RFID Localisation For Internet Of Things Smart Homes: A Survey

The Internet of Things (IoT) enables numerous business opportunities in fields as diverse as e-health, smart cities, smart homes, among many others. The IoT incorporates multiple long-range, short-range, and personal area wireless networks and technologies into the designs of IoT applications. Localisation in indoor positioning systems plays an important role in the IoT. Location Based IoT applications range from tracking objects and people in real-time, assets management, agriculture, assisted monitoring technologies for healthcare, and smart homes, to name a few. Radio Frequency based systems for indoor positioning such as Radio Frequency Identification (RFID) is a key enabler technology for the IoT due to its costeffective, high readability rates, automatic identification and, importantly, its energy efficiency characteristic. This paper reviews the state-of-the-art RFID technologies in IoT Smart Homes applications. It presents several comparable studies of RFID based projects in smart homes and discusses the applications, techniques, algorithms, and challenges of adopting RFID technologies in IoT smart home systems.Comment: 18 pages, 2 figures, 3 table

Real-time human ambulation, activity, and physiological monitoring:taxonomy of issues, techniques, applications, challenges and limitations

Automated methods of real-time, unobtrusive, human ambulation, activity, and wellness monitoring and data analysis using various algorithmic techniques have been subjects of intense research. The general aim is to devise effective means of addressing the demands of assisted living, rehabilitation, and clinical observation and assessment through sensor-based monitoring. The research studies have resulted in a large amount of literature. This paper presents a holistic articulation of the research studies and offers comprehensive insights along four main axes: distribution of existing studies; monitoring device framework and sensor types; data collection, processing and analysis; and applications, limitations and challenges. The aim is to present a systematic and most complete study of literature in the area in order to identify research gaps and prioritize future research directions

Intelligent Dynamic Traffic Light Sequence Using RFID

The proposed RFID traffic control avoids problems that usually arise with standard traffic control systems, especially those related to image processing and beam interruption techniques. This RFID technique deals with a multi-vehicle, multilane, multi road junction area. It provides an efficient time management scheme, in which a dynamic time schedule is worked out in real time for the passage of each traffic column. The real time operation of the system emulates the judgment of a traffic policeman on duty. The number of vehicles in each column and the routing are proprieties, upon which the calculations and the judgments are based

Data Fusion for Materials Location Estimation in Construction

Effective automated tracking and locating of the thousands of materials on construction sites improves material distribution and project performance and thus has a significant positive impact on construction productivity. Many locating technologies and data sources have therefore been developed, and the deployment of a cost-effective, scalable, and easy-to-implement materials location sensing system at actual construction sites has very recently become both technically and economically feasible. However, considerable opportunity still exists to improve the accuracy, precision, and robustness of such systems. The quest for fundamental methods that can take advantage of the relative strengths of each individual technology and data source motivated this research, which has led to the development of new data fusion methods for improving materials location estimation. In this study a data fusion model is used to generate an integrated solution for the automated identification, location estimation, and relocation detection of construction materials. The developed model is a modified functional data fusion model. Particular attention is paid to noisy environments where low-cost RFID tags are attached to all materials, which are sometimes moved repeatedly around the site. A portion of the work focuses partly on relocation detection because it is closely coupled with location estimation and because it can be used to detect the multi-handling of materials, which is a key indicator of inefficiency. This research has successfully addressed the challenges of fusing data from multiple sources of information in a very noisy and dynamic environment. The results indicate potential for the proposed model to improve location estimation and movement detection as well as to automate the calculation of the incidence of multi-handling

