Energy Management Systems in Sustainable Smart Cities Based on the Internet of Energy: A Technical Review

Abstract

In this paper, we exploit state-of-the-art energy management in sustainable smart cities employing the Internet of Energy (IoE). The primary goal of this study is to leverage cutting-edge energy management techniques through the IoE in sustainable smart cities to bring about significant improvements in clean energy processes while targeting environmental benefits, efficiency enhancements, sustainability, and cost reduction. In this work, we present a comprehensive exploration of energy management strategies within the context of IoE-enabled sustainable smart cities. Firstly, we provide a detailed classification of diverse energy management approaches pertinent to IoE-based sustainable smart cities. This classification covers a spectrum of methodologies, including scheduling optimization, the design of low-power device transceivers, cognitive frameworks, and the integration of cloud computing technology. Furthermore, we highlight the pivotal role of smart grids as fundamental elements in the establishment of smart cities. Within this context, we offer a comprehensive overview of the essential components that underlie smart grids, with a notable focus on the intricate realm of micro/nanogrids. Moreover, our research delves comprehensively into energy harvesting within the context of smart cities. We analyze crucial facets like receiver design, energy optimization methods, a variety of energy sources, efficient energy scheduling approaches, and the establishment of effective energy routing mechanisms. Additionally, we delve into the multifaceted nature of sustainable smart cities across various domains. Our investigation reaches its culmination in the creation of a novel conceptual framework and the identification of enabling technologies centered on effective energy management. Lastly, we contribute to the field by outlining the current research challenges and mapping potential research directions relevant to energy management within sustainable smart cities, capitalizing on the capabilities of the IoT.

1. Introduction

With the increasing demand for energy and the imperative to reduce carbon emissions, upgraded energy systems have become indispensable for cities. As urban areas expand and evolve, the demand for energy continues to surge. Implementing smart energy systems and adopting sustainable practices has become crucial for cities as they offer effective means to manage energy usage and mitigate carbon emissions. Cities utilize energy across various sectors, including residential buildings, commercial facilities, transportation, street lighting, and water and wastewater treatment, among others. These systems employ advanced technologies, such as sensors, meters, and data analytics, to optimize energy generation, distribution, and consumption. In today’s world, the Internet plays a pivotal role, facilitating the expansion of the Internet of Things (IoT) through modern wireless communication. In response to the challenges brought about by urbanization and population growth, smart city solutions leverage communication and networking technologies to enhance energy management [1]. Automatic communication is established among objects, systems, and humans, enabling seamless interaction between multiple entities. The fusion of 5G and the IoT is poised to play a transformative role in the energy sector, ushering in a new era of efficiency, connectivity, and sustainability. This dynamic pairing offers a suite of capabilities that promise to revolutionize how energy is generated, distributed, and consumed. With its ultra-low latency and high-speed connectivity, 5G empowers IoT devices to communicate seamlessly and instantaneously, enabling real-time monitoring and control of energy assets. In the energy sector for smart cities, 5G and the upcoming 6G networks play pivotal roles in enabling more efficient and sustainable systems. With 5G, we see the foundation for advanced connectivity, automation, and efficiency, particularly in applications like smart grids, smart buildings, and intelligent transportation systems. These networks allow for real-time data exchange, enabling precise control over energy consumption and the integration of renewable sources. Looking ahead to 6G, we anticipate even greater transformations. This technology has the potential to make smart grids smarter, facilitating better management of renewable energy sources and electric vehicles. Moreover, 6G can enhance smart buildings, allowing for more precise control of energy-consuming systems. In the realm of transportation, it offers opportunities for intelligent traffic management and predictive vehicle maintenance, ultimately reducing energy consumption and emissions. As these networks evolve, they continue to unlock fresh possibilities for a more energy-efficient and connected urban future.

This technology has also permeated the energy sector, promoting sustainable and clean development. The fusion of the IoT and energy has given rise to the concept of the smart grid (SG) or the Internet of Energy (IoE). As a popular technology, IoE integrates various forms of energy and leverages the internet to collect, organize, optimize, and manage energy networks. Therefore, solutions for sustainable smart cities should prioritize energy efficiency from both the citizen and environmental perspectives. The past decade has witnessed a surge in energy demand due to increased consumption in automation, factories, transportation, domestic appliances, healthcare, and other services. Moreover, information and communication technologies (ICTs) have evolved into significant energy consumers due to the generation of countless requests in various formats, including emails, audio–video transmissions, search engine queries, social media interactions, and so forth.

