Understanding collaborative workspaces:spatial affordances & time constraints

Abstract. This thesis presents a generic solution for indoor positioning and movement monitoring, positioning data collection and analysis with the aim of improving the interior design of collaborative workspaces. Since the nature of the work and the work attitude of employees varies in different workspaces, no general workspace layout can be applied to all situations. Tailoring workspaces according to the exact needs and requirements of the employees can improve collaboration and productivity. Here, an indoor positioning system based on Bluetooth Low Energy technology was designed and implemented in a pilot area (an IT company), and the position of the employees was monitored during a two months period. The pilot area consisted of an open workplace with workstations for nine employees and two sets of coffee tables, four meeting rooms, two coffee rooms and a soundproof phone booth. Thirteen remixes (BLE signal receivers) provided full coverage over the pilot area, while light durable BLE beacons, which were carried by employees acted as BLE signal broadcasters. The RSSIs of the broadcasted signals from the beacons were recorded by each remix within the range of the signal and the gathered data was stored in a database. The gathered RSSI data was normalized to decrease the effect of workspace obstacles on the signal strength. To predict the position of beacons based on the recorded RSSIs, a few approaches were tested, and the most accurate one was chosen, which provided an above 95% accuracy in predicting the position of each beacon every 3 minutes. This approach was a combination of fingerprinting with a Machine Learning-based Random Forest Classifier. The obtained position results were then used to extract various information about the usage pattern of different workspace areas to accurately access the current layout and the needs of the employees

Device Free Localisation Techniques in Indoor Environments

The location estimation of a target for a long period was performed only by device based localisation technique which is difficult in applications where target especially human is non-cooperative. A target was detected by equipping a device using global positioning systems, radio frequency systems, ultrasonic frequency systems, etc. Device free localisation (DFL) is an upcoming technology in automated localisation in which target need not equip any device for identifying its position by the user. For achieving this objective, the wireless sensor network is a better choice due to its growing popularity. This paper describes the possible categorisation of recently developed DFL techniques using wireless sensor network. The scope of each category of techniques is analysed by comparing their potential benefits and drawbacks. Finally, future scope and research directions in this field are also summarised

Evaluation of an indoor localization system for a mobile robot

Although indoor localization has been a wide researched topic, obtained results may not fit the requirements that some domains need. Most approaches are not able to precisely localize a fast moving object even with a complex installation, which makes their implementation in the automated driving domain complicated. In this publication, common technologies were analyzed and a commercial product, called Marvelmind Indoor GPS, was chosen for our use case in which both ultrasound and radio frequency communications are used. The evaluation is given in a first moment on small indoor scenarios with static and moving objects. Further tests were done on wider areas, where the system is integrated within our Robotics Operating System (ROS)-based self-developed 'Smart PhysIcal Demonstration and evaluation Robot (SPIDER)' and the results of these outdoor tests are compared with the obtained localization by the installed GPS on the robot. Finally, the next steps to improve the results in further developments are discussed

Past, Present, and Future of Simultaneous Localization And Mapping: Towards the Robust-Perception Age

Simultaneous Localization and Mapping (SLAM)consists in the concurrent construction of a model of the environment (the map), and the estimation of the state of the robot moving within it. The SLAM community has made astonishing progress over the last 30 years, enabling large-scale real-world applications, and witnessing a steady transition of this technology to industry. We survey the current state of SLAM. We start by presenting what is now the de-facto standard formulation for SLAM. We then review related work, covering a broad set of topics including robustness and scalability in long-term mapping, metric and semantic representations for mapping, theoretical performance guarantees, active SLAM and exploration, and other new frontiers. This paper simultaneously serves as a position paper and tutorial to those who are users of SLAM. By looking at the published research with a critical eye, we delineate open challenges and new research issues, that still deserve careful scientific investigation. The paper also contains the authors' take on two questions that often animate discussions during robotics conferences: Do robots need SLAM? and Is SLAM solved

Improving a wireless localization system via machine learning techniques and security protocols

