Yes / The advent of the Internet of Things has witnessed tremendous success in the application of wireless sensor networks and ubiquitous computing for diverse smart-based applications. The developed systems operate under different technologies using different methods to achieve their targeted goals. In this treatise, we carried out an inclusive survey on key indoor technologies and techniques, with to view to explore their various benefits, limitations, and areas for improvement. The mathematical formulation for simple localization problems is also presented. In addition, an empirical evaluation of the performance of these indoor technologies is carried out using a common generic metric of scalability, accuracy, complexity, robustness, energy-efficiency, cost and reliability. An empirical evaluation of performance of different RF-based technologies establishes the viability of Wi-Fi, RFID, UWB, Wi-Fi, Bluetooth, ZigBee, and Light over other indoor technologies for reliable IoT-based applications. Furthermore, the survey advocates hybridization of technologies as an effective approach to achieve reliable IoT-based indoor systems. The findings of the survey could be useful in the selection of appropriate indoor technologies for the development of reliable real-time indoor applications. The study could also be used as a reliable source for literature referencing on the subject of indoor location identification. / Supported in part by the Tertiary Education Trust Fund of the Federal Government of Nigeria, and in part by the European Union’s Horizon 2020 Research and Innovation Programme under Grant agreement H2020-MSCA-ITN-2016 SECRET-722424

Location-based services have experienced substantial growth in the last decade. However, despite extensive research efforts, sub-meter location accuracy with low-cost infrastructure continues to be elusive. Comparing with infrastructure-based solutions, infrastructure-free indoor localization has the major advantage of avoiding extra cost for infrastructure deployment. There are two typical types of infrastructure-free indoor localization solutions, i.e., Pedestrian Dead Reckoning (PDR)-based and fingerprint-based. PDR-based solutions rely on inertial measurement units to estimate the user's relative location. Despite the effort, many issues still remain in PDR systems. For example, any deployed smartphone-based PDR system needs to cope with the changing orientation of smartphone that the phone might be putting in a pocket, or being taken out to use, etc. In addition, the outputs of Micro Electro-Mechanical Systems (MEMS) sensors on smart devices vary over time which results in rapidly accumulated localization errors without external references. Fingerprint-based solutions utilize different types of location dependent parameters to estimate user's absolute location. Although fingerprint-based solutions are usually more practical than PDR-based, they suffer from laborious site survey process. In this dissertation, we aim to mitigate these challenges. First of all, illumination intensity is introduced as a new type of fingerprints to provide location references for PDR-based indoor localization. We propose IDyLL -- an indoor localization system using inertial measurement units (IMU) and photodiode sensors on smartphones. Using a novel illumination peak detection algorithm, IDyLL augments IMU-based pedestrian dead reckoning with location fixes. Moreover, we devise a burned-out detection algorithm for simultaneous luminary-assisted IPS and burned-out luminary detection. Experimental study using data collected from smartphones shows that IDyLL is able to achieve high localization accuracy at low costs. As for fingerprint collection, several frameworks are proposed to ease the laborious site survey process, without compromising fingerprint quality. We propose TuRF, a path-based fingerprint collection mechanism for site survey. MobiBee, a treasure hunt game, is further designed to take advantage of gamification and incentive models for fast fingerprint collection. Motivated by applying mobile crowdsensing for fingerprint collection, we propose ALSense, a distributed active learning framework, for budgeted mobile crowdsensing applications. Novel stream-based active learning strategies are developed to orchestrate queries of annotation data and the upload of unlabeled data from mobile devices. Extensive experiments demonstrate that ALSense can indeed achieve higher classification accuracy given fixed data acquisition budgets. Facing malicious behaviors, three types of location-related attacks and their corresponding detection algorithms are investigated. Experiments on both crowdsensed and emulated dataset show that the proposed algorithms can detect all three types of attacks with high accuracy. / Thesis / Doctor of Philosophy (PhD)

