The Creation of a Building Map Application for a University Setting

The use of navigational technology in mobile and web devices has sharply increased in recent years. With the capability to create interactive maps now available, navigating in real time between locations has become possible. This is especially essential in areas and organizations experiencing rapid expansion like Liberty University (LU). Therefore, the author proposes a project to create an interactive map application (IMA) for LU’s academic buildings that is scalable and usable through both the university’s website and with a mobile application. There are several considerations that must be taken into account when creating the LU map application, such as development methods, platforms, programming languages, software, userbase, and cost. The software development lifecycle is used in order to properly analyze, plan, design, and implement the LU map application. Activity, use-case, and entity-relationship diagrams are used as a basis for the implementation of the application which is created with a user-centric design and security as a priority. Despite some limitations to the LU map application, it provides a preliminary model of how an interactive building map could be used to provide students and faculty new ways to navigate a university setting

MAQS: a personalized mobile sensing system for indoor air quality monitoring.

ABSTRACT Most people spend more than 90% of their time indoors; indoor air quality (IAQ) influences human health, safety, productivity, and comfort. This paper describes MAQS, a personalized mobile sensing system for IAQ monitoring. In contrast with existing stationary or outdoor air quality sensing systems, MAQS users carry portable, indoor location tracking sensors that provide personalized IAQ information. To improve accuracy and energy efficiency, MAQS incorporates three novel techniques: (1) an accurate temporal n-gram augmented Bayesian room localization method that requires few Wi-Fi fingerprints; (2) an air exchange rate based IAQ sensing method, which measures general IAQ using only CO 2 sensors; and (3) a zone-based proximity detection method for collaborative sensing, which saves energy and enables data sharing among users. MAQS has been deployed and evaluated via user study. Detailed evaluation results demonstrate that MAQS supports accurate personalized IAQ monitoring and quantitative analysis with high energy efficiency

Impact of Indoor Location Information Reliability on Users’ Trust of an Indoor Positioning System

Indoor positioning systems have been used as a supplement to provide positioning in settings where GPS does not function. However, the accuracy of calculated results varies among techniques and algorithms used; system performance also differs across testing environments. As a result, users’ responses to and opinions of these positioning results could be different. Furthermore, user trust, most closely associated with their confidence in the system, will also vary. A relatively little studied topic is the effect of positioning variance on a user’s opinion or trust of such systems (GPS as well, for that matter). Therefore, understanding how user interaction with such systems (through trust) changes is important for achieving more usable positioning system design. An experiment was designed to examine if the sequence of location accuracy will affect users’ trust in an individual episode positioning result as well as the system overall. The simulated positioning system running on an iPad used for this experiment provides 10 priming positioning results at a specific category of accuracy. The accuracy is controlled and is presented as either 1. ACCURATE (within 5 meters of actual location), 2. INACCURATE (greater 15 meters), 0r 3. WRONG BUILDING (outside current building’s footprint). After one set of these priming locations a series of 55 post-priming locations across the same categories in addition to 10 CONTINUOUS locations (with between 6 and 15 meters of error) were presented. At each experimental site participants located themselves using the simulated system and rated their trust for that location. Variables obtained from the experiment include: 1. Two types of trust at each location (positioning trust and system trust); 2. Spatial abilities, sense of direction, and ancillary survey data (user characteristics). Results show that users’ trust varies among different accuracy categories and changes over time according to the system performance in association with their own characteristics. Specifically, the accuracy of the priming locations has an impact on users’ trust of later results. Besides, users’ trust in individual positioning results is quite variable and the variability is closely related to accuracy, while user trust of the overall system is less variable

