Digital Cities Roadmap

IoT-Based Architecture and Sustainable Buildings

Arun Solanki, Adarsh Kumar, Anand Nayyar

• Publisher: Wiley-Scrivener
• Year: 2021
• Print ISBN:
  9781119791591
• Edition: 1
• eBook ISBN:
  9781119792055
• Topic: Computer Science
• Subtopic: Artificial Intelligence (AI) & Semantics
• Index: Computer Science

DIGITAL CITIES ROADMAP

This book details applications of technology to efficient digital city infrastructure and its planning, including smart buildings.

Rapid urbanization, demographic changes, environmental changes, and new technologies are changing the views of urban leaders on sustainability, as well as creating and providing public services to tackle these new dynamics. Sustainable development is an objective by which the processes of planning, implementing projects, and development is aimed at meeting the needs of modern communities without compromising the potential of future generations. The advent of Smart Cities is the answer to these problems.

Digital Cities Roadmap provides an in-depth analysis of design technologies that lay a solid foundation for sustainable buildings. The book also highlights smart automation technologies that help save energy, as well as various performance indicators needed to make construction easier. The book aims to create a strong research community, to have a deep understanding and the latest knowledge in the field of energy and comfort, to offer solid ideas in the nearby future for sustainable and resilient buildings. These buildings will help the city grow as a smart city. The smart city has also a focus on low energy consumption, renewable energy, and a small carbon footprint.

Audience

The information provided in this book will be of value to researchers, academicians and industry professionals interested in IoT-based architecture and sustainable buildings, energy efficiency and various tools and methods used to develop green technologies for construction in smart cities.

1
The Use of Machine Learning for Sustainable and Resilient Buildings

Kuldeep Singh Kaswan¹* and Jagjit Singh Dhatterwal²

¹School of Computing Science and Engineering, Galgotias University, Greater Noida, India

²Department of Computer Science & Applications, PDM University, Bahadurgarh, India

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.

Keywords: Machine learning, big data analytics, Internet of Things, smart building, resilient building, sustainable building

1.1 Introduction of ML Sustainable Resilient Building

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 [1–5]. 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 [6–8]. This awareness can also contribute, and most significantly, innovative goods and services that change our lives drastically. For starters, smart meter readings may be used to help estimate and control power usage. To optimize this convenience, reduce expenses adapting to requirements of its residents, the SSRB requires sophisticated tools to understand, anticipate and make intelligent decisions. SSRB must also provide a variety of wearable sensor data linked to its patients and produce new remote sensors. SSRB algorithms include estimation, decision analysis, robots, smart devices, wireless sensor networks, interactive, web computing and cloud computing and include several other developments. Cognitive maintenance of offices is necessary in several SSRB programs for starters, fitness, safety, energy management, illumination, repair, the elderly and digital entertainment through these technologies.

1.2 Related Works

While several SB-focused survey papers have been released, none focuses on the role of data analysis and ML within SBs. All the relevant survey papers are comprehensively presented in Table 1.1.

Table 1.1 Report data of a survey.

Cite	Purpose	Limitations
Chan et al. [12]	A country and continent arranged project SH Review as well as the associated technologies for monitoring systems and assistive robotics.	It not emphasized on the importance of ML and big data analytics, it does not review and classify the papers according to the applications of SH
Alam et al. [13]	Research objectives and services-based review of SH projects; namely, comfort, healthcare, and security.	It not emphasized on the importance of ML and big data analytics for SB.
Lobaccaro et al. [14]	Review of existing software, hardware, and communications control systems for S.H and smart grid.	It not emphasized on the importance of ML and big data analytics. It also does not f...

Table of contents

• Cover
• Table of Contents
• Title Page
• Copyright
• Preface
• 1 The Use of Machine Learning for Sustainable and Resilient Buildings
• 2 Fire Hazard Detection and Prediction by Machine Learning Techniques in Smart Buildings (SBs) Using Sensors and Unmanned Aerial Vehicles (UAVs)
• 3 Sustainable Infrastructure Theories and Models
• 4 Blockchain for Sustainable Smart Cities
• 5 Contextualizing Electronic Governance, Smart City Governance and Sustainable Infrastructure in India: A Study and Framework
• 6 Revolutionizing Geriatric Design in Developing Countries: IoT-Enabled Smart Home Design for the Elderly
• 7 Sustainable E-Infrastructure for Blockchain-Based Voting System
• 8 Impact of IoT-Enabled Smart Cities: A Systematic Review and Challenges
• 9 Indoor Air Quality (IAQ) in Green Buildings, a Pre-Requisite to Human Health and Well-Being
• 10 An Era of Internet of Things Leads to Smart Cities Initiatives Towards Urbanization
• 11 Trip-I-Plan: A Mobile Application for Task Scheduling in Smart City’s Sustainable Infrastructure
• 12 Smart Health Monitoring for Elderly Care in Indoor Environments
• 13 A Comprehensive Study of IoT Security Risks in Building a Secure Smart City
• 14 Role of Smart Buildings in Smart City—Components, Technolo Indicators, Challenges, Future Research Opportunities
• 15 Effects of Green Buildings on the Environment
• Index
• End User License Agreement