This thesis presents a grid-based indoor radiolocation technique based on a Spatially Coherent Path Loss Model (SCPL). SCPL is a path loss model which characterizes the radio wave propagation in an environment by solely using Received Signal Strength (RSS) fingerprints. The propagation of the radio waves is characterized by uniformly dividing the environment into grid cells, followed by the estimation of the propagation parameters for each grid cell individually. By using SCPL and RSS fingerprints acquired at an unknown location, the distance between an agent and all the access point in an indoor environment can be determined. A least-squares based trilateration is then used as the global fix of location the agent in the environment. The result of the trilateration is then represented in a probability distribution function over the grid cells induced by SCPL. Since the proposed technique is able to locally model the propagation accounting for attenuation of non-uniform environmental irregularities, the characterization of the path loss in the indoor environment and radiolocation technique might yield improved results. The efficacy of the proposed technique was investigated with an experiment comparing SCPL and an indoor radiolocation technique based on a conventional path loss model. / Master of Science / This thesis presents a technique uses radio waves to localize an agent in an indoor environment. By characterizing the difference between transmitted and received power of the radio waves, the agent can determine how far it is away from the transmitting antennas, i.e. access points, placed in the environment. Since the power difference mainly results from obstructions in the environment, the attenuation profile of the environment carries a significant importance in radiolocation techniques. The proposed technique, called Spatially Coherent Path Loss Model (SCPL), characterizes the radio wave propagation, i.e. the attenuation, separately for different regions of the environment, unlike the conventional techniques employing global attenuation profiles. The localization environment is represented with grid-cell structure and the parameters of SCPL model describing the extent of the attenuation of the environment are estimated individually. After creating an attenuation profile of the environment, the agent localizes itself in the localization environment by using SCPL with signal powers received from the access points. This scheme of attenuation profiling constitutes the main contribution of the proposed technique. The efficacy and validity of the proposed technique was investigated with an experiment comparing SCPL and an indoor radiolocation technique based on a conventional path loss model.

The demand for accurate localization under indoor environments has increased dramatically in recent years. To be cost-effective, most of the localization solutions are based on the WiFi signals, utilizing the pervasive deployment of WiFi infrastructure and availability of the WiFi enabled mobile devices. In this thesis, we develop completed indoor localization solutions based on WiFi fingerprinting and machine learning approaches with two types of WiFi fingerprints including received signal strength indicator (RSSI) and channel state information (CSI). Starting from the low complexity algorithm, we propose a soft range limited K nearest neighbours (SRL-KNN) to address spatial ambiguity and the fluctuation of WiFi signals. SRL-KNN exploits RSSI and scales the fingerprint distance by a range factor related to the physical distance between the user’s previous position and the reference location in the database. Although utilizing the prior locations, SRL-KNN does not require knowledge of the exact moving speed and direction of the user. Besides, the idea of the soft range limiting factor can be applied to all of the existed probabilistic methods, i.e., parametric and nonparametric methods, to improve their performances. A semi-sequential short term memory step is proposed to add to the existed probabilistic methods to reduce their spatial ambiguity of fingerprints and boost significantly their localization accuracy. In the following research phase, instead of locating user's position one at a time as in the cases of conventional algorithms, our recurrent neuron networks (RNNs) solution aims at trajectory positioning and takes into account of the relation among RSSI measurements in a trajectory. The results using different types of RNN including vanilla RNN, long short-term memory (LSTM), gated recurrent unit (GRU) and bidirectional LSTM (BiLSTM) are presented. Next, the problem of localization using only one single router is analysed. CSI information will be adopted along with RSSI to enhance the localization accuracy. Each of the reference point (RP) is presented by a group of CSI measurements from several WiFi subcarriers which we call CSI images. The combination of convolutional neural network (CNN) and LSTM model is proposed. CNN extracts the useful information from several CSI values (CSI images), and then LSTM will exploit this information in sequential timesteps to determine the user's location. Finally, a fully practical passive indoor localization is proposed. Most of the conventional methods rely on the collected WiFi signal on the mobile devices (active information), which requires a dedicated software to be installed. Different from them, we leverage the received data of the routers (passive information) to locate the position of the user. The localization accuracy is investigated through experiments with several phones, e.g., Nexus 5, Samsung, Iphone and HTC, in hundreds of testing locations. The experimental results demonstrate that our proposed localization scheme achieves an average localization error of around 1.5 m when the phone is in idle mode, and approximately 1 m when it actively transmits data. / Graduate

This thesis presents a 6 degree of freedom (DOF) position and orientation tracking solution suitable for pedestrian motion tracking based on 6DOF low cost MEMS inertial measurement units. This thesis was conducted as an extension of the ongoing efforts of the Precision Personnel Location (PPL) project at WPI. Prior to this work most of the PPL research focus has been on Radio Frequency (RF) location estimation. The newly developed inertial based system supports data fusion with the aforementioned RF system in a system currently under development. This work introduces a methodology for the implementation of a position estimation system based upon a Kalman filter structure, constructed from industry standard inertial sensor specifications and analytic noise models. This methodology is important because it allows for both rapid filter construction derived solely from specified values and flexible system definitions. In the course of the project, three different sensors were accommodated using the automatic design tools that were constructed. This thesis will present the mathematical basis of the new inertial tracking system followed by the stages of filter design and implementation, and finally the results of several trials with actual inertial data captures, using both public reference data and inertial captures from a foot mounted sensor that was developed as part of this work.

