Energy Performance Testing of Smartphones: A First Look at Energy Bugs in Mobile Devices

Smartphones have revolutionized the way people live their daily lives, the way they communicate with each other and the way they access information on-line. A decade ago, desktop computers and laptops were the primary source to use internet and access on-line information. But with all the technological advancements, smartphones and tablets have taken over. An important factor that aided to the popularity of smartphones is different applications available on smartphones. Whether a user wants to play games, watch videos, read books, access on-line information or check his/her email, there are applications for each and every one of them. These applications have greatly enhanced the user experience on smartphones. According to an old saying, everything comes at a price. The same is the case with these smartphone applications. In addition to enhancing user experience and providing easy accessibility, they affect the smartphone battery consumption. They utilize the hardware resources and in turn consume the battery's energy. In comparison to the advancements in hardware and software industry, the development in battery technology is significantly slow. Even the battery energy density has little effect on the battery life with inefficient applications. Therefore there is a need: (a) for applications that efficiently utilize the smartphone battery, (b) to investigate the energy issues (energy bugs) in smartphones. For applications to be energy efficient; we need to have some testing methodologies so that the developers are aware of the energy consumption of their applications and can take appropriate measures while the applications are still in the development phase. Bugs are usually defined as an error in the system and energy bugs in smartphones are responsible for the unexpected and substantial battery drain. In order to research the energy bugs in smartphones, we need to have a comprehensive definition in context of software testing so that the developers can use it as a reference while testing their applications and improve the functionality of their applications. With the above objectives in mind, in this thesis we have proposed and implemented a methodology to efficiently reduce the configuration parameters of smartphone applications that will help in reduction of test cases and will efficiently reduce the testing time. We also validated our methodology by measurements and experiments on four different smartphones. We have investigated the energy issues in smartphones and have defined energy bug. We also validated our definition with measurements and experiments.4 month

Computer Vision in Wind Turbine Blade Inspections: An Analysis of Resolution Impact on Detection and Classification of Leading-Edge Erosion

Wind turbines, as critical components of the renewable energy industry, present unique maintenance challenges, particularly in remote or challenging locations such as offshore wind farms. These are amplified in the inspection of leading-edge erosion on wind turbine blades, a task still largely reliant on traditional methods. Emerging technologies like computer vision and object detection offer promising avenues for enhancing inspections, potentially reducing operational costs and human-associated risks. However, variability in image resolution, a critical factor for these technologies, remains a largely underexplored aspect in the wind energy context. This study explores the application of machine learning in detecting and categorizing leading edge erosion damage on wind turbine blades. YOLOv7, a state-of-the-art object detection model, is trained with a custom dataset consisting of images displaying various forms of leading edge erosion, representing multiple categories of damage severity. Trained model is tested on images acquired with three different tools, each providing images with a different resolution. The effect of image resolution on the performance of the custom object detection model is examined. The research affirms that the YOLOv7 model performs exceptionally well in identifying the most severe types of LEE damage, usually classified as Category 3, characterized by distinct visual features. However, the model's ability to detect less severe damage, namely Category 1 and 2, which are crucial for early detection and preventive measures, exhibits room for improvement. The findings point to a potential correlation between input image resolution and detection confidence in the context of wind turbine maintenance. These results stress the need for high-resolution images, leading to a discussion on the selection of appropriate imaging hardware and the creation of machine learning-ready datasets. The study thereby emphasizes the importance of industry-wide efforts to compile standardized image datasets and the potential impact of machine learning techniques on the efficiency of visual inspections and maintenance strategies. Future directions are proposed with the ultimate aim of enhancing the application of artificial intelligence in wind energy maintenance and management, enabling more efficient and effective operational procedures, and driving the industry towards a more sustainable future

Characterization of user mobility trajectories by implementing clustering techniques

Current and legacy technologies for wireless communications are facing an explosive demand of capacity and resources, triggered by an exponential growing of traffic, mainly due to the proliferation of smartphones and the introduction of demanding multimedia and video applications. There is the anticipation that future generation of wireless communications systems, 5G, will attend the growing demand on capacity and network resources, along with the necessity for blending novel technology concepts including Internet of Things, machine communications, the introduction of heterogeneous network architectures, massive arrays of antennas and dynamic spectrum allocation, among others. Moreover, self-organizing networks (SON) functions incorporated in present mobile communication standards provide limited levels of proactivity. Therefore, it is foreseen that future network are required of highly automation and real-time reaction to network problems, topology changes and dynamic parameterization. The flexibility to be introduced in 5G networks by incorporating virtualized hardware architecture and cloud computing, allow the inclusion of big data analytics capabilities for finding insights and taking advantage of the vast amounts of data generated in the network system. The full embodiment of big data analytics among the Radio Access Network optimization and planning processes, allow gathering an end to end knowledge and reaching the individual user level granularity. The purpose of this work is to provide a case of study for smartly processing collected data from mobility traces by using a hierarchical clustering function, an unsupervised method of data analytics, for characterizing the different user mobility trajectories to extract an individual user mobility profile. The methodology proposed references a knowledge discovery framework which uses Artificial Intelligence processes for finding insights in collected network data and the use of this knowledge for driving SON functions, other optimization and planning processes, and novel operator business cases

