Overview of positioning technologies from fitness-to-purpose point of view

Even though Location Based Services (LBSs) are being more and more widely-used and this shows a promising future, there are still many challenges to deal with, such as privacy, reliability, accuracy, cost of service, power consumption and availability. There is still no single low-cost positioning technology which provides position of its users seamlessly indoors and outdoors with an acceptable level of accuracy and low power consumption. For this reason, fitness of positioning service to the purpose of LBS application is an important parameter to be considered when choosing the most suitable positioning technology for an LBS. This should be done for any LBS application, since each application may need different requirements. Some location-based applications, such as location-based advertisements or Location-Based Social Networking (LBSN), do not need very accurate positioning input data, while for some others, e.g. navigation and tracking services, highly-accurate positioning is essential. This paper evaluates different positioning technologies from fitness-to-purpose point of view for two different applications, public transport information and family/friend tracking

Indoor location based services challenges, requirements and usability of current solutions

Indoor Location Based Services (LBS), such as indoor navigation and tracking, still have to deal with both technical and non-technical challenges. For this reason, they have not yet found a prominent position in people’s everyday lives. Reliability and availability of indoor positioning technologies, the availability of up-to-date indoor maps, and privacy concerns associated with location data are some of the biggest challenges to their development. If these challenges were solved, or at least minimized, there would be more penetration into the user market. This paper studies the requirements of LBS applications, through a survey conducted by the authors, identifies the current challenges of indoor LBS, and reviews the available solutions that address the most important challenge, that of providing seamless indoor/outdoor positioning. The paper also looks at the potential of emerging solutions and the technologies that may help to handle this challenge

Interpolation and extrapolation methods for WLAN-based positioning

WLAN-based positioning is obtaining more and more attention in the research field no-wadays. In order to create better Location Based Services (LBSs), the demand to achieve higher user location accuracy is increasing. This thesis aims at studying the ef-fect of different interpolation and extrapolation methods in the WLAN-based indoor positioning, based on collected WLAN data. Depending on the embraced positioning method, there are various errors in WLAN-based positioning, such as calibration error, measurement errors, shadowing, etc. The motivation of this work came from trying to decrease the positioning error in the ab-sence of complete information about the indoor environment. This can be done by using interpolation and extrapolation methods, which are widely used in image processing nowadays. However, they are also an available and efficient way to deal with WLAN-based positioning studies. Among interpolation methods, Delaunay triangulation can partly avoid introducing dis-tortions in the measurement databases. Therefore, it makes sense to investigate triangula-tion based methods and to study their usefulness in the WLAN context. Practically, it is very hard to extrapolate appropriately and the implementation of the extrapolation is much more complex than the one of the interpolation. Thus in this thesis, simple extrapo-lation methods have been performed. The results here are based on measurement data. The performance of each method is analyzed in terms of the error between the received signal strengths (RSS) coming from the measurements and the RSS obtained through interpolation and extrapolation. WLAN data was collected from several buildings of Tampere University of Technology. Results show that extrapolation methods may increase the RSS estimation error some-times because it is very hard to predict the outside range. However, with more accurate extrapolation, the error would decrease. The performances of natural neighbor, linear and cubic interpolation are similar. The highest impact on RSS estimation comes from the extrapolation

Characterization and Design Methodologies for Wearable Passive UHF RFID Tag Antennas for Wireless Body-Centric Systems