In Mobile Ad Hoc NETworks (MANETs) autonomous nodes act both as traffic originators and forwarders to form a multi-hop network. Out-of-range nodes are reachable through a process called routing, which is a challenging task due to the constraints of bandwidth and battery power. Stateless location-based routing schemes have been proposed to avoid complex route discovery and maintenance, whereby nodes make routing decisions based solely on the knowledge of their location, the location of their neighbors, and the location of the destination. Natural routing schemes based on these prerequisites suffer from problems like local maxima or loops. We mitigate those problems by proposing randomized routing algorithms, which outperform others in terms of the packet delivery ratio and throughput. The prerequisite for location-based routing is knowing the location of a node. Location information is more widely useful anyway for location-aware applications like security, health care, robotics, navigation etc. Locating a node indoors remains a challenging problem due to the unavailability of GPS signals under the roof. For this goal we choose the RSS (Received Signal Strength) as the relevant attribute of the signal due to its minimal requirements on the RF technology of the requisite modules. Then profiling based localization is considered that does not rely on any channel model (range-based) or the connectivity information (range-free), but rather exploits the context of a node to infer that information into the estimation. We propose a RSS profiling based indoor localization system, dubbed LEMON, based on low-cost low-power wireless devices that offers better accuracy than other RSS-based schemes. We then propose a simple RSS scaling trick to further improve the accuracy of LEMON. Furthermore, we study the effect of the node orientation, the number and the arrangement of the infrastructure nodes and the profiled samples, leading us to further insights about what can be effective node placement and profiling. We also consider alternate formulations of the localization problem, as a Bayesian network model as well as formulated in a combinatorial fashion. Then performance of different localization methods is compared and again LEMON ensures better accuracy. An effective room localization algorithm is developed, and both single and multiple channels are used to test its performance. Furthermore, a set of two-step localization algorithms is designed to make the LEMON robust in the presence of noisy RSS and faulty device behavior.

Localization of individual nodes in a wireless network is useful in many applications, e.g for tracking patients in hospitals. Using the Received Signal Strength Indicator, RSSI, for this purpose has been explored in numerous studies. It is energy efficient and rarely requires customised hardware of configuration. The possibility to use pre-configured, off-the-shelf products is especially important in large scale sensor network deployments. Using RSSI has, however, many drawbacks, since the radio signal is heavily affected by the surrounding envi- ronment. Most studies in this area discuss the impact of multipath effects. Our study on range based distance estimations, using the Telos hardware, shows that individual profiling requirements and antenna quality are equally challenging. Still, RSSI based indoor localization systems remains an active field of research. A multitude of approaches and algorithms have been proposed to gain accuracy in position estimations. The most common of these techniques are explored in this report. Based on previous work at The Polytechnic University of Catalonia, the Telos hardware has been integrated successfully with existing software to form local wireless sensor networks for indoor localization. We present applications developed on top of TinyOS, an operating system for embedded systems. These applications serve as a platform for related future work at The Polytechnic University of Catalonia and elsewhere.

Vision-based positioning systems are founded primarily on a simple image processing technique of identifying various visually significant key-points in an image and relating them to a known coordinate system in a scene. Fiducial markers are used as a means of providing the scene with a number of specific key-points, or features, such that computer vision algorithms can quickly identify them within a captured image. This thesis proposes a reliable vision-based positioning system which utilizes a unique pseudo-random fiducial marker. The marker itself offers 49 distinct feature points to be used in position estimation. Detection of the designed marker occurs after an integrated process of adaptive thresholding, k-means clustering, color classification, and data verification. The ultimate goal behind such a system would be for indoor localization implementation in low cost autonomous mobile platforms.

Vision-based positioning systems are founded primarily on a simple image processing technique of identifying various visually significant key-points in an image and relating them to a known coordinate system in a scene. Fiducial markers are used as a means of providing the scene with a number of specific key-points, or features, such that computer vision algorithms can quickly identify them within a captured image. This thesis proposes a reliable vision-based positioning system which utilizes a unique pseudo-random fiducial marker. The marker itself offers 49 distinct feature points to be used in position estimation. Detection of the designed marker occurs after an integrated process of adaptive thresholding, k-means clustering, color classification, and data verification. The ultimate goal behind such a system would be for indoor localization implementation in low cost autonomous mobile platforms.

The purpose of this thesis is to create an indoor rescue scenario with multiple self-localizing robots that are able to collaborate for a victim search. Victims are represented by RFID tags and detecting them combined with an accurate enough location data is considered as a successful finding. This setup is created for use in a laboratory assignment at Linköping University. We consider the indoor localization problem by trying to use as few sensors as possible and implement three indoor localization methods - odometry based, passive RFID based, and our approach by fusing both sensor data with particle filter.The Results show that particle filter based localization performs the best in comparison to the two other implemented methods and satisfies the accuracy requirements stated for the scenario. The victim search problem is solved by an ant mobility (pheromone-based) approach which integrates our localization method and provides a collaborative navigation through the rescue area. The purpose of the pheromone mobility approach is to achieve a high coverage with an acceptable resource consumption.Experiments show that area is covered with approximately 30-40% overhead in traveled distance comparing to an optimal path.