Big Data in Smart-Cities: Current Research and Challenges

Smart-cities are an emerging paradigm containing heterogeneous network infrastructure, ubiquitous sensing devices, big-data processing and intelligent control systems. Their primary aim is to improve the quality of life of the citizens by providing intelligent services in a wide variety of aspects like transportation, healthcare, entertainment, environment, and energy. In order to provide such services, the role of big-data and its analysis is extremely important as it enables to obtain valuable insights into the large data generated by the smart-cities.  In this article, we investigate the state-of-art research efforts directed towards big-data analytics in a smart-city context. Specifically, first we present a big-data centric taxonomy for the smart-cities to bring forth a generic overview of the importance of big-data paradigm in a smart-city environment. This is followed by the presentation of a top-level snapshot of the commonly used big-data analytical platforms. Due to the heterogeneity of data being collected by the smart-cities, often with conflicting processing requirements, suitable analytical techniques depending upon the data type are also suggested. In addition to this, a generic four-tier big-data framework comprising of the sensing hub, storage hub, processing hub and application hub is also proposed that can be applied in any smart-city context. This is complemented by providing the common big-data applications in a smart-city and presentation of ten selected case studies of smart-cities across the globe. Finally, the open challenges are highlighted in order to give future research directions

Intelligent Transportation System for Smart-Cities using Fuzzy Logic

According to United Nations population statistics 2017, the world population is 7.6 billion and is growing rapidily alomost 11 billion by end of 21 century with a 70% chance of continued growth, this rapid increasing population have created low standards of living in cities. Smart Cities are facing pressures associated with due innovations and globalization to improve their citizens life. Computational intelligence is the study of adaptive mechanism to facilitate intelligent behavior in changing and complex environments. Traffic congestion and monitoring has become one of the critical issues in big cities. The adaptive mechanism of computational intelligence in changing the behavior of complex environments of smart city is very effective. The developing framework and services for smart-city requires sound infrastructure, latest current technology adoption. A framework model with the integration of cloud-data, social network (SN) services that is collecting stream data with smart sensors in the context of smart cities is proposed. The adaptive mechanism of computational intelligence in changing thebehavior of complex environments of smart city is very effective. A radical framework that enables the analysis of big-data sets stemming from Social Networking (SN) sites. Smart cities understanding is a broad concept only city transportation sector is focused in this article. Fuzzy logic modeling techniques are used in many fields i.e. medical, engineering. business and computing related problems. To solve various traffic management issues in cities a detailed analysis of fuzzy logic system is proposed. This paper presents an analysis of the results achieved using Fuzzy Logic System (FLS) for smart cities. The results are verified using MATLAB Simulation

GEOSPATIAL BIG DATA ANALYTICS FOR SUSTAINABLE SMART CITIES

Growing urbanization cause environmental problems such as vast amount of carbon emissions and pollution all over the world.Smart Infrastructure and Smart Environment are two significant components of the smart city paradigm that can create opportunities for ensuring energy conservation, preventing ecological degradation, and using renewable energy sources. Since a great portion of the data contains location information, geospatial intelligence is a key technology for sustainable smart cities. We need a holistic framework for the smart governance of cities by utilizing key technological drivers such as big data, Geographic Information Systems (GIS), cloud computing, Internet of Things (IoT). Geospatial Big Data applications offer predictive data science tools such as grid computing and parallel computing for efficient and fast processing to build a sustainable smart city ecosystem. Effective management of big data in storage, visualization, analytics, and analysis stages can foster green building, green energy, and net zero targets of countries. Parallel computing systems have the ability to scale up analysis on geospatial big data platforms which is key for ocean, atmosphere, land, and climate applications. In this study, it is aimed to create the necessary technical infrastructure for smart city applications with a holistic big data management approach. Thus, a smart city model framework is developed for Smart Environment and Smart Governance components and performance comparison of Dask-GeoPandas and Apache Sedona parallel processing systems are carried out. Apache Sedona performed better on the performance test during read, write, join and clustering operations.</p

Water demand estimation and outlier detection from smart meter data using classification and Big Data methods

Automatic Meter Reading (AMR) systems are being deployed in many cities to obtain insight into the status and the behavior of District Metering Area (DMA) with more granularity. Until now, the water consumption readings of the population were taken one per month or one each two-months. In contrast, AMR systems provide hourly readings for households and more frequent readings for big consumers. On the one hand, this paper aims at predicting water demand and detect suspicious behaviors – e.g. a leak, a smart meter break down or even a fraud – by extracting water consumption patterns. On the other hand, the main contribution of this paper, a software framework, based on Big Data techniques, is presented to tackle the barriers of traditional data storage and data analysis since the volume of AMR data collected by Water Utilities is enormous and it is continuously growing because this technology is expanding .Peer ReviewedPostprint (author’s final draft

