INDOOR POSITIONING SYSTEM

Nowadays, personal positioning systems are more
necessary to build many location-based services. Pedestrian Dead
Reckoning (PDR), which is a pedestrian positioning technique
using the accelerometer sensor to recognize pattern of steps, is an
alternative method that has advantages in terms of
infrastructure-independent. However, the variation of walking
pattern on each individual will make some difficulties for the
system to detect displacement. This is really interested authors to
develop a sensor-based positioning system that applied generally
to all individuals. In the test, 15 test subjects was taken with the
distance of each 10m, 20m and 30m.
Experiment begins with the feasibility test of accelerometer
sensor. In this work, a smartphone with average sampling rate
63.79 Hz and standard deviation of 1.293 is used to records the
acceleration. Then, the acceleration data are analyzed to detect
step and to estimate the travelled distance using several methods.
Detection of steps are able to make an average error of 2.925%,
while the most nearly correct displacement estimation is using
Scarlet experimental method which is make a distance average
error of 1.39metres at all the traveled distance.

Interiors of buildings are often represented as two-dimensional spaces with attributes attached to them. Examples can be found everywhere, from architectural design plans to maps showing evacuation routes in emergency cases. Most of the navigation programs use primarily 2D plans for visualization and communication. Some exceptions are historical buildings and museums, which do offer navigation through textured, detailed 3D indoor models. However, these models are not structured with respect to the functionality of the building but largely with respect to the navigation/visualisation route.

Disastrous accidents (fire, chemical releases, earthquake, terrorist attacks, etc) in large public and residential buildings (discotheques, cafes, trade and industrial buildings) usually result in tragic consequences for people and environments. Such accidents have clearly showed that need for reliable systems supporting rescue operations is urgently appealing. Amongst all, giving appropriate information to the ordinary people in/around the affected area considering the disaster developments (available exists, assessable corridors, etc.) and the human factors (age, gender, disability) are of critical importance for the success of the rescue operation.

Indoor navigation is gaining an increasing interest among researchers in many domains. In many cases users need to orient quickly in complex environments, which is not always the target of current routing algorithms. The paper reviews current indoor path-finding approaches and discusses some of the limitations. In order to support a natural movement in buildings, typically for emergencies, we purpose a new indoor path-finding approach, that is, the " door-to-door " approach. We present an algorithm, which is applied to 2D floor plan of buildings with complex indoor structure. The algorithm consists of two-level routing: one is to get coarse route between rooms, and the other one is applied to single rooms to acquire the detailed route. Ultimately, several instances are given to illustrate advantages and feasibility of the door-to-door approach. From the test results it is evident this algorithm runs well even on quite complex floor plans. The paper concludes with a discussion of future work, which is to extend the routing approach into 3D, i.e. considering the vertical direction, indoor obstacles and path-finding in 3D scenarios.

Indoor navigation is a fast developing area, but researchers are facing various
challenges. One of them is the 3D model that could support the navigation. After reviewing
current development of models for indoor navigation, in this paper we generalize essential
emergency phases of buildings and clarify requirements for indoor navigation models. Then
typical indoor navigation models are compared and their pros and cons are given. Based on
these work, we propose a 3D network called “Indoor Visibility Structure”, which makes use of
3D geometry & semantics of buildings and allow for automatic generation of navigation
network. The proposed approach is designed to resolve navigation in complex buildings.
Ultimately, by relying on the IVS, its updating mechanism, and corresponding routing strategy
considering indoor real-time changes, the ‘door-to-door’ navigation within buildings could be
achieved.

The Internet of Things (IoT) enables numerous business opportunities in fields as diverse as e-health, smart cities, smart homes, among many others. The IoT incorporates multiple long-range, short-range, and personal area wireless networks and technologies into the designs of IoT applications. Localisation in indoor positioning systems plays an important role in the IoT. Location Based IoT applications range from tracking objects and people in real-time, assets management, agriculture, assisted monitoring technologies for healthcare, and smart homes, to name a few. Radio Frequency based systems for indoor positioning such as Radio Frequency Identification (RFID) is a key enabler technology for the IoT due to its cost-effective, high readability rates, automatic identification and, importantly, its energy efficiency characteristic. This paper reviews the state-of-the-art RFID technologies in IoT Smart Homes applications. It presents several comparable studies of RFID based projects in smart homes and discusses the applications, techniques, algorithms, and challenges of adopting RFID technologies in IoT smart home systems.

