RSSI Based Indoor Localization for Smartphone Using Fixed and Mobile Wireless Node

Nowadays with the dispersion of wireless networks, smartphones and diverse related services, different localization techniques have been developed. Global Positioning System (GPS) has a high rate of accuracy for outdoor localization but the signal is not available inside of buildings. Also other existing methods for indoor localization have low accuracy. In addition, they use fixed infrastructure support. In this paper, we present a novel system for indoor localization, which also works well outside. We have developed a mathematical model for estimating location (distance and direction) of a mobile device using wireless technology. Our experimental results on Smartphones (Android and iOS) show good accuracy (an error less than 2.5 meters). We have also used our developed system in asset tracking and complex activity recognition

Improved Localization Algorithms in Indoor Wireless Environment

Localization has been considered as an important precondition for the location-dependent applications such as mobile tracking and navigation.To obtain specific location information, we usually make use of Global Positioning System(GPS), which is the most common plat- form to acquire localization information in outdoor environments. When targets are in indoor environment, however, the GPS signal is usually blocked, so we also consider other assisted positioning techniques in order to obtain accurate position of targets. In this thesis, three different schemes in indoor environment are proposed to minimize localization error by placing refer- ence nodes in optimum locations, combining the localization information from accelerometer sensor in smartphone with Received Signal Strength (RSS) from reference nodes, and utilizing frequency diversity in Wireless Fidelity (WiFi) environment. Deployments of reference nodes are vital for locating nearby targets since they are used to estimate the distances from them to the targets. A reference nodes’ placement scheme based on minimizing the average mean square error of localization over a certain region is proposed in this thesis first and is applied in different localization regions which are circular, square and hexagonal for illustration of the flexibility of the proposed scheme. Equipped with accelerometer sensor, smartphone provides useful information which out- puts accelerations in three different directions. Combining acceleration information from smart- phones and signal strength information from reference nodes to prevent the accumulated error from accelerometer is studied in this thesis. The combined locating error is narrowed by as- signing different weights to localization information from accelerometer and reference nodes. In indoor environment, RSS technology based localization is the most common way to imply since it require less additional hardware compared to other localization technologies. However, RSS can be affected greatly by complex circumstance as well as carrier frequency. Utilization of diverse frequencies to improve localization performance is proposed in the end of this thesis along with some experiments applied on Software Defined Platform (SDR)

Sulautettu ohjelmistototeutus reaaliaikaiseen paikannusjärjestelmään

Asset tracking often necessitates wireless, radio-frequency identification (RFID). In practice, situations often arise where plain inventory operations are not sufficient, and methods to estimate movement trajectory are needed for making reliable observations, classification and report generation. In this thesis, an embedded software application for an industrial, resource-constrained off-the-shelf RFID reader device in the UHF frequency range is designed and implemented. The software is used to configure the reader and its air-interface operations, accumulate read reports and generate events to be reported over network connections. Integrating location estimation methods to the application facilitates the possibility to make deploying middleware RFID solutions more streamlined and robust while reducing network bandwidth requirements. The result of this thesis is a functional embedded software application running on top of an embedded Linux distribution on an ARM processor. The reader software is used commercially in industrial and logistics applications. Non-linear state estimation features are applied, and their performance is evaluated in empirical experiments.Tavaroiden seuranta edellyttää usein langatonta radiotaajuustunnistustekniikkaa (RFID). Käytännön sovelluksissa tulee monesti tilanteita joissa pelkkä inventointi ei riitä, vaan tarvitaan menetelmiä liikeradan estimointiin luotettavien havaintojen ja luokittelun tekemiseksi sekä raporttien generoimiseksi. Tässä työssä on suunniteltu ja toteutettu sulautettu ohjelmistosovellus teolliseen, resursseiltaan rajoitettuun ja kaupallisesti saatavaan UHF-taajuusalueen RFID-lukijalaitteeseen. Ohjelmistoa käytetään lukijalaitteen ja sen ilmarajapinnan toimintojen konfigurointiin, lukutapahtumien keräämiseen ja raporttien lähettämiseen verkkoyhteyksiä pitkin. Paikkatiedon estimointimenetelmien integroiminen ohjelmistoon mahdollistaa välitason RFID-sovellusten toteuttamisen aiempaa suoraviivaisemin ja luotettavammin, vähentäen samalla vaatimuksia tietoverkon kaistanleveydelle. Työn tuloksena on toimiva sulautettu ohjelmistosovellus, jota ajetaan sulautetussa Linux-käyttöjärjestelmässä ARM-arkkitehtuurilla. Lukijaohjelmistoa käytetään kaupallisesti teollisuuden ja logistiikan sovelluskohteissa. Epälineaarisia estimointiominaisuuksia hyödynnetään, ja niiden toimivuutta arvioidaan empiirisin kokein

