Future Mobile Communications: LTE Optimization and Mobile Network Virtualization

Providing QoS while optimizing the LTE network in a cost efficient manner is very challenging. Thus, radio scheduling is one of the most important functions in mobile broadband networks. The design of a mobile network radio scheduler holds several objectives that need to be satisfied, for example: the scheduler needs to maximize the radio performance by efficiently distributing the limited radio resources, since the operator's revenue depends on it. In addition, the scheduler has to guarantee the user's demands in terms of their Quality of Service (QoS). Thus, the design of an effective scheduler is rather a complex task. In this thesis, the author proposes the design of a radio scheduler that is optimized towards QoS guarantees and system performance optimization. The proposed scheduler is called Optimized Service Aware Scheduler (OSA). The OSA scheduler is tested and analyzed in several scenarios, and is compared against other well-known schedulers. A novel wireless network virtualization framework is also proposed in this thesis. The framework targets the concepts of wireless virtualization applied within the 3GPP Long Term Evolution (LTE) system. LTE represents one of the new mobile communication systems that is just entering the market. Therefore, LTE was chosen as a case study to demonstrate the proposed wireless virtualization framework. The framework is implemented in the LTE network simulator and analyzed, highlighting the many advantages and potential gain that the virtualization process can achieve. Two potential gain scenarios that can result from using network virtualization in LTE systems are analyzed: Multiplexing gain coming from spectrum sharing, and multi-user diversity gain. Several LTE radio analytical models, based on Continuous Time Markov Chains (CTMC) are designed and developed in this thesis. These models target the modeling of three different time domain radio schedulers: Maximum Throughput (MaxT), Blind Equal Throughput (BET), and Optimized Service Aware Scheduler (OSA). The models are used to obtain faster results (i.e., in a very short time period in the order of seconds to minutes), compared to the simulation results that can take considerably longer periods, such as hours or sometimes even days. The model results are also compared against the simulation results, and it is shown that it provides a good match. Thus, it can be used for fast radio dimensioning purposes. Overall, the concepts, investigations, and the analytical models presented in this thesis can help mobile network operators to optimize their radio network and provide the necessary means to support services QoS differentiations and guarantees. In addition, the network virtualization concepts provides an excellent tool that can enable the operators to share their resources and reduce their cost, as well as provides good chances for smaller operators to enter the market

Automated network optimisation using data mining as support for economic decision systems

The evolution from wired voice communications to wireless and cloud computing services has led to the rapid growth of wireless communication companies attempting to meet consumer needs. While these companies have generally been able to achieve quality of service (QoS) high enough to meet most consumer demands, the recent growth in data hungry services in addition to wireless voice communication, has placed significant stress on the infrastructure and begun to translate into increased QoS issues. As a result, wireless providers are finding difficulty to meet demand and dealing with an overwhelming volume of mobile data. Many telecommunication service providers have turned to data analytics techniques to discover hidden insights for fraud detection, customer churn detection and credit risk analysis. However, most are illequipped to prioritise expansion decisions and optimise network faults and costs to ensure customer satisfaction and optimal profitability. The contribution of this thesis in the decision-making process is significant as it initially proposes a network optimisation scheme using data mining algorithms to develop a monitoring framework capable of troubleshooting network faults while optimising costs based on financial evaluations. All the data mining experiments contribute to the development of a super–framework that has been tested using real-data to demonstrate that data mining techniques play a crucial role in the prediction of network optimisation actions. Finally, the insights extracted from the super-framework demonstrate that machine learning mechanisms can draw out promising solutions for network optimisation decisions, customer segmentation, customers churn prediction and also in revenue management. The outputs of the thesis seek to help wireless providers to determine the QoS factors that should be addressed for an efficient network optimisation plan and also presents the academic contribution of this research

Machine Learning Framework for the Sustainable Maintenance of Building Facilities