Integrated communication infrastructure has become a must-have facility for modern
public buildings and offices. To cover this need, several commercial products exist on the market,
but most of them require advanced technical skill to operate, while others require manual and timeconsuming
operations. This work proposes an intelligent displaying and alerting system (called
SICIAD), implemented over an integrated communication infrastructure with support for wireless
ePaper and iBeacon technologies to enhance displaying static and dynamic information, as well as
to ease the indoor orientation of guests. A centralized display management console is implemented,
as well as procedures for automatically displaying different types of notifications. An Android
mobile application is developed which enables indoor user location and guidance. The system
targets educational and research institutions but could also cope with public institutions such as
museums and hospitals. Remote authentication is supported in research facilities through eduroam
technology, access being provided by the user's distant institution of affiliation. Secure multiplelevel
access to the system is provided to users, from guests to system administrators, based on
locally defined policies. Functional validation and performance evaluation aspects are presented for
the proposed system.

In this paper we present a distributed positioning
system for indoor environments based on a network of compact
independent anchor nodes operating as specialized WiFi access
points. Each node is built with component-off-the-shelves and it is
capable of standard IEEE 802.11 connectivity at 2.45GHz. The
enabling technology for the localization is the Switched Beam
Antenna (SBA) equipped in each node, which permits a space
division multiple access at network-level.
The positional information is the result of a maximum likeli-
hood estimation driven by the expected signal space partition of
the constellation, and it is tolerant to noisy power measurements,
such as Received Signal Strength Indicator, thanks to angular
filtering capability of the SBA, which in addition operates in
circular polarization.
Experimental validations demonstrate the performance of a
3-anchors network, operating within the IEEE 802.11 protocol,
monitoring a single nomadic node inside a 7m 2 indoor square
area. It results that 68% of the square area is covered with
a localization error below 50cm, with a mean error of 47cm.
Inside the triangular mesh defined by the three anchors, the
mean error drops to 39cm, with 88% of the area being below
50cm. In addition, the maximum error is always below 77cm.

Existing global positioning systems (GPS) applications are normally denied in indoor environments where, in spite of this limitation, a number of interesting applications are evolving into commercial products. At present, for indoor positioning, there are few cost-effective alternatives to GPS. One promising approach is based on the received signal strength indicator (RSSI) estimation, which is available in every IEEE standard compliant wireless transceiver, and includes useful information about the mutual positions between two position reference nodes. Extrapolating distance evaluation from RSSI is not reliable without the adoption of an accurate channel model [1],[2]. By using an anchor node/router with an array of directive antennas, it is possible to implement a spatial subdivision aimed at achieving an estimation of the direction of arrival (DoA) by evaluating the ratios between the single anchor’s antennas, and thus independently of the channel characteristics. This approach makes use of already existing spectral-based DoA localization algorithms ([3],[4]), but all of them assume a real-time analysis of both the amplitude and the phase of the received signals. In this article, we demonstrate analytically the capability of the MUSIC algorithm [3] to elaborate only the RSSI readings. We also compare the DoA localization performance using the classical information set (i.e., amplitude and phase) versus the use of the RSSI set (obtained simply by reading transceiver standard registers). In conclusion, we introduce some fundamental array design principles for optimal MUSIC RSSI implementation, with a real implementation tracking test.

Personal  positioning  system  needs  portable,
flexible,  and  inexpensive  sensor.  Nowadays,  most  of
smartphones  in  the  market  have  been  equipped  with
accelerometer and magnetometer to support some basic
features. Utilizing these sensors as  a  positioning system
is  an  interesting  proposition  because  it  doesn’t  depend
on  outside  infrastructures  and  can  be  widely  used  by
many people with their smartphone.
In  this  study,  accelerometer  and  magnetometer  sensor
are used to estimate  pedestrian positions  based on  dead
reckoning  method.  Dead  reckoning  is  a  positioning
method  where  subsequent  position  is  estimated  by
adding displacement to the previous position. Moreover,
our system is not only utilizing smartphone as an input
sensor,  but  also  implementing  PDR  (Pedestrian  Dead
Reckoning)  in  real-time  mode  in  the  smartphone  that
has limited resources.
Our  experiment  was  performed  with  30  test  subjects,
walking  in  3  tracks  with  distances  of  51.54m,  77.79m,
and  82.67m  respectively.  The  result  shows  that  the
system  was  able  to  estimate  user  position  with  average
error less than 4.4% of total distance