Radio Frequency Identification (RFID) is a wireless automatic identification technology that utilizes electrically active tags – low-cost and low-power wireless communication devices that let themselves transparently and unobstructively be embedded into everyday objects to remotely track information of the object’s physical location, origin, and ownership. At ultra-high frequencies (UHF), this technology uses propagating electromagnetic waves for communication, which enables the fast identification of tags at large distances. A passive RFID tag includes two main components; a tag antenna and an RFID integrate circuit (tag IC). A passive tag relies solely on the external power harvested from an incident electromagnetic wave to run its circuitry and for data transmission. The passiveness makes the tag maintenance-free, simple, and low-cost, allowing large-scale commercial applications in the supply chain, ticketing, and asset tracking. The future of RFID, however, lies in the transition from traditional embedded applications to wearable intelligent systems, in which the tags are seamlessly integrated with everyday clothing. Augmented with various ambient and biochemical sensors, the tag is capable of detecting physical parameters of its environment and providing continuous monitoring of human vital signs. Tremendous amount of tagged entities establish an intelligent infrastructure that is personalized and tailored to the needs of each individual and ultimately, it recedes into the background of our daily life. Although wearable tags in intelligent systems have the enormous potential to revolutionize the quality of human life, the emerging wearable RFID applications introduce new challenges for designers developing efficient and sophisticated RFID systems. Traditional tag design parameters and solutions will no longer respond to the new requirements. Instead, the whole RF community must adopt new methods and unconventional approaches to achieve advanced wearable tags that are highly transparently integrated into our daily life. In this research work, an empirical as well as a theoretical approach is taken to address the above-mentioned wearable RFID tag challenges. Exploiting new analysis tools in combination with computational electromagnetics, a novel technique to model the human body in UHF applications for initiating the design of optimized wearable tags is developed. Further, fundamental unprecedented UHF characteristics of advanced wearable electronics materials – electro-textiles, are established. As an extremely important outcome of this research work, innovative optimization methodologies for the promotion of novel and advanced wearable UHF antennas are proposed. Particularly, it is evidenced that proper embroidery fabrication techniques have the great potential to realize wearable tag antennas exhibiting excellent RF performance and structural properties for the seamless integration with clothing. The kernel of this research work is the realization of a flexible and fully embroidered passive UHF RFID patch tag prototype achieving optimized performance in close vicinity of the high-permittivity and dissipative human body. Its performance may be considered as a benchmark for future wearable antenna designs. This shows that this research work outcome forms an important contribution to the state of the art and a milestone in the development towards wearable intelligence

Investigations of Dempster-Shafer theory in the context of WLAN-based indoor localization

Accurate user's locations and real-time location estimations in indoor environments, are important parameters to achieve reliable Location Based Services (LBSs). Non-Bayesian frameworks are gaining more and more interest in order to improve the location accuracy indoors when WLAN positioning is used. The main objective of this thesis is to study the feasibility of Dempster Shafer non-Bayesian combining in the context of received signal strength (RSS)-based indoor WLAN localization. The motivation of our work has been to look for new approaches in order to try to deal better with the incomplete or erroneous data measurements used in the training phase of any WLAN positioning algorithm. State-of-art studies show that the accuracy of mobile position estimation by WLAN localization algorithms with the Bayesian framework is not satisfactory. Thus, it makes sense to try to investigate non-Bayesian approaches and to see their usefulness in the context of WLAN localization. First, a comprehensive analysis of various DST combining rules with RSS-based positioning methods has been performed. Then, the idea has been implemented via MATLAB simulator and the outputs were compared to the Bayesian approaches. The comparison is in terms of root mean square errors, correct floor detection probabilities and error radius and we used real-field data measurements as test data. Typically, the current published research work based on non-Bayesian frameworks in the context of wireless localization is limited to fingerprinting methods. Both the fingerprinting and the path-loss model using the DST frameworks are carried out in this thesis. The thesis results contain two parts. The first one examines the fingerprinting with various DST combination while the other one deals with the path-loss and DST combination. The positioning accuracy estimated by Bayesian framework is compared to the DST and a high correlation between these two has been observed. As expected, the Bayesian framework results are slightly less accurate (on average) than the DST, because the DST fuse RSS from multiple access points with different beliefs or underlying uncertainty and allows the uncertainty to be a model parameter