A Client-Centric Data Streaming Technique for Smartphones: An Energy Evaluation

With advances in microelectronic and wireless communication technologies, smartphones have computer-like capabilities in terms of computing power and communication bandwidth. They allow users to use advanced applications that used to be run on computers only. Web browsing, email fetching, gaming, social networking, and multimedia streaming are examples of wide-spread smartphone applications. Unsurprisingly, network-related applications are dominant in the realm of smartphones. Users love to be connected while they are mobile. Streaming applications, as a part of network-related applications, are getting increasingly popular. Mobile TV, video on demand, and video sharing are some popular streaming services in the mobile world. Thus, the expected operational time of smartphones is rising rapidly. On the other hand, the enormous growth of smartphone applications and services adds up to a significant increase in complexity in the context of computation and communication needs, and thus there is a growing demand for energy in smartphones. Unlike the exponential growth in computing and communication technologies, the growth in battery technologies is not keeping up with the rapidly growing energy demand of these devices. Therefore, the smartphone's utility has been severely constrained by its limited battery lifetime. It is very important to conserve the smartphone's battery power. Even though hardware components are the actual energy consumers, software applications utilize the hardware components through the operating system. Thus, by making smartphone applications energy-efficient, the battery lifetime can be extended. With this view, this work focuses on two main problems: i) developing an energy testing methodology for smartphone applications, and ii) evaluating the energy cost and designing an energy-friendly downloader for smartphone streaming applications. The detailed contributions of this thesis are as follows: (i) it gives a generalized framework for energy performance testing and shows a detailed flowchart that application developers can easily follow to test their applications; (ii) it evaluates the energy cost of some popular streaming applications showing how the download strategy that an application developer adopts may adversely affect the energy savings; (iii) it develops a model of an energy-friendly downloader for streaming applications and studies the effects of the downloader's parameters regarding energy consumption; and finally, (iv) it gives a mathematical model for the proposed downloader and validates it by means of experiments

Security Enhancement by Identifying Attacks Using Machine Learning for 5G Network

Need of security enhancement for 5G network has been increased in last decade. Data transmitted over network need to be secure from external attacks. Thus there is need to enhance the security during data transmission over 5G network. There remains different security system that focus on identification of attacks. In order to identify attack different machine learning mechanism are considered. But the issue with existing research work is limited security and performance issue. There remains need to enhance security of 5G network. To achieve this objective hybrid mechanism are introduced. Different treats such as Denial-of-Service, Denial-of-Detection, Unfair use or resources are classified using enhanced machine learning approach. Proposed work has make use of LSTM model to improve accuracy during decision making and classification of attack of 5G network. Research work is considering accuracy parameters such as Recall, precision and F-Score to assure the reliability of proposed model. Simulation results conclude that proposed model is providing better accuracy as compared to conventional model

Proposal of an adaptive infotainment system depending on driving scenario complexity

