A case study: Failure prediction in a real LTE network

Mobile traffic and number of connected devices have been increasing exponentially nowadays, with customer expectation from mobile operators in term of quality and reliability is higher and higher. This places pressure on operators to invest as well as to operate their growing infrastructures. As such, telecom network management becomes an essential problem. To reduce cost and maintain network performance, operators need to bring more automation and intelligence into their management system. Self-Organizing Networks function (SON) is an automation technology aiming to maximize performance in mobility networks by bringing autonomous adaptability and reducing human intervention in network management and operations. Three main areas of SON include self-configuration (auto-configuration when new element enter the network), self-optimization (optimization of the network parameters during operation) and self-healing (maintenance). The main purpose of the thesis is to illustrate how anomaly detection methods can be applied to SON functions, in particularly self-healing functions such as fault detection and cell outage management. The thesis is illustrated by a case study, in which the anomalies - in this case, the failure alarms, are predicted in advance using performance measurement data (PM data) collected from a real LTE network within a certain timeframe. Failures prediction or anomalies detection can help reduce cost and maintenance time in mobile network base stations. The author aims to answer the research questions: what anomaly detection models could detect the anomalies in advance, and what type of anomalies can be well-detected using those models. Using cross-validation, the thesis shows that random forest method is the best performing model out of the chosen ones, with F1-score of 0.58, 0.96 and 0.52 for the anomalies: Failure in Optical Interface, Temperature alarm, and VSWR minor alarm respectively. Those are also the anomalies can be well-detected by the model

Context-awareness for mobile sensing: a survey and future directions

The evolution of smartphones together with increasing computational power have empowered developers to create innovative context-aware applications for recognizing user related social and cognitive activities in any situation and at any location. The existence and awareness of the context provides the capability of being conscious of physical environments or situations around mobile device users. This allows network services to respond proactively and intelligently based on such awareness. The key idea behind context-aware applications is to encourage users to collect, analyze and share local sensory knowledge in the purpose for a large scale community use by creating a smart network. The desired network is capable of making autonomous logical decisions to actuate environmental objects, and also assist individuals. However, many open challenges remain, which are mostly arisen due to the middleware services provided in mobile devices have limited resources in terms of power, memory and bandwidth. Thus, it becomes critically important to study how the drawbacks can be elaborated and resolved, and at the same time better understand the opportunities for the research community to contribute to the context-awareness. To this end, this paper surveys the literature over the period of 1991-2014 from the emerging concepts to applications of context-awareness in mobile platforms by providing up-to-date research and future research directions. Moreover, it points out the challenges faced in this regard and enlighten them by proposing possible solutions

