Using iBeacon for Navigation and Proximity Awareness in Smart Buildings

Guests in unfamiliar buildings often do not know where to go to or what is around them. This project sought to alleviate this problem using a combination of smartphone and iBeacon technology. Received signal strength (RSS) information was obtained from iBeacon devices deployed in Atwater Kent Labs on the WPI campus. This information was fed into an Android application, which localized the user using a least mean squares (LMS) algorithm and provided contextual information to the user about their surroundings

Fusing Bluetooth Beacon Data with Wi-Fi Radiomaps for Improved Indoor Localization

Indoor user localization and tracking are instrumental to a broad range of services and applications in the Internet of Things (IoT) and particularly in Body Sensor Networks (BSN) and Ambient Assisted Living (AAL) scenarios. Due to the widespread availability of IEEE 802.11, many localization platforms have been proposed, based on the Wi-Fi Received Signal Strength (RSS) indicator, using algorithms such as K-Nearest Neighbour (KNN), Maximum A Posteriori (MAP) and Minimum Mean Square Error (MMSE). In this paper, we introduce a hybrid method that combines the simplicity (and low cost) of Bluetooth Low Energy (BLE) and the popular 802.11 infrastructure, to improve the accuracy of indoor localization platforms. Building on KNN, we propose a new positioning algorithm (dubbed i-KNN) which is able to filter the initial fingerprint dataset (i.e., the radiomap), after considering the proximity of RSS fingerprints with respect to the BLE devices. In this way, i-KNN provides an optimised small subset of possible user locations, based on which it finally estimates the user position. The proposed methodology achieves fast positioning estimation due to the utilization of a fragment of the initial fingerprint dataset, while at the same time improves positioning accuracy by minimizing any calculation errors

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

A smart phone based multi-floor indoor positioning system for occupancy detection

At present there is a lot of research being done simulating building environment with artificial agents and predicting energy usage and other building performance related factors that helps to promote understanding of more sustainable buildings. To understand these energy demands it is important to understand how the building spaces are being used by individuals i.e. the occupancy pattern of individuals. There are lots of other sensors and methodology being used to understand building occupancy such as PIR sensors, logging information of Wi-Fi APs or ambient sensors such as light or CO2 composition. Indoor positioning can also play an important role in understanding building occupancy pattern. Due to the growing interest and progress being made in this field it is only a matter of time before we start to see extensive application of indoor positioning in our daily lives. This research proposes an indoor positioning system that makes use of the smart phone and its built-in integrated sensors; Wi-Fi, Bluetooth, accelerometer and gyroscope. Since smart phones are easy to carry helps participants carry on with their usual daily work without any distraction but at the same time provide a reliable pedestrian positioning solution for detecting occupancy. The positioning system uses the traditional Wi-Fi and Bluetooth fingerprinting together with pedestrian dead reckoning to develop a cheap but effective multi floor positioning solution. The paper discusses the novel application of indoor positioning technology to solve a real world problem of understanding building occupancy. It discusses the positioning methodology adopted when trying to use existing positioning algorithm and fusing multiple sensor data. It also describes the novel approach taken to identify step like motion in absence of a foot mounted inertial system. Finally the paper discusses results from limited scale trials showing trajectory of motion throughout the Nottingham Geospatial Building covering multiple floors

Smart learning environment: Teacher’s role in assessing classroom attention

The main purpose of this article is to investigate the impact of teacher’s position on students’ performance in higher education. A new pedagogical approach based on collaborative learning is used due to the design of a smart learning environment (SLE). This workspace uses, respectively, information and communication technologies (ICT) and radio frequency identification (RFID)-based indoor positioning system in order to examine students’ perceptions and the involvement of groups into this smart classroom. The merge of interactive multimedia system, ubiquitous computing and several handheld devices should lead to a successful active learning process. Firstly, we provide a detailed description of the proposed collaborative environment using mainly new technologies and indoor location system serving as a platform for evaluating attention. The research provides an obvious consensus on the teacher’s role in assessing classroom attention. We discuss our preliminary results on how teacher’s position influences essentially students’ participation. Our first experiments show that the integration of novel technologies in the area of higher education is extremely promoting the traditional way of teaching. The smart classroom model has been recommended to support this evolution. As a result, the found results indicate that the teacher’s position increases the learner’s motivation, engagement and effective learning

