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Big Data in Smart Cities
Abstract
Government and IT companies are investing and planning to adopt smart city concept in their cities. With the concept of smart city, multiple technologies to improve health, education, transportation, energy comes which needs millions of sensors to be deployed and a huge amount of data is generated by these nodes on a daily basis. From here comes the buzz word " Big Data ". A study by Mckinsey Global estimates that 43 trillion GB of data will be generated by 2020. In one second 22810 GB of internet traffic exists, 2325044 emails are sent, 7474 Tweets are posted, 1229 Instagram photos are uploaded [1]. Data is likely to grow drastically in the upcoming years. A large number of applications are equipped with sensors that measures multiple variable and generate a bulk amount of data. In this paper we are going to review the concepts of big data and how it supports smart cities in implementing smart technologies and making smart decision. This paper will also reveal that though various techniques are available but there are still some challenges to be overcome to achieve better utilization of big data.
... To this end, data is a fundamental to building blocks of smart cities and since data is coming from heterogeneous sources, there are various challenges that are peculiar to retrieving data in our city and must be addressed. Finally, as data and IoT are the backbone of smart city infrastructure, it is crucial to manage data for transition of cities into smart cities (Walia, 2018). ...
"Data is the new oil discovery. Every time we send a message, make a call or complete a transaction, we leave digital traces. A digital version of our physical universe lies within data created and this data is projected to reach 44 zettabyte by 2020. Data is already a key component for business, but today it is moving from the business background to becoming the lifeline of our cities. This has made it more important than ever to understand the consequences of data, notably how it affects people's lives." (Urbanization, 2017) When Wolfgang Stolper (1966) wrote the book titled, "Planning without Facts: Lessons in Resource Allocation from Nigeria's Development", he was emphasizing the necessity of data in planning towards sustainable human settlements. With two-thirds of the world's population expected to live in cities by 2050, United Nations have characterized cities as growing at record speed (Urbanization, 2017). As such, the quest to make cities function more efficiently has never been more urgent. However, city managers wrestle with reduced revenues, inadequate and ageing infrastructure, and the challenge of creating a truly sustainable city. Regardless, city dwellers are now demanding more than ever, of benefits cities should provide e.g. more tailored services, greater access to information, and better value for money, etc. (Spatial, 2014). Massachusetts Institute of Technology (MIT), Centre for Advanced Urbanism described the age we are now as an "era of urban growth whereby the rules have changed and paradigms of urbanism desperately need calibration to meet today's global challenges". With this untold responsibility, city managers and urban planners are starting to realize the importance of data as an ingredient in connecting the dots within a city. This might have been influenced by the hype around the relationship between cities and data, especially so-called big data as emphasized by technological giants. For example, Monsivais (2017) has elaborated that data have become critical to the fabric of human life over the years, and since the evolution of cities revolve around the activities of people, the interplay between cities and human activities can only be understood by insight from records of these activities. For large numbers of tech companies, cities, and even a growing number of urban planners, data promises to solve all manner of urban problems, from predictive policing to improving traffic flow to promoting energy efficiency (Florida, 2016).
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.
The goal of the present thematic series is to showcase some of the most relevant contributions submitted to the 'Telecom Italia Big Data Challenge 2014' and to provide a discussion venue about recent advances in the appplication of mobile phone and social media data to the study of individual and collective behaviors. Particular attention is devoted to data-driven studies aimed at understanding city dynamics. These studies include: modeling individual and collective traffic patterns and automatically identifying areas with traffic congestion, creating high-resolution population estimates for Milan inhabitants, clustering urban dynamics of migrants and visitors traveling to a city for business or tourism, and investigating the relationship between urban communication and urban happiness.
In this paper, we introduce the concept of the smart city, summarize its development process, analyze the construction motivation and objective of the smart city in China and elaborate the supporting technologies for the smart city. Then we propose a smart city infrastructure which is based on digital city, the Internet of Things (IOT) and cloud computing technologies. Smart city will achieve comprehensive awareness and management of people and things to provide various intelligent services. Smart city with mass sensors will continuously collect vast amounts of data. Big data in smart city also bring many problems and challenges. To deal with those big data-related issues, we propose a strategy mainly base on the cloud computing and data mining. After that, we presente a framework for cloud platform and propose the suggestion of establish smart city operation center. In the end, we look forward to a bright future for the smart city.