Organic indoor location : infrastructure and applications

Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2010.This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Cataloged from student-submitted PDF version of thesis.Includes bibliographical references (p. 55-57).We describe OIL, a system that uses the existing wireless infrastructure of a building to enable a mobile device to discover its indoor location. One of the main goals behind OIL is to enable non-expert users to contribute the data that is required for localization. Toward this we have developed (1) a server-client architecture for aggregating and distributing data; (2) a caching scheme that enables client devices to estimate indoor location; (3) a simple user interface for contributing data; and (4) a way to indicate how uncertain localization estimates are. We evaluate our system with a nine-day, nineteen-person user study that took place on campus as well as a deployment of the system at an off-campus long-term specialized care facility. We also describe how to use a person's indoor location trace (i.e. the rooms they had visited and the times of each visit) to build a content-based recommendation system for academic seminars. Such a system would learn about a user's preferences implicitly, placing no burden on the user. We evaluate a prototype recommendation system based on data gathered from a user study in which participants ranked seminars.by Benjamin W. Charrow.M.Eng

Energy Efficient Geo-Localization for a Wearable Device

During the last decade there has been a surge of smart devices on markets around the world. The latest trend is devices that can be worn, so called wearable devices. As for other mobile devices, effective localization are of great interest for many different applications of these devices. However they are small and usually set a high demand on energy efficiency, which makes traditional localization techniques unfeasible for them to use. In this thesis we investigate and succeed in providing a localization solution for a wearable camera that is both accurate and energy efficient. Localization is done through a combination of Wi-Fi and GPS positioning with a mean accuracy of 27 m. Furthermore we utilize an activity recognition algorithm with data from an accelerometer to decide when a new position estimate should be obtained. Our evaluation of the algorithm shows that by applying this method, 83.2 % of the position estimates can be avoided with an insignificant loss in accuracy

Design of an adaptive RF fingerprint indoor positioning system

RF fingerprinting can solve the indoor positioning problem with satisfactory accuracy, but the methodology depends on the so-called radio map calibrated in the offline phase via manual site-survey, which is costly, time-consuming and somewhat error-prone. It also assumes the RF fingerprint’s signal-spatial correlations to remain static throughout the online positioning phase, which generally does not hold in practice. This is because indoor environments constantly experience dynamic changes, causing the radio signal strengths to fluctuate over time, which weakens the signal-spatial correlations of the RF fingerprints. State-of-the-arts have proposed adaptive RF fingerprint methodology capable of calibrating the radio map in real-time and on-demand to address these drawbacks. However, existing implementations are highly server-centric, which is less robust, does not scale well, and not privacy-friendly. This thesis aims to address these drawbacks by exploring the feasibility of implementing an adaptive RF fingerprint indoor positioning system in a distributed and client-centric architecture using only commodity Wi-Fi hardware, so it can seamlessly integrate with existing Wi-Fi network and allow it to offer both networking and positioning services. Such approach has not been explored in previous works, which forms the basis of this thesis’ main contribution. The proposed methodology utilizes a network of distributed location beacons as its reference infrastructure; hence the system is more robust since it does not have any single point-of-failure. Each location beacon periodically broadcasts its coordinate to announce its presence in the area, plus coefficients that model its real-time RSS distribution around the transmitting antenna. These coefficients are constantly self-calibrated by the location beacon using empirical RSS measurements obtained from neighbouring location beacons in a collaborative fashion, and fitting the values using path loss with log-normal shadowing model as a function of inter-beacon distances while minimizing the error in a least-squared sense. By self-modelling its RSS distribution in real-time, the location beacon becomes aware of its dynamically fluctuating signal levels caused by physical, environmental and temporal characteristics of the indoor environment. The implementation of this self-modelling feature on commodity Wi-Fi hardware is another original contribution of this thesis. Location discovery is managed locally by the clients, which means the proposed system can support unlimited number of client devices simultaneously while also protect user’s privacy because no information is shared with external parties. It starts by listening for beacon frames broadcasted by nearby location beacons and measuring their RSS values to establish the RF fingerprint of the unknown point. Next, it simulates the reference RF fingerprints of predetermined points inside the target area, effectively calibrating the site’s radio map, by computing the RSS values of all detected location beacons using their respective coordinates and path loss coefficients embedded inside the received beacon frames. Note that the coefficients model the real-time RSS distribution of each location beacon around its transmitting antenna; hence, the radio map is able to adapt itself to the dynamic fluctuations of the radio signal to maintain its signal-spatial correlations. The final step is to search the radio map to find the reference RF fingerprint that most closely resembles the unknown sample, where its coordinate is returned as the location result. One positioning approach would be to first construct a full radio map by computing the RSS of all detected location beacons at all predetermined calibration points, then followed by an exhaustive search over all reference RF fingerprints to find the best match. Generally, RF fingerprint algorithm performs better with higher number of calibration points per unit area since more locations can be classified, while extra RSS components can help to better distinguish between nearby calibration points. However, to calibrate and search many RF fingerprints will incur substantial computing costs, which is unsuitable for power and resource limited client devices. To address this challenge, this thesis introduces a novel algorithm suitable for client-centric positioning as another contribution. Given an unknown RF fingerprint to solve for location, the proposed algorithm first sorts the RSS in descending order. It then iterates over this list, first selecting the location beacon with the strongest RSS because this implies the unknown location is closest to the said location beacon. Next, it computes the beacon’s RSS using its path loss coefficients and coordinate information one calibration point at a time while simultaneously compares the result with the measured value. If they are similar, the algorithm keeps this location for subsequent processing; else it is removed because distant points relative to the unknown location would exhibit vastly different RSS values due to the different site-specific obstructions encountered by the radio signal propagation. The algorithm repeats the process by selecting the next strongest location beacon, but this time it only computes its RSS for those points identified in the previous iteration. After the last iteration completes, the average coordinate of remaining calibration points is returned as the location result. Matlab simulation shows the proposed algorithm only takes about half of the time to produce a location estimate with similar positioning accuracy compared to conventional algorithm that does a full radio map calibration and exhaustive RF fingerprint search. As part of the thesis’ contribution, a prototype of the proposed indoor positioning system is developed using only commodity Wi-Fi hardware and open-source software to evaluate its usability in real-world settings and to demonstrate possible implementation on existing Wi-Fi installations. Experimental results verify the proposed system yields consistent positioning accuracy, even in highly dynamic indoor environments and changing location beacon topologies