The importance of sustainable building maintenance is growing as part of the Sustainable Building concept. The integration and implementation of new technologies such as the Internet of Things (IoT), smart sensors, and information and communication technology (ICT) into building facilities generate a large amount of data that will be utilized to better manage the sustainable building maintenance and staff. Anomaly prediction models assist facility managers in informing operators to perform scheduled maintenance and visualizing predicted facility anomalies on building information models (BIM). This study proposes a Machine Learning (ML) anomaly prediction model for sustainable building facility maintenance using an IoT sensor network and a BIM model. The suggested framework shows the data management technique of the anomaly prediction model in the 3D building model. The case study demonstrated the framework’s competence to predict anomalies in the heating ventilation air conditioning (HVAC) system. Furthermore, data collected from various simulated conditions of the building facilities was utilized to monitor and forecast anomalies in the 3D model of the fan coil. The faults were then predicted using a classification model, and the results of the models are introduced. Finally, the IoT data from the building facility and the predicted values of the ML models are visualized in the building facility’s BIM model and the real-time monitoring dashboard, respectively

Beyond Wealth and Health: Psycho-Social Factors and Retirement Planning and Expectations in the U.S.

Thesis advisor: Christina MatzRetirement is a significant transition in an individual’s life course. More and more people are working past traditional retirement ages. Planning before retirement has been shown to relate to a number of positive outcomes and lead to a smoother transition to a retired life, such as more retirement savings, better retirement satisfaction, better social life, health, and mental health. However, most of the studies about retirement to date have focused on the impact of health and wealth in preparing for a successful retirement. This dissertation examines three issues related to retirement planning and expectations: (1) How do work and family relationships relate to having a plan to reduce or stop work and expected retirement timing in late life, and are there gender and occupational differences in these relationships? (2) How do workplace experiences relate to expectations to retire earlier or later than what is normative in different occupations? (3) Does sense of control explain the relationship between involuntary retirement and retirement satisfaction? To answer the three questions, the author adopts the role theory, the age norm theory, and the theory of self-efficacy to explain the background and findings. The data for this dissertation comes from the Health and Retirement Study (HRS), a nationally representative dataset that captures the information about the health and retirement issues among adults over age 50 in the U.S. This proposed study uses pooled cross-sectional data from waves 2012 and 2014. Ordinary least squares (OLS) regression and logistic regression were used to examine the effect of work and family relationships and the plans/retirement timing of pre-retirees. Multinomial logistic regression was used to examine workplace factors that contribute to the non-normative retirement age expectations. Mediation analysis was used to study how personal mastery, perceived constraints, and domain-specific control mediates the relationship between involuntary retirement and retirement satisfaction.Thesis (PhD) — Boston College, 2019.Submitted to: Boston College. Graduate School of Social Work.Discipline: Social Work

Routing optimization algorithms in integrated fronthaul/backhaul networks supporting multitenancy