The Internet of Things (IoT) is now shaping our
cities to make them more connected, convenient, and intelligent.
However, this change will highly rely on extracted values and
insights from the big data generated by our cities via sensors,
devices, and human activities. Many existing studies and projects
have been done to make our cities smart, focusing more on how
to deploy various sensors and devices and then collect data from
them. However, this is just the first step towards smart cities and
next step will be to make good use of the collected data and enable
context-awareness and intelligence into all kinds of applications
and services via a flexible big data platform. In this paper, we
introduce the system architecture and the major design issues of
a live City Data and Analytics Platform, namely CiDAP. More
importantly, we share our experience and lessons learned from
building this practical system for a large scale running smart city
testbed, SmartSantander. Our work provides a valuable example
to future Smart City platform designers so that they can foresee
some practice issues and refer to our solution when building their
own smart city data platforms.
I define big data with respect to its size but pay particular attention to the fact that the data I am referring to is urban data, that is, data for cities that are invariably tagged to space and time. I argue that this sort of data are largely being streamed from sensors, and this represents a sea change in the kinds of data that we have about what happens where and when in cities. I describe how the growth of big data is shifting the emphasis from longer term strategic planning to short-term thinking about how cities function and can be managed, although with the possibility that over much longer periods of time, this kind of big data will become a source for information about every time horizon. By way of conclusion, I illustrate the need for new theory and analysis with respect to 6 months of smart travel card data of individual trips on Greater London’s public transport systems.
Data will soon become one of the most precious treasures we have ever had, 43
trillion gigabytes of data will be created by 2020 according to a study made by
Mckinsey Global Institute, it is estimated that 2.3 trillion gigabytes of data
is created each day and most companies in the US have 100.000 gigabytes of data
stored. Data is recorded, stored and analyzed to enable technology and services
that the world relies on every day, this technology is getting smarter and we
will be soon living in a world of smart services or what is called smart
cities. This article presents an overview of the topic pointing to its actual
status and forecasting the crucial roles it will play in the future, we will
define big data analytics and smart cities and talk about their potential
contributions in changing our way of living and finally we will discuss the
possible down side of this upcoming technologies and how it can fool us,
violate our privacy and turn us into puppets or technology slaves.
We have a clear idea today about the necessity and usefulness of making cities smarter, the potential market size, and trials and tests. However, it seems that business around Smart Cities is having difficulties taking off and is thus running short of projected potentials. This article looks into why this is the case and proposes a procedure to make smart cities happen based on big data exploitation through the API stores concept. To this end, we first review involved stakeholders and the ecosystem at large. We then propose a viable approach to scale business within that ecosystem. We also describe the available ICT technologies and finally exemplify all findings by means of a sustainable smart city application. Over the course of the article, we draw two major observations, which are seen to facilitate sustainable smart city development. First, independent smart city departments (or the equivalent) need to emerge, much like today's well accepted IT departments, which clearly decouple the political element of the improved city servicing from the underlying technologies. Second, a coherent three-phase smart city rollout is vital, where in phase 1 utility and revenues are generated; in phase 2 only-utility service is also supported; and in phase 3, in addition, a fun/leisure dimension is permitted.
A USAF sponsored MITRE research team undertook four separate, domain-specific case studies about Big Data applications. Those case studies were initial investigations into the question of whether or not data quality issues encountered in Big Data collections are substantially different in cause, manifestation, or detection than those data quality issues encountered in more traditionally sized data collections. The study addresses several factors affecting Big Data Quality at multiple levels, including collection, processing, and storage. Though not unexpected, the key findings of this study reinforce that the primary factors affecting Big Data reside in the limitations and complexities involved with handling Big Data while maintaining its integrity. These concerns are of a higher magnitude than the provenance of the data, the processing, and the tools used to prepare, manipulate, and store the data. Data quality is extremely important for all data analytics problems. From the study's findings, the " truth about Big Data " is there are no fundamentally new DQ issues in Big Data analytics projects. Some DQ issues exhibit returns to scale effects, and become more or less pronounced in Big Data analytics, though. Big Data Quality varies from one type of Big Data to another and from one Big Data technology to another.