Nodes localization in Wireless Sensor Networks (WSN) has arisen as a very challenging problem in the research community. Most of the applications for WSN are not useful without a priori known nodes positions. One solution to the problem is by adding GPS receivers to each node. Since this is an expensive approach and inapplicable for indoor environments, we need to find an alternative intelligent mechanism for determining nodes location. In this paper, we propose our cluster-based approach of multidimensional scaling (MDS) technique. Our initial experiments show that our algorithm outperforms MDS-MAP[8], particularly for irregular topologies in terms of accuracy.

This paper presents an investigation about Switched Beam Antennas (SBA) applied to WiFi nodes for indoor positioning systems based on RSSI measurement. Given practical consideration on costs, the proposed node is based low-cost commercial components capable of standard WiFi connectivity at 2.45 GHz.
The SBA is the enabling technology for Beam Diversity Multiple Access. Upon the reception of radio messages from
generic mobile devices, the node estimates the DoA of the
incoming signals on the basis of a likelihood criterion driven
by beam diversity.
SBA design has great impact on the localization performance.
In this paper an investigation around the shape, and in particular on the dihedral angle is presented. Thanks to a model based on trustful electromagnetic simulations, some conclusions on the general design principles of the SBA’s are drawn.

In recent years, there has been an ongoing interest in indoor positioning systems. Many designs have been proposed which have employed a wide variety of algorithms, including Wi-Fi and image processing algorithms. Although current experiments have been designed that incorporates the use of individual algorithms, there is still much to account for, such as the lack of accuracy in Wi-Fi Localization techniques, and the lack of speed in image processing. In this study, a two-phase framework was designed to have one algorithm compensate for the other's weakness. The algorithms used in this study were Wi-Fi Localization and image processing techniques. This framework implemented Wi-Fi Localization with routers in order to determine the user's rough location, and applied image processing as a means to improve the accuracy of the predicted location. Techniques that involved image masking and low-resolution imagery were also integrated to improve speed without jeopardizing accuracy. Tests have shown that the framework had better speed and accuracy as compared to using these algorithms individually, and it surpassed the accuracy of a number of current indoor positioning systems. Further analysis also allowed to determine the limitations of the framework, and suggestions were raised for additional refinement.

Localization is the technique to determine the position of people or assets. The position information enables location-based protocols to navigate, to track or to monitor a person or an asset. Recently, indoor localization systems have been designed to provide location information of persons and devices in the indoor environment. The techniques used for location detection in the outdoor environment (like GPS) cannot be used in the indoor environment because there is no line-of sight communication possible and no sky-view in indoor environment. In big cities with plenty of high rise buildings GPS cannot work properly. The deployment of a system for obtaining location information in the indoor environment is a challenging task because of a large number of obstacles and interference of different frequencies. In this paper we studied the existing approaches and classify them based on the enabling technology. We further compared the existing approaches based on some parameters (like cost, accuracy) and compiled the result in tabular format.

In this paper, we leverage spatial model to process indoor localization results and then improve the track consisting of measured locations. We elaborate different parts of spatial model such as geometry, topology and semantics, and then present how they contribute to the processing of indoor tracks. The initial results of our experiment reveal that spatial model can support us to overcome problems such as tracks intersecting with obstacles and unstable shifts between two location measurements. In the future, we will investigate more exceptions of indoor tracking results and then develop additional spatial methods to reduce errors of indoor tracks.

