacity – Volume 1 comprises the first part of contributions to the 11th International Conference on Bearing Capacity of Roads, Railways and Airfields (2022). In anticipation of the event, it unveils state-of-the-art information and research on the latest policies, traffic loading measurements, in-situ measurements and condition surveys, functional testing, deflection measurement evaluation, structural performance prediction for pavements and tracks, new construction and rehabilitation design systems, frost affected areas, drainage and environmental effects, reinforcement, traditional and recycled materials, full scale testing and on case histories of road, railways and airfields. This edited work is intended for a global audience of road, railway and airfield engineers, researchers and consultants, as well as building and maintenance companies looking to further upgrade their practices in the field