Technology and Talent Strategies for Sustainable Smart Cities

As the size of the population is growing and the capacity of the planet Earth is limited, human beings are searching for sustainable and technology-enabled solutions to support society, ecology and economy. One of the solutions has been developing smart sustainable cities. Smart sustainable cities are cities as systems, where their infrastructure, different subsystems and different functional domains are virtually connected to the information and communication technologies (ICT) and internet via sensors and devices and the Internet of Things (IoT), to collect and process real-time Big Data and make efficient, effective and sustainable solutions for a democratic and liveable city for its various stakeholders. This chapter explores the concepts and practices of sustainable smart cities across the globe and explores the use of technologies such as IoT, Blockchain technology and Cloud computing, etc. their challenges and then presents a view on business models for sustainable smart cities.

India launched Smart City Mission in 2015 with an objective of development of 100 smart cities with a completion deadline in 2019 that was extended till June 2023. Smart City Mission is an important mission in the backdrop that urban population in India is projected to be 67.55 crore in 2035 from 48.30 crore in 2020. Further, by 2035, the percentage of population in India at mid-year residing in ‘urban area’ will be 43.2% as per the United Nations – Habitat's World Cities Report 2022 and it will be just next to China's urban population in 2035 that is projected at 1.05 billion. A recent World Bank report (2022) estimated that India will need to invest US (United States) $840 billion over the next 15 years, i.e. US $55 billion per annum – into urban infrastructure if it has to effectively meet the needs of its fast-growing urban population.

This chapter focuses on financing of sustainable smart cities in India. This chapter summarises financing options explored by the government in the beginning, challenges faced in financing of Smart City Mission in India over a period due to various developments such as pandemic, delay in execution of projects under the Smart City Mission, among others. Finally, suggestions have been given for making financing means effective and sustainable. These suggestions are based on the gaps between the ‘financing means thought of’ in the beginning and ‘financing means actually applied’ while executing Smart City Mission in India. Financing part is worth exploring in the background that India had the fiscal deficit at 3.9% of Gross Domestic Product (GDP) in 2015–2016 and most recently, the country had the fiscal deficit at 6.71% of GDP in FY22. And the country also dealt with the pandemic like other economies and provided COVID-19 vaccine free of cost to all citizens. Insights are useful for any other economy with a similar sustainable and smart city mission while facing resource constraints.

From the perspective of any nation, rural areas generally present a comparable set of problems, such as a lack of proper healthcare, education, living conditions, wages and market opportunities. Some nations have created and developed the concept of smart villages during the previous few decades, which effectively addresses these issues. The landscape of traditional agriculture has been radically altered by digital agriculture, which has also had a positive economic impact on farmers and those who live in rural regions by ensuring an increase in agricultural production. We explored current issues in rural areas, and the consequences of smart village applications, and then illustrate our concept of smart village from recent examples of how emerging digital agriculture trends contribute to improving agricultural production in this chapter.

The purpose of this chapter is to develop academic answers to the key rural areas and smart villages and digital agriculture. This chapter analyses the National level initiatives of Government of India Mission to convert rural areas into smart cities. The Union Ministry of urban development collaborates with State Government and nominate a particular city or cities in their state. Financial incentives or benefits will be provided to enhance the quality of the city. Coimbatore being a cosmopolitan city it is also a combination of rural villages and urban township. The main objective of this chapter is to identify and explore the initiatives of SMART CITIES MISSION a joint venture activity initiated by Government of India and State Government of Tamil Nadu. The results clearly indicate how digital technologies play a pivotal role to enhance the quality of eco-friendly initiatives and to improve the smart villages and agriculture. The key recommendations are the lessons learnt from other smart cities initiatives in other states and how Coimbatore can be an example and adopt key takeaways from other states and cities around the world.

The concept of governance recognises the power dependency that exists between institutions that are engaged in collective action. Government, according to UNESCAP, is a process through which choices are made and executed or rejected. Corporate governance, international governance, national governance and municipal governance are just a few examples of how the term governance may be employed. Governance was also cited by UNESCAP as a player in government. To build a smart city and economy, national level of governance focuses on freedom of media, country history and traditions, civil society, private sector and good government. All those elements are important to build a smart economy and smart city. This chapter discusses the role of government to ensure good governance and good citizen policy choices that benefit the smart city and economy in Bangladesh.