A reliable and sustainable energy supply is a fundamental requirement for the successful establishment of smart cities [2]. While both fossil fuels and renewable energy play crucial roles, environmental considerations take precedence in smart city development, making renewable energy sources the primary and most viable choice for meeting future energy demands [3]. For policymakers and urban planners involved in the development of smart cities, a key priority should be the promotion of abundant renewable energy sources. It is crucial to emphasize that offering a diverse range of energy options for residents is not enough; policymakers and urban planners must also take into account factors such as affordability and accessibility, all within the broader framework of sustainability [4]. To address challenges like traffic congestion in rapidly growing urban areas, a smart approach to public transportation systems, integrating IoT technology, becomes imperative. Moreover, given the critical role of renewable energy sources in global development, especially in the face of climate change, the integration of emerging technologies like artificial intelligence (AI) holds potential for tackling future energy sustainability challenges. To ensure a sustainable energy supply for smart cities, it is imperative to establish an integrated infrastructure that seamlessly integrates emerging technologies like IoT and advanced mobile communication systems.

The energy industry is undergoing significant transformation due to advances in technology, changing consumer preferences, and growing concerns about climate change. As global energy demand continues to increase, the traditional methods of energy production are facing limitations and challenges. In response to the increasing demand for energy and the challenges posed by the energy crisis, countries around the world are adopting new strategies to address their energy needs. One of the key approaches is to shift away from fossil fuels and towards renewable energy sources, such as solar, wind, hydro, geothermal, and biomass. To address these challenges, the energy industry is adopting new technologies such as renewable energy sources, energy storage systems, smart grids, and advanced analytics to improve energy efficiency and reduce carbon emissions [5]. Renewable energies are produced using both conventional large-scale projects and distributed generation technologies [6,7,8]. This approach offers numerous benefits, including reduced transmission losses, increased grid stability, and enhanced energy security. As the use of renewable energy sources and distributed generation technologies becomes more widespread, managing energy networks is becoming increasingly complex. The energy networks now involve a diverse range of stakeholders, including energy producers, consumers, and distribution and transmission systems. To address these challenges, the energy industry is turning to IoT technologies, which are used to monitor and manage energy systems in real time [9,10]. By leveraging the IoT, energy networks are optimized to ensure that energy is distributed efficiently and that energy supply matches demand.

The articles [11,12,13,14] provide insights into the latest research and developments in the energy sector, offering a deeper understanding of this evolving field. The IoE is a concept that involves integrating various energy-related technologies with the IoT, creating a network of interconnected devices that communicate and exchange data [15]. This interconnected system enhances the efficiency and effectiveness of energy systems, spanning from generation to consumption [16,17]. It encompasses technologies like smart grids [18], renewable energy sources, and energy storage systems, enabling the creation of energy-efficient smart homes and buildings that reduce consumption and costs [19]. The IoE facilitates superior monitoring and control of energy systems, optimizing energy use while minimizing waste. Digitalization—the transformation of traditional business processes and services using digital technologies—has made a profound impact across industries worldwide [20]. With the widespread adoption of digital tools like cloud computing, AI [21], big data analytics, and the IoT [22], businesses are becoming more efficient, productive, and competitive. “Energy 4.0” is a broader concept that encompasses the digitalization of the energy sector, integrating Industry 4.0 technologies and principles [23]. This relationship with Industry 4.0 [24] and its potential impact on the energy sector are discussed in detail. The article [23] provides a comprehensive overview of the Energy 4.0 concept, highlighting its transformative potential and showcasing the companies and technologies driving this transformation [25,26,27].