This paper analyzes the benefits of big data for smart cities and the potential of the knowledge discovery from sensed data. Big data enables real-time systems monitoring, management, optimization and anticipation. In this work we present some examples of applications of big data analysis in two scenarios of smart cities. One of them describes the services provided in the SmartCampus of the University of Murcia. The second example is focused on a tram service scenario where thousands of transit-card transactions should be processed. The results obtained after applying the most appropriate big data techniques in both scenarios show how it is possible to provide efficiently services like the management of the energy consumption and comfort in buildings, and the transport congestion in the context of smart cities.
A new academic center in New York City joins other efforts to harness data from disparate sources with the goal of improving city life.
Big Data is conceived as the powerful tool to exploit all the potential of the Internet of Things and the Smart Cities. Historically several of the human-related behaviours have been modelled with Poisson distribution, but a new dimension of understanding about the human behaviours is reached through all the gathered data in the emerging smart environment. This work analyses the data from the European Project SmartSantander. This work has correlated the traffic behaviour with respect to the temperature in the Santander City. This has been presented as the evolution of both flows present a similar behaviour. The traffic distribution, aggregated by temperature bins, follows up a Poisson distribution model. Thereby, allowing interpolate and predict complex behaviours based on simple measures such as the temperature. At the same time, this data presents a burst behaviour (human dynamics), when the data is analysed in sequence, instead of aggregated by temperature bins. Therefore, this work concludes that human-related behaviours can be described with both, Poisson and Human Dynamics distribution, depending on how the data is represented and aggregated.
ICT is becoming increasingly pervasive to urban environments and providing the necessary basis for sustainability and resilience of the smart future cities. Often ICT tools for a smart city deal with different application domains e.g. land use, transport, energy, and rarely provide an integrated information perspective to deal with sustainability and socioeconomic growth of the city. Smart cities can benefit from such information using Big, and often real-time cross-thematic, data collection, processing, integration and sharing through inter-operable services deployed in a Cloud environment. However, such information utilisation requires appropriate software tools, services and technologies to collect, store, analyse and visualise large amounts of data from the city environment, citizens and various departments and agencies at city scale. This paper presents a theoretical perspective on the smart cities focused Big data processing and analysis by proposing a Cloud-based analysis service that can be further developed to generate information intelligence and support decision-making in smart future cities context.
This paper acts as a primer on an economic outlook at the value and pricing of big data. We introduce a simple taxonomy, discuss rights to access and analyze the case of big data as a common pool resource.
Big Data analytics can revolutionize the healthcare industry. It can improve operational efficiencies, help predict and plan responses to disease epidemics, improve the quality of monitoring of clinical trials, and optimize healthcare spending at all levels from patients to hospital systems to governments. This paper provides an overview of Big Data, applicability of it in healthcare, some of the work in progress and a future outlook on how Big Data analytics can improve overall quality in healthcare systems.
This paper describes the architecture and design of a novel system for supporting large-scale real-time data analysis for future power grid systems. The widespread deployment of renewable generation, smart grid controls, energy storage, plug-in hybrids, and new conducting materials will require fundamental changes in the operational concepts and principal components of the grid. As a result, the whole system becomes highly dynamic and requires constant adjusting based on real time data. Even though millions of sensors such as phase measurement units (PMU) and smart meters are being widely deployed, a data layer that can analyze this amount of data in real time is needed. Unlike the data fabric in other cloud services, the data layer for smart grids has some unique design requirements. First, this layer must provide real time guarantees. Second, this layer must be scalable to allow a large number of applications to access the data from millions of sensors in real time. Third, reliability is critical and this layer must be able to continue to provide service in face of failures. Fourth, this layer must be secure. We address these challenges though a scalable system architecture that integrates the I/O and data processing capability in a devise set of devices. Data process operations can be placed anywhere from sensors, data storage devices, to control centers. We further employ compression to improve performance. We design a lightweight compression customized for power grid data. Our system can reduce end-to-end response time by reduce I/O overhead through compression and overlap compression operations with I/O. The initial prototype of our system was demonstrated with several use cases from PNNL's FPGI and show that our system can provide real time guarantees to a diverse set of applications.
Traces of moving objects in a city, which depict lots of semantics concerning human mobility and city dynamics, are becoming increasingly important. In this article, we first give a brief introduction to trace data; then we present six research issues in trace analysis and mining, and survey the state-of-the-art methods; finally, five promising application domains in smart cities are discussed.
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