As the world continues to urbanise, cities face increasing pressure to become more sustainable, efficient and livable. Sustainable smart cities are emerging as a promising solution to this challenge, leveraging technology and data to improve urban systems and services while reducing environmental impact. This chapter provides an overview of the concept of sustainable smart cities and its implications for urban development. It explores the key features of sustainable smart cities, including their focus on technology, data and citizen engagement and the challenges they are facing in terms of infrastructure, data management, social equity, environmental sustainability, governance and regulations. The chapter also highlights the implications of sustainable smart cities for urban planners, policymakers and other stakeholders, emphasising the need for collaborative approaches that engage citizens and stakeholders in the design and implementation of smart city initiatives.

A Digital Twin (DT) is a digital replica of an artefact which is updated on real-time or semi–real-time basis. In 2017, Gartner listed DT as one of the top 10 emerging technologies of the year. Since then, there have been numerous attempts to develop architecture and reference models for DTs, and in some studies, DT construction for real-world case studies is reported. This chapter attempts to provide a contextualised background on DT for smart cities. It also discusses various stakeholders involved in devising and/or employing DTs in a smart city. The chapter concludes with a set of recommendations for the training requirements of final DT users.

Successful smart cities' implementation will require organisational leadership decision-making competences. The foundation of smart cities is digital technologies; many of these technologies are emerging technologies that require IT skills, which are scarce and will exacerbate the battle for talent between organisations. Filling the talent gap will necessitate global hiring, which has implications for organisational culture, cultural diversity and organisational leadership. Organisational cultural mix is an important contributor to leadership decision-making. However, decision-making is underpinned by trust. Blockchain is an emerging technology that has the potential to engender organisational trust in decision-making and, by extension, in the leadership with the ‘right’ organisational culture. Smart cities will be required to leverage emerging technologies to give business performance a competitive advantage and use emerging technologies’ applications to build a sustainable competitive advantage.

This study aims to identify blockchain-related innovation trends that can improve trust networks in a smart city's transport and supply chain networks. Trust networks are crucial in building and maintaining the trust of citizens in smart cities. By promoting transparency and accountability, facilitating collaboration and innovation, enhancing citizen participation and protecting privacy and security, trust networks can help to ensure that smart cities are developed and implemented in a responsible and sustainable way. A systematic literature review identifies 60 conceptual and empirical studies. This research focuses on the current problems and developing procurement and supply chain strategy and the potential benefits of using blockchain in these areas. It suggests ways for the smart city's transport and supply chain networks to utilise blockchain to improve operations and supply chain strategy and identifies innovation trends related to blockchain. The study also includes a systematic literature review and Blockchain Transformation and Influence model as the basis to enhance trust networks in the supply chain.

The malfunction variables of power stations are related to the areas of weather, physical structure, control and load behaviour. To predict temporal power failure is difficult due to their unpredictable characteristics. As high accuracy is normally required, the estimation of failures of short-term temporal prediction is highly difficult. This study presents a method for converting stochastic behaviour into a stable pattern, which can subsequently be used in a short-term estimator. For this conversion, K-means clustering is employed, followed by Long-Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) algorithms are used to perform the Short-term estimation. The environment, the operation and the generated signal factors are all simulated using mathematical models. Weather parameters and load samples have been collected as part of a data set. Monte-Carlo simulation using MATLAB programming has been used to conduct experimental estimation of failures. The estimated failures of the experiment are then compared with the actual system temporal failures and found to be in good match. Therefore, for any future power grid, there is a testbed ready to estimate the future failures.