The recent advancements made in Internet of Things (IoT) devices have brought forth new opportunities for technologies and systems to be integrated into our everyday life. In this work, we investigate how edge nodes can effectively utilize 802.11 wireless beacon frames being broadcast from pre-existing access points in a building to achieve room-level localization. We explain the needed hardware and software for this system and demonstrate a proof of concept with experimental data analysis. Improvements to localization accuracy are shown via machine learning by implementing the random forest algorithm. Using this algorithm, historical data can train the model and make more informed decisions while tracking other nodes in the future. We also include multiple security protocols that can be taken to reduce the threat of both physical and digital attacks on the system. These threats include access point spoofing, side channel analysis, and packet sniffing, all of which are often overlooked in IoT devices that are rushed to market. Our research demonstrates the comprehensive combination of affordability, accuracy, and security possible in an IoT beacon frame-based localization system that has not been fully explored by the localization research community

Identification and Mitigation of NLOS based on Channel Information Rules for Indoor UWB Localization

Indoor localization is an emerging technology that can be utilized for developing products and services for commercial usage, public safety, military applications and so forth. Commercially it can be applied to track children, people with special needs, help navigate blind people, locate equipment, mobile robots, etc. The objective of this thesis is to enable an indoor mobile vehicle to determine its location and thereby making it capable of autonomous localization under Non-light of sight (NLOS) conditions. The solution developed is based on Ultra Wideband (UWB) based Indoor Positioning System (IPS) in the building. The proposed method increases robustness, scalability, and accuracy of location. The out of the box system of DecaWave TREK1000 provides tag tracking features but has no method to detect and mitigate location inaccuracies due to the multipath effect from physical obstacles found in an indoor environment. This NLOS condition causes ranges to be positively biased, hence the wrong location is reported. Our approach to deal with the NLOS problem is based on the use of Rules Classifier, which is based on channel information. Once better range readings are achieved, approximate location is calculated based on Time of Flight (TOF). Moreover, the proposed rule based IPS can be easily implemented on hardware due to the low complexity. The measurement results, which was obtained using the proposed mitigation algorithm, show considerable improvements in the accuracy of the location estimation which can be used in different IPS applications requiring centimeter level precision. The performance of the proposed algorithm is evaluated experimentally using an indoor positioning platform in a laboratory environment, and is shown to be significantly better than conventional approaches. The maximum positioning error is reduced to 15 cm for NLOS using both an offline and real time tracking algorithm extended from the proposed approach

Location estimation in smart homes setting with RFID systems

Indoor localisation technologies are a core component of Smart Homes. Many applications within Smart Homes benefit from localisation technologies to determine the locations of things, objects and people. The tremendous characteristics of the Radio Frequency Identification (RFID) systems have become one of the enabler technologies in the Internet of Things (IOT) that connect objects and things wirelessly. RFID is a promising technology in indoor positioning that not only uniquely identifies entities but also locates affixed RFID tags on objects or subjects in stationary and real-time. The rapid advancement in RFID-based systems has sparked the interest of researchers in Smart Homes to employ RFID technologies and potentials to assist with optimising (non-) pervasive healthcare systems in automated homes. In this research localisation techniques and enabled positioning sensors are investigated. Passive RFID sensors are used to localise passive tags that are affixed to Smart Home objects and track the movement of individuals in stationary and real-time settings. In this study, we develop an affordable passive localisation platform using inexpensive passive RFID sensors. To fillful this aim, a passive localisation framework using minimum tracking resources (RFID sensors) has been designed. A localisation prototype and localisation application that examined the affixed RFID tag on objects to evaluate our proposed locaisation framework was then developed. Localising algorithms were utilised to achieve enhanced accuracy of localising one particular passive tag which that affixed to target objects. This thesis uses a general enough approach so that it could be applied more widely to other applications in addition to Health Smart Homes. A passive RFID localising framework is designed and developed through systematic procedures. A localising platform is built to test the proposed framework, along with developing a RFID tracking application using Java programming language and further data analysis in MATLAB. This project applies localisation procedures and evaluates them experimentally. The experimental study positively confirms that our proposed localisation framework is capable of enhancing the accuracy of the location of the tracked individual. The low-cost design uses only one passive RFID target tag, one RFID reader and three to four antennas