Sustainable Supply Chain Management in Smart City Design: A Case Study of Al Khobar City Centre

Abstract—The purpose of this study is to analyze the impact of supply chain management (SCM) on smart city development. More specifically, we investigate the connections between smart cities, and supply network characteristics (supply network structure and governance mechanisms). Urban spaces, which have high acoustic quality, are a rare resource that must be protected to achieve the sustainable urban development as well as health and comfort of the urban space users. Urban morphology and urban environment are major topics for various urban studies using multiple approaches. Many of these approaches focus firstly on the visual analysis of urban area, while a shortage of urban sound quality studies can be highlighted. In this study, we propose the sound-walk procedure to evaluate the urban supply chain managements in Khobar city center. This work aims to understand how the use of supply chain management concept can help in environmental issues in urban areas. The relationships between smart cities, big data and supply networks cannot be described simply by using a linear, cause-and-effect framework. Accordingly, we have proposed an integrative framework that can be used in future empirical studies to analyze smart cities and big data implications on SCM. This will allow city-users and planners to determine and identify the urban supply chain management, and suggest that a decrease of noise level is not the only way to improve the urban environment

Big data & artificial intelligence of things for sustainable smart cities - prosing a framework with an innovation systems perspective

The United nation projects that the world population could grow to 9.7 billion in 2050 and can reach a peak of around 10.4 billion in 2080s. With this rise in population cities pose a challenge in meeting the needs of growing demand sustainably over the coming years. There is ever increasing density of population within cities due to growing urbanization. There is an urgency in solving challenges due to this demographic change which has led to the need of developing innovative solutions in urban planning leading to smart city. Sustainability is a major agenda within the smart city planning and development but has a lot of challenges too for implementation. Technology is playing a key factor in enabling sustainability agenda of smart cities with big data and Artificial Intelligence of Things(AIoT) leading the innovation within the ecosystem. There is huge data generated and collected today from diverse data sources within a city, but the need is to utilize the data and overcome the sustainability challenges within smart cities. Objective of this study is to identify how sustainability can be addressed through smart city implementations and provide a framework for sustainable smart cities which can efficiently utilize data for building smart applications. This study also looks at the role of Big Data & AIoT within smart city implementations and how it impacts the sustainability. Stakeholder’s involvement and challenges related to data within smart city ecosystem are also considered for both literature review and insights collected from data analysis. The framework is recommended based on extensive literature reviewed and analysis of primary data collected through semi-structured interviews with experts involved in smart city implementations. Smart city implementation needs proper policies delineated for governance and use of data. Data governance and security are one of the primary concerns within smart city ecosystem. Interoperability and reuse of data within systems is also an issue which is common across smart city initiatives

An Integrated Big and Fast Data Analytics Platform for Smart Urban Transportation Management

(c) 20xx IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.[EN] Smart urban transportation management can be considered as a multifaceted big data challenge. It strongly relies on the information collected into multiple, widespread, and heterogeneous data sources as well as on the ability to extract actionable insights from them. Besides data, full stack (from platform to services and applications) Information and Communications Technology (ICT) solutions need to be specifically adopted to address smart cities challenges. Smart urban transportation management is one of the key use cases addressed in the context of the EUBra-BIGSEA (Europe-Brazil Collaboration of Big Data Scientific Research through Cloud-Centric Applications) project. This paper specifically focuses on the City Administration Dashboard, a public transport analytics application that has been developed on top of the EUBra-BIGSEA platform and used by the Municipality stakeholders of Curitiba, Brazil, to tackle urban traffic data analysis and planning challenges. The solution proposed in this paper joins together a scalable big and fast data analytics platform, a flexible and dynamic cloud infrastructure, data quality and entity matching algorithms as well as security and privacy techniques. By exploiting an interoperable programming framework based on Python Application Programming Interface (API), it allows an easy, rapid and transparent development of smart cities applications.This work was supported by the European Commission through the Cooperation Programme under EUBra-BIGSEA Horizon 2020 Grant [Este projeto e resultante da 3a Chamada Coordenada BR-UE em Tecnologias da Informacao e Comunicacao (TIC), anunciada pelo Ministerio de Ciencia, Tecnologia e Inovacao (MCTI)] under Grant 690116.Fiore, S.; Elia, D.; Pires, CE.; Mestre, DG.; Cappiello, C.; Vitali, M.; Andrade, N.... (2019). An Integrated Big and Fast Data Analytics Platform for Smart Urban Transportation Management. IEEE Access. 7:117652-117677. https://doi.org/10.1109/ACCESS.2019.2936941S117652117677