Indoor localization using place and motion signatures

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Aeronautics and Astronautics, 2013.This electronic version was submitted and approved by the author's academic department as part of an electronic thesis pilot project. The certified thesis is available in the Institute Archives and Special Collections.Cataloged from department-submitted PDF version of thesis.Includes bibliographical references (p. 141-153).Most current methods for 802.11-based indoor localization depend on either simple radio propagation models or exhaustive, costly surveys conducted by skilled technicians. These methods are not satisfactory for long-term, large-scale positioning of mobile devices in practice. This thesis describes two approaches to the indoor localization problem, which we formulate as discovering user locations using place and motion signatures. The first approach, organic indoor localization, combines the idea of crowd-sourcing, encouraging end-users to contribute place signatures (location RF fingerprints) in an organic fashion. Based on prior work on organic localization systems, we study algorithmic challenges associated with structuring such organic location systems: the design of localization algorithms suitable for organic localization systems, qualitative and quantitative control of user inputs to "grow" an organic system from the very beginning, and handling the device heterogeneity problem, in which different devices have different RF characteristics. In the second approach, motion compatibility-based indoor localization, we formulate the localization problem as trajectory matching of a user motion sequence onto a prior map. Our method estimates indoor location with respect to a prior map consisting of a set of 2D floor plans linked through horizontal and vertical adjacencies. To enable the localization system, we present a motion classification algorithm that estimates user motions from the sensors available in commodity mobile devices. We also present a route network generation method, which constructs a graph representation of all user routes from legacy floor plans. Given these inputs, our HMM-based trajectory matching algorithm recovers user trajectories. The main contribution is the notion of path compatibility, in which the sequential output of a classifier of inertial data producing low-level motion estimates (standing still, walking straight, going upstairs, turning left etc.) is examined for metric/topological/semantic agreement with the prior map. We show that, using only proprioceptive data of the quality typically available on a modern smartphone, our method can recover the user's location to within several meters in one to two minutes after a "cold start."by Jun-geun Park.Ph.D