Signals of Opportunity for Positioning Purposes

O ver the last years, location-based services (LBS) have become popular due to the emergence of smartphones with capabilities of positioning their user’s location on Earth at unprecedented speed and convenience. Behind such feat are the technological advances in global navigation satellite systems (GNSS), such as Galileo, Globalnaya Navigazionnaya Sputnikovaya Sistema (GLONASS), Global Positioning Service (GPS) and Beidou. The easiness of smartphones and the improvement of positioning technology has driven LBS to be at the core of many business models. Some of these business models rely on the user’s location to pick him up on a car, relinquish a meal to him, offer insights on sports performance, locate items to be picked up on a warehouse, among many others.While LBS are driving the need to continuously locate the user at higher degrees of accuracy and across any environment, be it in a city park, an urban canyon or inside a corporate office, some of these environments pose a challenge for GNSS. Indoor environments are particularly challenging for GNSS due to the attenuation and strong multipath imposed by walls and building materials. Such challenges and difficulties in signal acquisition have led to the development of solutions and technologies to improve positioning in indoor environments.While there are several commercial systems available to fulfill the needs of most LBS in indoor environments, most of these are not feasible to deploy at a global scale due to their infrastructure costs. Hence, several solutions have sought to build upon existing infrastructure to provide positioning information.Building upon existing infrastructure is what leads to the main topic of this thesis, the concept of signals of opportunity (SoO). A SoO is any wireless signal that can be exploited for a positioning purpose despite its initial design seeking to fulfill a different purpose. A few examples of these signals are IEEE 802.11 signals, commonly known as WiFi, Bluetooth, digital video broadcasting - terrestrial (DVB-T) and many of the cellular signals, such as long-term evolution (LTE), universal mobile telecommunications system (UMTS) and global mobile system (GSM).The goal of this thesis is to address various challenges related to SoO for positioning. From the identification of SoO at the physical layer, how to merge them at the algorithmic level and how to put them in use for a cognitive positioning system (CPS)

User perception-based quantitative studies of Location Based Services of today and tomorrow

Modern Location Based Services (LBS) are not any more limited to navigation or routing services, but they have flourished in every sphere of life whether it is regular activity tracker or family finder. The continuous advancement of location technologies, such as GNSS and cellular in outdoors and WLAN in indoors, opens new challenges for the LBS providers. Due to the emergence of location-enabled smartphone technologies, the use of location based services and applications has increased remarkably in the last few years. This forces the LBS providers to think beyond the boundaries. Therefore, the analysis of the user needs, behavior, perception and preference becomes one of the key factors and eventually prerequisites for success in this sector. The thesis comprises a survey focusing on LBS from different perspectives, such as localization knowledge, privacy concerns and LBS usage, and an analysis based on the responses from 118 volunteer participants. The analysis begins with the classification of the users with respect to their “technical knowledge in localization”, “privacy concerns” and “LBS usage” based on the survey results, and it continues with the analysis of the correlation and similarity between the user classes. The user classes are compared based on the Mann-Whitney-Wilcoxon, Fligner-Policello and unpaired t-test in terms of preferences similarity. The user perceptions with respect to the cost and feature preferences are also analyzed per user class. The aim of the thesis is to illustrate how the LBS preferences differ among various user classes and how the user classes may correlate. The main findings of the analysis are that the user’s background class has a significant impact on the preferences. Moreover, the high-level knowledge users have similar preferences as the high-level usage users, even though the correlation among the user classes is very low. Another interesting finding of this analysis is that the high-level knowledge users are relatively less willing to pay for LBS applications in comparison to the other user classes. From the privacy-concern based classification, it is observed that most of the users have certain privacy concerns, which represents one of the barriers in the LBS development. Finally, it can be inferred that the statistical analysis and the comparative results justify the empirical user classification derived in this thesis

User perception-based quantitative studies of Location Based Services of today and tomorrow