The energy system model, which has been established for decades, continues to evolve and adapt to incorporate emerging concepts and technologies, as highlighted in [28]. This type of model plays a crucial role in the planning, operation, and management of power systems. In a related study [29], the authors introduced a distribution network growth-planning model that considers factors such as investment size, location, and timing, as well as network enhancements. In [30], a simulation of a smart grid utilizing software agents is conducted to capture the dynamic behavior of a smart city, with a focus on electricity. Typically, the modeling of an electric system involves the application of stochastic programming, which aims to minimize an objective function while accounting for specific constraints, as mentioned in [31]. Therefore, the utilization of urban planning models becomes imperative in order to attain sustainable development, as emphasized in [32]. In [33], a model is introduced that evaluates the availability of renewable energy and urban energy supply plants across different locations, aiming to identify the optimal sites and types of generation considering geographical limitations. This approach aids in determining the most suitable locations and types of energy generation installations based on the specific constraints of the area. A limited number of cities take a holistic approach to urban planning that encourages synergy among energy-related initiatives across various levels, as opposed to concentrating solely on either renewable energy or energy efficiency [34]. Likewise, reference [35] explores various techniques for improving energy efficiency in urban planning by incorporating information technology. These approaches highlight the significance of considering multiple aspects of energy management and leveraging technological advancements to optimize energy usage in urban environments. Integrating a smart load node within the framework of a smart grid paradigm introduces an innovative home energy management system capable of effectively controlling and optimizing both smart and non-smart loads for enhanced energy efficiency [36]. Additionally, reference [37] details the use of an artificial neural network in a home energy management system, employing Bluetooth Low Energy (BLE) technology. These advancements in infrastructure technology aim to achieve substantial energy savings across various domains, including infrastructure, transportation, buildings, electricity generation, and distribution, with the objective of reducing greenhouse gas emissions and promoting ecological sustainability. Similarly, in [38], a human-centric smart home energy management system is proposed, leveraging smart grid technologies. An enhanced evolutionary algorithm is employed in [39] to develop an intelligent residential energy management system (IREMS) for residential buildings, aimed at reducing electricity expenses. These studies highlight the growing interest in innovative approaches to energy management, integrating technology and intelligent systems for enhanced energy efficiency and cost reduction. In [40], a practical energy hub management system (EHMS) is introduced, which utilizes a neural network (NN) called the Housing Convenient Demand Profile Estimator (HCDPE) to estimate the demand profiles of residential buildings. This system aims to optimize energy usage and management in housing complexes. Additionally, [41] presents two power management solutions for enabling vehicle-to-grid (V2G) technology in grid-associated microgrids with a supervisory power imbalance. These solutions address the challenges of integrating plug-in electric vehicles (PEVs) into the grid and optimizing their power flow. Furthermore, [42] describes a smart and energy-efficient system for smart grid cyber–physical systems (CPSs), employing coalition-based game theory to achieve optimal energy management and resource allocation. These studies contribute to the development of advanced technologies and strategies for efficient energy management and integration of renewable energy sources in smart grid systems. In [43], a smart-grid power-driven coordinated multi-point (CoMP) transmission scheme is proposed, incorporating active energy management techniques. The study formulates an infinite-horizon optimization problem to achieve optimal downlink transmission that is sufficiently robust to control reservations, aiming to enhance the efficiency and reliability of power delivery in smart grid systems. On the other hand, [44] introduces an intelligent dynamic energy management system (I-DEMS) designed for smart microgrids. This system employs intelligent algorithms and advanced control strategies to optimize energy utilization, storage, and distribution within the microgrid, contributing to improved energy efficiency and reliability. These studies highlight the importance of innovative approaches and intelligent systems in the effective management of energy resources in smart grid and microgrid environments. In [45], a distributed home energy management system with storage (HoMeS) is proposed, which consists of compound microgrids and multiple clients. The system aims to optimize energy management and storage within microgrid networks, enabling efficient energy consumption and improved grid stability. On the other hand, [46] discusses the development of smart grids based on game theory and price elasticity concepts. These approaches leverage economic incentives and behavioral modeling to manage and control energy consumption patterns, allowing for demand response and efficient utilization of energy resources. It immediately becomes evident that a pertinent case study demonstrating the successful application of this approach holds the potential to offer invaluable insights into the tangible advantages and inherent challenges associated with the deployment of a hybrid storage solution integrating supercapacitors (SCs) alongside batteries. This illustration would not only underscore the practical feasibility of such a configuration but also shed light on its real-world implications for enhancing energy storage system efficiency and durability within decentralized DC nano- and microgrids [47]. The issue of remote power delivery would be addressed by focusing on standalone microgrid systems. These systems, designed to provide electricity to inaccessible areas, incorporate a photovoltaic (PV) setup and a wind energy conversion system (WECS) driven by a permanent magnet synchronous generator (PMSG). The study [48] demonstrates the practicality of this approach, highlighting the integration of renewable sources in addressing energy access challenges. Through meticulous design and control strategies, the case study showcases the feasibility of establishing a reliable and sustainable energy solution for remote regions.