Mención Internacional en el título de doctorEsta tesis pretende ayudar en la definición y el diseño de la quinta generación de redes de telecomunicaciones (5G) a través del modelado matemático de las diferentes cualidades que las caracterizan. En general, la ambición de estos modelos es realizar una optimización de las redes, ensalzando sus capacidades recientemente adquiridas para mejorar la eficiencia de los futuros despliegues tanto para los usuarios como para los operadores. El periodo de realización de esta tesis se corresponde con el periodo de investigación y definición de las redes 5G, y, por lo tanto, en paralelo y en el contexto de varios proyectos europeos del programa H2020. Por lo tanto, las diferentes partes del trabajo presentado en este documento cuadran y ofrecen una solución a diferentes retos que han ido apareciendo durante la definición del 5G y dentro del ámbito de estos proyectos, considerando los comentarios y problemas desde el punto de vista de todos los usuarios finales, operadores y proveedores. Así, el primer reto a considerar se centra en el núcleo de la red, en particular en cómo integrar tráfico fronthaul y backhaul en el mismo estrato de transporte. La solución propuesta es un marco de optimización para el enrutado y la colocación de recursos que ha sido desarrollado teniendo en cuenta restricciones de retardo, capacidad y caminos, maximizando el grado de despliegue de Unidades Distribuidas (DU) mientras se minimizan los agregados de las Unidades Centrales (CU) que las soportan. El marco y los algoritmos heurísticos desarrollados (para reducir la complexidad computacional) son validados y aplicados a redes tanto a pequeña como a gran (nivel de producción) escala. Esto los hace útiles para los operadores de redes tanto para la planificación de la red como para el ajuste dinámico de las operaciones de red en su infraestructura (virtualizada). Moviéndonos más cerca de los usuarios, el segundo reto considerado se centra en la colocación de servicios en entornos de nube y borde (cloud/edge). En particular, el problema considerado consiste en seleccionar la mejor localización para cada función de red virtual (VNF) que compone un servicio en entornos de robots en la nube, que implica restricciones estrictas en las cotas de retardo y fiabilidad. Los robots, vehículos y otros dispositivos finales proveen competencias significativas como impulsores, sensores y computación local que son esenciales para algunos servicios. Por contra, estos dispositivos están en continuo movimiento y pueden perder la conexión con la red o quedarse sin batería, cosa que reta aún más la entrega de servicios en este entorno dinámico. Así, el análisis realizado y la solución propuesta abordan las restricciones de movilidad y batería. Además, también se necesita tener en cuenta los aspectos temporales y los objetivos conflictivos de fiabilidad y baja latencia en el despliegue de servicios en una red volátil, donde los nodos de cómputo móviles actúan como una extensión de la infraestructura de cómputo de la nube y el borde. El problema se formula como un problema de optimización para colocación de VNFs minimizando el coste y también se propone un heurístico eficiente. Los algoritmos son evaluados de forma extensiva desde varios aspectos por simulación en escenarios que reflejan la realidad de forma detallada. Finalmente, el último reto analizado se centra en dar soporte a servicios basados en el borde, en particular, aprendizaje automático (ML) en escenarios del Internet de las Cosas (IoT) distribuidos. El enfoque tradicional al ML distribuido se centra en adaptar los algoritmos de aprendizaje a la red, por ejemplo, reduciendo las actualizaciones para frenar la sobrecarga. Las redes basadas en el borde inteligente, en cambio, hacen posible seguir un enfoque opuesto, es decir, definir la topología de red lógica alrededor de la tarea de aprendizaje a realizar, para así alcanzar el resultado de aprendizaje deseado. La solución propuesta incluye un modelo de sistema que captura dichos aspectos en el contexto de ML supervisado, teniendo en cuenta tanto nodos de aprendizaje (que realizan las computaciones) como nodos de información (que proveen datos). El problema se formula para seleccionar (i) qué nodos de aprendizaje e información deben cooperar para completar la tarea de aprendizaje, y (ii) el número de iteraciones a realizar, para minimizar el coste de aprendizaje mientras se garantizan los objetivos de error predictivo y tiempo de ejecución. La solución también incluye un algoritmo heurístico que es evaluado ensalzando una topología de red real y considerando tanto las tareas de clasificación como de regresión, y cuya solución se acerca mucho al óptimo, superando las soluciones alternativas encontradas en la literatura.This thesis aims to help in the definition and design of the 5th generation of telecommunications networks (5G) by modelling the different features that characterize them through several mathematical models. Overall, the aim of these models is to perform a wide optimization of the network elements, leveraging their newly-acquired capabilities in order to improve the efficiency of the future deployments both for the users and the operators. The timeline of this thesis corresponds to the timeline of the research and definition of 5G networks, and thus in parallel and in the context of several European H2020 programs. Hence, the different parts of the work presented in this document match and provide a solution to different challenges that have been appearing during the definition of 5G and within the scope of those projects, considering the feedback and problems from the point of view of all the end users, operators and providers. Thus, the first challenge to be considered focuses on the core network, in particular on how to integrate fronthaul and backhaul traffic over the same transport stratum. The solution proposed is an optimization framework for routing and resource placement that has been developed taking into account delay, capacity and path constraints, maximizing the degree of Distributed Unit (DU) deployment while minimizing the supporting Central Unit (CU) pools. The framework and the developed heuristics (to reduce the computational complexity) are validated and applied to both small and largescale (production-level) networks. They can be useful to network operators for both network planning as well as network operation adjusting their (virtualized) infrastructure dynamically. Moving closer to the user side, the second challenge considered focuses on the allocation of services in cloud/edge environments. In particular, the problem tackled consists of selecting the best the location of each Virtual Network Function (VNF) that compose a service in cloud robotics environments, that imply strict delay bounds and reliability constraints. Robots, vehicles and other end-devices provide significant capabilities such as actuators, sensors and local computation which are essential for some services. On the negative side, these devices are continuously on the move and might lose network connection or run out of battery, which further challenge service delivery in this dynamic environment. Thus, the performed analysis and proposed solution tackle the mobility and battery restrictions. We further need to account for the temporal aspects and conflicting goals of reliable, low latency service deployment over a volatile network, where mobile compute nodes act as an extension of the cloud and edge computing infrastructure. The problem is formulated as a cost-minimizing VNF placement optimization and an efficient heuristic is proposed. The algorithms are extensively evaluated from various aspects by simulation on detailed real-world scenarios. Finally, the last challenge analyzed focuses on supporting edge-based services, in particular, Machine Learning (ML) in distributed Internet of Things (IoT) scenarios. The traditional approach to distributed ML is to adapt learning algorithms to the network, e.g., reducing updates to curb overhead. Networks based on intelligent edge, instead, make it possible to follow the opposite approach, i.e., to define the logical network topology around the learning task to perform, so as to meet the desired learning performance. The proposed solution includes a system model that captures such aspects in the context of supervised ML, accounting for both learning nodes (that perform computations) and information nodes (that provide data). The problem is formulated to select (i) which learning and information nodes should cooperate to complete the learning task, and (ii) the number of iterations to perform, in order to minimize the learning cost while meeting the target prediction error and execution time. The solution also includes an heuristic algorithm that is evaluated leveraging a real-world network topology and considering both classification and regression tasks, and closely matches the optimum, outperforming state-of-the-art alternatives.This work has been supported by IMDEA Networks InstitutePrograma de Doctorado en Ingeniería Telemática por la Universidad Carlos III de MadridPresidente: Pablo Serrano Yáñez-Mingot.- Secretario: Andrés García Saavedra.- Vocal: Luca Valcarengh