Exploring Audio Sensing in Detecting Social Interactions Using Smartphone Devices

In recent years, the fast proliferation of smartphones devices has provided powerful and portable methodologies for integrating sensing systems which can run continuously and provide feedback in real-time. The mobile crowd-sensing of human behaviour is an emerging computing paradigm that offers a challenge of sensing everyday social interactions performed by people who carry smartphone devices upon themselves. Typical smartphone sensors and the mobile crowd-sensing paradigm compose a process where the sensors present, such as the microphone, are used to infer social relationships between people in diverse social settings, where environmental factors can be dynamic and the infrastructure of buildings can vary. The typical approaches in detecting social interactions between people consider the use of co-location as a proxy for real-world interactions. Such approaches can under-perform in challenging situations where multiple social interactions can occur within close proximity to each other, for example when people are in a queue at the supermarket but not a part of the same social interaction. Other approaches involve a limitation where all participants of a social interaction must carry a smartphone device with themselves at all times and each smartphone must have the sensing app installed. The problem here is the feasibility of the sensing system, which relies heavily on each participant's smartphone acting as nodes within a social graph, connected together with weighted edges of proximity between the devices; when users uninstall the app or disable background sensing, the system is unable to accurately determine the correct number of participants. In this thesis, we present two novel approaches to detecting co-located social interac- tions using smartphones. The first relies on the use of WiFi signals and audio signals to distinguish social groups interacting within a few meters from each other with 88% precision. We orchestrated preliminary experiments using WiFi as a proxy for co-location between people who are socially interacting. Initial results showed that in more challenging scenarios, WiFi is not accurate enough to determine if people are socially interacting within the same social group. We then made use of audio as a second modality to capture the sound patterns of conversations to identify and segment social groups within close proximity to each other. Through a range of real-world experiments (social interactions in meeting scenarios, coffee shop scenarios, conference scenarios), we demonstrate a technique that utilises WiFi fingerprinting, along with sound fingerprinting to identify these social groups. We built a system which performs well, and then optimized the power consumption and improved the performance to 88% precision in the most challenging scenarios using duty cycling and data averaging techniques. The second approach explores the feasibility of detecting social interactions without the need of all social contacts to carry a social sensing device. This work explores the use of supervised and unsupervised Deep Learning techniques before concluding on the use of an Autoencoder model to perform a Speaker Identification task. We demonstrate how machine learning can be used with the audio data collected from a singular device as a speaker identification framework. Speech from people is used as the input to our Autoencoder model and then classified against a list of "social contacts" to determine if the user has spoken a person before or not. By doing this, the system can count the number of social contacts belonging to the user, and develop a database of common social contacts. Through the use 100 randomly-generated social conversations and the use of state-of-the-art Deep Learning techniques, we demonstrate how this system can accurately distinguish new and existing speakers from a data set of voices, to count the number of daily social interactions a user encounters with a precision of 75%. We then optimize the model using Hyperparameter Optimization to ensure that the model is most optimal for the task. Unlike most systems in the literature, this approach would work without the need to modify the existing infrastructure of a building, and without all participants needing to install the same ap