Detection and compensation methods for self-healing in self-organizing networks

Uno de los elementos clave en la definición de los recientes estándares de comunicaciones móviles del 3rd Generation Partnership Project (3GPP), LTE (Long Term Evolution) y LTEAdvanced, es la consideración de funciones que se puedan ejecutar de manera automática. Este tipo de redes se conocen como redes Auto-Organizadas (Self-Organizing Networks, SON). Las funciones SON permiten hacer frente al importante incremento en tamaño y complejidad que han experimentado las redes de comunicaciones móviles en los últimos años. El número de usuarios es cada vez mayor y los servicios requieren gran cantidad de recursos y altas tasas de transmisión por lo que la gestión de estas redes se está convirtiendo en una tarea cada vez más compleja. Además, cuando las redes de quinta generación (5G) se implanten, la complejidad y el coste asociado a estas nuevas redes será todavía mayor. En este contexto, las funciones SON resultan imprescindibles para llevar a cabo la gestión de estas redes tan complejas. El objetivo de SON es definir un conjunto de funcionalidades que permitan automatizar la gestión de las redes móviles. Mediante la automatización de las tareas de gestión y optimización es posible reducir los gastos de operación y capital (OPEX y CAPEX). Las funciones SON se clasifican en tres grupos: Auto- Configuración, Auto-Optimización y Auto-Curación. Las funciones de Auto-Configuración tienen como objetivo la definición de los distintos parámetros de configuración durante la fase de planificación de una red o después de la introducción de un nuevo elemento en una red ya desplegada. Las funciones de Auto-Optimización pretenden modificar los parámetros de configuración de una red para maximizar el rendimiento de la misma y adaptarse a distintos escenarios. Las funciones de Auto- Curación tienen como objetivo detectar y diagnosticar posibles fallos en la red que afecten al funcionamiento de la misma de manera automática. Cuando un fallo es detectado en una celda este puede ser recuperado (función de recuperación) o compensado (función de compensación). Uno de los principales desafíos relacionado con las funciones SON es el desarrollo de métodos eficientes para la automatización de las tareas de optimización y mantenimiento de una red móvil. En este sentido, la comunidad científica ha centrado su interés en la definición de métodos de Auto-Configuración y Auto-Optimización siendo las funciones de Auto-Curación las menos exploradas. Por esta razón, no es fácil encontrar algoritmos de detección y compensación realmente eficientes. Muchos estudios presentan métodos de detección y compensación que producen buenos resultados pero a costa de una gran complejidad. Además, en muchos casos, los algoritmos de detección y compensación se presentan como solución general para distintos tipos de fallo lo que hace que disminuya la efectividad. Por otro lado, la investigación ha estado tradicionalmente enfocada a la búsqueda de soluciones SON basadas en modelos analíticos o simulados. Sin embargo, el principal desafío ahora está relacionado con la explotación de datos reales disponibles con el objetivo de crear una base del conocimiento útil que maximice el funcionamiento de las actuales soluciones SON. Esto es especialmente interesante en el área de las funciones de Auto-Curación. En este contexto, la disponibilidad de un histórico de datos es crucial para entender cómo funciona la red en condiciones normales o cuando se producen fallos y como estos fallos afectan a la calidad de servicio experimentada por los usuarios. El principal objetivo de esta tesis es el desarrollo de algoritmos eficientes de detección y compensación de fallos en redes móviles. En primer lugar, se propone un método de detección de celdas caídas basado en estadísticas de traspasos. Una de las principales características de este algoritmo es que su simplicidad permite detectar celdas caídas en cualquier red inmediatamente después de acceder a los indicadores de funcionamiento de la misma. En segundo lugar, una parte importante de la tesis está centrada en la función de compensación. Por un lado, se propone una novedosa metodología de compensación de celdas caídas. Este nuevo método permite adaptar la compensación a la degradación específica provocada por la celda caída. Una vez que se detecta un problema de celda caída, se realiza un análisis de la degradación producida por este fallo en las celdas vecinas. A continuación, diferentes algoritmos de compensación se aplican a las distintas celdas vecinas en función del tipo de degradación detectado. En esta tesis se ha llevado a cabo un estudio de esta fase de análisis utilizando datos de una red real actualmente en uso. Por otro lado, en esta tesis también se propone un método de compensación que considera un fallo diferente al de celda caída. En concreto, se propone un método de compensación para un fallo de cobertura débil basado en modificaciones del margen de traspaso. Por último, aunque es interesante evaluar los métodos propuestos en redes reales, no siempre es posible. Los operadores suelen ser reacios a probar métodos que impliquen cambios en los parámetros de configuración de los elementos de la red. Por esta razón, una parte de esta tesis ha estado centrada en la implementación de un simulador dinámico de nivel de sistema que permita la evaluación de los métodos propuestos

Avances y perspectivas de la ingeniería 4.0

Este libro es resultado de los trabajos finales presentados en el I Congreso Internacional de Ingeniería 4.0 realizado los días 24 y 25 de octubre de 2019 organizado por la Facultad de Ingeniería de la Universidad Libre Seccional Pereira