Integration of Data Driven Technologies in Smart Grids for Resilient and Sustainable Smart Cities: A Comprehensive Review

A modern-day society demands resilient, reliable, and smart urban infrastructure for effective and in telligent operations and deployment. However, unexpected, high-impact, and low-probability events such as earthquakes, tsunamis, tornadoes, and hurricanes make the design of such robust infrastructure more complex. As a result of such events, a power system infrastructure can be severely affected, leading to unprecedented events, such as blackouts. Nevertheless, the integration of smart grids into the existing framework of smart cities adds to their resilience. Therefore, designing a resilient and reliable power system network is an inevitable requirement of modern smart city infras tructure. With the deployment of the Internet of Things (IoT), smart cities infrastructures have taken a transformational turn towards introducing technologies that do not only provide ease and comfort to the citizens but are also feasible in terms of sustainability and dependability. This paper presents a holistic view of a resilient and sustainable smart city architecture that utilizes IoT, big data analytics, unmanned aerial vehicles, and smart grids through intelligent integration of renew able energy resources. In addition, the impact of disasters on the power system infrastructure is investigated and different types of optimization techniques that can be used to sustain the power flow in the network during disturbances are compared and analyzed. Furthermore, a comparative review analysis of different data-driven machine learning techniques for sustainable smart cities is performed along with the discussion on open research issues and challenges

Triangulum City Dashboard: An Interactive Data Analytic Platform for Visualizing Smart City Performance

Cities are becoming smarter by incorporating hardware technology, software systems, and network infrastructure that provide Information Technology (IT) systems with real-time awareness of the real world. What makes a “smart city” functional is the combined use of advanced infrastructure technologies to deliver its core services to the public in a remarkably efficient manner. City dashboards have drawn increasing interest from both city operators and citizens. Dashboards can gather, visualize, analyze, and inform regional performance to support the sustainable development of smart cities. They provide useful tools for evaluating and facilitating urban infrastructure components and services. This work proposes an interactive web-based data visualization and data analytics toolkit supported by big data aggregation tools. The system proposed is a cloud-based prototype that supports visualization and real-time monitoring of city trends while processing and displaying large data sets on a standard web browser. However, it is capable of supporting online analysis processing by answering analytical queries and producing graphics from multiple resources. The aim of this platform is to improve communication between users and urban service providers and to give citizens an overall view of the city’s state. The conceptual framework and architecture of the proposed platform are explored, highlighting design challenges and providing insight into the development of smart cities. Moreover, results and the potential statistical analysis of important city services offered by the system are introduced. Finally, we present some challenges and opportunities identified through the development of the city data platform.publishedVersio

Investigating the current approach to developing data governance in the Canadian smart city

Smart cities have grown in prevalence as cities take advantage of big data and connected technologies to address the issues of sustainable urban development in the face of their growing urban populations. Data governance is necessary to smart cities to ensure integrity, accessibility, and accountability of data. There is also a growing concern about having proper data governance to protect citizens’ digital rights and democracy. Though these concerns are pressing, there is a gap in understanding the data governance strategies of city governments and the roles that they play in developing those strategies. Additionally, literature on smart cities often focuses on data privacy and security instead of discussing data governance comprehensively and does not discuss the role of the city. This thesis aims to address this gap by understanding the current state of data governance of proposed Canadian smart cities, through identifying their data governance decisions and classifying them into the roles they are adopting. The Smart Cities Challenge in Canada presented an opportunity to study proposed smart cities for their data governance decisions and the role of the city through content analysis, using concepts from Khatri and Brown’s (2010) data governance framework and Bayat and Kawalek’s (2018) model of data governance city roles. The analysis found that the proposed Canadian smart cities are planning to develop their smart city projects and data governance using an approach driven by open and collaborative principles. This open and collaborative approach adopted by the Canadian smart cities prioritizes data governance activities that address the data access, data principles, and data lifecycle decision domains, in conjunction to the cities taking on roles that emphasize transparency, co-creation, and high stakeholder involvement. Openness and collaboration are discussed to be critical to the success of smart cities, as they can drive mechanisms to help address the challenges of trust and achieve and maintain democratic accountability. This open and collaborative state of smart city data governance also supports a transformation of the smart city discourse, moving away from vendor-driven and citizen-driven smart cities and towards government-driven smart cities. The study outlines considerations for the proposed Canadian smart cities and their stakeholders to act on the gaps in their data governance strategies as identified in the results. Future smart cities are recommended to proactively use an open and collaborative approach in developing their smart city plans and data governance strategies