Big Data Analytics Algorithm, Data Type and Tools in Smart City: A Systematic Literature Review
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paper_title: Driving innovation through big open linked data (BOLD): Exploring antecedents using interpretive structural modelling

paper_content:

Innovation is vital to find new solutions to problems, increase quality, and improve profitability. Big open linked data (BOLD) is a fledgling and rapidly evolving field that creates new opportunities for innovation. However, none of the existing literature has yet considered the interrelationships between antecedents of innovation through BOLD. This research contributes to knowledge building through utilising interpretive structural modelling to organise nineteen factors linked to innovation using BOLD identified by experts in the field. The findings show that almost all the variables fall within the linkage cluster, thus having high driving and dependence powers, demonstrating the volatility of the process. It was also found that technical infrastructure, data quality, and external pressure form the fundamental foundations for innovation through BOLD. Deriving a framework to encourage and manage innovation through BOLD offers important theoretical and practical contributions.
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paper_title: Cloud Support Data Management Infrastructure for Upcoming Smart Cities

paper_content:

Abstract This paper presents a novel large scale data management model for future smart cities. The system is exploiting the emerging cloud computing services availability over the globe by processing the collected data at the edge. To handle the large scale requirements in smart city, we introduce a Mobile Edge Computing (MEC) framework in order to increase the reliability of the deployed applications. MEC is a promising framework to reduce the cloud core utilization as well as to provide applications that require low latency to mobile end users. As near as possible to the location of the end user, MEC relates to the mobile network applications and data stream acceleration through caching and/or compressing of relevant data at the edge of the mobile network. Although MEC is not yet deployed in real-life systems, recent studies discussed some technical details and related concepts. This paper studies the definition of MEC and similar concepts with respect to typical application scenarios, like in smart cities, and provides scope and limitations that may be encountered when implementing and deploying MEC. Then, a MEC framework is used to explore the data management infrastructure for upcoming smart cities.
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paper_title: Smart campus features, technologies, and applications: A systematic literature review

paper_content:

Research in the smart campus area is still growing, where every researcher defines the concept of smart campus with a less thorough perspective that has not been conical in the same conception of the concept. In this paper, we summarize the existing condition of smart campus development in term of features, supported technologies, and applications were built using systematic literature review (SLR) as the standard methodology used to solve any problems by tracing the results of previous research. The problems declared in SLR are commonly called as research question (RQ). To achieve that goal, we define some RQs related to that scope and clarify each question by tracing previous research papers which are indexed in reputable journal databases such as IEEE Xplore, Scopus, Springerlink, and ScienceDirect. After synthesizing 29 articles, the results are: contactless technology provides an easier way to enter data when accessing a particular room or equipment than using a keyboard; IoT supports an easier way to report real-time environment status; cloud computing is used to organize various information effectively and provide data services; iCampus becomes a popular smart campus model and if we map the applications that have been built into iCampus; there is no applications as part of iHealth domain. The main contribution of smart campus development based on previous research is to make easier in all campus aspect life. The contribution expected through this paper is to provide an overview for researchers who want to build applications in a campus as research challenge so that they can use appropriate technology and meet the characteristics of smart campus.
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paper_title: Big data and smart cities: a public sector organizational learning perspective

paper_content:

Abstract Public sector organizations (city authorities) have begun to explore ways to exploit big data to provide smarter solutions for cities. The way organizations learn to use new forms of technology has been widely researched. However, many public sector organisations have found themselves in new territory in trying to deploy and integrate this new form of technology (big data) to another fast moving and relatively new concept (smart city). This paper is a cross-sectional scoping study—from two UK smart city initiatives—on the learning processes experienced by elite (top management) stakeholders in the advent and adoption of these two novel concepts. The findings are an experiential narrative account on learning to exploit big data to address issues by developing solutions through smart city initiatives. The findings revealed a set of moves in relation to the exploration and exploitation of big data through smart city initiatives: (a) knowledge finding; (b) knowledge reframing; (c) inter-organization collaborations and (d) ex-post evaluations. Even though this is a time-sensitive scoping study it gives an account on a current state-of-play on the use of big data in public sector organizations for creating smarter cities. This study has implications for practitioners in the smart city domain and contributes to academia by operationalizing and adapting Crossan et al’s (Acad Manag Rev 24(3): 522–537, 1999) 4I model on organizational learning.
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paper_title: The Research Question in Social Research: What is its Role?