Data-Driven Operational and Safety Analysis of Emerging Shared Electric Scooter Systems

The rapid rise of shared electric scooter (E-Scooter) systems offers many urban areas a new micro-mobility solution. The portable and flexible characteristics have made E-Scooters a competitive mode for short-distance trips. Compared to other modes such as bikes, E-Scooters allow riders to freely ride on different facilities such as streets, sidewalks, and bike lanes. However, sharing lanes with vehicles and other users tends to cause safety issues for riding E-Scooters. Conventional methods are often not applicable for analyzing such safety issues because well-archived historical crash records are not commonly available for emerging E-Scooters. Perceiving the growth of such a micro-mobility mode, this study aimed to investigate E-Scooter operations and safety by collecting, processing, and mining various unconventional data sources. First, origin-destination (OD) data were collected for E-Scooters to analyze how E-Scooters have been used in urban areas. The key factors that drive users to choose E-Scooters over other options (i.e., shared bikes and taxis) were identified. Concerning user safety tied to the growing usage, we further assessed E-Scooter user guidelines in urban areas in the U.S. Scoring models have been developed for evaluating the adopted guidelines. It was found that the areas with E-Scooter systems have notable disparities in terms of the safety factors considered in the guidelines. Built upon the usage and policy analyses, this study also creatively collected news reports as an alternative data source for E-Scooter safety analysis. Three-year news reports were collected for E-Scooter-involved crashes in the U.S. The identified reports are typical crash events with great media impact. Many detailed variables such as location, time, riders’ information, and crash type were mined. This offers a lens to highlight the macro-level crash issues confronted with E-Scooters. Besides the macro-level safety analysis, we also conducted micro-level analysis of E-Scooter riding risk. An all-in-one mobile sensing system has been developed using the Raspberry Pi platform with multiple sensors including GPS, LiDAR, and motion trackers. Naturalistic riding data such as vibration, speed, and location were collected simultaneously when riding E-Scooters. Such mobile sensing technologies have been shown as an innovative way to help gather valuable data for quantifying riding risk. A demonstration on expanding the mobile sensing technologies was conducted to analyze the impact of wheel size and riding infrastructure on E-Scooter riding experience. The quantitative analysis framework proposed in this study can be further extended for evaluating the quality of road infrastructure, which will be helpful for understanding the readiness of infrastructure for supporting the safe use of micro-mobility systems. To sum up, this study contributes to the literature in several distinct ways. First, it has developed mode choice models for revealing the use of E-Scooters among other existing competitive modes for connecting urban metro systems. Second, it has systematically assessed existing E-Scooter user guidelines in the U.S. Moreover, it demonstrated the use of surrogate data sources (e.g., news reports) to assist safety studies in cases where there is no available crash data. Last but not least, it developed the mobile sensing system and evaluation framework for enabling naturalistic riding data collection and risk assessment, which helps evaluate riding behavior and infrastructure performance for supporting micro-mobility systems

