Spatiotemporal Wireless Sensor Network Field Approximation with Multilayer Perceptron Artificial Neural Network Models

As sensors become increasingly compact and dependable in natural environments, spatially-distributed heterogeneous sensor network systems steadily become more pervasive. However, any environmental monitoring system must account for potential data loss due to a variety of natural and technological causes. Modeling a natural spatial region can be problematic due to spatial nonstationarities in environmental variables, and as particular regions may be subject to specific influences at different spatial scales. Relationships between processes within these regions are often ephemeral, so models designed to represent them cannot remain static. Integrating temporal factors into this model engenders further complexity. This dissertation evaluates the use of multilayer perceptron neural network models in the context of sensor networks as a possible solution to many of these problems given their data-driven nature, their representational flexibility and straightforward fitting process. The relative importance of parameters is determined via an adaptive backpropagation training process, which converges to a best-fit model for sensing platforms to validate collected data or approximate missing readings. As conditions evolve over time such that the model can no longer adapt to changes, new models are trained to replace the old. We demonstrate accuracy results for the MLP generally on par with those of spatial kriging, but able to integrate additional physical and temporal parameters, enabling its application to any region with a collection of available data streams. Potential uses of this model might be not only to approximate missing data in the sensor field, but also to flag potentially incorrect, unusual or atypical data returned by the sensor network. Given the potential for spatial heterogeneity in a monitored phenomenon, this dissertation further explores the benefits of partitioning a space and applying individual MLP models to these partitions. A system of neural models using both spatial and temporal parameters can be envisioned such that a spatiotemporal space partitioned by k-means is modeled by k neural models with internal weightings varying individually according to the dominant processes within the assigned region of each. Evaluated on simulated and real data on surface currents of theGulf ofMaine, partitioned models show significant improved results over single global models

Topological Signals of Singularities in Ricci Flow

We implement methods from computational homology to obtain a topological signal of singularity formation in a selection of geometries evolved numerically by Ricci flow. Our approach, based on persistent homology, produces precise, quantitative measures describing the behavior of an entire collection of data across a discrete sample of times. We analyze the topological signals of geometric criticality obtained numerically from the application of persistent homology to models manifesting singularities under Ricci flow. The results we obtain for these numerical models suggest that the topological signals distinguish global singularity formation (collapse to a round point) from local singularity formation (neckpinch). Finally, we discuss the interpretation and implication of these results and future applications.Comment: 24 pages, 14 figure

Classification and Clustering of Shared Images on Social Networks and User Profile Linking

The ever increasing prevalence of smartphones and the popularity of social network platforms have facilitated instant sharing of multimedia content through social networks. However, the ease in taking and sharing photos and videos through social networks also allows privacy-intrusive and illegal content to be widely distributed. As such, images captured and shared by users on their profiles are considered as significant digital evidence for social network data analysis. The Sensor Pattern Noise (SPN) caused by camera sensor imperfections during the manufacturing process mainly consists of the Photo-Response Non-Uniformity (PRNU) noise that can be extracted from taken images without hacking the device. It has been proven to be an effective and robust device fingerprint that can be used for different important digital image forensic tasks, such as image forgery detection, source device identification and device linking. Particularly, by fingerprinting the camera sources captured a set of shared images on social networks, User Profile Linking (UPL) can be performed on social network platforms. The aim of this thesis is to present effective and robust methods and algorithms for better fulfilling shared image analysis based on SPN. We propose clustering and classification based methods to achieve Smartphone Identification (SI) and UPL tasks, given a set of images captured by a known number of smartphones and shared on a set of known user profiles. The important outcome of the proposed methods is UPL across different social networks where the clustered images from one social network are applied to fingerprint the related smartphones and link user profiles on the other social network. Also, we propose two methods for large-scale image clustering of different types of the shared images by users, without prior knowledge about the types and number of the smartphones

Distributed spatial prediction for radio environment maps reconstruction in heterogeneous wireless networks

