Vehicular Ad-hoc Networks (VANETs) provide networks for smart vehicles and will enable future systems to provide services that enhance the overall transportation experience. However, these applications require consideration to possible damage to both property and human life. Communication between vehicles requires data immutability and user privacies to provide safe operation of the system. Blockchains can provide these properties and more to create a more secure and decentralized system. However, a chain’s security comes from the chain length. VANETs’ ephemeral connections provide harm limits how much data can be exchanged during vehicle rendezvous. This thesis investigates lightweight blockchains that operate with lower overheads. A survey of current techniques to accomplish this are discussed in Chapter 1. Two techniques are demonstrated within two separate environments to demonstrate the network overhead reductions when using a lightweight blockchain with respect to network and storage loads within these VANET environments.

Yes / One of the common cardiac disorders is a cardiac attack called Myocardial infarction (MI), which occurs due to the blockage of one or more coronary arteries. Timely treatment of MI is important and slight delay results in severe consequences. Electrocardiogram (ECG) is the main diagnostic tool to monitor and reveal the MI signals. The complex nature of MI signals along with noise poses challenges to doctors for accurate and quick diagnosis. Manually studying large amounts of ECG data can be tedious and time-consuming. Therefore, there is a need for methods to automatically analyze the ECG data and make diagnosis. Number of studies has been presented to address MI detection, but most of these methods are computationally expensive and faces the problem of overfitting while dealing real data. In this paper, an effective computer-aided diagnosis (CAD) system is presented to detect MI signals using the convolution neural network (CNN) for urban healthcare in smart cities. Two types of transfer learning techniques are employed to retrain the pre-trained VGG-Net (Fine-tuning and VGG-Net as fixed feature extractor) and obtained two new networks VGG-MI1 and VGG-MI2. In the VGG-MI1 model, the last layer of the VGG-Net model is replaced with a specific layer according to our requirements and various functions are optimized to reduce overfitting. In the VGG-MI2 model, one layer of the VGG-Net model is selected as a feature descriptor of the ECG images to describe it with informative features. Considering the limited availability of dataset, ECG data is augmented which has increased the classification performance. A standard well-known database Physikalisch-Technische Bundesanstalt (PTB) Diagnostic ECG is used for the validation of the proposed framework. It is evident from experimental results that the proposed framework achieves a high accuracy surpasses the existing methods. In terms of accuracy, sensitivity, and specificity; VGG-MI1 achieved 99.02%, 98.76%, and 99.17%, respectively, while VGG-MI2 models achieved an accuracy of 99.22%, a sensitivity of 99.15%, and a specificity of 99.49%. / This project was funded by University of Jeddah, Jeddah, Saudi Arabia (Project number: UJ-02-018-ICGR).

Yes / This article aims to assess critically different definitions and indicators of smart cities. Drawing on exemplary case studies, the author proposes a typology of four categories of smart cities: type A are the world leaders who pioneer ideas not predicated on smart city projects; type B are aspirational cities punching above their weight; type C are surprise transformers that use the smart city concept to propel real transformation; and type D are cases where smart city projects do not directly address the main urban problems. The discussion highlights the need to prevent ‘smart-wash’ by avoiding superficial technological solutions that chase symptoms but not causes of some of the complex urban challenges that they are intending to address. In conclusion, the author considers the public policy implications of applying these typologies to cities in general with particular reference to the Global South / British Academy: [grant number IPM 15008]

Yes / Since the 1980s, the digital revolution has been both a negative and positive force. Within a few weeks of the Covid-19 outbreak, lockdown accelerated the adoption of digital solutions at an unprecedented pace, creating unforeseen opportunities for scaling up alternative approaches to social and economic life. But it also brought digital risks and threats that placed new demands on policymakers. This article assembles evidence from different areas of social science expertise about the impacts of Covid-19 in digitised societies and policy responses. The authors show how the pandemic supported changes in data collection techniques and dissemination practices for official statistics, and how seemingly insuperable obstacles to the implementation of e-health treatments were largely overcome. They demonstrate how the ethics of artificial intelligence became a primary concern for government legislation at national and international levels, and how the features enabling smart cities to act as drivers of productivity did not necessarily give them an advantage during the pandemic. At the micro-level, families are shown to have become ‘digital by default’, as children were exposed to online risks and opportunities. Globally, the spread of the pandemic provided a fertile ground for cybercrime, while digital disinformation and influencing risked becoming normalised and domesticated.

