Wi-Fi Location Determination for Semantic Locations

In Wi-Fi location determination literature, little attention is paid to locations that do not have numeric, geometric coordinates, though many users prefer the convenience of non-coordinate locations (consider the ease of giving a street address as opposed to giving latitude and longitude). It is not often easy to tell from the title or abstract of a Wi-Fi location determination article whether or not it has applicability to semantic locations such as room-level names. This article surveys the literature through 2011 on Wi-Fi localization for symbolic locations

AN INDOOR BLUETOOTH-CENTRIC PROXIMITY BASED POSITIONING SYSTEM

In recent years, positioning and navigation become an important topic in research. The most popular positioning system is an outdoor positioning called Global Positioning System (GPS). However, due to the influence of weak signal strength, weather conditions, diverse geographical and living environments, GPS sometimes cannot support indoor positioning and, if it can, the 5-10 meters error range does not meet the indoor positioning requirement. In order to provide a better solution with higher accuracy for indoor localization, we can benefit from the proliferation of indoor communication devices. Different technologies such as WiFi, Radio Frequency Identification (RFID) and Ultra-wideband (UWB) have been commonly used in indoor positioning systems. However, WiFi has a high energy consumption for indoor localization, as it consumes 3 to 10 watts per hour in the case of using 3 routers to do the job. In addition, due to its dependency on reference tags, the overall cost of the RFID-based approaches may usually cost more than $300 which is economically prohibitive. In terms of UWB, its low area coverage brings great challenges to popularizing its acceptance as a device for indoor positioning. The Bluetooth Low Energy (BLE) based iBeacon solution primarily focuses on the proximity based detection, and its low power consumption and low price bring great potential for its popularity. In this report, assuming that the resident owns a smartphone which is powered on, we present an iBeacon based indoor positioning system and provide some strategies and algorithms to overcome the indoor noise of possibly weak indoor Bluetooth signals. In our system, the Received Signal Strength Index (RSSI) is pre-processed to eliminate noise. Then, the distance between a mobile device and a BLE signal source can be calculated by combination use of pre-processed RSSI, Kalman Filter, and machine learning. In the end, the current mobile device position can be determined by using a triangulation algorithm. Our experimental results, acquired through running experiments in a real-world scenario, show that the localization error can be as low as 0.985m in the 2D environment. We also compared our results against other works with the same research objectives

iBeacon-based indoor positioning system: from theory to practical deployment

Developing an indoor positioning system became essential when global positioning system signals could not work well in indoor environments. Mobile positioning can be accomplished via many radio frequency technology such as Bluetooth low energy (BLE), wireless fidelity (Wi-Fi), ultra-wideband (UWB), and so on. With the pressing need for indoor positioning systems, we, in this work, present a deployment scheme for smartphone using Bluetooth iBeacons. Three main parts, hardware deployment, software deployment, and positioning accuracy assessment, are discussed carefully to find the optimal solution for a complete indoor positioning system. Our application and experimental results show that proposed solution is feasible and indoor positioning system is completely attainable

Path Recognition with DTW in a Distributed Environment

The Internet of Things is a concept, where various devices are connected in a network and data is exchanged between them. With the help of Internet of Things applications, it is possible to access sensors remotely to collect data from the physical world. The collected data contains potential knowledge, which could be revealed by applying machine learning techniques. Due to the rapid development of Internet of Things applications, the amount of collected data increases enormously. In order to perform computations on large datasets, distributed computing technologies are used. Recognizing people’s movements is a popular topic in the context of the Internet of Things. Movement patterns are usually sequential and continuous, and can therefore be encoded in the form of time series. Since the Dynamic-Time-Warping (DTW) is an established algorithm for processing time series data, it is chosen as a similarity measure for different movement patterns. Moreover, based on the DTW results, the movements are classified. In this thesis, we provide an implementation for the recognition of movement patterns. The prototype is built on Apache Spark and Apache Hadoop and uses their distributed computation possibilities. In an experiment, data from probands is collected and evaluated. Finally, the algorithm performance and accuracy is measured

Multi-Slot BLE Raw Database for Accurate Positioning in Mixed Indoor/Outdoor Environments

The technologies and sensors embedded in smartphones have contributed to the spread of disruptive applications built on top of Location Based Services (LBSs). Among them, Bluetooth Low Energy (BLE) has been widely adopted for proximity and localization, as it is a simple but efficient positioning technology. This article presents a database of received signal strength measurements (RSSIs) on BLE signals in a real positioning system. The system was deployed on two buildings belonging to the campus of the University of Extremadura in Badajoz. the database is divided into three different deployments, changing in each of them the number of measurement points and the configuration of the BLE beacons. the beacons used in this work can broadcast up to six emission slots simultaneously. Fingerprinting positioning experiments are presented in this work using multiple slots, improving positioning accuracy when compared with the traditional single slot approach

Design and Implementation of an RSSI-Based Bluetooth Low Energy Indoor Localization System

Indoor Positioning System (IPS) is a crucial technology that enables medical staff and hospital managements to accurately locate and track persons or assets inside the medical buildings. Among other technologies, Bluetooth Low Energy (BLE) can be exploited for achieving an energy-efficient and low-cost solution. This work presents the design and implementation of an received signal strength indicator (RSSI)-based indoor localization system. The paper shows the implementation of a low complex weighted k-Nearest Neighbors algorithm that processes raw RSSI data from connection-less iBeacon's. The designed hardware and firmware are implemented around the low-power and low-cost nRF52832 from Nordic Semiconductor. Experimental evaluation with the real-time data processing has been evaluated and presented in a 7.2 m by 7.2 m room with furniture and 5 beacon nodes. The experimental results show an average error of only 0.72 m in realistic conditions. Finally, the overall power consumption of the fixed beacon with a periodic advertisement of 100 ms is only 50 uA at 3 V, which leads to a long-lasting solution of over one year with a 500 mAh coin battery.Comment: This article has been accepted for publication in the proceedings of the 2021 IEEE International Conference on Wireless and Mobile Computing, Networking And Communications (WiMob). DOI: 10.1109/WiMob52687.2021.960627

Optimization of Algorithms in Relation to iBeacon

The boom of portable electronics and high-speed wireless networks has brought changes throughout society, including development in positioning systems. Indoor localization is more and more important. With modern technology, we are able to track people in shopping complexes and offer them discounts for surrounding goods. The following text deals with the design and description of methods to determine user’s position based on fingerprint technology. The text focuses on the description of algorithms in relation to the iBeacon. Three main algorithms were described in the text. The following text describes the implementation of Knn algorithm. The main goal of this paper is to clearly describe the basic positioning algorithms for the readers, introduce implementation of the Knn algorithm and its usage in real environment

Indoor self-localization via bluetooth low energy beacons

Indoor localization is concerned with mapping sensory data to physical locations inside buildings. Location of a user or a mobile device is an essential part of the context, and is therefore very useful for pervasive computing applications. Many proposals exist for solving the localization problem, typically based on image or radio signal processing, though the problem is still generally considered to be open, especially when costs and privacy constraints play an important role. In this paper, we propose a solution based on the emerging Bluetooth Low Energy (BLE) standard and off-the-shelf hardware. Such approach proves to satisfy economic constraints, while challenging in terms of accurate location. To translate beacon signals into locations, we consider several approaches, i.e., cosine similarity, nearest neighbourhood classification, and the nearest beacon. Our experiments indicate a vector based approach as the most suited one. In fact, we show its effectiveness in an actual office deployment consisting of five indoor areas: three multiuser offices, a social corner, and a hallway. We achieve 90% and 80% for accuracy and F-measure, respectively