Increasing demand on Internet of Things (IoT) requires specific communication technology to handle low-cost power consumption and coverage requirements. Several Low Power Wide Area Network (LPWAN) technologies emerged; however, Long Range Wide Area Networks (LoRaWAN) is the key technology to address these issues. LoRaWAN uses a unique LoRa modulation to provide long range data communication with low power consumption; this will lead LoRaWAN to be the key technology for IoT infrastructures. In this study, LoRaWAN performance is analysed with an experimental TestBed environment by considering communication range, data rates, packet losses and energy consumption for indoor usage. Obtained results are summarized and discussed possible future research directions

—Fingerprinting based localization is one of the most widely used indoor localization methods. This method is divided into two phases: during the off-line training phase, fingerprints within the area of interest are collected and stored in a fingerprint database; during the on-line mapping phase, the real-time location of a device is estimated by mapping itself to the most accurate fingerprint in the database. The efficiency of the mapping process is one of the key challenges of the on-line phase, and is mostly characterized by the localization accuracy and the response time. Clustering methods have been introduced to reduce computational overhead. In this paper, we propose a dynamic clustering method leveraging the inertial information of the target device. An active area is dynamically computed around the prior position. The target mapping space is significantly reduced with this active area. This method can be integrated with other clustering algorithms to overcome the edge problem and remove outliers. We evaluate this method compared with the state-of-the-art methods on a body sensor based localization system. The results show that the accuracy, precision and response time of the system are improved greatly.

In last years the interest in indoor localization techniques is grown very quickly: due to huge increase of interest in domotic technologies and to fast improvement of capabilities for mobile devices, which are becoming always-connected support points for standard users, the capability of making spatial data-contextualization has become an important point of interest. Existing GPS technologies aren’t capable of realizing sufficient accuracy needed for indoor positioning, so it is necessary to find alternative cost-effective methods to resolve localization problem. Excluding odometry-based tracking methods (which present a lot of uncertainty for making an absolute positioning), famous signal DoA (Direction Of Arrival) spectral-based localization algorithms like CAPON, ESPRIT, MUSIC[1],[2] provide valid localization accuracy assuming device capability to analyze in real-time physical parameters (amplitude/phase) of received radiocommunication signals to a spatial antenna array. This requirement could be achieved using ad-hoc radio equipment, which causes an increase of production costs: maybe, it is far better to develop some kind of new algorithms capable of using standard IEEE wireless protocols information (like RSS indicator, RSSI) for positioning elaboration. In this article, it will be analytically demonstrated the capability of MUSIC[1] algorithm to elaborate RSSI standard transceiver values (placing some measurement conditions), and there will be shown a comparison between DoA localizations using the classical information set (amplitude/phase) and the RSSI set (obtained simply reading transceiver standard registers).

The paper presents the analysis of long term accuracy of the localization solution based on Wi-Fi signals. The localization model is built using random forest algorithm and it was tested using data collected between years 2012–2014 inside of a six floor building.

In this paper, Performance improvement techniques for two popular RSSI based indoor localization methods have
been studied experimentally by using Wi-Fi modems. The improvement of RF Fingerprinting and RSSI Multi-lateration methods
have been suggested from different aspects for both line-of-sight (LoS) and non-line-of-sight (nLoS) medium in an indoor
environment. Various testing scenarios have been examined for comparison of the two methods, as the performance level in RF
Fingerprinting is mainly depend on the number of modems, as well as the density of training data and, the multilateration method
is mainly depend on correctly modeling of the path loss exponent. Optimizing and defining a unique path loss exponent for each
of the wireless transmitter modems, testing in a LoS and a nLoS medium, changing the number of transmitters, etc, have been
tried and performance plots have been shown for comparison purposes.

Navigation systems are known to provide time and location information for easy and accurate navigation in a specified environment. While Global Positioning System (GPS) has recorded a considerable success for navigating outdoors, the absence of GPS indoors has made orientation in an indoor environment challenging. Furthermore, existing technologies and methods of indoor positioning and navigation, such as WLAN, Bluetooth and Infrared, have been complex, inaccurate, expensive and challenging to implement; thereby limiting the usability of these technologies in less developed countries. This limitation of navigation services makes it difficult and time-consuming to locate a destination in indoor and closed spaces. Hence, recent works with Near Field Communication (NFC) has kindled interest in positioning and navigation.