Tesi en modalitat Doctorat industrialPla de Doctorat industrial de la Generalitat de CatalunyaThe PhD research project is framed within the plan of industrial doctorates of the “Generalitat de Catalunya”. During the investigation, most of the work was carried out at the facilities of the vehicle manufacturer SEAT, specifically at the information and entertainment (infotainment) department. In the same way, there was a continuous cooperation with the telematics department of the UPC. The main objective of the project consisted in the design and validation of an adaptive infotainment system dependent on the driving complexity. The system was created with the purpose of increasing driver’ experience while guaranteeing a proper level of road safety. Given the increasing number of application and services available in current infotainment systems, it becomes necessary to devise a system capable of balancing these two counterparts. The most relevant parameters that can be used for balancing these metrics while driving are: type of services offered, interfaces available for interacting with the services, the complexity of driving and the profile of the driver. The present study can be divided into two main development phases, each phase had as outcome a real physical block that came to be part of the final system. The final system was integrated in a vehicle and validated in real driving conditions. The first phase consisted in the creation of a model capable of estimating the driving complexity based on a set of variables related to driving. The model was built by employing machine learning methods and the dataset necessary to create it was collected from several driving routes carried out by different participants. This phase allowed to create a model capable of estimating, with a satisfactory accuracy, the complexity of the road using easily extractable variables in any modern vehicle. This approach simplify the implementation of this algorithm in current vehicles. The second phase consisted in the classification of a set of principles that allow the design of the adaptive infotainment system based on the complexity of the road. These principles are defined based on previous researches undertaken in the field of usability and user experience of graphical interfaces. According to these of principles, a real adaptive infotainment system with the most commonly used functionalities; navigation, radio and media was designed and integrated in a real vehicle. The developed system was able to adapt the presentation of the content according to the estimation of the driving complexity given by the block developed in phase one. The adaptive system was validated in real driving scenarios by several participants and results showed a high level of acceptance and satisfaction towards this adaptive infotainment. As a starting point for future research, a proof of concept was carried out to integrate new interfaces into a vehicle. The interface used as reference was a Head Mounted screen that offered redundant information in relation to the instrument cluster. Tests with participants served to understand how users perceive the introduction of new technologies and how objective benefits could be blurred by initial biases.El proyecto de investigación de doctorado se enmarca dentro del plan de doctorados industriales de la Generalitat de Catalunya. Durante la investigación, la mayor parte del trabajo se llevó a cabo en las instalaciones del fabricante de vehículos SEAT, específicamente en el departamento de información y entretenimiento (infotainment). Del mismo modo, hubo una cooperación continua con el departamento de telemática de la UPC. El objetivo principal del proyecto consistió en el diseño y la validación de un sistema de información y entretenimiento adaptativo que se ajustaba de acuerdo a la complejidad de la conducción. El sistema fue creado con el propósito de aumentar la experiencia del conductor y garantizar un nivel adecuado en la seguridad vial. El proyecto surge dado el número creciente de aplicaciones y servicios disponibles en los sistemas actuales de información y entretenimiento; es por ello que se hace necesario contar con un sistema capaz de equilibrar estas dos contrapartes. Los parámetros más relevantes que se pueden usar para equilibrar estas métricas durante la conducción son: el tipo de servicios ofrecidos, las interfaces disponibles para interactuar con los servicios, la complejidad de la conducción y el perfil del conductor. El presente estudio se puede dividir en dos fases principales de desarrollo, cada fase tuvo como resultado un componente que se convirtió en parte del sistema final. El sistema final fue integrado en un vehículo y validado en condiciones reales de conducción. La primera fase consistió en la creación de un modelo capaz de estimar la complejidad de la conducción en base a un conjunto de variables relacionadas con la conducción. El modelo se construyó empleando "Machine Learning Methods" y el conjunto de datos necesario para crearlo se recopiló a partir de varias rutas de conducción realizadas por diferentes participantes. Esta fase permitió crear un modelo capaz de estimar, con una precisión satisfactoria, la complejidad de la carretera utilizando variables fácilmente extraíbles en cualquier vehículo moderno. Este enfoque simplifica la implementación de este algoritmo en los vehículos actuales. La segunda fase consistió en la clasificación de un conjunto de principios que permiten el diseño del sistema de información y entretenimiento adaptativo basado en la complejidad de la carretera. Estos principios se definen en base a investigaciones anteriores realizadas en el campo de usabilidad y experiencia del usuario con interfaces gráficas. De acuerdo con estos principios, un sistema de entretenimiento y entretenimiento real integrando las funcionalidades más utilizadas; navegación, radio y audio fue diseñado e integrado en un vehículo real. El sistema desarrollado pudo adaptar la presentación del contenido según la estimación de la complejidad de conducción dada por el bloque desarrollado en la primera fase. El sistema adaptativo fue validado en escenarios de conducción reales por varios participantes y los resultados mostraron un alto nivel de aceptación y satisfacción hacia este entretenimiento informativo adaptativo. Como punto de partida para futuras investigaciones, se llevó a cabo una prueba de concepto para integrar nuevas interfaces en un vehículo. La interfaz utilizada como referencia era una pantalla a la altura de los ojos (Head Mounted Display) que ofrecía información redundante en relación con el grupo de instrumentos. Las pruebas con los participantes sirvieron para comprender cómo perciben los usuarios la introducción de nuevas tecnologías y cómo los sesgos iniciales podrían difuminar los beneficios.Postprint (published version

Smart Home System

This project involves the design and implementation of a Smart Home system using IoT solutions. Three types of sensors, namely an occupancy sensor, a light sensor and a temperature sensor, along with a security camera are used and incorporated with a microcontroller in a master/slave architecture via Zigbee, a short-range network communication. The data collected from these sensors is transmitted to a cloud-based platform through Wi-Fi for analyzing and downloading to personal smartphones via a designated user interface. The entire system can be controlled both by users’ smartphones and by personal computers