paper_content:

This paper addresses the role of the research question in social research. It outlines what is taken to be the conventional view in many methodological discussions, namely, that research questions guide decisions about research design and research methods. This position is taken to imply that social researchers typically take the view that research methods need to be tailored to the research questions that guide an investigation. The paper questions how far this position pertains to actual research practice. Drawing on interviews with researchers about their practices in relation to mixed‐method research, two discourses were found in the transcripts. A particularistic discourse that reflects the traditional view, whereby mixed‐method research is viewed as only appropriate when research questions warrant it, was uncovered. In addition, a universalistic discourse, which sees mixed‐method research as more generally superior, was also uncovered. The implications of these viewpoints for understanding the role ...
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paper_title: Ontology‐based activity recognition in intelligent pervasive environments

paper_content:

Purpose – This paper aims to serve two main purposes. In the first instance it aims to it provide an overview addressing the state‐of‐the‐art in the area of activity recognition, in particular, in the area of object‐based activity recognition. This will provide the necessary material to inform relevant research communities of the latest developments in this area in addition to providing a reference for researchers and system developers who ware working towards the design and development of activity‐based context aware applications. In the second instance this paper introduces a novel approach to activity recognition based on the use of ontological modeling, representation and reasoning, aiming to consolidate and improve existing approaches in terms of scalability, applicability and easy‐of‐use.Design/methodology/approach – The paper initially reviews the existing approaches and algorithms, which have been used for activity recognition in a number of related areas. From each of these, their strengths and w...
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paper_title: Applications of big data to smart cities

paper_content:

Many governments are considering adopting the smart city concept in their cities and implementing big data applications that support smart city components to reach the required level of sustainability and improve the living standards. Smart cities utilize multiple technologies to improve the performance of health, transportation, energy, education, and water services leading to higher levels of comfort of their citizens. This involves reducing costs and resource consumption in addition to more effectively and actively engaging with their citizens. One of the recent technologies that has a huge potential to enhance smart city services is big data analytics. As digitization has become an integral part of everyday life, data collection has resulted in the accumulation of huge amounts of data that can be used in various beneficial application domains. Effective analysis and utilization of big data is a key factor for success in many business and service domains, including the smart city domain. This paper reviews the applications of big data to support smart cities. It discusses and compares different definitions of the smart city and big data and explores the opportunities, challenges and benefits of incorporating big data applications for smart cities. In addition it attempts to identify the requirements that support the implementation of big data applications for smart city services. The review reveals that several opportunities are available for utilizing big data in smart cities; however, there are still many issues and challenges to be addressed to achieve better utilization of this technology.
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paper_title: Applied Graph Theory to Real Smart City Logistic Problems

paper_content:

Abstract In a recent project in the Region of North Jutland in Denmark, an empirical study of the mobile broadband conditions was carried out by installing measurement equipment on two cars driving around everywhere in the Region. This paper provides an overview of the system designed for carrying out the project, focusing on the solution for the logistics problem presenting well-defined challenges. We present a feasible solution for the non-trivial problem of planning routes, guaranteeing 100% coverage of the 19.000 km of roads in the Region and having hard computational time constrains. The solution is a combination of applied graph theory and Geographic Information Systems (GIS), framed into a realistic context to be able to carry out the project. One of the future foreseen application fields of cellular networks is to provide efficient network connectivity to cyber physical systems for the required data transactions between the devices and data processing units. Hence, the results derived from similar measurement projects, among many others, is the evaluation of whether the broadband infrastructure is currently ready for the deployment of Smart City outdoor cyber physical systems and implicitly, which steps should be taken towards the deployment of these systems.
---
paper_title: Design of IoT Systems and Analytics in the Context of Smart City Initiatives in India

paper_content:

The rapid growth of population and industrialization has paved way for the use of technologies like the Internet of Things which gave rise to the concept of smart cities. India as a developing country has a great prospect in developing technologies to make the cities smart. As urbanization occurs the demand for resources and efficient servicing will increase. To achieve this in a smart and efficient way, connected device (IoT) could be used. The possible design of an IoT system based on surveys performed on similar smart solutions implemented has been discussed in this paper. Urbanization and population growth has led to higher demand for resources like water which are of scarce. There is a keen interest from the organizations and government to make proper usage of water. The same can be achieved by proper monitoring and management of water distribution systems. The paper discusses the use of Machine learning techniques to smart city management aspects like smart water management which include water demand forecasting, water quality monitoring and anomaly detection.
---
paper_title: Big Data Analytics Embedded Smart City Architecture for Performance Enhancement through Real-Time Data Processing and Decision-Making

paper_content:

The concept of the smart city is widely favored, as it enhances the quality of life of urban citizens, involving multiple disciplines, that is, smart community, smart transportation, smart healthcare, smart parking, and many more. Continuous growth of the complex urban networks is significantly challenged by real-time data processing and intelligent decision-making capabilities. Therefore, in this paper, we propose a smart city framework based on Big Data analytics. The proposed framework operates on three levels: data generation and acquisition level collecting heterogeneous data related to city operations, data management and processing level filtering, analyzing, and storing data to make decisions and events autonomously, and application level initiating execution of the events corresponding to the received decisions. In order to validate the proposed architecture, we analyze a few major types of dataset based on the proposed three-level architecture. Further, we tested authentic datasets on Hadoop ecosystem to determine the threshold and the analysis shows that the proposed architecture offers useful insights into the community development authorities to improve the existing smart city architecture.
---
paper_title: Big Data Logistics: A health-care Transport Capacity Sharing Model

paper_content:

The growth of cities in the 21st century has put more pressure on resources and conditions of urban life. There are several reasons why the health-care industry is the focus of this investigation. For instance, in the UK various studies point to the lack of failure of basic quality control procedures and misalignment between customer needs and provider services and duplication of logistics practices. The development of smart cities and big data present unprecedented challenges and opportunities for operations managers; they need to develop new tools and techniques for network planning and control. Our paper aims to make a contribution to big data and city operations theory by exploring how big data can lead to improvements in transport capacity sharing. We explore using Markov models the integration of big data with future city (health-care) transport sharing. A mathematical model was designed to illustrate how sharing transport load (and capacity) in a smart city can improve efficiencies in meeting demand for city services. The results from our analysis of 13 different sharing/demand scenarios are presented. A key finding is that the probability for system failure and performance variance tends to be highest in a scenario of high demand/zero sharing.
---
paper_title: Video big data in smart city: Background construction and optimization for surveillance video processing

paper_content:

Abstract Transforming infrastructures, buildings and services with the sensed data from the Internet of Things (IoT) technique has drawn wide attention. Enormous video data from city surveillance cameras poses huge challenges of transmission, storage and analysis, which necessitates new video compression technologies. The fusion of video data generated from smart city could be used to support city management and urban policy. Based on the specific characteristics of surveillance video, which are successive pictures have very strong correlations and each picture can be divided into background and foreground, this work proposes a block-level background modeling (BBM) algorithm to support long-term reference structure for efficient surveillance video coding. A rate–distortion optimization for surveillance source (SRDO) algorithm is also developed to improve the coding performance. Experimental results show that the proposed BBM and SRDO can significantly improve the compression performance, which can effectively support diverse video applications in smart city.
---
paper_title: A resilient and distributed near real-time traffic forecasting application for Fog computing environments

paper_content:

In this paper we propose an architecture for a city-wide traffic modeling and prediction service based on the Fog Computing paradigm. The work assumes an scenario in which a number of distributed antennas receive data generated by vehicles across the city. In the Fog nodes data is collected, processed in local and intermediate nodes, and finally forwarded to a central Cloud location for further analysis. We propose a combination of a data distribution algorithm, resilient to back-haul connectivity issues, and a traffic modeling approach based on deep learning techniques to provide distributed traffic forecasting capabilities. In our experiments, we leverage real traffic logs from one week of Floating Car Data (FCD) generated in the city of Barcelona by a road-assistance service fleet comprising thousands of vehicles. FCD was processed across several simulated conditions, ranging from scenarios in which no connectivity failures occurred in the Fog nodes, to situations with long and frequent connectivity outage periods. For each scenario, the resilience and accuracy of both the data distribution algorithm, and the learning methods were analyzed. Results show that the data distribution process running in the Fog nodes is resilient to back-haul connectivity issues and is able to deliver data to the Cloud location even in presence of severe connectivity problems. Additionally, the proposed traffic modeling and forecasting method exhibits better behavior when run distributed in the Fog instead of centralized in the Cloud, especially when connectivity issues occur that force data to be delivered out of order to the Cloud.
---
paper_title: Enabling Next Generation Logistics and Planning for Smarter Societies

paper_content:

Abstract: Social media has revolutionized our societies. It has made fundamental impact on the way we work and live. More importantly, social media is gradually becoming a key pulse of smart societies by sensing the information about the people and their spatio-temporal experiences around the living spaces. Big data and computational intelligence technologies are helping us to manage and analyze large amounts of data generated by the social media, such as twitter, and make informed decisions about us and the living spaces. This paper reports our preliminary work on the use of social media for the detection of spatio-temporal events related to logistics and planning. Specifically, we use big data and AI platforms including Hadoop, Spark, and Tableau, to study twitter data about London. Moreover, we use the Google Maps Geocoding API to locate the tweeters and make additional analysis. We find and locate congestion around the London city. We also discover that, during a certain period, top third tweeted words were about job and hiring, leading us to locate the source of the tweets which happened to be originating from around the Canary Wharf area, UK's major financial center. The results presented in the paper have been obtained using 500,000 tweets.
---
paper_title: Applied Graph Theory to Real Smart City Logistic Problems

paper_content:

Abstract In a recent project in the Region of North Jutland in Denmark, an empirical study of the mobile broadband conditions was carried out by installing measurement equipment on two cars driving around everywhere in the Region. This paper provides an overview of the system designed for carrying out the project, focusing on the solution for the logistics problem presenting well-defined challenges. We present a feasible solution for the non-trivial problem of planning routes, guaranteeing 100% coverage of the 19.000 km of roads in the Region and having hard computational time constrains. The solution is a combination of applied graph theory and Geographic Information Systems (GIS), framed into a realistic context to be able to carry out the project. One of the future foreseen application fields of cellular networks is to provide efficient network connectivity to cyber physical systems for the required data transactions between the devices and data processing units. Hence, the results derived from similar measurement projects, among many others, is the evaluation of whether the broadband infrastructure is currently ready for the deployment of Smart City outdoor cyber physical systems and implicitly, which steps should be taken towards the deployment of these systems.
---
paper_title: Towards cloud based big data analytics for smart future cities

paper_content:

A large amount of land-use, environment, socio-economic, energy and transport data is generated in cities. An integrated perspective of managing and analysing such big data can answer a number of science, policy, planning, governance and business questions and support decision making in enabling a smarter environment. This paper presents a theoretical and experimental perspective on the smart cities focused big data management and analysis by proposing a cloud-based analytics service. A prototype has been designed and developed to demonstrate the effectiveness of the analytics service for big data analysis. The prototype has been implemented using Hadoop and Spark and the results are compared. The service analyses the Bristol Open data by identifying correlations between selected urban environment indicators. Experiments are performed using Hadoop and Spark and results are presented in this paper. The data pertaining to quality of life mainly crime and safety & economy and employment was analysed from the data catalogue to measure the indicators spread over years to assess positive and negative trends.
---
paper_title: Design of IoT Systems and Analytics in the Context of Smart City Initiatives in India

paper_content:

The rapid growth of population and industrialization has paved way for the use of technologies like the Internet of Things which gave rise to the concept of smart cities. India as a developing country has a great prospect in developing technologies to make the cities smart. As urbanization occurs the demand for resources and efficient servicing will increase. To achieve this in a smart and efficient way, connected device (IoT) could be used. The possible design of an IoT system based on surveys performed on similar smart solutions implemented has been discussed in this paper. Urbanization and population growth has led to higher demand for resources like water which are of scarce. There is a keen interest from the organizations and government to make proper usage of water. The same can be achieved by proper monitoring and management of water distribution systems. The paper discusses the use of Machine learning techniques to smart city management aspects like smart water management which include water demand forecasting, water quality monitoring and anomaly detection.
---
paper_title: Big Data Logistics: A health-care Transport Capacity Sharing Model

paper_content:

The growth of cities in the 21st century has put more pressure on resources and conditions of urban life. There are several reasons why the health-care industry is the focus of this investigation. For instance, in the UK various studies point to the lack of failure of basic quality control procedures and misalignment between customer needs and provider services and duplication of logistics practices. The development of smart cities and big data present unprecedented challenges and opportunities for operations managers; they need to develop new tools and techniques for network planning and control. Our paper aims to make a contribution to big data and city operations theory by exploring how big data can lead to improvements in transport capacity sharing. We explore using Markov models the integration of big data with future city (health-care) transport sharing. A mathematical model was designed to illustrate how sharing transport load (and capacity) in a smart city can improve efficiencies in meeting demand for city services. The results from our analysis of 13 different sharing/demand scenarios are presented. A key finding is that the probability for system failure and performance variance tends to be highest in a scenario of high demand/zero sharing.
---
paper_title: Video big data in smart city: Background construction and optimization for surveillance video processing

paper_content:

Abstract Transforming infrastructures, buildings and services with the sensed data from the Internet of Things (IoT) technique has drawn wide attention. Enormous video data from city surveillance cameras poses huge challenges of transmission, storage and analysis, which necessitates new video compression technologies. The fusion of video data generated from smart city could be used to support city management and urban policy. Based on the specific characteristics of surveillance video, which are successive pictures have very strong correlations and each picture can be divided into background and foreground, this work proposes a block-level background modeling (BBM) algorithm to support long-term reference structure for efficient surveillance video coding. A rate–distortion optimization for surveillance source (SRDO) algorithm is also developed to improve the coding performance. Experimental results show that the proposed BBM and SRDO can significantly improve the compression performance, which can effectively support diverse video applications in smart city.
---
paper_title: The core enabling technologies of big data analytics and context-aware computing for smart sustainable cities: a review and synthesis

paper_content:

Data sensing, information processing, and networking technologies are being fast embedded into the very fabric of the contemporary city to enable the use of innovative solutions to overcome the challenges of sustainability and urbanization. This has been boosted by the new digital transition in ICT. Driving such transition predominantly are big data analytics and context-aware computing and their increasing amalgamation within a number of urban domains, especially as their functionality involve more or less the same core enabling technologies, namely sensing devices, cloud computing infrastructures, data processing platforms, middleware architectures, and wireless networks. Topical studies tend to only pass reference to such technologies or to largely focus on one particular technology as part of big data and context-aware ecosystems in the realm of smart cities. Moreover, empirical research on the topic, with some exceptions, is generally limited to case studies without the use of any common conceptual frameworks. In addition, relatively little attention has been given to the integration of big data analytics and context-aware computing as advanced forms of ICT in the context of smart sustainable cities. This endeavor is a first attempt to address these two major strands of ICT of the new wave of computing in relation to the informational landscape of smart sustainable cities. Therefore, the purpose of this study is to review and synthesize the relevant literature with the objective of identifying and distilling the core enabling technologies of big data analytics and context-aware computing as ecosystems in relevance to smart sustainable cities, as well as to illustrate the key computational and analytical techniques and processes associated with the functioning of such ecosystems. In doing so, we develop, elucidate, and evaluate the most relevant frameworks pertaining to big data analytics and context-aware computing in the context of smart sustainable cities, bringing together research directed at a more conceptual, analytical, and overarching level to stimulate new ways of investigating their role in advancing urban sustainability. In terms of originality, a review and synthesis of the technical literature has not been undertaken to date in the urban literature, and in doing so, we provide a basis for urban researchers to draw on a set of conceptual frameworks in future research. The proposed frameworks, which can be replicated and tested in empirical research, will add additional depth and rigor to studies in the field. In addition to reviewing the important works, we highlight important applications as well as challenges and open issues. We argue that big data analytics and context-aware computing are prerequisite technologies for the functioning of smart sustainable cities of the future, as their effects reinforce one another as to their efforts for bringing a whole new dimension to the operating and organizing processes of urban life in terms of employing a wide variety of big data and context-aware applications for advancing sustainability.
---
paper_title: Enabling Next Generation Logistics and Planning for Smarter Societies

paper_content:

Abstract: Social media has revolutionized our societies. It has made fundamental impact on the way we work and live. More importantly, social media is gradually becoming a key pulse of smart societies by sensing the information about the people and their spatio-temporal experiences around the living spaces. Big data and computational intelligence technologies are helping us to manage and analyze large amounts of data generated by the social media, such as twitter, and make informed decisions about us and the living spaces. This paper reports our preliminary work on the use of social media for the detection of spatio-temporal events related to logistics and planning. Specifically, we use big data and AI platforms including Hadoop, Spark, and Tableau, to study twitter data about London. Moreover, we use the Google Maps Geocoding API to locate the tweeters and make additional analysis. We find and locate congestion around the London city. We also discover that, during a certain period, top third tweeted words were about job and hiring, leading us to locate the source of the tweets which happened to be originating from around the Canary Wharf area, UK's major financial center. The results presented in the paper have been obtained using 500,000 tweets.
---
paper_title: Applications of big data to smart cities

paper_content:

Many governments are considering adopting the smart city concept in their cities and implementing big data applications that support smart city components to reach the required level of sustainability and improve the living standards. Smart cities utilize multiple technologies to improve the performance of health, transportation, energy, education, and water services leading to higher levels of comfort of their citizens. This involves reducing costs and resource consumption in addition to more effectively and actively engaging with their citizens. One of the recent technologies that has a huge potential to enhance smart city services is big data analytics. As digitization has become an integral part of everyday life, data collection has resulted in the accumulation of huge amounts of data that can be used in various beneficial application domains. Effective analysis and utilization of big data is a key factor for success in many business and service domains, including the smart city domain. This paper reviews the applications of big data to support smart cities. It discusses and compares different definitions of the smart city and big data and explores the opportunities, challenges and benefits of incorporating big data applications for smart cities. In addition it attempts to identify the requirements that support the implementation of big data applications for smart city services. The review reveals that several opportunities are available for utilizing big data in smart cities; however, there are still many issues and challenges to be addressed to achieve better utilization of this technology.
---
paper_title: Towards cloud based big data analytics for smart future cities

paper_content:

A large amount of land-use, environment, socio-economic, energy and transport data is generated in cities. An integrated perspective of managing and analysing such big data can answer a number of science, policy, planning, governance and business questions and support decision making in enabling a smarter environment. This paper presents a theoretical and experimental perspective on the smart cities focused big data management and analysis by proposing a cloud-based analytics service. A prototype has been designed and developed to demonstrate the effectiveness of the analytics service for big data analysis. The prototype has been implemented using Hadoop and Spark and the results are compared. The service analyses the Bristol Open data by identifying correlations between selected urban environment indicators. Experiments are performed using Hadoop and Spark and results are presented in this paper. The data pertaining to quality of life mainly crime and safety & economy and employment was analysed from the data catalogue to measure the indicators spread over years to assess positive and negative trends.
---
paper_title: Cloud Support Data Management Infrastructure for Upcoming Smart Cities

paper_content:

Abstract This paper presents a novel large scale data management model for future smart cities. The system is exploiting the emerging cloud computing services availability over the globe by processing the collected data at the edge. To handle the large scale requirements in smart city, we introduce a Mobile Edge Computing (MEC) framework in order to increase the reliability of the deployed applications. MEC is a promising framework to reduce the cloud core utilization as well as to provide applications that require low latency to mobile end users. As near as possible to the location of the end user, MEC relates to the mobile network applications and data stream acceleration through caching and/or compressing of relevant data at the edge of the mobile network. Although MEC is not yet deployed in real-life systems, recent studies discussed some technical details and related concepts. This paper studies the definition of MEC and similar concepts with respect to typical application scenarios, like in smart cities, and provides scope and limitations that may be encountered when implementing and deploying MEC. Then, a MEC framework is used to explore the data management infrastructure for upcoming smart cities.
---
paper_title: Big Data Analytics Embedded Smart City Architecture for Performance Enhancement through Real-Time Data Processing and Decision-Making

paper_content:

The concept of the smart city is widely favored, as it enhances the quality of life of urban citizens, involving multiple disciplines, that is, smart community, smart transportation, smart healthcare, smart parking, and many more. Continuous growth of the complex urban networks is significantly challenged by real-time data processing and intelligent decision-making capabilities. Therefore, in this paper, we propose a smart city framework based on Big Data analytics. The proposed framework operates on three levels: data generation and acquisition level collecting heterogeneous data related to city operations, data management and processing level filtering, analyzing, and storing data to make decisions and events autonomously, and application level initiating execution of the events corresponding to the received decisions. In order to validate the proposed architecture, we analyze a few major types of dataset based on the proposed three-level architecture. Further, we tested authentic datasets on Hadoop ecosystem to determine the threshold and the analysis shows that the proposed architecture offers useful insights into the community development authorities to improve the existing smart city architecture.
---
paper_title: A Master Data Management Solution to Unlock the Value of Big Infrastructure Data for Smart, Sustainable and Resilient City Planning

paper_content:

Abstract In recent years, many governments have launched various smart city or smart infrastructure initiatives to improve the quality of citizens’ life and help city managers / planners optimize the operation and management of urban infrastructures. By deploying internet of things (IoT) to infrastructure systems, high-volume and high-variety of data pertinent to the condition and performance of infrastructure systems along with the behaviors of citizens can be gathered, processed, integrated and analyzed through cloud-based infrastructure asset management systems, ubiquitous mobile applications and big data analytics platforms. Nonetheless, how to fully exploit the value of ‘big infrastructure data’ is still a key challenge facing most stakeholders. Unless data is shared by different infrastructure systems in an interoperable and consistent manner, it is difficult to realize the smart infrastructure concept for efficient smart city planning, not to mention about developing appropriate resilience and sustainable programs. To unlock the value of big infrastructure data for smart, sustainable and resilient city planning, a master data management (MDM) solution is proposed in this paper. MDM has been adopted in the business sector to orchestrate operational and analytical big data applications. In order to derive a suitable MDM solution for smart, sustainable and resilient city planning, commercial and open source MDM systems, smart city standards, smart city concept models, smart community infrastructure frameworks, semantic web technologies will be critically reviewed, and feedback and requirements will be gathered from experts who are responsible for developing smart, sustainable and resilient city programs. A case study which focuses on the building and transportation infrastructures of a selected community in Hong Kong will be conducted to pilot the proposed MDM solution.
---
paper_title: A resilient and distributed near real-time traffic forecasting application for Fog computing environments

paper_content:

In this paper we propose an architecture for a city-wide traffic modeling and prediction service based on the Fog Computing paradigm. The work assumes an scenario in which a number of distributed antennas receive data generated by vehicles across the city. In the Fog nodes data is collected, processed in local and intermediate nodes, and finally forwarded to a central Cloud location for further analysis. We propose a combination of a data distribution algorithm, resilient to back-haul connectivity issues, and a traffic modeling approach based on deep learning techniques to provide distributed traffic forecasting capabilities. In our experiments, we leverage real traffic logs from one week of Floating Car Data (FCD) generated in the city of Barcelona by a road-assistance service fleet comprising thousands of vehicles. FCD was processed across several simulated conditions, ranging from scenarios in which no connectivity failures occurred in the Fog nodes, to situations with long and frequent connectivity outage periods. For each scenario, the resilience and accuracy of both the data distribution algorithm, and the learning methods were analyzed. Results show that the data distribution process running in the Fog nodes is resilient to back-haul connectivity issues and is able to deliver data to the Cloud location even in presence of severe connectivity problems. Additionally, the proposed traffic modeling and forecasting method exhibits better behavior when run distributed in the Fog instead of centralized in the Cloud, especially when connectivity issues occur that force data to be delivered out of order to the Cloud.
---

Title: Big Data Analytics Algorithm, Data Type and Tools in Smart City: A Systematic Literature Review

Section 1: INTRODUCTION
Description 1: Provide an overview of the significance of big data and its application in smart cities, explaining the research problem and the objectives of the systematic literature review (SLR).

Section 2: METHODOLOGY
Description 2: Describe the methodology employed in conducting the systematic literature review, including the use of Prisma and the phases involved in the identification, screening, eligibility, and inclusion of relevant research.

Section 3: PROPOSE METHOD
Description 3: Explain the specific research methods and activities undertaken to identify, select, extract, and synthesize data from the studies included in the review, detailing the research questions and search strategies used.

Section 4: RESEARCH RESULT AND DISCUSSION
Description 4: Present and discuss the findings of the research, categorizing them into sub-sections based on the algorithms, data types, and tools identified for big data analytics in smart cities.

Section 5: CONCLUSION AND FUTURE WORK
Description 5: Summarize the key findings of the review, highlight the most frequently used algorithms, data types, and tools in smart cities, and outline potential directions for future research in this field.