While navigating, users in less developed nations face several challenges, such as infrastructure complexity, high-cost solution, inaccuracy and usability. However, this research focuses on providing interventions to alleviate usability challenges, in order to strengthen the overall accuracy and the navigation effectiveness in stringent environments through the experiential manipulation of technical attributes of the positioning and navigation system in indoor environments. Therefore, this study adopted the realist ontology and the positivist epistemological approach. It followed a quantitative and experimental method of empirical enquiry, and software engineering and synthesis research methods. The study entails three implementation processes, namely map generation, positioning framework and navigation service using a prototype mobile navigation application that uses the NFC technology. It used open-source software and hardware engineering tools, instruments and technologies, such as Ubuntu Linux, Android Software Development Kit, Arduino, NFC APIs and PandaBoard. The data was collected and the findings evaluated in three stages: pre-test, experiment and post-test.

Our approach and findings revealed that the capability of NFC in leveraging its low-cost infrastructure of passive tags, its availability in mobile devices and the ubiquity of the mobile device provided a cost-effective solution with impressive accuracy and usability. Based on the outcome of the positioning and navigation study with NFC, the positioning accuracy (i.e. the distance between the mobile device and the NFC tag) was less than 9 cm. The prototype navigation system used on an android emulator and a mobile device showed that the usability improved after the post-test from 44% to 96% based on the feedbacks given by respondents who tested the application in an indoor environment. Furthermore, the evaluation and findings in this work showed that NFC is a viable alternative to resolve the challenges identified in previous solutions and technologies.

Full Text: http://hdl.handle.net/20.500.11838/2290

In recent years, localization systems for indoor vicinity using the present wireless local area (WLAN) network infrastructure have been proposed. Such positioning systems create the usage of location fingerprinting instead of direction or time of arrival techniques for deciding the location of mobile users. However experimental study associated to such localization systems have been proposed, high attenuation and signal scattering related to greater density of wall attenuation still affecting the indoor positioning performance. This paper presents an analytical model for minimizing high signal attenuation effect for WLAN fingerprinting indoor positioning systems. The model employs the probabilistic algorithm that using signal relation method.

Currently, the health care problem for older people is becoming of considerable size and involves the whole civilized world. The number of people in need of assistance is constantly growing due to life extension, and the social cost is becoming unsustainable. Technological evolution and IT development can help contain spending by providing efficient health services and improving the quality of life. This article highlights methodological and technical aspects to support the development of an integrated system for locating and monitoring people in their homes or nursing homes. This system complements the home care service by avoiding hospitalization in health facilities with more significant financial burdens.

Human activity monitoring technologies are one of the essential systems for elderly care. Advances in electronic systems, sensor technologies, and communication network protocols have enabled a new generation of integrated health systems to be created. The solution presented in this document represents an integrated system prototype that provides an efficient technological tool to caregivers operating promptly and ensures efficient performance throughout the entire healthcare system process. This solution differs from previous works for the coexistence of a series of innovative aspects. Human activity recognition is based on combining different types of information: environmental data, physiological data, inertial data, and indoor location data of patients; CNN network for locating position and activities; Virtual Reality System (VR) for optimizing the neural network and related training.

Hybrid positioning frameworks use various sensors and algorithms to enhance positioning through different types of fusion. The optimisation of the fusion process requires the testing of different algorithm parameters and optimal lowas well as high-level sensor fusion techniques. The presented OpenHPS open source hybrid positioning system is a modular framework managing individual nodes in a process network, which can be configured to support concrete positioning use cases or to adapt to specific technologies. This modularity allows developers to rapidly develop and optimise their positioning system while still providing them the flexibility to add their own algorithms. In this paper we discuss how a process network developed with OpenHPS can be used to realise a customisable indoor positioning solution with an offline and online stage, and how it can be adapted for high accuracy or low latency. For the demonstration and validation of our indoor positioning solution, we further compiled a publicly available dataset containing data from WLAN access points, BLE beacons as well as several trajectories that include IMU data.

This dataset (https://doi.org/10.5281/zenodo.4744379) contains fingerprint information of WLAN access points and BLE beacons with a known position and IMU sensor data. Data was collected on the floor of the Web and Information Systems Engineering (WISE) Lab at the VUB (Pleinlaan 9, 3rd floor) with 110 training reference points and 30 test data points. Each reference point was recorded for 20 seconds in four different orientations. In this README document we go in depth into how the data was collected and the structure of the dataset.