Enhancing Mobility in Low Power Wireless Sensor Networks

In the early stages of wireless sensor networks (WSNs), low data rate traffic patterns are assumed as applications have a single purpose with simple sensing task and data packets are generated at a rate of minutes or hours. As such, most of the proposed communication protocols focus on energy efficiency rather than high throughput. Emerging high data rate applications motivate bulk data transfer protocols to achieve high throughput. The basic idea is to enable nodes to transmit a sequence of packets in burst once they obtain a medium. However, due to the low-power, low-cost nature, the transceiver used in wireless sensor networks is prone to packet loss. Especially when the transmitters are mobile, packet loss becomes worse. To reduce the energy expenditure caused by packet loss and retransmission, a burst transmission scheme is required that can adapt to the link dynamics and estimate the number of packets to transmit in burst. As the mobile node is moving within the network, it cannot always maintain a stable link with one specific stationary node. When link deterioration is constantly detected, the mobile node has to initiate a handover process to seamlessly transfer the communication to a new relay node before the current link breaks. For this reason, it is vital for a mobile node to (1) determine whether a fluctuation in link quality eventually results in a disconnection, (2) foresee potential disconnection well ahead of time and establish an alternative link before the disconnection occurs, and (3) seamlessly transfer communication to the new link. In this dissertation, we focus on dealing with burst transmission and handover issues in low power mobile wireless sensor networks. To this end, we begin with designing a novel mobility enabled testing framework as the evaluation testbed for all our remaining studies. We then perform an empirical study to investigate the link characteristics in mobile environments. Using these observations as guidelines, we propose three algorithms related to mobility that will improve network performance in terms of latency and throughput: i) Mobility Enabled Testing Framework (MobiLab). Considering the high fluctuation of link quality during mobility, protocols supporting mobile wireless sensor nodes should be rigorously tested to ensure that they produce predictable outcomes before actual deployment. Furthermore, considering the typical size of wireless sensor networks and the number of parameters that can be configured or tuned, conducting repeated and reproducible experiments can be both time consuming and costly. The conventional method for evaluating the performance of different protocols and algorithms under different network configurations is to change the source code and reprogram the testbed, which requires considerable effort. To this end, we present a mobility enabled testbed for carrying out repeated and reproducible experiments, independent of the application or protocol types which should be tested. The testbed consists of, among others, a server side control station and a client side traffic ow controller which coordinates inter- and intra-experiment activities. ii) Adaptive Burst Transmission Scheme for Dynamic Environment. Emerging high data rate applications motivate bulk data transfer protocol to achieve high throughput. The basic idea is to enable nodes to transmit a sequence of packets in burst once they obtain a medium. Due to the low-power and low-cost nature, the transceiver used in wireless sensor networks is prone to packet loss. When the transmitter is mobile, packet loss becomes even worse. The existing bulk data transfer protocols are not energy efficient since they keep their radios on even while a large number of consecutive packet losses occur. To address this challenge, we propose an adaptive burst transmission scheme (ABTS). In the design of the ABTS, we estimate the expected duration in which the quality of a specific link remains stable using the conditional distribution function of the signal-to-noise ratio (SNR) of received acknowledgment packets. We exploit the expected duration to determine the number of packets to transmit in burst and the duration of the sleeping period. iii) Kalman Filter Based Handover Triggering Algorithm (KMF). Maintaining a stable link in mobile wireless sensor network is challenging. In the design of the KMF, we utilized combined link quality metrics in physical and link layers, such as Received Signal Strength Indicator (RSSI) and packet success rate (PSR), to estimate link quality fluctuation online. Then Kalman filter is adopted to predict link dynamics ahead of time. If a predicted link quality fulfills handover trigger criterion, a handover process will be initiated to discover alternative relay nodes and establish a new link before the disconnection occurs. iv) Mobile Sender Initiated MAC Protocol (MSI-MAC). In cellular networks, mobile stations are always associated with the nearest base station through intra- and inter-cellular handover. The underlying process is that the quality of an established link is continually evaluated and handover decisions are made by resource rich base stations. In wireless sensor networks, should a seamless handover be carried out, the task has to be accomplished by energy-constraint, resource-limited, and low-power wireless sensor nodes in a distributed manner. To this end, we present MSI-MAC, a mobile sender initiated MAC protocol to enable seamless handover

Applications

Volume 3 describes how resource-aware machine learning methods and techniques are used to successfully solve real-world problems. The book provides numerous specific application examples: in health and medicine for risk modelling, diagnosis, and treatment selection for diseases in electronics, steel production and milling for quality control during manufacturing processes in traffic, logistics for smart cities and for mobile communications

Applications

Volume 3 describes how resource-aware machine learning methods and techniques are used to successfully solve real-world problems. The book provides numerous specific application examples: in health and medicine for risk modelling, diagnosis, and treatment selection for diseases in electronics, steel production and milling for quality control during manufacturing processes in traffic, logistics for smart cities and for mobile communications