Failure Prediction Model for Oil Pipelines

Abstract Failure Predicting Model for Oil Pipelines Bassem Abdrabou Oil and gas pipelines are considered the safest means to transport petroleum products comparing to railway and highway transportations. They transport millions of dollars’ worth of goods every day. However, accidents happen every year and some of these accidents inflict catastrophic impact on the environment and result in great economic loss. In order to maintain safety of the pipelines, several inspection techniques have been developed in the last decades. Despite the accuracy of these techniques, they are very costly and time consuming. Similarly, several failure predicting and condition assessment models have been developed in the last decade; however, most of these models are limited to one type of failure, such as corrosion failure, or mainly depend on expert opinion which makes their output seemingly subjective. The present research develops an objective model of failure prediction for oil pipelines depending on the available historical data on pipelines' accidents. Two approaches were used to fulfill this objective: the artificial neural network (ANN) and the Multi Nomial Logit (MNL). The ANN is used to develop a model to predict failure due to mechanical, corrosion or third party, which collectively account for 88% of oil pipeline accidents. This model had a prediction accuracy of 68.5%. Another ANN model is developed to predict only corrosion or third party failure with a prediction accuracy of 72.2%. The Average Validity Percentage (AVP) for the two models is 73.7 and 72.8, respectively. The MNL approach is used to develop a model that predicts failures caused by mechanical, corrosion or third party elements with a prediction accuracy of 68.4% and Pseudo R Squared of 0.42. The Average Validity Percentage (AVP) for this MNL approach is 73.7%. This model also generates a probability equation for each type of failure. The three developed models show convincing results, since they are based on solid historical failure data for the last 38 years, with no subjectivity or ambiguity. These models could easily be used by oil pipeline operators to identify the type of failure threatening each pipeline so that appropriate preventive and corrective measures can be planned. The models also help to prioritize in-line inspection of different pipeline segments according to the predicted type of failure

Physical layer security for machine type communication networks

Abstract. We examine the physical layer security for machine type communication networks and highlight a secure communication scenario that consists of a transmitter Alice, which employs Transmit Antenna Selection, while a legitimate receiver Bob that uses Maximum Ratio Combining, as well as an eavesdropper Eve. We provide a solution to avoid eavesdropping and provide ways to quantify security and reliability. We obtain closed-form expressions for Multiple-Input Multiple-Output and Multi-antenna Eavesdropper (MIMOME) scenario. The closed{-}form expressions for three useful variations of MIMOME scenario, i.e., MISOME, MIMOSE, and MISOSE are also provided. A low cost and less complex system for utilizing the spatial diversity in multiple antennas system, while guaranteeing secrecy and reliability. Similarly, it is also assumed that Alice, Bob, and Eve can estimate their channel state information, and then we evaluate the performance of closed-form expressions in terms of secrecy outage probability and provide Monte Carlo simulations to corroborate the proposed analytical framework