A novel approach employing event-triggered distributed hybrid control to ensure an integrated energy system (IES) operates both securely and economically is presented in [49]. Three novel hyperspectral image (HSI) classification methods, outperforming conventional techniques, including graph convolutional networks (GCNs), are proposed to address complex challenges and enhance classification performance. These methods are essential for various applications, such as target detection, environmental management, and mineral mapping, within the field of hyperspectral remote sensing technology [50]. In [51], the authors offer insights into the versatility of graph convolutional networks (GCNs) across multiple domains, with a specific focus on their emerging role in the energy sector. This exploration of recent developments highlights the potential of GCNs to transform computational intelligence, leading to enhanced energy management and efficiency. Furthermore, a hybrid policy-based reinforcement learning (HPRL) adaptive energy management system tailored to achieve optimal performance in an island group energy system facing constraints in energy transmission is discussed in [52]. In addition, the study offers a comprehensive overview of previously published research in the realms of microgrids and associated energy management modeling and solution techniques [53,54]. These studies highlight the integration of advanced technologies and economic principles in the design and operation of smart grid systems for effective energy management. This paper addresses the pivotal role of energy management in establishing sustainable smart cities, optimizing consumption, enhancing energy generation and storage efficiency, and leveraging data-driven strategies to mitigate waste and emissions. It encompasses energy-efficient urban technologies, including harvesting, optimization, scheduling, and routing, culminating in a conceptual framework for energy-driven sustainable smart cities.

1.1. Motivation

The ongoing global transition toward urbanization presents both opportunities and challenges for modern societies. As urban centers continue to expand, the need for sustainable and efficient management of resources, particularly energy, has become paramount. The increasing demand for energy, coupled with the imperative to mitigate environmental impact, has spurred the emergence of smart cities—intelligent urban ecosystems that leverage technology to enhance quality of life while minimizing resource consumption. Central to this endeavor is the integration of the IoT and its derivative, the IoE, which promises to revolutionize energy production, distribution, and consumption. However, while the promise of IoE-enabled sustainable smart cities is captivating, it demands a comprehensive understanding of multifaceted challenges and complexities. This work aims to address this gap by delving into a myriad of energy-related aspects within the context of smart cities. By exploring energy management techniques, smart grid implementations, energy harvesting strategies, and the interdisciplinary nature of sustainable urban living, this study strives to provide insights that will inform the development of resilient, efficient, and environmentally conscious urban environments.

1.2. Background

The evolution of urban landscapes has necessitated a rethinking of traditional energy paradigms. The integration of renewable energy sources, the rise of decentralized energy generation, and the potential of IoT-driven energy optimization have spurred considerable research interest. Smart grids, as integral components of modern urban infrastructure, offer a promising solution to effectively manage energy supply and demand while accommodating renewable sources. Moreover, the advent of energy harvesting technologies presents novel avenues for energy acquisition. By capturing energy from ambient sources, cities can enhance their energy autonomy and reduce dependency on conventional grids. These technologies, when combined with intelligent scheduling and routing mechanisms, have the potential to significantly enhance energy efficiency and reduce the ecological footprint of urban centers. As the demand for sustainable urban living intensifies, this study’s exploration of energy management, smart grids, energy harvesting, and the broader realm of sustainable smart cities contributes to a more holistic understanding of the challenges and opportunities in achieving energy-efficient urban ecosystems. By addressing these complexities, this work aims to lay the foundation for informed decision-making and innovative solutions that will shape the cities of the future.

