The education industry around the globe is undergoing major transformations. Organizations, such as Coursera are advancing new business models for education. A number of major industries have dropped degrees from the job requirements. While the economics of higher education institutions are under threat in a continuing gloomy global economy, digital and lifelong learners are increasingly demanding new teaching and learning paradigms from educational institutions. There is an urgent need to transform teaching and learning landscape in order to drive global economic growth. The use of distance eTeaching and eLearning (DTL) is on the rise among digital natives alongside our evolution toward smart societies. However, the DTL systems today lack the necessary sophistication due to several challenges including data analysis and management, learner-system interactivity, system cognition, resource planning, agility, and scalability. This paper proposes a personalised Ubiquitous eTeaching & eLearning (UTiLearn) framework that leverages Internet of Things, big data, supercomputing, and deep learning to provide enhanced development, management, and delivery of teaching and learning in smart society settings. A proof of concept UTiLearn system has been developed based on the framework. A detailed design, implementation, and evaluation of the UTiLearn system, including its five components, are provided using 11 widely used datasets.

Machine learning (ML) allows the Internet of Things (IoT) to gain hidden insights from the treasure trove of sensed data and be truly ubiquitous without explicitly looking for knowledge and data patterns. Without ML, IoT cannot withstand the future requirements of businesses, governments, and individual users. The primary goal of IoT is to perceive what is happening in our surroundings and allow automation of decision-making through intelligent methods, which will mimic the decisions made by humans. In this paper, we classify and discuss the literature on ML-enabled IoT from three perspectives: data, application, and industry. We elaborate with dozens of cutting-edge methods and applications through a review of around 300 published sources on how ML and IoT work together to play a crucial role in making our environments smarter. We also discuss emerging IoT trends, including the Internet of Behavior (IoB), pandemic management, connected autonomous vehicles, edge and fog computing, and lightweight deep learning. Further, we classify challenges to IoT in four classes: technological, individual, business, and society. This paper will help exploit IoT opportunities and challenges to make our societies more prosperous and sustainable.

Road transportation is the backbone of modern economies, albeit it annually costs $1.25$ million deaths and trillions of dollars to the global economy, and damages public health and the environment. Deep learning is among the leading-edge methods used for transportation-related predictions, however, the existing works are in their infancy, and fall short in multiple respects, including the use of datasets with limited sizes and scopes, and insufficient depth of the deep learning studies. This paper provides a novel and comprehensive approach toward large-scale, faster, and real-time traffic prediction by bringing four complementary cutting-edge technologies together: big data, deep learning, in-memory computing, and Graphics Processing Units (GPUs). We trained deep networks using over 11 years of data provided by the California Department of Transportation (Caltrans), the largest dataset that has been used in deep learning studies. Several combinations of the input attributes of the data along with various network configurations of the deep learning models were investigated for training and prediction purposes. The use of the pre-trained model for real-time prediction was explored. The paper contributes novel deep learning models, algorithms, implementation, analytics methodology, and software tool for smart cities, big data, high performance computing, and their convergence.

The importance of emergency response systems cannot be overemphasized today due to the many manmade and natural disasters in the recent years such as September 2001 and the recent Japan earthquake and tsunami disaster. The overall cost of the Japan disaster alone is estimated to have exceeded 300 billion USD. Transportation and telecommunications play a critical role in disaster response and management in order to minimize loss of human life, economic cost and disruptions. Our research is concerned with developing emergency response systems for disasters of various scales with a focus on transportation systems which exploit ICT developments. In this paper, we leverage Intelligent Transportation Systems (ITS) including VANETs (Vehicular Ad hoc Networks), mobile and Cloud computing technologies to propose an intelligent disaster management system. The system is intelligent because it is able to gather information from multiple sources and locations, including from the point of incident, and is able to make effective strategies and decisions, and propagate the information to vehicles and other nodes in real-time. The effectiveness of our system is demonstrated through modelling the impact of a disaster on a real city transport environment and comparing it with the case where our disaster management system was in place. We report great benefits derived from the adoption of our proposed system in terms of improved and balanced traffic flow and smooth evacuation.

Rapid transit systems or metros are a popular choice for high-capacity public transport in urban areas due to several advantages including safety, dependability, speed, cost, and lower risk of accidents. Existing studies on metros have not considered appropriate holistic urban transport models and integrated use of cutting-edge technologies. This paper proposes a comprehensive approach toward large-scale and faster prediction of metro system characteristics by employing the integration of four leading-edge technologies: big data, deep learning, in-memory computing, and Graphics Processing Units (GPUs). Using London Metro as a case study, and the Rolling Origin and Destination Survey (RODS) (real) dataset, we predict the number of passengers for six time intervals (a) using various access transport modes to reach the train stations (buses, walking, etc.); (b) using various egress modes to travel from the metro station to their next points of interest (PoIs); (c) traveling between different origin-destination (OD) pairs of stations; and (d) against the distance between the OD stations. The prediction allows better spatiotemporal planning of the whole urban transport system, including the metro subsystem, and its various access and egress modes. The paper contributes novel deep learning models, algorithms, implementation, analytics methodology, and software tool for analysis of metro systems.