One of the most neglected sources of energy loss is streetlights that generate too much light in areas where it is not required. Energy waste has enormous economic and environmental effects. In addition, due to the conventional manual nature of operation, streetlights are frequently seen being turned ‘ON’ during the day and ‘OFF’ in the evening, which is regrettable even in the twenty-first century. These issues require automated streetlight control in order to be resolved. This study aims to develop a novel streetlight controlling method by combining a smart transport monitoring system powered by computer vision technology with a closed circuit television (CCTV) camera that allows the light-emitting diode (LED) streetlight to automatically light up with the appropriate brightness by detecting the presence of pedestrians or vehicles and dimming the streetlight in their absence using semantic image segmentation from the CCTV video streaming. Consequently, our model distinguishes daylight and nighttime, which made it feasible to automate the process of turning the streetlight ‘ON’ and ‘OFF’ to save energy consumption costs. According to the aforementioned approach, geo-location sensor data could be utilised to make more informed streetlight management decisions. To complete the tasks, we consider training the U-net model with ResNet-34 as its backbone. Validity of the models is guaranteed with the use of assessment matrices. The suggested concept is straightforward, economical, energy-efficient, long-lasting and more resilient than conventional alternatives.

Banking traces back to 2000 BC in Assyria, India and Sumeria. Merchants used to give grain loans to farmers and traders to carry goods between cities. In ancient Greece and Roman Empire, lenders in temples, provided loans, and accepted deposits while performed change of money. The archaeological evidence uncovered in India and China corroborates this. The major development in banking came predominantly in the mediaeval, Renaissance Italy, with the major cities Florence, Venice and Genoa being the financial centres. Technology has become an inherent and integral part of our lives. We are generating a huge amount of data in transfer, storage and usage, with greater demands of ubiquitous accessibility, inducing an enormous impact on industry and society. With the emergence of smarter cities and societies, the security challenges pertinent to data become greater, impending impact on the consumer protection and security. The aim of this chapter is to highlight if SSI and passwordless authentication using FIDO-2 protocol assuage security concerns such as authentication and authorisation while preserving the individual's privacy.

This research navigates the confluence of data analytics, machine learning, and artificial intelligence to revolutionize the management of urban services in smart cities. The study thoroughly investigated with advanced tools to scrutinize key performance indicators integral to the functioning of smart cities, thereby enhancing leadership and decision-making strategies. Our work involves the implementation of various machine learning models such as Logistic Regression, Support Vector Machine, Decision Tree, Naive Bayes, and Artificial Neural Networks (ANN), to the data. Notably, the Support Vector Machine and Bernoulli Naive Bayes models exhibit robust performance with an accuracy rate of 70% precision score. In particular, the study underscores the employment of an ANN model on our existing dataset, optimized using the Adam optimizer. Although the model yields an overall accuracy of 61% and a precision score of 58%, implying correct predictions for the positive class 58% of the time, a comprehensive performance assessment using the Area Under the Receiver Operating Characteristic Curve (AUC-ROC) metrics was necessary. This evaluation results in a score of 0.475 at a threshold of 0.5, indicating that there's room for model enhancement. These models and their performance metrics serve as a key cog in our data analytics pipeline, providing decision-makers and city leaders with actionable insights that can steer urban service management decisions. Through real-time data availability and intuitive visualization dashboards, these leaders can promptly comprehend the current state of their services, pinpoint areas requiring improvement, and make informed decisions to bolster these services. This research illuminates the potential for data analytics, machine learning, and AI to significantly upgrade urban service management in smart cities, fostering sustainable and livable communities. Moreover, our findings contribute valuable knowledge to other cities aiming to adopt similar strategies, thus aiding the continued development of smart cities globally.

The changing environment and competitive market forces have brought many changes in the business sector that have put organisations under immense pressure. The use of psychometric assessments and behavioural profiling help organisations to determine individuals' abilities, aptitudes, personality traits, values and factors which intrinsically motivate them and assist in bringing the right people on board who fit well within the organisational culture and can contribute towards the performance goals. Although behavioural profiling and psychometric assessments are accepted worldwide, however, developing countries particularly the public sector still relies on conventional recruitment methods and the adaptation of contemporary behavioural profiling and psychometric assessments is a challenge. Therefore, this chapter evaluates how the adaptation of behavioural profiling and psychometric assessments in the civil service exams in developing countries can improve the selection process and ultimately can help to improve the quality of public services, capacity building and achieving sustainability goals.