Enhancing Mobility in Low Power Wireless Sensor Networks

In the early stages of wireless sensor networks (WSNs), low data rate traffic patterns are assumed as applications have a single purpose with simple sensing task and data packets are generated at a rate of minutes or hours. As such, most of the proposed communication protocols focus on energy efficiency rather than high throughput. Emerging high data rate applications motivate bulk data transfer protocols to achieve high throughput. The basic idea is to enable nodes to transmit a sequence of packets in burst once they obtain a medium. However, due to the low-power, low-cost nature, the transceiver used in wireless sensor networks is prone to packet loss. Especially when the transmitters are mobile, packet loss becomes worse. To reduce the energy expenditure caused by packet loss and retransmission, a burst transmission scheme is required that can adapt to the link dynamics and estimate the number of packets to transmit in burst. As the mobile node is moving within the network, it cannot always maintain a stable link with one specific stationary node. When link deterioration is constantly detected, the mobile node has to initiate a handover process to seamlessly transfer the communication to a new relay node before the current link breaks. For this reason, it is vital for a mobile node to (1) determine whether a fluctuation in link quality eventually results in a disconnection, (2) foresee potential disconnection well ahead of time and establish an alternative link before the disconnection occurs, and (3) seamlessly transfer communication to the new link. In this dissertation, we focus on dealing with burst transmission and handover issues in low power mobile wireless sensor networks. To this end, we begin with designing a novel mobility enabled testing framework as the evaluation testbed for all our remaining studies. We then perform an empirical study to investigate the link characteristics in mobile environments. Using these observations as guidelines, we propose three algorithms related to mobility that will improve network performance in terms of latency and throughput: i) Mobility Enabled Testing Framework (MobiLab). Considering the high fluctuation of link quality during mobility, protocols supporting mobile wireless sensor nodes should be rigorously tested to ensure that they produce predictable outcomes before actual deployment. Furthermore, considering the typical size of wireless sensor networks and the number of parameters that can be configured or tuned, conducting repeated and reproducible experiments can be both time consuming and costly. The conventional method for evaluating the performance of different protocols and algorithms under different network configurations is to change the source code and reprogram the testbed, which requires considerable effort. To this end, we present a mobility enabled testbed for carrying out repeated and reproducible experiments, independent of the application or protocol types which should be tested. The testbed consists of, among others, a server side control station and a client side traffic ow controller which coordinates inter- and intra-experiment activities. ii) Adaptive Burst Transmission Scheme for Dynamic Environment. Emerging high data rate applications motivate bulk data transfer protocol to achieve high throughput. The basic idea is to enable nodes to transmit a sequence of packets in burst once they obtain a medium. Due to the low-power and low-cost nature, the transceiver used in wireless sensor networks is prone to packet loss. When the transmitter is mobile, packet loss becomes even worse. The existing bulk data transfer protocols are not energy efficient since they keep their radios on even while a large number of consecutive packet losses occur. To address this challenge, we propose an adaptive burst transmission scheme (ABTS). In the design of the ABTS, we estimate the expected duration in which the quality of a specific link remains stable using the conditional distribution function of the signal-to-noise ratio (SNR) of received acknowledgment packets. We exploit the expected duration to determine the number of packets to transmit in burst and the duration of the sleeping period. iii) Kalman Filter Based Handover Triggering Algorithm (KMF). Maintaining a stable link in mobile wireless sensor network is challenging. In the design of the KMF, we utilized combined link quality metrics in physical and link layers, such as Received Signal Strength Indicator (RSSI) and packet success rate (PSR), to estimate link quality fluctuation online. Then Kalman filter is adopted to predict link dynamics ahead of time. If a predicted link quality fulfills handover trigger criterion, a handover process will be initiated to discover alternative relay nodes and establish a new link before the disconnection occurs. iv) Mobile Sender Initiated MAC Protocol (MSI-MAC). In cellular networks, mobile stations are always associated with the nearest base station through intra- and inter-cellular handover. The underlying process is that the quality of an established link is continually evaluated and handover decisions are made by resource rich base stations. In wireless sensor networks, should a seamless handover be carried out, the task has to be accomplished by energy-constraint, resource-limited, and low-power wireless sensor nodes in a distributed manner. To this end, we present MSI-MAC, a mobile sender initiated MAC protocol to enable seamless handover

A fuzzy logic approach to localisation in wireless local area networks

This thesis examines the use and value of fuzzy sets, fuzzy logic and fuzzy inference in wireless positioning systems and solutions. Various fuzzy-related techniques and methodologies are reviewed and investigated, including a comprehensive review of fuzzy-based positioning and localisation systems. The thesis is aimed at the development of a novel positioning technique which enhances well-known multi-nearest-neighbour (kNN) and fingerprinting algorithms with received signal strength (RSS) measurements. A fuzzy inference system is put forward for the generation of weightings for selected nearest-neighbours and the elimination of outliers. In this study, Monte Carlo simulations of a proposed multivariable fuzzy localisation (MVFL) system showed a significant improvement in the root mean square error (RMSE) in position estimation, compared with well-known localisation algorithms. The simulation outcomes were confirmed empirically in laboratory tests under various scenarios. The proposed technique uses available indoor wireless local area network (WLAN) infrastructure and requires no additional hardware or modification to the network, nor any active user participation. The thesis aims to benefit practitioners and academic researchers of system positioning