Collaborative Traffic Offloading for Mobile Systems

Due to the popularity of smartphones and mobile streaming services, the growth of traffic volume in mobile networks is phenomenal. This leads to huge investment pressure on mobile operators' wireless access and core infrastructure, while the profits do not necessarily grow at the same pace. As a result, it is urgent to find a cost-effective solution that can scale to the ever increasing traffic volume generated by mobile systems. Among many visions, mobile traffic offloading is regarded as a promising mechanism by using complementary wireless communication technologies, such as WiFi, to offload data traffic away from the overloaded mobile networks. The current trend to equip mobile devices with an additional WiFi interface also supports this vision. This dissertation presents a novel collaborative architecture for mobile traffic offloading that can efficiently utilize the context and resources from networks and end systems. The main contributions include a network-assisted offloading framework, a collaborative system design for energy-aware offloading, and a software-defined networking (SDN) based offloading platform. Our work is the first in this domain to integrate energy and context awareness into mobile traffic offloading from an architectural perspective. We have conducted extensive measurements on mobile systems to identify hidden issues of traffic offloading in the operational networks. We implement the offloading protocol in the Linux kernel and develop our energy-aware offloading framework in C++ and Java on commodity machines and smartphones. Our prototype systems for mobile traffic offloading have been tested in a live environment. The experimental results suggest that our collaborative architecture is feasible and provides reasonable improvement in terms of energy saving and offloading efficiency. We further adopt the programmable paradigm of SDN to enhance the extensibility and deployability of our proposals. We release the SDN-based platform under open-source licenses to encourage future collaboration with research community and standards developing organizations. As one of the pioneering work, our research stresses the importance of collaboration in mobile traffic offloading. The lessons learned from our protocol design, system development, and network experiments shed light on future research and development in this domain.Yksi mobiiliverkkojen suurimmista haasteista liittyy liikennemäärien eksponentiaaliseen kasvuun. Tämä verkkoliikenteen kasvu johtuu pitkälti suosituista videopalveluista, kuten YouTube ja Netflix, jotka lähettävät liikkuvaa kuvaa verkon yli. Verkon lisääntynyt kuormitus vaatii investointeja verkon laajentamiseksi. On tärkeää löytää kustannustehokkaita tapoja välittää suuressa mittakaavassa sisältöä ilman mittavia infrastruktuuri-investointeja. Erilaisia liikennekuormien ohjausmenetelmiä on ehdotettu ratkaisuksi sisällönvälityksen tehostamiseen mobiiliverkoissa. Näissä ratkaisuissa hyödynnetään toisiaan tukevia langattomia teknologioita tiedonvälityksen tehostamiseen, esimerkiksi LTE-verkosta voidaan delegoida tiedonvälitystä WiFi-verkoille. Useimmissa kannettavissa laitteissa on tuki useammalle langattomalle tekniikalle, joten on luonnollista hyödyntää näiden tarjoamia mahdollisuuksia tiedonvälityksen tehostamisessa. Tässä väitöskirjassa tutkitaan liikennekuormien ohjauksen toimintaa ja mahdollisuuksia mobiiliverkoissa. Työssä esitetään uusi yhteistyöpohjainen liikennekuormien ohjausjärjestelmä, joka hyödyntää päätelaitteiden ja verkon tilannetietoa liikennekuormien optimoinnissa. Esitetty järjestelmä ja arkkitehtuuri on ensimmäinen, joka yhdistää energiankulutuksen ja kontekstitiedon liikennekuormien ohjaukseen. Väitöskirjan keskeisiä tuloksia ovat verkon tukema liikennekuormien ohjauskehikko, yhteistyöpohjainen energiatietoinen optimointiratkaisu sekä avoimen lähdekoodin SoftOffload-ratkaisu, joka mahdollistaa ohjelmistopohjaisen liikennekuormien ohjauksen. Esitettyjä järjestelmiä arvioidaan kokeellisesti kaupunkiympäristöissä älypuhelimia käyttäen. Työn tulokset mahdollistavat entistä energiatehokkaammat liikennekuormien ohjausratkaisut ja tarjoavat ideoita ja lähtökohtia tulevaan 5G kehitystyöhön

Optimization of Handover, Survivability, Multi-Connectivity and Secure Slicing in 5G Cellular Networks using Matrix Exponential Models and Machine Learning

