Bootstrapping Urban Planning: Addressing Big Data Issues in Smart Cities

Bootstrapping Urban Planning: Addressing Big Data Issues in Smart Cities

Ankur Lohachab (Kurukshetra University, India)
Copyright: © 2020 |Pages: 30
DOI: 10.4018/978-1-5225-9742-1.ch009

Abstract

Rapid growth of embedded devices and population density in IoT-based smart cities provides great potential for business and opportunities in urban planning. For addressing the current and future needs of living, smart cities have to revitalize the potential of big data analytics. However, a colossal amount of sensitive information invites various computational challenges. Moreover, big data generated by the IoT paradigm acquires different characteristics as compared to traditional big data because it contains heterogeneous unstructured data. Despite various challenges in big data, enterprises are trying to utilize its true potential for providing proactive applications to the citizens. In this chapter, the author finds the possibilities of the role of big data in the efficient management of smart cities. Representative applications of big data, along with advantages and disadvantages, are also discussed. By delving into the ongoing research approaches in securing and providing privacy to big data, this chapter is concluded by highlighting the open research issues in the domain.
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Introduction

IoT envisages enormous number of smart devices and embedded systems which empowers physical objects with pervasive sensing, seeing, hearing, and communication with each other. As a result, IoT can be considered as a big outlook for future Internet which provides a new scope of opportunities. The promise of Smart Cities ensures the transformation in various areas of human life including transportation, education, health, and energy. Smart Cities led to the concept of smart communities in which distinct electronic devices are inter-connected with each other and generally produce high-quality two-way interactive multimedia content. This multimedia content along with colossal amount of incommensurable types of datasets generated by heterogeneous IoT devices are collectively termed as Big Data. As compared with traditional data, Big Data contains more unstructured data that also require real-time analysis. Mainly, three aspects are used for characterizing Big Data: (a) it cannot be classified into regular relational database, (b) it is in enormous amount, and (c) it is captured, processed, and generated expeditiously. An observation from McKinsey & Company suggests that Big Data create productive, competitive, and economic value in the five core sectors. Record creation of data due to its deep detailing is eliciting attention of everyone.

Along with IoT, Cloud Computing is a major breakthrough technology which is used as an alternative for providing dedicated storage space, software, and even expensive hardware to the users according to their uses and needs. The reason for adoption of Cloud Computing among common users is that it minimizes infrastructure cost by providing virtual resources and parallel processing with anytime, anywhere user access, and efficient management (Bhushan & Gupta, 2018; Chen, Mao, & Liu, 2014; Bhushan & Gupta, 2017). The said advantages motivate organizations for using the virtualized environment in the Smart Cities scenario. The increasing popularity of IoT devices and personal digital assistants has taken the Cloud Computing concept to prominence peak due to their limited storage capacity, processing capability, and constrained energy resources. The concepts of Cloud Computing, IoT, and Big Data are coalescing as IoT provides users the convenience to interact with their physical objects, Cloud Computing provides the fundamental engine through the use of virtualization, and Big Data provides users the capability of using commodity computing for processing their queries in a timely and efficient manner.

Despite the fact that these smart connected objects are used for reducing traffic congestion, fighting crime, making local decisions more open, and foster economic development, they are creating the Big Data that require excessive amount of energy which is also responsible for increasing greenhouse gases. Researchers and industrialists see Big Data as an opportunity for developing new solutions and analyzing new problems. Big Data can be seen as one of the driving technology for drastic increase in development of machine learning algorithms (Labrinidis & Jagadish, 2012). For enhancement of Smart City services, Big Data is mined, processed, and stored efficiently in order to help managers for taking right decisions in real-time according to the provided information (Caragliu, Bo, & Nijkamp, 2011). Although analyzing datasets of network flows, logs, and system events is always considered as a problem, nevertheless this Big Data driven information security is utilized for forensics and intrusion detection.

Key Terms in this Chapter

Smart Grid: For optimized consumption, supply, and generation of electric energy, the concept of smart grids integrates the traditional energy networks with next generation grids that are enabled with remote automation, communication, and computation.

Smart Healthcare: Smart healthcare can be defined as an integration of patients and doctors onto a common platform for intelligent health monitoring by analyzing day-to-day human activities.

Data Provenance: Data provenance is associated with the records of the inputs, systems, entities, and processes that influence the data of interest, and provide historical records of the data and its origins.

Urban Planning: Urban planning may be described as a specialized technical and political procedure which is concerned with the design and development of land usage and the built environment, which includes air, water, and the physical and virtual infrastructure passing into and out of urban zones, such as communication, transportation, and dissemination networks.

Smart Transportation: Smart transportation is defined as the integration of modern technologies, innovations, and management strategies in transportation systems, that aim to provide enhanced services associated with different modes of transport and traffic management, and enable users to be actively informed regarding safe and smarter use of transport networks.

Smart Governance: Smart governance is about the use of technology and innovation for facilitating and supporting enhanced decision making and planning. It is associated with improving the democratic processes and transforming the ways that public services are delivered.

Internet of Things (IoT): IoT can be defined as the idea of envisaging enormous number of smart devices and embedded systems which empowers physical objects with pervasive sensing, seeing, hearing, and communication with each other.

Smart Cities: Smart cities can be generally defined as a conceptual development model which makes collective use of humans and technology for the expansion of collaborative urban development.

Big Data: Big data is collection of datasets that could not be acquired, processed, perceived, and managed by traditional hardware and software tools within a specific time.

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