Occupancy monitoring and prediction in ambient intelligent environment

Occupancy monitoring and prediction as an influential factor in the extraction of occupants' behavioural patterns for the realisation of ambient intelligent environments is addressed in this research. The proposed occupancy monitoring technique uses occupancy detection sensors with unobtrusive features to monitor occupancy in the environment. Initially the occupancy detection is conducted for a purely single-occupant environment. Then, it is extended to the multipleoccupant environment and associated problems are investigated. Along with the occupancy monitoring, it is aimed to supply prediction techniques with a suitable occupancy signal as the input which can enhance efforts in developing ambient intelligent environments. By predicting the occupancy pattern of monitored occupants, safety, security, the convenience of occupants, and energy saving can be improved. Elderly care and supporting people with health problems like dementia and Alzheimer disease are amongst the applications of such an environment. In the research, environments are considered in different scenarios based on the complexity of the problem including single-occupant and multiple-occupant scenarios. Using simple sensory devices instead of visual equipment without any impact on privacy and her/his normal daily activity, an occupant is monitored in a living or working environment in the single-occupant scenario. ZigBee wireless communication technology is used to collect signals from sensory devices such as motion detection sensors and door contact sensors. All these technologies together including sensors, wireless communication, and tagging are integrated as a wireless sensory agent

Automated Site Data Acquisition for Effective Project Control

Tracking and control of construction projects depend primarily on the accuracy, frequency and time required to collect actual onsite data of construction operations that characterize the work progress. Earned Value Analysis (EVA) is being used for reporting work progress and for forecasting project status at completion and at any future time horizon. Critical to its reliable application is accurate and timely data for quantifying the budgeted cost of work performed. Automated site data acquisition has received considerable attention in recent years to circumvent the limitations of manual data collection. The limitations of existing models lie in their inability to measure the progress of different types of construction activities from inception to completion in near real-time. The objective of this research is to study and analyze the characteristics of automated data acquisition technologies in construction. This thesis is carried out with a focus on automating the process of data collection and project control. An automated model is developed that integrates project visualization-information aspects, automated site data acquisition and earned value analysis. The developed model consists of two main frameworks; one is for data acquisition and the other for data analysis and processing. Data acquisition is carried out using the integrated automated data acquisition technologies embedded in the tablet PC used in this research as well as others, referred to here as independent, automated data acquisition technologies. The developed model makes full use of 4D BIM to provide visualization and pertinent information of activities in progress. The developed model embraces the human factors to augment the visualization-information aspects, localization technologies and development of progress templates. The developed model demonstrated the significance of data fusion of a wide range of automated site data acquisition technologies and visualization-information technologies. A prime focus is placed on extensive field studies and experimental work. Field studies on construction jobsite are conducted utilizing a wide range of technologies such as 3D Scanner, RFID and GPS. Extensive experimental work is conducted to develop deployment protocols for utilization of Ultra High Frequency (UHF) passive RFID in localization and material tracking. The field and lab work resulted in a number of observations, findings, and lessons learned for deployment of passive RFID in construction. The results presented in this study demonstrate the potential use of short range RFIDs in location estimation and material tracking in a cost-effective manner for indoor construction operations. The developed method for location identification and material tracking using RFID technology can be used to obtain information required for scalable, near-real-time decision-making and timely tracking of project status. The developed methods and algorithms are implemented in prototype software. It consists of two computational frameworks; BIM+ and Control+. BIM+ is Two-Tier software, which utilizes an object-oriented BIM model. So, it can be used as an advanced tool for data acquisition through the user’s tablet PC. Control+ is Three-Tier Web-Based software for processing the captured data from the tablet PC and independent automated data acquisition technologies. The developed model, methods, algorithms and software constitute a step ahead of current progress reporting applications and expand upon automated site data acquisition technologies and visualization-information technologies use in construction

Data Analysis From an Internet Of Things System in a Gas Station Convenience Store

