Applying data mining in telecommunications

This thesis applies data mining in commercial settings in the telecommunications industry. The research for this thesis has been performed at T-Mobile Netherlands B.V. and the methods described in some of the chapters have been also applied in Deutsche Telekom subsidiaries in other countries. We had a rare opportunity to work on real commercial data sets and have the results of our research deployed in practice. Throughout this thesis we describe some of the challenges that data miners (or data scientists) meet when working on business problems and our solutions to these problems. The complex data sets we were analyzing contained in certain cases millions of records. In this research we were using simple methods combined in innovative ways to achieve results that were either an improvement on how the business was previously solving these problems or solving important business problems that were not addressed before in such detail. We address the stages of CRISP-DM (CRoss Industry Standard Process for Data Mining), and our main focus is on the stages least covered in literature.T-Mobile Netherlands B.V.Algorithms and the Foundations of Software technolog

An integrated approach to rotorcraft human factors research

As the potential of civil and military helicopters has increased, more complex and demanding missions in increasingly hostile environments have been required. Users, designers, and manufacturers have an urgent need for information about human behavior and function to create systems that take advantage of human capabilities, without overloading them. Because there is a large gap between what is known about human behavior and the information needed to predict pilot workload and performance in the complex missions projected for pilots of advanced helicopters, Army and NASA scientists are actively engaged in Human Factors Research at Ames. The research ranges from laboratory experiments to computational modeling, simulation evaluation, and inflight testing. Information obtained in highly controlled but simpler environments generates predictions which can be tested in more realistic situations. These results are used, in turn, to refine theoretical models, provide the focus for subsequent research, and ensure operational relevance, while maintaining predictive advantages. The advantages and disadvantages of each type of research are described along with examples of experimental results

Final report on the evaluation of RRM/CRRM algorithms

Deliverable public del projecte EVERESTThis deliverable provides a definition and a complete evaluation of the RRM/CRRM algorithms selected in D11 and D15, and evolved and refined on an iterative process. The evaluation will be carried out by means of simulations using the simulators provided at D07, and D14.Preprin

Framework to facilitate smooth handovers between mobile IPv6 networks

Fourth generation (4G) mobile communication networks are characterised by heterogeneous access networks and IP based transport technologies. Different access technologies give users choices to select services such as levels of Quality of Service (QoS) support, business models and service providers. Flexibility of heterogeneous access is compounded by the overhead of scanning to discover accessible services, which added to the handoff latency. This thesis has developed mechanisms for service discovery and service selection, along with a novel proposal for mobility management architectures that reduced handoff latency. The service discovery framework included a service advertisement data repository and a single frequency band access mechanism, which enabled users to explore services offered by various operators with a reduced scanning overhead. The novel hierarchical layout of the repository enabled it to categorise information into various layers and facilitate location based information retrieval. The information made available by the repository included cost, bandwidth, Packet Loss (PL), latency, jitter, Bit Error Rate (BER), location and service connectivity information. The single frequency band access mechanism further enabled users to explore service advertisements in the absence of their main service providers. The single frequency access mechanism broadcasted service advertisements information piggybacked onto a router advertisement packet on a reserved frequency band for advertisements. Results indicated that scanning 13 channels on 802.11 b interface takes 189ms whereas executing a query with maximum permissible search parameters on the service advertisement data repository takes 67ms. A service selection algorithm was developed to make handoff decisions utilising the service advertisements acquired from the service discovery framework; based on a user's preference. The selection algorithm reduced the calculation overhead by eliminating unsuitable networks; based on interface compatibility, service provider location, unacceptable QoS (Quality of service) and unacceptable cost; from the selection process. The selection algorithm utilised cost, bandwidth, PL, latency, jitter, BER and terminal power for computing the most suitable network. Results indicated that the elimination based approach has improved the performance of the algorithm by 35% over non- elimination oriented selection procedures, even after utilising more selection parameters. The service discovery framework and the service selection algorithm are flexible enough to be employed in most mobility management architectures. The thesis recommends Seamless Mobile Internet Protocol (SMIP) as a mobility management scheme based on the simulation results. The SMIP protocol, a combination of Hierarchical Mobile Internet Protocol (HMIP) and Fast Mobile Internet Protocol (FMIP), suffered hand off latency increases when undergoing a global handoff due to HMIP. The proposed modification to the HMIP included the introduction of a coverage area overlap, to reduce the global handoff latency. The introduction of a Home Address (HA) in Wireless Local Area Networks (WLAN) binding table enabled seamless handoffs from WLANs by having a redirection mechanism for the user's packets after handoff. The thesis delivered a new mobility management architecture with mechanisms for service discovery and service selection. The proposed framework enabled user oriented, application centric and terminal based approach for selecting IPv6 networks

Edge IoT Driven Framework for Experimental Investigation and Computational Modeling of Integrated Food, Energy, and Water System

As the global population soars from today’s 7.3 billion to an estimated 10 billion by 2050, the demand for Food, Energy, and Water (FEW) resources is expected to more than double. Such a sharp increase in demand for FEW resources will undoubtedly be one of the biggest global challenges. The management of food, energy, water for smart, sustainable cities involves a multi-scale problem. The interactions of these three dynamic infrastructures require a robust mathematical framework for analysis. Two critical solutions for this challenge are focused on technology innovation on systems that integrate food-energy-water and computational models that can quantify the FEW nexus. Information Communication Technology (ICT) and the Internet of Things (IoT) technologies are innovations that will play critical roles in addressing the FEW nexus stress in an integrated way. The use of sensors and IoT devices will be essential in moving us to a path of more productivity and sustainability. Recent advancements in IoT, Wireless Sensor Networks (WSN), and ICT are one lever that can address some of the environmental, economic, and technical challenges and opportunities in this sector. This dissertation focuses on quantifying and modeling the nexus by proposing a Leontief input-output model unique to food-energy-water interacting systems. It investigates linkage and interdependency as demand for resource changes based on quantifiable data. The interdependence of FEW components was measured by their direct and indirect linkage magnitude for each interaction. This work contributes to the critical domain required to develop a unique integrated interdependency model of a FEW system shying away from the piece-meal approach. The physical prototype for the integrated FEW system is a smart urban farm that is optimized and built for the experimental portion of this dissertation. The prototype is equipped with an automated smart irrigation system that uses real-time data from wireless sensor networks to schedule irrigation. These wireless sensor nodes are allocated for monitoring soil moisture, temperature, solar radiation, humidity utilizing sensors embedded in the root area of the crops and around the testbed. The system consistently collected data from the three critical sources; energy, water, and food. From this physical model, the data collected was structured into three categories. Food data consists of: physical plant growth, yield productivity, and leaf measurement. Soil and environment parameters include; soil moisture and temperature, ambient temperature, solar radiation. Weather data consists of rainfall, wind direction, and speed. Energy data include voltage, current, watts from both generation and consumption end. Water data include flow rate. The system provides off-grid clean PV energy for all energy demands of farming purposes, such as irrigation and devices in the wireless sensor networks. Future reliability of the off-grid power system is addressed by investigating the state of charge, state of health, and aging mechanism of the backup battery units. The reliability assessment of the lead-acid battery is evaluated using Weibull parametric distribution analysis model to estimate the service life of the battery under different operating parameters and temperatures. Machine learning algorithms are implemented on sensor data acquired from the experimental and physical models to predict crop yield. Further correlation analysis and variable interaction effects on crop yield are investigated