Automated Productivity Models for Earthmoving Operations

Earthmoving operations have significant importance, particularly for civil infrastructure projects. The performance of these operations should be monitored regularly to support timely recognition of undesirable productivity variances. Although productivity assessment occupies high importance in earthmoving operations, it does not provide sufficient information to assist project managers in taking the necessary actions in a timely manner. Assessment only is not capable of identifying problems encountered in these operations and their causes. Many studies recognized conditions and related factors that influence productivity of earthmoving operations. These conditions are mainly project-specific and vary from one project to another. Most of reported work in the literature focused on assessment rather than analysis of productivity. This study presents three integrated models that automate productivity measurement and analysis processes with capabilities to detect different adverse conditions that influence the productivity of earthmoving operations. The models exploit innovations in wireless and remote sensing technologies to provide project managers, contractors, and decision makers with a near-real-time automated productivity measurement and analysis. The developed models account for various uncertainties associated with earthmoving projects. The first model introduces a fuzzy-based standardization for customizing the configuration of onsite data acquisition systems for earthmoving operations. While the second model consists of two interrelated modules. The first is a customized automated data acquisition module, where a variety of sensors, smart boards, and microcontrollers are used to automate the data acquisition process. This module encompasses onsite fixed unit and a set of portable units attached to each truck used in the earthmoving fleet. The fixed unit is a communication gateway (Meshlium®), which has integrated MySQL database with data processing capabilities. Each mobile unit consists of a microcontroller equipped with a smart board that hosts a GPS module as well as a number of sensors such as accelerometer, temperature and humidity sensors, load cell and automated weather station. The second is a productivity measurement and analysis module, which processes and analyzes the data collected automatically in the first module. It automates the analysis process using data mining and machine learning techniques; providing a near-real-time web-based visualized representation of measurement and analysis outcomes. Artificial Neural Network (ANN) was used to model productivity losses due to the existence of different influencing conditions. Laboratory and field work was conducted in the development and validation processes of the developed models. The work encompassed field and scaled laboratory experiments. The laboratory experiments were conducted in an open to sky terrace to allow for a reliable access to GPS satellites. Also, to make a direct connection between the data communication gateway (Meshlium®), initially installed on a PC computer to observe the received data latency. The laboratory experiments unitized 1:24 scaled loader and dumping truck to simulate loading, hauling and dumping operations. The truck was instrumented with the microcontroller equipped with an accelerometer, GPS module, load cell, and soil water content sensor. Thirty simulated earthmoving cycles were conducted using the scaled equipment. The collected data was recorded in a micro secure digital (SD) card in a comma separated value (CSV) format. The field work was carried out in the city of Saint-Laurent, Montreal, Quebec, Canada using a passenger vehicle to mimic the hauling truck operational modes. Fifteen Field simulated earthmoving cycles were performed. In this work two roads with different surface conditions, but of equal length (1150 m) represented the haul and return roads. These two roads were selected to validate the developed road condition analysis algorithm and to study the model’s capability in determining the consequences of adverse road conditions on the haul and return durations and thus on the tuck and fleet productivity. The data collected from the lab experiments and field work was used as input for the developed model. The developed model has shown perfect recognition of the state of truck throughout the fifteen field simulated earthmoving cycles. The developed road condition analysis algorithm has demonstrated an accuracy of 83.3% and 82.6% in recognizing road bumps and potholes, respectively. Also, the results indicated tiny variances in measuring the durations compared with actual durations using time laps displayed on a smart cell telephone; with an average invalidity percentage AIP% of 1.89 % and 1.33% for the joint hauling and return duration and total cycle duration, respectively

Smart Indoor Positioning/Location and Navigation: A Lightweight Approach

In this paper a new location indoor system is presented, which shows the position and orientation of the user in closed environments, as well as the optimal route to his destination through location tags. This system is called Labelee, and it makes easier the interaction between users and devices through QR code scanning or by NFC tag reading, because this technology is increasingly common in the latest smartphones. With this system, users could locate themselves into an enclosure with less interaction

Intelligent evacuation management systems: A review

Crowd and evacuation management have been active areas of research and study in the recent past. Various developments continue to take place in the process of efficient evacuation of crowds in mass gatherings. This article is intended to provide a review of intelligent evacuation management systems covering the aspects of crowd monitoring, crowd disaster prediction, evacuation modelling, and evacuation path guidelines. Soft computing approaches play a vital role in the design and deployment of intelligent evacuation applications pertaining to crowd control management. While the review deals with video and nonvideo based aspects of crowd monitoring and crowd disaster prediction, evacuation techniques are reviewed via the theme of soft computing, along with a brief review on the evacuation navigation path. We believe that this review will assist researchers in developing reliable automated evacuation systems that will help in ensuring the safety of the evacuees especially during emergency evacuation scenarios