RÉSUMÉ : Le numérique est de plus en plus populaire et peut être appliquée à plusieurs industries et entreprises afin d'améliorer la productivité et extraire des informations de marketing. Ce travail de recherche s’adresse sur le potentiel des applications d'exploration de données dans un magasin numérisé de vente au détail traditionnel. L'objectif est de démontrer que grâce à un système IoT, des informations peuvent être extraites à partir des données collectées à l'aide des méthodes appropriées, tel que les méthodes d'exploration de données. Nos objectifs ont été réalisés en installant des capteurs Bluetooth dans un dépanneur de station d’essence dans la ville de Laval et en recueillant des données provenant des appareils Bluetooth des clients. Ces appareils incluent tous les téléphones intelligents et les montres intelligentes équipés de la technologie Bluetooth. Une collecte automatisée a été faite sur une durée de une semaine. À partir des données collectées, une première analyse a été effectué pour trouver une corrélation entre le RSSI et les distances réelles dans le but de tracer le mouvement des clients dans le magasin. Ces analyses ont montré que la précision des capteurs n’est pas assez forte pour démontrer un mouvement précis des clients. Pour s’adapter au manque de précision observé, la prochaine étape a été de regarder les données des capteurs comme des événements de présences ou absences dans les zones autours de chaque capteur. Avec les présences identifiées, une proportion de volume d’activité dans chaque zone a été établi comme donnée pour être utilisée avec les rapports de ventes du magasin pour en construire un arbre de décision. Nos résultats ont démontré que des informations peuvent être extraites à partir de la construction de ces arbres de décision qui contiennent des données venant d'un système IoT bien mis en place dans un environnement de vente au détail traditionnel.----------ABSTRACT : Digitalization is increasingly popular and can be applied to multiple industries and businesses to improve productivity and extract marketing insights. This research work looks at the potential of data mining applications in a digitalized traditional retail store. The goal is to demonstrate that through the means of an IoT system, insight can be extracted from the collected data with the proper tools, such as data mining methods. This has been done by installing Bluetooth beacons in a gas station convenience store in the city of Laval and collecting data coming from the customers Bluetooth devices. These devices include all smartphones and smart watches equipped with Bluetooth. An automated collection of data was done for a duration of one week. From the collected data, a first analysis was done to find a correlation between the RSSI and real distances to trace customers pathways within the store. These analysis showed us that the sensors precisions are not high enough to show a precise client pathway within the store. To adapt to this lack of precision, the next step was to look at the data from the sensors as events of presences or absences in the zones around each sensor. With each presence identified, a proportion of volume of activity in each zone has been established as data to be used with the store’s sales report to build a decision tree. Our results have showed that useful information can be extracted from a properly constructed decision tree with data coming from an IoT system put in place in a traditional retail environment

New Approach of Indoor and Outdoor Localization Systems

Accurate determination of the mobile position constitutes the basis of many new applications. This book provides a detailed account of wireless systems for positioning, signal processing, radio localization techniques (Time Difference Of Arrival), performances evaluation, and localization applications. The first section is dedicated to Satellite systems for positioning like GPS, GNSS. The second section addresses the localization applications using the wireless sensor networks. Some techniques are introduced for localization systems, especially for indoor positioning, such as Ultra Wide Band (UWB), WIFI. The last section is dedicated to Coupled GPS and other sensors. Some results of simulations, implementation and tests are given to help readers grasp the presented techniques. This is an ideal book for students, PhD students, academics and engineers in the field of Communication, localization & Signal Processing, especially in indoor and outdoor localization domains

Device-free indoor localisation with non-wireless sensing techniques : a thesis by publications presented in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Electronics and Computer Engineering, Massey University, Albany, New Zealand

Global Navigation Satellite Systems provide accurate and reliable outdoor positioning to support a large number of applications across many sectors. Unfortunately, such systems do not operate reliably inside buildings due to the signal degradation caused by the absence of a clear line of sight with the satellites. The past two decades have therefore seen intensive research into the development of Indoor Positioning System (IPS). While considerable progress has been made in the indoor localisation discipline, there is still no widely adopted solution. The proliferation of Internet of Things (IoT) devices within the modern built environment provides an opportunity to localise human subjects by utilising such ubiquitous networked devices. This thesis presents the development, implementation and evaluation of several passive indoor positioning systems using ambient Visible Light Positioning (VLP), capacitive-flooring, and thermopile sensors (low-resolution thermal cameras). These systems position the human subject in a device-free manner (i.e., the subject is not required to be instrumented). The developed systems improve upon the state-of-the-art solutions by offering superior position accuracy whilst also using more robust and generalised test setups. The developed passive VLP system is one of the first reported solutions making use of ambient light to position a moving human subject. The capacitive-floor based system improves upon the accuracy of existing flooring solutions as well as demonstrates the potential for automated fall detection. The system also requires very little calibration, i.e., variations of the environment or subject have very little impact upon it. The thermopile positioning system is also shown to be robust to changes in the environment and subjects. Improvements are made over the current literature by testing across multiple environments and subjects whilst using a robust ground truth system. Finally, advanced machine learning methods were implemented and benchmarked against a thermopile dataset which has been made available for other researchers to use

Eyes-Free Vision-Based Scanning of Aligned Barcodes and Information Extraction from Aligned Nutrition Tables

Visually impaired (VI) individuals struggle with grocery shopping and have to rely on either friends, family or grocery store associates for shopping. ShopMobile 2 is a proof-of-concept system that allows VI shoppers to shop independently in a grocery store using only their smartphone. Unlike other assistive shopping systems that use dedicated hardware, this system is a software only solution that relies on fast computer vision algorithms. It consists of three modules - an eyes free barcode scanner, an optical character recognition (OCR) module, and a tele-assistance module. The eyes-free barcode scanner allows VI shoppers to locate and retrieve products by scanning barcodes on shelves and on products. The OCR module allows shoppers to read nutrition facts on products and the tele-assistance module allows them to obtain help from sighted individuals at remote locations. This dissertation discusses, provides implementations of, and presents laboratory and real-world experiments related to all three modules