Las previsiones indican que el tráfico de datos móviles se multiplicará por siete en el periodo de 2016 a 2021, creciendo con una tasa agregada anual del 47%. Para satisfacer esta demanda, tanto la industria como la academia se están centrando en las redes de quinta generación o 5G. Las redes 5G se espera que constituyan un entorno complejo e interconectado, que además proporciones múltiples servicios y aplicaciones a un número masivo de usuarios y máquinas. En este concepto se incluye la necesidad de dar soporte o de crear servicios para el paradigma conocido como el Internet de las Cosas (IoT), donde la visión es la de crear un entorno de todo conectado con todo en todo momento, con aplicaciones fundamentalmente de dos tipos, comunicaciones masivas de tipo máquina y aplicaciones de misión crítica. En este contexto, un factor muy importante es el de la conectividad, que no puede experimentar discontinuidades de ningún tipo. Unas de las consecuencias de este cambio de visión de las comunicaciones es que se tiende hacia un entorno puramente heterogéneo, tanto desde el punto de vista de los dispositivos a los que se va a dar servicio, como desde el punto de vista del tipo de redes que van a dar servicio a estos dispositivos. Las redes heterogéneas ofrecerán conectividad ubicua para aplicaciones IoT a través de una variedad de técnicas de coordinación y cooperación. Además, será necesario que la red sea consciente del entorno, principalmente mediante el uso de información de tipo contextual en tiempo real. 5G tendrá la capacidad de extraer y procesar información contextual diversa, junto con información de localización, para mejorar el rendimiento general del sistema. En este marco, los mapas de entorno radio (REM) son herramientas que recopilan información contextual y de localización, dando servicio tanto a las tecnologías tradicionales como a las novedosas y disruptivas que se espera que puedan solucionar los retos asociados a la 5G. En esta Tesis, la información contextual es básicamente la información del enlace o las variaciones del canal inalámbrico, modelado de forma estadística como un sistema dinámico de escala múltiple, incluyendo los efectos de la propagación a gran escala y los efectos a pequeña escala como el desvanecimiento. Dado que el canal inalámbrico depende de la ubicación, se pueden utilizar herramientas de regresión estándar para la predicción de canales en REM. Dentro de las técnicas de regresión espacial, una técnica proveniente de la geoestadística llamada Kriging y la regresión de procesos Gaussianos o GPR son probablemente las técnicas más conocidas y aplicadas para la reconstrucción de REM. Sus inconvenientes son, la necesidad de realizar una predicción de canal centralizada y su complejidad computacional. Para abordar estas limitaciones, en esta Tesis, los REM se reconstruyen mediante un algoritmo distribuido con formación de agrupaciones de sensores incremental o DICA basado en la técnica de Kriging, y mediante una versión distribuida de GPR. La complejidad de los métodos propuestos se analiza y los resultados de la simulación se presentan para mostrar la eficacia de las soluciones propuestas en cuanto a reconstrucción de REM.Mobile data traffic is expected to grow sevenfold at a compound annual growth rate of 47 percent from 2016 to 2021. To meet these demands, wireless communication researchers and designers are turning their attention towards fifth generation (5G) networks. 5G will be a key enabler for the Internet of Things (IoT), whose vision is to create an environment of everything connected everywhere and provide a platform to massive machinetype communications and mission-critical applications. Heterogeneous networks will offer ubiquitous connectivity for IoT applications through a variety of coordination and cooperation techniques. Provisioning services and supporting diverse applications requires the network to be context-aware, utilizing contextual information in real-time. 5G will have the ability to extract and process various contextual information coupled with location information to improve the overall system performance. Radio environment map (REM) is a powerful tool that leverages link contextual information and location information, to support both the traditional and disruptive technologies in addressing the challenges of 5G. Link context refers in this Thesis to the evolution of the wireless propagation channel, which can be probabilistically modeled as a multi-scale dynamical system consisting of path-loss, shadowing and small-scale fading. Since the wireless channel is location-dependent, standard regression tools can be used for channel prediction in REMs. Kriging and Gaussian process regression (GPR) are popular spatial regression tools from Geo-statistics and machine learning, respectively. Drawbacks of Kriging and GPR are a traditional centralized prediction and their computing complexity. To address these limitations, in this Thesis, REMs are developed using a distributed incremental clustering algorithm (DICA) and distributed GPR to minimize the computational complexity of kriging and GPR, respectively. DICA is a kriging based interpolation method that employs the least number of closest measurements to leverage short range variations in the local neighborhood of the unmeasured location. Distributed GPR distributes the overall computations among the independent mobile agents. Learning and prediction phases of GPR are achieved by first performing local prediction and then combining the local information using a consensus scheme to obtain a global estimate. In addition, a novel distributed learning method based on importance sampling suitable for kriging and GPR is presented. The complexity of the proposed methods is analyzed and simulation results are presented to showcase the algorithm efficacy to REM reconstruction