1.3. Key Contributions

We acknowledge the importance of energy management for IoE-driven sustainable smart cities, and we highlight the following key points:

• The paper presents a classification of energy management approaches for IoE-based sustainable smart cities, including scheduling optimization, low-power device transceivers, cognitive frameworks, and cloud computing technology.
• Highlights the significance of smart grids as a fundamental element in the establishment of smart cities and provides an overview of their essential components, with a particular focus on micro/nanogrids.
• Investigates energy harvesting within smart cities, with a particular emphasis on receiver design, energy optimization, energy sources, energy scheduling, and energy routing.
• Explores sustainable smart cities in various domains, including the development of a new conceptual framework and enabling technologies based on energy management.
• Discusses forthcoming research avenues and identifies unresolved challenges in the context of energy management for sustainable smart cities enabled by the IoT.

The rest of this paper is organized as follows (and as also presented in Figure 1): A global market analysis of the energy sector is discussed in Section 2. The evolution of the energy revolution is presented in Section 3. Section 4 and Section 5 describe recent advancements in the energy sector and the classification of energy management in sustainable smart cities, respectively. Energy harvesting in smart cities is presented in Section 6. Section 7 and Section 8 explain green energy and the conceptual framework, respectively. Enabling technologies in the energy sector are discussed in Section 9. The security concerns related to the IoE in a sustainable smart city are presented in Section 10. Current research challenges and research directions are described in Section 11. Finally, a conclusion is drawn in Section 12.

2. Energy Sector Trends: A Global Analysis

While the world has made tremendous progress in expanding access to power, boosting renewable energy use in the electricity sector, and improving energy efficiency over the last decade, it still falls short of delivering affordable, dependable, sustainable, and modern energy to all. With the world’s urban population expected to reach 68.4 percent of the total population by 2050, action is needed to address not only the increase in fossil fuel consumption in cities but also the excessive load on local power plants due to the imbalance in energy consumption [55]. Buildings and infrastructure, for instance, account for over 40% of worldwide energy use, with the residential sector accounting for about 7%. Therefore, technological ideas that successfully save energy in homes are of considerable interest [56]. Global access to electricity has improved, with an average annual electrification rise of 0.876 percentage points. This has resulted in a rise from 83% in 2010 to 90% in 2019. The worldwide access deficit has decreased from 1.22 billion in 2010 to 759 million in 2019. The energy-as-a-service market, which refers to the provision of energy services and solutions to customers, was valued at approximately USD 65.4 billion in 2021. Experts predict that this market will experience significant growth in the coming years, with a projected market size of USD 157.87 billion by 2030. This translates to a compound annual growth rate (CAGR) of 10.29% from 2022 to 2030. Essentially, the market is expected to more than double in size over the next decade due to increasing demand for energy solutions and services [57]. The global energy management systems market was valued at USD 45.11 billion in 2021, with an expected compound annual growth rate (CAGR) of 14.55% from 2022 to 2030, as illustrated in Figure 2. By 2030, the market is projected to reach approximately USD 153.62 billion. This projection indicates a significant increase in demand for energy management systems in the coming years, as more organizations aim to enhance energy efficiency and reduce their carbon footprint [58]. However, it is worth noting that despite tremendous efforts, an estimated 660 million people may still lack internet access by 2030 [59]. To successfully execute the 2030 Sustainable Development Agenda [60], there is a critical need to establish a robust set of indicators and statistical data. These resources are crucial for monitoring progress, informing policy choices, and strengthening accountability. On 6 July 2017, the United Nations General Assembly formally approved the global indicator framework. This framework was seamlessly integrated into the assembly’s resolution concerning the Statistical Commission’s role in supporting the 2030 Sustainable Development Agenda [61]. In the context of energy supply, transmission, distribution, and demand, this plan becomes particularly valuable [62]. To reshape energy provisioning strategies for larger settlements, it is essential to explore innovative energy systems with new technological options. Effective alternatives encompass photovoltaic (PV)-powered heat pumps for heating, the introduction of bio-methane into grids, passive building designs, and small-scale combined heat and power (CHP) systems with heat storage [63]. Additionally, implementing policies such as the use of electric vehicles (EVs) [64], evaluating common frameworks for the interaction between intelligent transportation and EVs in smart cities [65], adopting a regular and reasonable electricity pricing strategy that contributes to grid security [66], and ensuring adequate investment and government support [67] are crucial steps in advancing sustainable energy practices. These key aspects are essential for the development of intelligent energy systems within smart cities. The effective management of energy in smart urban environments has gained significant prominence in both industry and research circles. To aid governments in transitioning towards smart cities, the International Telecommunication Union (ITU) has partnered with the IEEE to establish a smart cities community. Several major companies, including Honeywell, IBM, Intel, Cisco, and Schneider Electric, are actively engaged in the development of energy-efficient solutions for smart cities. Research funded by the Seventh Framework Programme (FP7) of the European Community.