Managing trust and reliability for indoor tracking systems

Indiana University-Purdue University Indianapolis (IUPUI)Indoor tracking is a challenging problem. The level of accepted error is on a much smaller scale than that of its outdoor counterpart. While the global positioning system has become omnipresent, and a widely accepted outdoor tracking system it has limitations in indoor environments due to loss or degradation of signal. Many attempts have been made to address this challenge, but currently none have proven to be the de-facto standard. In this thesis, we introduce the concept of opportunistic tracking in which tracking takes place with whatever sensing infrastructure is present – static or mobile, within a given indoor environment. In this approach many of the challenges (e.g., high cost, infeasible infrastructure deployment, etc.) that prohibit usage of existing systems in typical application domains (e.g., asset tracking, emergency rescue) are eliminated. Challenges do still exist when it comes to provide an accurate positional estimate of an entities location in an indoor environment, namely: sensor classification, sensor selection, and multi-sensor data fusion. We propose an enhanced tracking framework that through the infusion of QoS-based selection criteria of trust and reliability we can improve the overall accuracy of the tracking estimate. This improvement is predicated on the introduction of learning techniques to classify sensors that are dynamically discovered as part of this opportunistic tracking approach. This classification allows for sensors to be properly identified and evaluated based upon their specific behavioral characteristics through performance evaluation. This in-depth evaluation of sensors provides the basis for improving the sensor selection process. A side effect of obtaining this improved accuracy is the cost, found in the form of system runtime. This thesis provides a solution for this tradeoff between accuracy and cost through an optimization function that analyzes this tradeoff in an effort to find the optimal subset of sensors to fulfill the goal of tracking an object as it moves indoors. We demonstrate that through this improved sensor classification, selection, data fusion, and tradeoff optimization we can provide an improvement, in terms of accuracy, over other existing indoor tracking systems

Simulation of Local Climate Control in Shared Offices Based on Occupants Locations and Preferences

It is estimated that building energy consumption (BEC) accounts for one-third of the total global energy consumption, and Heating, Cooling, and Air-conditioning (HVAC) accounts for almost half of the energy consumption of buildings. To efficiently achieve more energy saving from the HVAC systems, narrowing the gap between the actual energy consumed and the demanded heating and cooling loads is found to be a promising strategy. Therefore, occupancy-driven HVAC management is attracting great attention. On the other hand, future smart buildings will have the ability to detect and locate the occupants, and adjust the HVAC system accordingly, which is expected to result in considerable energy savings. This research proposes a local climate control strategy in open space, such as shared offices, by dividing the space into zones according to the number of HVAC terminal units and adjusting the operation of each terminal unit based on occupants’ preferences and presence in the zone. To evaluate the performance regarding energy consumption and occupancy thermal comfort, and the feasibility of the proposed local climate control, three case studies are implemented. The occupancy presence pattern is captured by a Bluetooth Low Energy (BLE)-based tracking system. Based on a four-week test carried out in a graduate laboratory in Concordia University, the occupancy profiles and different HVAC operation scenarios are created as the inputs of the building energy simulation. The simulation is run for three months for cooling and the results show that, with the adoption of the proposed local climate control strategy, 15% or 36% of the energy consumption can be saved compared with applying a dynamic schedule using a motion detector or a fixed schedule, respectively. In addition, the occupants’ comfort level can be increased by an average of 6%. In addition, sensitivity analysis is conducted with respect to the factors affecting the effectiveness of the proposed climate control strategy and the HVAC setpoint temperature. It is concluded that the proposed local climate control strategy is effective in reducing the energy consumption and improving occupancy thermal comfort, however, the extent of the effectiveness depends on factors of building properties, occupancy attributes, and HVAC operation

Proceedings. 9th 3DGeoInfo Conference 2014, [11-13 November 2014, Dubai]

It is known that, scientific disciplines such as geology, geophysics, and reservoir exploration intrinsically use 3D geo-information in their models and simulations. However, 3D geo-information is also urgently needed in many traditional 2D planning areas such as civil engineering, city and infrastructure modeling, architecture, environmental planning etc. Altogether, 3DGeoInfo is an emerging technology that will greatly influence the market within the next few decades. The 9th International 3DGeoInfo Conference aims at bringing together international state-of-the-art researchers and practitioners facilitating the dialogue on emerging topics in the field of 3D geo-information. The conference in Dubai offers an interdisciplinary forum of sub- and above-surface 3D geo-information researchers and practitioners dealing with data acquisition, modeling, management, maintenance, visualization, and analysis of 3D geo-information