An iOS Application for Visually Impaired Individuals to Assist with Crossing Roads

In day-to-day life, visually impaired individuals face the problem of crossing roads by themselves. This project was designed and built to solve this key issue. The system is supposed to give the user a warning before approaching a crosswalk for their safety and also give information about when it is safe to cross the road. An iOS application was developed to address the problem since recent studies have discovered that a vast number of visually impaired individuals are using smartphones (iPhones in particular) due to the ease and convenience it brings to their daily life. The application should be able to: notify the user through audio and haptic vibration, inform the user about crossing with 100% confidence with audio, all the input should be voice-based. In addition, it shouldn’t need any internet, shouldn’t consume much battery power, and run in the background with other applications. The application goal is to be used in Queens, NY in particular. For warning notification, we used iBeacon to know if a user is approaching a crosswalk. Currently, the iBeacon detection has only an error distance of +/-3 feet. In our implementation, every output of the application is audio and haptic vibration, and the inputs are all voice and haptic touch which are standard for the visually impaired. The application informs the user when it is safe to cross by detecting traffic lights 30 frames per second using the phone\u27s built-in camera and an offline image processing model. The model was built in Caffe2 and then converted to CoreML for this project. The model alone has an accuracy of 92.9% and on top of that, we added cross-checking which increases the confidence level according to Bayes Theorem. Also, there is an additional layer of state machine which refines the output using possible timing of the traffic light to give 100% confident signal to cross the road and makes sure we don’t make the user wait more than 10 seconds without any decision. The application is only 30MB in size, doesn’t need any internet connection, and has low to average power consumption. The application can also run in the background allowing low power consumption and the capability to work with other applications. We have done both unit testing and integration testing to confirm every component of the application. The only thing that is left is to test the application in the real world by visually impaired individuals to confirm this initial prototype

Evolution of social networks through smart phones and radio sensors

Social interactions have been an integral part of human civilization. It reflects human society and its evolution. The first challenge in the research of social networks is data acquisition. The high cost and low efficiency are always restrictions. In addition, most existing studies of network problems are using single datasets to build their network models. It is paramount to find a general method of obtaining a highly accurate network model to represent social interactions. Therefore, we propose a cross-platform system and strategy to collect data through radio sensors and design a combined scheme with multiple datasets in order to settle this problem. Moreover, we use complex network theory to build our network models. The next challenge is network dynamic. A larger number of real-world networks are dynamic, i.e. social networks, as the topology of a network changes over time. It is also hard to describe the topological variance of the network using a static network model where it does not have any time features. Thus, we propose a weighted temporal network model to illustrate the time effect of social network problems. In this study, we also analyze the effect of friendship on human social interactions and activities. The relationships among networks are shown as well. Furthermore, we show the combined network model provides a highly efficient way to construct social networks

A Noticeboard application using context aware services

Thesis submitted in partial fulfillment of the requirements for the Degree of Master of Science in Mobile Telecommunication and Innovation at (MSc.MTI) at Strathmore UniversityThe dissemination of information on campuses using noticeboards is both inefficient and ineffective due to the static, unreliable and limiting nature of the boards. This implies that people who need to post information for consumption by the public are constrained in terms of reach, poor feedback mechanisms and a general lack of security. Posts can be tampered with by malicious people or even removed by competitors. Due to their fixed nature, noticeboards also do not cater for context, meaning that a lot of posts are consumed outside of their actionable spaces, meaning that people might never get a chance to act on them. There is therefore a need for a smart noticeboard system using the power of context aware services that allows for notices to be sent directly to user’s devices only when they are in the correct context. The aim of this research is to develop a noticeboard application that uses the power of context aware services. With such a solution, students are able to receive notices through an application when they are in the correct and most effective context and be in a better position to react or act on them accordingly. They are also able to save notifications on their mobile devices for later use or sharing. The solution therefore supports the communications departments by providing an easy, paperless way to setup notices and to track interactions with each post. The proposed system is developed and tested on the Android platform coupled with an analytical backend for post manipulation and presenting summaries analytical data for the communication department. Object Oriented Analysis and Agile development methodologies were applied to develop a robust and dynamic, context aware noticeboard system. The final prototype was tested to ensure that the requirements were met by the developer and the potential users. Tests included functional testing and usability testing