Many countries around the world are undergoing massive urbanization campaigns at an unprecedented rate, heralded by promises of economical prosperity and bolstered population health and well-being. Projections indicate that by 2050, nearly 68% of the world populace will reside in these urban environments. However, rapid growth at such an exceptional scale poses unique challenges pertaining to environmental quality and food production, which can negate the effectiveness of the aforementioned boons. As such, there is an emphasis on mitigating these negative effects through the construction of smart and connected communities (S&CC), which integrate both artificial intelligence (AI) and the Internet of Things (IoT). This coupling of intelligent technologies also poses interesting system design challenges pertaining to the fusion of the diverse, heterogeneous datasets available to IoT environments, and the ability to learn multiple S&CC problem sets concurrently. Attention-based Transformer networks are of particular interest given their success across diverse fields of natural language processing (NLP), computer vision, time-series regression, and multi-modal data fusion in recent years. This begs the question whether Transformers can be further diversified to leverage fusions of IoT data sources for heterogeneous multi-task learning in S&CC trade spaces. This is a fundamental question that this thesis seeks to answer. Indeed, the key contribution of this thesis is the design and application of Transformer networks for developing AI systems in emerging smart cities. This is executed within a collaborative U.S.-Indonesia effort between Virginia Tech, the University of Colorado Boulder, the Universitas Gadjah Mada, and the Institut Teknologi Bandung with the goal of growing smart and sustainable garden alleys in Makassar City, Indonesia. Specifically, a proof-of-concept AI nerve-center is proposed using a backbone of pure-encoder Transformer architectures to learn a diverse set of tasks such as multivariate time-series regression, visual plant disease classification, and image-time-series fusion. To facilitate the data fusion tasks, an effective algorithm is also proposed to synthesize heterogeneous feature sets, such as multivariate time-series and time-correlated images. Moreover, a hyperparameter tuning framework is also proposed to standardize and automate model training regimes. Extensive experimentation shows that the proposed Transformer-based systems can handle various input data types via custom sequence embedding techniques, and are naturally suited to learning a diverse set of tasks. Further, the results also show that multi-task learners increase both memory and computational efficiency while maintaining comparable performance to both single-task variants, and non-Transformer baselines. This demonstrates the flexibility of Transformer networks to learn from a fusion of IoT data sources, their applicability in S&CC trade spaces, and their further potential for deployment on edge computing devices. / Master of Science / Many countries around the world are undergoing massive urbanization campaigns at an unprecedented rate, heralded by promises of economical prosperity and bolstered population health and well-being. Projections indicate that by 2050, nearly 68% of the world populace will reside in these urban environments. However, rapid growth at such an exceptional scale poses unique environmental and food cultivation challenges. Hence, there is a focus on reducing these negative effects through building smart and connected communities (S&CC). The term connected is derived from the integration of small, low-cost devices which gather information from the surrounding environment, called the Internet of Things (IoT). Likewise, smart is a term derived from the integration of artificial intelligence (AI), which is used to make informed decisions based on IoT-collected information. This coupling of intelligent technologies also poses its own unique challenges pertaining to the blending of IoT data with highly diverse characteristics. Of specific interest is the design of AI models that can not only learn from a fusion of this diverse information, but also learn to perform multiple tasks in parallel. Attention-based networks are a relatively new category of AI which learn to focus on, or attend to, the most important portions of an arbitrary data sequence. Transformers are AI models which are designed using attention as their backbone, and have been employed to much success in many fields in recent years. This success begs the question whether Transformers can be further extended to put the smart in S&CC. The overarching goal of this thesis is to design and implement a Transformer-based AI system for emerging smart cities. In particular, this is accomplished within a U.S.-Indonesia collaborative effort with the goal of growing smart and sustainable garden alleys in Makassar City, Indonesia.