Estimating Footfall From Passive Wi-Fi Signals: Case Study with Smart Street Sensor Project

Measuring the distribution and dynamics of the population at granular level both spatially and temporally is crucial for understanding the structure and function of the built environment. In this era of big data, there have been numerous attempts to undertake this using the preponderance of unstructured, passive and incidental digital data which are generated from day-to-day human activities. In attempts to collect, analyse and link these widely available datasets at a massive scale, it is easy to put the privacy of the study subjects at risk. This research looks at one such data source - Wi-Fi probe requests generated by mobile devices - in detail, and processes it into granular, long-term information on number of people on the retail high streets of the United Kingdom (UK). Though this is not the first study to use this data source, the thesis specifically targets and tackles the uncertainties introduced in recent years by the implementation of features designed to protect the privacy of the users of Wi-Fi enabled mobile devices. This research starts with the design and implementation of multiple experiments to examine Wi-Fi probe requests in detail, then later describes the development of a data collection methodology to collect multiple sets of probe requests at locations across London. The thesis also details the uses of these datasets, along with the massive dataset generated by the ‘Smart Street Sensor’ project, to devise novel data cleaning and processing methodologies which result in the generation of a high quality dataset which describes the volume of people on UK retail high streets with a granularity of 5 minute intervals since August 2015 across 1000 locations (approx.) in 115 towns. This thesis also describes the compilation of a bespoke ‘Medium data toolkit’ for processing Wi-Fi probe requests (or indeed any other data with a similar size and complexity). Finally, the thesis demonstrates the value and possible applications of such footfall information through a series of case studies. By successfully avoiding the use of any personally identifiable information, the research undertaken for this thesis also demonstrates that it is feasible to prioritise the privacy of users while still deriving detailed and meaningful insights from the data generated by the users

Data Outsourcing on Cloud using Secret Key Distribution for Privacy Preserving

Cloud computing has turn out to be a trend with the delivery of innumerable advantages. Cloud has come to be a rising widely recognized that brings about diverse era and computing mind for internet at very low rate. Massive garage centres are supplied with the aid of the cloud which may be accessed without problem from any corner of the centre and at any time however there are positive problems and demanding situations faced thru the person at the same time as using cloud computing with reference to protection. But new stressful situations popped out to ensure Confidentiality, integrity and access control of the records. To deal with the ones troubles we will be inclined to suggest a topic depend that makes use of threshold cryptography internal which records proprietor divides clients in businesses and offers single key to every organization within the imply time, that single key (separate thru approach that will become special mystery key) is distribute to each purchaser of that cluster for decoding of information. The most function of this subject is that cut once more the number of safety key and it additionally make sure that absolutely attested users can get entry to the outsourced know-how

Globally Gridded Satellite (GridSat) Observations for Climate Studies

Geostationary satellites have provided routine, high temporal resolution Earth observations since the 1970s. Despite the long period of record, use of these data in climate studies has been limited for numerous reasons, among them: there is no central archive of geostationary data for all international satellites, full temporal and spatial resolution data are voluminous, and diverse calibration and navigation formats encumber the uniform processing needed for multi-satellite climate studies. The International Satellite Cloud Climatology Project set the stage for overcoming these issues by archiving a subset of the full resolution geostationary data at approx.10 km resolution at 3 hourly intervals since 1983. Recent efforts at NOAA s National Climatic Data Center to provide convenient access to these data include remapping the data to a standard map projection, recalibrating the data to optimize temporal homogeneity, extending the record of observations back to 1980, and reformatting the data for broad public distribution. The Gridded Satellite (GridSat) dataset includes observations from the visible, infrared window, and infrared water vapor channels. Data are stored in the netCDF format using standards that permit a wide variety of tools and libraries to quickly and easily process the data. A novel data layering approach, together with appropriate satellite and file metadata, allows users to access GridSat data at varying levels of complexity based on their needs. The result is a climate data record already in use by the meteorological community. Examples include reanalysis of tropical cyclones, studies of global precipitation, and detection and tracking of the intertropical convergence zone