Digital Cities Roadmap. Группа авторов

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Digital Cities Roadmap - Группа авторов


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outlines the components of smart cities and IoT technologies used in smart cities for establishing relationships between industries and their services, and includes a table showing various sectors providing different services and related principal issues of IoT technologies. Finally, the challenges of smart cities, urbanization, and IoT are highlighted. The perceived concept of the smart city appears to initiate the new standards for urban city planning. Urban planners imagine the city of the future as smart and economical. This initiative will always remain critical for development and sustainability. Chapter 11, “Trip-I-Plan: A Mobile Application for Task Scheduling in Smart City’s Sustainable Infrastructure,” provides comprehensive, automatic task rescheduling for mobile application. This approach will enhance the growth of smart city workers’ planning and boost the growth of smart sustainable infrastructure. Here, a comparative study of existing mobile applications of task scheduling is also presented.

      Chapter 14, “Role of Smart Buildings in Smart City: Components, Technologies, Indicators, Challenges, and Future Research Opportunities,” presents various indicators, technologies, components, and features of smart buildings in any smart city. General architectures are subsequently discussed along with the various supporting technologies and requirements of smart buildings for smart cities. The chapter ends with a discussion of the different challenges followed by future research opportunities in the domain of smart buildings in a smart city. Chapter 15, “Effects of Green Buildings on the Environment,” discusses concerns related to rapidly increasing environmental and sustainability issues like urbanization, climate change, loss of biodiversity and degradation of resources, which highlight the need for advancements in housing. Green building is the theory, science and styling of buildings planned and constructed in accordance with a minimum impact on the surroundings by reducing utilization of water, energy, and disturbances in the surrounding environment in which the building is located. This contribution is an attempt to appraise the value of green buildings compared to standard buildings. An attempt is also made to illustrate the available good practices regarding green structures in India.

      The information provided in this book will be an incentive to the researchers, academicians and industry professionals interested in IoT-based architecture and sustainable buildings. The book also provides a platform to exchange knowledge in the field of energy efficiency and various tools and methods used to develop green technologies for construction in smart cities.

       The Editors

      January 2021

      1

      The Use of Machine Learning for Sustainable and Resilient Buildings

       Abstract

      The use of Artificial Intelligence to ensure that intelligent and resilient buildings are sustainably developed. The intelligence displayed in buildings by electronic devices and software operated systems is artificial intelligence which perceives the building environment and takes actions aimed at optimizing output in a given context or constraint. A complex, sensitive infrastructure that ensures efficient, cost-effective and environmentally acceptable conditions for every occupant by constantly communicating with its four basic elements: locations (components, frameworks, facilities); processes (automation, control systems), staff (services, users) and management (maintaining, performance) and processes (controlling, systems); and they separate current technologies into two major groups, occupantcentered and energy-centered facilities. The first level approaches that use ML for occupant dimensions, including (1) occupancy and identity estimations, (2) behavior recognition and (3) choice and enforcement estimates. The approach in the second-class category used ML to approximate energy or device-related aspects. It is divided into three categories, (1) estimating the energy profiling and demand, (2) profiling and detection of faults of devices, and (3) sensor inferiority. In this chapter, we focus on guided study, unrestricted learning and improving learning. The main variants, implications of specific parameter choices are explored and we generate standard algorithms. Finally, discuss some of the challenges and opportunities in the built environment to apply machine learning.

      The hyperconnectivity generated by IoT will enhance the assurance of Smart Sustainable Resilient Building (SSRB) as all basic construction facilities and goods from your home electronics to your plant vessels have now been connected [15]. Nevertheless, this hyperconnectivity could hinder the control of SSRBs at the same time. In particular, massive quantities of streaming data are required from SSRB and its residents. The management of large data streams is becoming more and more relevant with ML, testing, compaction, learning and filtration technologies. In order to obtain a greater interpretation of human beings than their environment computers, the amount of sensory data obtained by sensors and devices needs to be processed by algorithms, converted into details and derived expertise [68]. This awareness can also contribute, and most significantly, innovative goods and services that change our lives drastically. For starters, smart meter


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