Title from PDF of title page, viewed January 31, 2023Dissertation advisor: Cory BeardVitaIncludes bibliographical references (pages 173-194)Dissertation (Ph.D.)--Department of Computer Science and Electrical Engineering. University of Missouri--Kansas City, 2022This works proposes optimization of cellular handovers, cellular network survivability modeling, multi-connectivity and secure network slicing using matrix exponentials and machine learning techniques. We propose matrix exponential (ME) modeling of handover arrivals with the potential to much more accurately characterize arrivals and prioritize resource allocation for handovers, especially handovers for emergency or public safety needs. With the use of a ‘B’ matrix for representing a handover arrival, we have a rich set of dimensions to model system handover behavior. We can study multiple parameters and the interactions between system events along with the user mobility, which would trigger a handoff in any given scenario. Additionally, unlike any traditional handover improvement scheme, we develop a ‘Deep-Mobility’ model by implementing a deep learning neural network (DLNN) to manage network mobility, utilizing in-network deep learning and prediction. We use the radio and the network key performance indicators (KPIs) to train our model to analyze network traffic and handover requirements. Cellular network design must incorporate disaster response, recovery and repair scenarios. Requirements for high reliability and low latency often fail to incorporate network survivability for mission critical and emergency services. Our Matrix Exponential (ME) model shows how survivable networks can be designed based on controlling numbers of crews, times taken for individual repair stages, and the balance between fast and slow repairs. Transient and the steady state representations of system repair models, namely, fast and slow repairs for networks consisting of multiple repair crews have been analyzed. Failures are exponentially modeled as per common practice, but ME distributions describe the more complex recovery processes. In some mission critical communications, the availability requirements may exceed five or even six nines (99.9999%). To meet such a critical requirement and minimize the impact of mobility during handover, a Fade Duration Outage Probability (FDOP) based multiple radio link connectivity handover method has been proposed. By applying such a method, a high degree of availability can be achieved by utilizing two or more uncorrelated links based on minimum FDOP values. Packet duplication (PD) via multi-connectivity is a method of compensating for lost packets on a wireless channel. Utilizing two or more uncorrelated links, a high degree of availability can be attained with this strategy. However, complete packet duplication is inefficient and frequently unnecessary. We provide a novel adaptive fractional packet duplication (A-FPD) mechanism for enabling and disabling packet duplication based on a variety of parameters. We have developed a ‘DeepSlice’ model by implementing Deep Learning (DL) Neural Network to manage network load efficiency and network availability, utilizing in-network deep learning and prediction. Our Neural Network based ‘Secure5G’ Network Slicing model will proactively detect and eliminate threats based on incoming connections before they infest the 5G core network elements. These will enable the network operators to sell network slicing as-a-service to serve diverse services efficiently over a single infrastructure with higher level of security and reliability.Introduction -- Matrix exponential and deep learning neural network modeling of cellular handovers -- Survivability modeling in cellular networks -- Multi connectivity based handover enhancement and adaptive fractional packet duplication in 5G cellular networks -- Deepslice and Secure5G: a deep learning framework towards an efficient, reliable and secure network slicing in 5G networks -- Conclusion and future scop

Insights from the Inventory of Smart Grid Projects in Europe: 2012 Update

By the end of 2010 the Joint Research Centre, the European Commission’s in-house science service, launched the first comprehensive inventory of smart grid projects in Europe1. The final catalogue was published in July 2011 and included 219 smart grid and smart metering projects from the EU-28 member states, Switzerland and Norway. The participation of the project coordinators and the reception of the report by the smart grid community were extremely positive. Due to its success, the European Commission decided that the project inventory would be carried out on a regular basis so as to constantly update the picture of smart grid developments in Europe and keep track of lessons learnt and of challenges and opportunities. For this, a new on-line questionnaire was launched in March 2012 and information on projects collected up to September 2012. At the same time an extensive search of project information on the internet and through cooperation links with other European research organizations was conducted. The resulting final database is the most up to date and comprehensive inventory of smart grids and smart metering projects in Europe, including a total of 281 smart grid projects and 90 smart metering pilot projects and rollouts from the same 30 countries that were included in the 2011 inventory database. Projects surveyed were classified into three categories: R&D, demonstration or pre-deployment) and deployment, and for the first time a distinction between smart grid and smart metering projects was made. The following is an insight into the 2012 report.JRC.F.3-Energy securit