There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality.

Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

I and i

Data Mining Practical Machine Learning Tools and Techniques

Machine learning for microbial identification and antimicrobial susceptibility testing on MALDI-TOF mass spectra: a systematic review

The next revolution of industrial applications, known as smart industry or Industry 4.0, will rely on Internet of Things (IoT) to automate the monitoring, inspection, and control of industrial equipment and processes. In Industry 4.0, efficient content delivery is one of the fundamental challenges to be addressed. Nowadays, the promising solution for content delivery in smart industrial applications is the use of hierarchical caching systems at the network edge (5G small cells). This approach reduces the delay for content delivery and helps improve the performance of smart industrial applications. However, the caching management is a challenging and complex task, especially in those scenarios of shared storage resources on edge devices to support multiple concurrent applications (e.g., industrial, mobile users, and connected cars applications). In this article, we study the performance of a shared edge caching system for content delivery in smart industry and connected cars applications. To do so, we propose a mathematical framework to model the performance of a hierarchical shared edge caching system. The proposed mathematical framework considers the distinct content catalogs of the different applications (e.g., industrial and connected cars applications) and content request characteristics from industrial IoT devices and vehicles. Numerical results show that the performance of the shared edge caching system is sensitive to vehicular mobility (i.e., vehicular speed).

Modeling and Analysis of a Shared Edge Caching System for Connected Cars and Industrial IoT-Based Applications

Traffic clustering, forecasting, and management play a crucial role in improving network efficiency, network quality, load balancing (LB), and energy saving of mobile networks. Especially, in 5G networks, a dense heterogeneous architecture of various types of cells (macro cells and small cells) make traffic management become more complicated. Moreover, investigating and understanding traffic patterns of a huge number of cells are challenging issues, but valuable for network operators. On the other hand, big data, machine learning (ML), software-defined network (SDN), and network functions virtualization (NFV) have recently been proposed as emerging technologies and the necessary tools for empowering the SON of 5G to address the intensive computation and optimization issues. In this study, the authors applied those technologies to build a practical and powerful framework for clustering, forecasting, and managing traffic behaviors for a huge number of base stations with different statistical traffic characteristics of different types of cells (GSM, 3G, 4G). Besides, several applications based on traffic forecasting were also introduced. Finally, the performance of the proposed models was evaluated by applying them to a real dataset that collected traffic KPIs (key performance indicators) of more than 6000 cells of a real network during the years, 2016 and 2017.

Applying Big Data, Machine Learning, and SDN/NFV to 5G Traffic Clustering, Forecasting, and Management

In the 5G era, the operational cost of network is expected to increase significantly because of networks' densification, the concurrent operation at multiple frequency bands, and the simultaneous use of different medium access as latent components of 5G. To decrease the operational cost of networks, engineers have tuned to self-organizing networks (SON) that facilitate automatic operation of a network. Challenges have emerged, however, that hinder the current SON paradigm from meeting the requirements of 5G. To overcome these challenges, researchers have proposed a framework for empowering SON with big data. The framework of big data empowered SON analyzes the relationship between key performance indicators (KPIs) and related network parameters (NPs) using machine learning tools; with those parameters, the framework develops regression models using Gaussian process. These models are then applied to the SON engine to be further optimized for operation. The problem, however, is that the methods of finding NPs related to KPI differ case by case. In addition, it is not easy to apprehend the relationship between a KPI and the various NPs related to that KPI with the Gaussian process regression model because it is a single regression. In this paper, to alleviate these two problems, we propose multiple regression models based on MapReduce, where a KPI is the dependent variable and NPs are the independent variables; then we also describe implementation issues with MapReduce.

Multiple regression models for a big data empowered SON framework

In future, more devices such as wearable devices will be connected to the networks. This will increase simultaneous handovers. The coverage of a cell will be small because a superhigh frequency used in 5G wireless networks does not propagate very far. This trend will increase the number of neighbour cell lists and it will accelerate the change of neighbour cell lists since the coverage of cells can be altered by the environment. Meanwhile, the ANR technology will be essential in 5G networks. Since the network environment in the future is not similar to the present, the strategy of ANR should also be different from the present. First, since practical neighbour cell lists in each cell are changed frequently and individually, it is necessary to optimize them frequently and individually. Second, since the neighbour cell lists in each cell are not changed similarly, it is necessary to operate ANR flexibly. To respond to these issues, we propose to use network function virtualization (NFV) for ANR. To evaluate the proposed strategies, we measured additional resource consumption and the latency of handover if neighbour cell lists are not optimized when UEs perform handover simultaneously. These experiments are conducted using Amarisoft LTE-100 Platform.