Modern Location Based Services (LBS) are not any more limited to navigation or routing services, but they have flourished in every sphere of life whether it is regular activity tracker or family finder. The continuous advancement of location technologies, such as GNSS and cellular in outdoors and WLAN in indoors, opens new challenges for the LBS providers. Due to the emergence of location-enabled smartphone technologies, the use of location based services and applications has increased remarkably in the last few years. This forces the LBS providers to think beyond the boundaries. Therefore, the analysis of the user needs, behavior, perception and preference becomes one of the key factors and eventually prerequisites for success in this sector. The thesis comprises a survey focusing on LBS from different perspectives, such as localization knowledge, privacy concerns and LBS usage, and an analysis based on the responses from 118 volunteer participants. The analysis begins with the classification of the users with respect to their “technical knowledge in localization”, “privacy concerns” and “LBS usage” based on the survey results, and it continues with the analysis of the correlation and similarity between the user classes. The user classes are compared based on the Mann-Whitney-Wilcoxon, Fligner-Policello and unpaired t-test in terms of preferences similarity. The user perceptions with respect to the cost and feature preferences are also analyzed per user class. The aim of the thesis is to illustrate how the LBS preferences differ among various user classes and how the user classes may correlate. The main findings of the analysis are that the user’s background class has a significant impact on the preferences. Moreover, the high-level knowledge users have similar preferences as the high-level usage users, even though the correlation among the user classes is very low. Another interesting finding of this analysis is that the high-level knowledge users are relatively less willing to pay for LBS applications in comparison to the other user classes. From the privacy-concern based classification, it is observed that most of the users have certain privacy concerns, which represents one of the barriers in the LBS development. Finally, it can be inferred that the statistical analysis and the comparative results justify the empirical user classification derived in this thesis

Contextually and identity aware 5G services

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University LondonThe fifth generation (5G) mobile networks aim to be ten times faster than the existing 4G connection, whilst providing low latency, and flexibility. Hence, various alterations are planned to the existing network infrastructure to be able to reach the 5G expected performance levels. The main technologies that were used, to ensure high performance, flexible network, and efficient resource allocation, are Software Defined Network and Network Function Virtualization. As these technologies are replacing the device-based architecture with, a service-based architecture. This thesis provides a design of location database interactive web interface and interactive mobile application. The implementation of real time video streaming location server, the streaming system's performance parameters demonstrated a high level of QoS (0.07ms jitter and 9.53ms delay). In regard to experimental examination, it measured the localisation coverage, accuracy measurements and a highly scalable security solution. The localisation coverage and accuracy measurements were achieved through the mmWave and VLC link transmitters. The proposed simulated annealing algorithm aimed at data optimisation for location measurements accuracy showed results of the average location error of x and y which showed significant improvement from x= 22.5 and y=21.6 to x=11.09 and y= 11.63. The proposed indoor location security solution showed significant results, as it provides a high scalability solution using the VNF. The solution showed that it was not 100% effective, as some of the fake discover packets still reached the DHCP server. This was due to the high load of traffic passing through the network. Nonetheless, 90% of the fake DHCP discover packets never reached the DHCP server because the scripts began blocking all fake discover packets after realising it was an attack. This conveys that the proposed system was able to run successfully without crashing or overloading the controller. Overall, the main challenges facing 5G have been addressed with their proposed solutions, which showed promising results. Conclusively showing that there is a lot more space for technological advancements to support the future of mobile networks.European Union’s Horizon 2020 research program - the Internet of Radio-Light (IoRL) project H2020-ICT 761992

Cooperative positioning studies based on WLANs

Location information and location-based service have gained importance in recent years because, based on their concept, a new business market has been opened which encompass emergency services, security, monitoring, tracking, logistics, etc. Nowadays, the most developed positioning systems, namely the Global Navigation Satellite Systems (GNSS), are meant for outdoor use. In order to integrate outdoor and indoor localization in the same mobile application, several lines of research have been created for the purpose of investigating the possibility of wireless network technologies and of overcoming the challenges faced by GNSS in performing localization and navigation in indoor environments. The benefit in using wireless networks is that they provide a minimally invasive solution which is based on software algorithms that can be implemented and executed in the Mobile Station (MS) or in a Location Server connected to the network. This thesis focuses on the development of localization approaches based on Received Signal Strength (RSS) and applied in WLANs. Such approaches demonstrated in recent research advances that RSS-based localization algorithms are the simplest existing approaches due to the fact that the RSSs are most accessible existing measurements. RSS measurements can be used with two main algorithms, which are addressed in this thesis: Fingerprinting method (FP) and Pathloss method (PL). These two methods can be applied in both cooperative and non-cooperative algorithms. Such algorithms are evaluated here in terms of Root Mean Square Error (RMSE) for both simulated and real-field data