Construction progress monitoring has been perceived as one of the key factors that prompt the achievement of a construction project. However, assessing the progress is time consuming, costly and obliges specialized personnel to reduce disagreements and approximate the actual performance to the original plan as close as possible. Image processing is a promising method that has been developed for automated monitoring of construction projects. It has attracted increasing attention for progress monitoring, quality assurance and work space analyses. Nonetheless, remarkable drawbacks still remain in image processing, particularly for outdoor environment such as construction progress monitoring. The principle downside of image processing goes back to the image resolution. Ambient lightning condition significantly affects the image quality which does affect the accuracy of data, extracted from related images. Much research strives to reduce the level of errors for data extraction but so far none has been able to deliver complete satisfactory and reliable result. In this research a novel approach based on thermal image analysis is presented. The new method consists of three phases: First, collecting the thermal and original images by utilizing Infrared-Camera. Second, estimating the position of captured images by the use of wireless sensor network implemented in the work space. Finally, the 3D plan will be updated automatically in the Building Information Modeling (BIM) software. The preliminary experimental results from an actual concrete building construction site show the feasibility of inferring the actual state of progress by the use of thermal images to overcome the limitation of vision monitoring. 

AUTOMATED CONSTRUCTION PROGRESS MONITORING USING THERMAL IMAGES AND WIRELESS SENSOR NETWORKS. Available from: https://www.researchgate.net/publication/275024236_AUTOMATED_CONSTRUCTION_PROGRESS_MONITORING_USING_THERMAL_IMAGES_AND_WIRELESS_SENSOR_NETWORKS#share [accessed Sep 11, 2015].

Nowadays, Wireless Sensor Networks are
becoming ubiquitous and increasingly attract investigators to
focus on different aspects of them. In terms of localization
estimation system, location algorithm play an important role to
reach high accuracy. Many algorithms have been developed to
improve the accuracy of the system, but so far none has been able
to deliver complete satisfactory and reliable result. This paper
addresses a filter for smoothing the received signal strength
index (RSSI) based on Principle Component Analysis. To
enhance the accuracy of the system and overcome unwanted
signals, the proposed algorithm is utilized to reduce the noise for
triangulation localization approach instead of reducing the
database in fingerprint approach. Our algorithm divided each 10
received packets to a group and replaced a representative signal
for each group. A realistic demo system of the CC2430/CC2431
has been implemented in the fifth floor of Xi’an JiaotongLiverpool
University to represent the feasibility and accuracy of
our algorithm. The results show the enhancement in localization
accuracy in normal speed by at least 2 meters.

Nowadays, smartphones and tablet computers are everywhere, and these mobile devices offer a lot of applications, such as the location-based services (LBS). The location-based services not only can provide the users’ locations, but also extend a lot of services, like navigation services and traffic information services, etc. Currently, the main stream of positioning technology is the global positioning system (GPS), but it is not suitable for indoor applications and services. The more suitable indoor positioning technology is the RF signal pattern matching method; whereas it will take a lot of time and man powers to build the RF map. In order to reduce the costs of time and man powers, this study proposes a system to build the indoor RF map through sensory detectors (ex. Xtion or Kinect) on the off-line stage. Through the capture of the depth information and the object image, we can build RF prediction propagation models from the environment information with less time and manpower cost. With built-in sensors (accelerometers and gyroscopes) of smart phones, we can estimate the users’ moving directions and distances in the environment with better positioning accuracy in online stage. Eventually, the paper cut down running time about 78% and obtains the double samples simultaneously. The positioning error is 2.73 meters indicated in the experimental results.

Recent advancement in ubiquitous computing and proliferation of mobile technology makes location based system (LBS) popular. In past few years numerous systems has been developed and different techniques has been implemented for achieving highest accuracy in LBS. RF Trilateration is an indoor localization technique based on Receive Signal Strength(RSS) to distance conversion. In this paper, the impact of path loss exponent for energy to distance conversion in RF Trilateration technique has been analyzed in an nLoS environment. This paper suggests the modelling of path loss exponent factor in an nLoS environment for achieving higher accuracy and low error rate for RF Trilateration Technique.