Yes / The emerging information revolution makes it necessary to manage vast amounts of unstructured data rapidly. As the world is increasingly populated by IoT devices and sensors that can sense their surroundings and communicate with each other, a digital environment has been created with vast volumes of volatile and diverse data. Traditional AI and machine learning techniques designed for deterministic situations are not suitable for such environments. With a large number of parameters required by each device in this digital environment, it is desirable that the AI is able to be adaptive and self-build (i.e. self-structure, self-configure, self-learn), rather than be structurally and parameter-wise pre-defined. This study explores the benefits of self-building AI and machine learning with unsupervised learning for empowering big data analytics for smart city environments. By using the growing self-organizing map, a new suite of self-building AI is proposed. The self-building AI overcomes the limitations of traditional AI and enables data processing in dynamic smart city environments. With cloud computing platforms, the selfbuilding AI can integrate the data analytics applications that currently work in silos. The new paradigm of the self-building AI and its value are demonstrated using the IoT, video surveillance, and action recognition applications. / Supported by the Data to Decisions Cooperative Research Centre (D2D CRC) as part of their analytics and decision support program and a La Trobe University Postgraduate Research Scholarship.

Yes / The wider use of Internet of Things (IoT) makes it possible to create smart cities. The purpose of this paper is to identify key IoT challenges and understand the relationship between these challenges to support the development of smart cities. Design/methodology/approach: Challenges were identified using literature review, and prioritised and elaborated by experts. The contextual interactions between the identified challenges and their importance were determined using Interpretive Structural Modelling (ISM). To interrelate the identified challenges and promote IoT in the context of smart cities, the dynamics of interactions of these challenges were analysed using an integrated Matrice d’Impacts Croisés Multiplication Appliqués à un Classement (MICMAC)-ISM approach. MICMAC is a structured approach to categorise variables according to their driving power and dependence. Findings: Security and privacy, business models, data quality, scalability, complexity and governance were found to have strong driving power and so are key challenges to be addressed in sustainable cities projects. The main driving challenges are complexity and lack of IoT governance. IoT adoption and implementation should therefore focus on breaking down complexity in manageable parts, supported by a governance structure. Practical implications: This research can help smart city developers in addressing challenges in a phase-wise approach by first ensuring solid foundations and thereafter developing other aspects. Originality/value: A contribution originates from the integrated MICMAC-ISM approach. ISM is a technique used to identify contextual relationships among definite elements, whereas MICMAC facilitates the classification of challenges based on their driving and dependence power. The other contribution originates from creating an overview of challenges and theorising the contextual relationships and dependencies among the challenges.

A cidade de São Paulo é o município mais populoso do Brasil, caracterizado por uma segregação urbana responsável por inúmeros problemas relacionados a mobilidade urbana. As ações atuais para resolver os problemas de mobilidade urbana têm pouco aprofundamento em questões tecnológicas e melhorias dos sistemas computacio- nais existentes como as necessárias ao Sistema Integrado de Monitoramento e Transporte (SIM), utilizado para gestão e monitoramento do transporte público por ônibus de São Paulo. Uma das possíveis melhorias é integrar o SIM às Redes Sociais. Com essa perspectiva de integração, esse trabalho tem como objetivo uti- lizar tweets e dados do SIM na caracterização de eventos de exceção e de seus respectivos impactos no sistema de transporte público por ônibus da cidade de São Paulo. Para alcançar tal objetivo, esse trabalho propõe utilizar tweets publicados por instituições governamentais responsáveis por reportar eventos de exceção, dados dos módulos AVL (Automatic Vehicle Location) do SIM, responsáveis por rastrear e localizar os ônibus do município e GTFS (General Transit Feed Specification) da SPTrans. Visando alcançar o objetivo proposto, classificamos manualmente 60.984 tweets e treinamos diferentes modelos por meio de algoritmos de aprendizado de máquina supervisionado para identificar eventos de exceção. Além disso, propomos uma nova metodologia para extrair e geolocalizar os endereços dos eventos de exceção, por meio de Processamento de Língua Natural e Expressão Regular. Com isso, demonstramos que é possível correlacionar os dados desses eventos com os dados históricos do SIM e da GTFS, para caracterizar como o transporte público por ônibus da cidade de São Paulo é impactado nesses cenários. Adicionalmente, propomos uma arquitetura distribuída para exploração e visualização de grandes volumes de dados relacionados a transporte público / The city of São Paulo is the most populous municipality in Brazil, characterized by an urban segregation responsible for numerous problems related to urban mobility. The current actions to solve the problems of urban mobility have little deepening in technological issues and improvements of existing computer systems such as those required for the Integrated Monitoring and Transport System (in the Portuguese acronym: SIM), used for the management and monitoring of public transport by buses of the city of São Paulo. One of the possible improvements is integrating the SIM with Social Networks. With this perspective of integration, this work aims to use tweets and data from SIM in the characterization of exception events and their respective impacts on the public transport system by buses of the city of São Paulo. In order to achieve this objective, this work proposes to use tweets published by governmental institutions responsible for reporting exception events, data from SIM Automatic Vehicle Location (AVL) modules, responsible for the tracking and locating of urban buses and data from SPTrans GTFS (General Transit Feed Specification). In order to reach the proposed goal, we manually classified 60,984 tweets and trained different models through supervised machine learning algorithms to identify exception events. In addition, we propose a new methodology to extract and geolocalize the addresses of the exception events, through Natural Language Processing and Regular Expression. Using that approaches, we show that it is possible to correlate the data of these events with the historical data of the SIM and GTFS, to characterize how the public transport by bus of the city of São Paulo is impacted in these scenarios. Additionally, we propose a distributed architecture for exploration and visualization of large volumes of data related to public transport