/pdf/virtualized-anr-to-manage-resources-for-optimization-of-1eg20f9cxx.pdf

Virtualized ANR to Manage Resources for Optimization of Neighbour Cell Lists in 5G Mobile Wireless Networks

MALDI-TOF mass spectrometry has high social and economic value in rapid identification of microorganisms based on the protein mass profile represented in a mass spectrum of the microorganism. Numerous studies have been conducted to identify microorganisms using MALDI-TOF MS. Markers are characteristics that can be used to uniquely distinguish microorganisms. Microorganisms can be identified by applying markers selected based on the extracted mass information. Previous studies demonstrated that combining mass information extracted by MALDI-TOF MS with machine-learning techniques can improve microorganism classification. Classification of microorganisms is particularly difficult and critical for mycobacteria because various pathogens should be treated with different prescriptions, although they exhibit similar compositions. It is quite challenging to accurately identify mycobacteria using conventional methods because their MALDI-TOF MS patterns are similar to each other. In this study, we propose a support vector machine model for improving the distinction of similar species by learning positive and negative markers separately extracted in each group. We classified species in the Mycobacterium abscessus group and Mycobacterium fortuitum group. Our novel approach applies negative markers to classify similar species and improves the identification of similar species using a combination of positive and negative markers.

SVM Classification Model of Similar Bacteria Species using Negative Marker: Based on Matrix-Assisted Laser Desorption/Ionization Time-of-Flight Mass Spectrometry

With the large increases in video content, mobile network operators (MNOs) are persistently seeking to lower their content access latency and backhaul traffic. To mitigate these challenges, operators have started developing an operator-owned content caching system. However, in such networks, it is difficult to provide cost-effective and reliable cache performance for dynamically changing traffic using traditional proprietary network equipment. Meanwhile, network function virtualization (NFV) technology has become a key enabler in providing new business models with reduced costs and enhanced service agility to mobile networks. In addition, dynamic scaling of virtualized network functions can be used to leverage adaptable management of network services on the fly. In this study, we introduce a system architecture of MNO-driven virtualized caching-service based on an NFV standard platform that uses an integer linear programming (ILP)-based centralized optimization algorithm. Our proposed system employs mainly two techniques: a hierarchical caching model and a hybrid (centralized and distributed) orchestration scheme to optimize cache function with fine-grained control. Through experiments, we show that the conceptual MNO caching service is an effective strategy for reducing the traffic burden and enhancing the quality of service (QoS) for streaming video.

Design and Analysis of Virtualized Caching Service on Cellular Infrastructure

In the 5G era, the operational cost of mobile wireless networks will significantly increase. Further, massive network capacity and zero latency will be needed because everything will be connected to mobile networks. Thus, self-organizing networks (SON) are needed, which expedite automatic operation of mobile wireless networks, but have challenges to satisfy the 5G requirements. Therefore, researchers have proposed a framework to empower SON using big data. The recent framework of a big data-empowered SON analyzes the relationship between key performance indicators (KPIs) and related network parameters (NPs) using machine-learning tools, and it develops regression models using a Gaussian process with those parameters. The problem, however, is that the methods of finding the NPs related to the KPIs differ individually. Moreover, the Gaussian process regression model cannot determine the relationship between a KPI and its various related NPs. In this paper, to solve these problems, we proposed multivariate multiple regression models to determine the relationship between various KPIs and NPs. If we assume one KPI and multiple NPs as one set, the proposed models help us process multiple sets at one time. Also, we can find out whether some KPIs are conflicting or not. We implement the proposed models using MapReduce.

/pdf/multivariate-multiple-regression-models-for-a-big-data-2mh48t32ix.pdf

Multivariate Multiple Regression Models for a Big Data-Empowered SON Framework in Mobile Wireless Networks

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