IEEE Access Special Section Editorial: Big Data Technology and Applications in Intelligent Transportation

During the last few years, information technology and transportation industries, along with automotive manufacturers and academia, are focusing on leveraging intelligent transportation systems (ITS) to improve services related to driver experience, connected cars, Internet data plans for vehicles, traffic infrastructure, urban transportation systems, traffic collaborative management, road traffic accidents analysis, road traffic flow prediction, public transportation service plan, personal travel route plans, and the development of an effective ecosystem for vehicles, drivers, traffic controllers, city planners, and transportation applications. Moreover, the emerging technologies of the Internet of Things (IoT) and cloud computing have provided unprecedented opportunities for the development and realization of innovative intelligent transportation systems where sensors and mobile devices can gather information and cloud computing, allowing knowledge discovery, information sharing, and supported decision making. However, the development of such data-driven ITS requires the integration, processing, and analysis of plentiful information obtained from millions of vehicles, traffic infrastructures, smartphones, and other collaborative systems like weather stations and road safety and early warning systems. The huge amount of data generated by ITS devices is only of value if utilized in data analytics for decision-making such as accident prevention and detection, controlling road risks, reducing traffic carbon emissions, and other applications which bring big data analytics into the picture

Integrated intelligent systems for industrial automation: the challenges of Industry 4.0, information granulation and understanding agents .

The objective of the paper consists in considering the challenges of new automation paradigm Industry 4.0 and reviewing the-state-of-the-art in the field of its enabling information and communication technologies, including Cyberphysical Systems, Cloud Computing, Internet of Things and Big Data. Some ways of multi-dimensional, multi-faceted industrial Big Data representation and analysis are suggested. The fundamentals of Big Data processing with using Granular Computing techniques have been developed. The problem of constructing special cognitive tools to build artificial understanding agents for Integrated Intelligent Enterprises has been faced

Towards Massive Machine Type Communications in Ultra-Dense Cellular IoT Networks: Current Issues and Machine Learning-Assisted Solutions

The ever-increasing number of resource-constrained Machine-Type Communication (MTC) devices is leading to the critical challenge of fulfilling diverse communication requirements in dynamic and ultra-dense wireless environments. Among different application scenarios that the upcoming 5G and beyond cellular networks are expected to support, such as eMBB, mMTC and URLLC, mMTC brings the unique technical challenge of supporting a huge number of MTC devices, which is the main focus of this paper. The related challenges include QoS provisioning, handling highly dynamic and sporadic MTC traffic, huge signalling overhead and Radio Access Network (RAN) congestion. In this regard, this paper aims to identify and analyze the involved technical issues, to review recent advances, to highlight potential solutions and to propose new research directions. First, starting with an overview of mMTC features and QoS provisioning issues, we present the key enablers for mMTC in cellular networks. Along with the highlights on the inefficiency of the legacy Random Access (RA) procedure in the mMTC scenario, we then present the key features and channel access mechanisms in the emerging cellular IoT standards, namely, LTE-M and NB-IoT. Subsequently, we present a framework for the performance analysis of transmission scheduling with the QoS support along with the issues involved in short data packet transmission. Next, we provide a detailed overview of the existing and emerging solutions towards addressing RAN congestion problem, and then identify potential advantages, challenges and use cases for the applications of emerging Machine Learning (ML) techniques in ultra-dense cellular networks. Out of several ML techniques, we focus on the application of low-complexity Q-learning approach in the mMTC scenarios. Finally, we discuss some open research challenges and promising future research directions.Comment: 37 pages, 8 figures, 7 tables, submitted for a possible future publication in IEEE Communications Surveys and Tutorial

Computationally intensive, distributed and decentralised machine learning: from theory to applications

Machine learning (ML) is currently one of the most important research fields, spanning computer science, statistics, pattern recognition, data mining, and predictive analytics. It plays a central role in automatic data processing and analysis in numerous research domains owing to widely distributed and geographically scattered data sources, powerful computing clouds, and high digitisation requirements. However, aspects such as the accuracy of methods, data privacy, and model explainability remain challenging and require additional research. Therefore, it is necessary to analyse centralised and distributed data processing architectures, and to create novel computationally intensive explainable and privacy-preserving ML methods, to investigate their properties, to propose distributed versions of prospective ML baseline methods, and to evaluate and apply these in various applications. This thesis addresses the theoretical and practical aspects of state-of-the-art ML methods. The contributions of this thesis are threefold. In Chapter 2, novel non-distributed, centralised, computationally intensive ML methods are proposed, their properties are investigated, and state-of-the-art ML methods are applied to real-world data from two domains, namely transportation and bioinformatics. Moreover, algorithms for ‘black-box’ model interpretability are presented. Decentralised ML methods are considered in Chapter 3. First, we investigate data processing as a preliminary step in data-driven, agent-based decision-making. Thereafter, we propose novel decentralised ML algorithms that are based on the collaboration of the local models of agents. Within this context, we consider various regression models. Finally, the explainability of multiagent decision-making is addressed. In Chapter 4, we investigate distributed centralised ML methods. We propose a distributed parallelisation algorithm for the semi-parametric and non-parametric regression types, and implement these in the computational environment and data structures of Apache SPARK. Scalability, speed-up, and goodness-of-fit experiments using real-world data demonstrate the excellent performance of the proposed methods. Moreover, the federated deep-learning approach enables us to address the data privacy challenges caused by processing of distributed private data sources to solve the travel-time prediction problem. Finally, we propose an explainability strategy to interpret the influence of the input variables on this federated deep-learning application. This thesis is based on the contribution made by 11 papers to the theoretical and practical aspects of state-of-the-art and proposed ML methods. We successfully address the stated challenges with various data processing architectures, validate the proposed approaches in diverse scenarios from the transportation and bioinformatics domains, and demonstrate their effectiveness in scalability, speed-up, and goodness-of-fit experiments with real-world data. However, substantial future research is required to address the stated challenges and to identify novel issues in ML. Thus, it is necessary to advance the theoretical part by creating novel ML methods and investigating their properties, as well as to contribute to the application part by using of the state-of-the-art ML methods and their combinations, and interpreting their results for different problem setting