The volume of information in the Internet is growing exponentially. The ability to find intelligible information among vast amounts of data is transforming the human vision of the universe and everything within it. The underlying question then becomes which methods or techniques can be applied to transform the raw data into something intelligible, active and personal? This question is explored in this document by investigating techniques that improve intelligence for systems in order to make them perceptive/active to the recent information shared by each individual. Consequently, the main objective of this thesis is to enhance the experience of the user (individual) by providing a broad perspective about an event, which could result in improved ideas and better decisions. Therefore, three different data sources (individual data, sensor data, web data) have been investigated. This thesis includes research into techniques that process, interpret and reduce these data. By aggregating these techniques into a platform it is possible to deliver personalised information to applications and services. The contribution of this thesis is twofold. First, it presents a novel process that has shifted its focus from IoT technology to the user (or smart citizen). Second, this research shows that huge volumes of data can be reduced if the underlying sensor signal has adequate spectral properties to be filtered and good results can be obtained when employing a filtered sensor signal in applications. By investigating these areas it is possible to contribute to this new interconnected society by offering socially aware applications and services. / Sem resumo

Este trabalho apresenta uma plataforma para coleta e análise de imagens urbanas, que integra Interfaces de Programação de Aplicativos \"Application Programming Interfaces\" (APIs) de sistemas de busca de imagens, Sistemas de Informações Geográficas (SIGs), mapas digitais e técnicas de visão computacional. Esta plataforma, INACITY, permite que usuários selecionem regiões de interesse e capturem elementos de relevância para a arquitetura urbana, como, por exemplo árvores e buracos em ruas. A implementação da plataforma foi feita de maneira a permitir que novos módulos possam ser facilmente incluídos ou substituídos possibilitando a introdução de outras APIs de mapas, SIGs e filtros de Visão Computacional. Foram realizados experimentos com as imagens obtidas através do \"Google Street View\" onde árvores são capturadas em áreas de bairros inteiros em questão de minutos, um ganho significativo quando comparado com o procedimento manual para levantamento deste tipo de dado. Além disso, também são apresentados resultados comparativos entre os métodos de visão computacional propostos para a detecção de árvores em imagens com outros métodos heurísticos, em um conjunto onde as árvores estão marcadas manualmente e assim as taxas de precisão e de redescoberta de cada algoritmo podem ser avaliadas e comparadas. / This project presents a platform that integrates Application Programming Interfaces (APIs), image retrieval systems, Geographical Information Systems (GISes), digital maps and Computer Vision techniques to collect and analyse urban images. The platform, INACITY (an acronym for INvestigate and Analyse a City), empowers users allowing them to select a region over a map and see urban features inside that region that have relevance to the urban architecture context, for instance trees. The implementation is extensible and it is designed to make it easy to add or replace new modules, for instance, to add a new API to present a map, different GISes and other Computer Vision filters.