Commission has focused on energy-efficient smart cities over the last few years. Internet-powered smart devices offer automated and efficient solutions that enhance overall effectiveness. However, the widespread deployment of these numerous small devices constantly sharing data poses significant challenges for the IoT in terms of coverage, security, complexity, costs, and energy consumption. This underscores the need for an approach to conserve energy, ensuring cost-effective and demand-based resource utilization. Furthermore, the IoT has played a substantial role in addressing society’s energy requirements, sustainable development, performance enhancements, demands, and security concerns. Its applications have extended into the industrial sector through Industry 4.0, facilitating productivity improvements, effective management, and the meeting of high-energy demands. Recently, a cloud-based energy management solution for smart buildings was introduced to tackle energy management issues in industrial contexts [68]. Governments and major corporations are actively involved in various projects aimed at advancing energy efficiency and smart city planning. Some notable initiatives include STEEP (Systems Thinking for Efficient Energy Planning) [69], STEP UP (Strategies Towards Energy Performance and Urban Planning) [70], PLEEC (Planning for Energy Efficient Cities) [71], the ZenN (Nearly Zero Energy) project [72], Residential Renovation Towards Nearly Zero Energy Cities (R2CITIES) [73], and Resource Efficient Cities Implementing Advanced Smart City Solutions (READY) [74]. The Smart Cities Mission (SCM), encompassing 100 cities, has a primary goal of equipping urban centers with high-speed internet connectivity, intelligent transportation systems, and sustainable renewable energy solutions [75]. IoT-based smart solutions, such as those offered by ALMANAC (Reliable Smart Secure Internet of Things for Smart Cities) [76], play a pivotal role in transforming a smart city into a more sustainable and environmentally friendly environment. Another initiative, titled “Reliable, Resilient, and Secure IoT for Smart City Applications”, is focused on creating, assessing, and validating a framework for IoT-powered smart city applications. This framework empowers smart devices to operate with greater energy efficiency [77]. Notable projects in the energy sector are detailed in

Table 1. Technologies based on existing literature in the healthcare domain.

Project Name	References	Year	City Involved	Purpose
STEEP	[69]	2013–2015	San Sebastian (Spain), Bristol (UK), and Florence (Italy)	The potential uses of sustainable energy solutions such as PVs, cogeneration, geothermal heat pumps, thermal energy storage, waste heat recovery, and smart meters were explored.
STEP UP	[70]	2012–2015	Ghent (Belgium), Glasgow (Scotland), Gothenburg (Sweden), and Riga (Latvia)	The project focused on using combined heat and power (CHP) and renewable energy sources such as biomass, geothermal, and solar thermal.
PLEEC	[71]	2014–2016	(Sweden), Turku (Finland), Santiago de Compostela (Spain), Jyvaskyla (Finland), Tartu (Estonia), and Stoke-on-Trent (Estonia).	Technologies utilized included water management, electricity grids, heating/cooling grids, and renewable energy sources.
ZenN	[72]	2013–2017	Oslo (Norway), Malmo (Sweden), Eibar (Spain), and Grenoble (France)	The project aimed to lower energy consumption in buildings and neighborhoods, utilizing PV and solar thermal panels, heat pumps, and cogeneration.
R2CITIES	[73]	2013–2018	Valladolid (Spain), Genoa (Italy), and Istanbul (Turkey)	The project employed PV and solar thermal energy, storage, and distribution systems to attain near-zero-energy cities.
READY	[74]	2014–2019	Aarhus (Denmark) and Vaxjo (Sweden)	The project created smart city electric grid systems and mobility solutions, utilizing PV, batteries, district heating systems, and intelligent control.
SCM	[75]	2015–ongoing	100 cities in India	The main aim is to equip cities with high-speed internet connectivity, smart transportation systems, and renewable energy solutions.

.

3. The Evolution of the Energy Revolution

The evolution of energy refers to the way in which energy sources have changed and developed over time [11]. Throughout human history, societies have relied on different forms of energy to power their homes, businesses, and technologies. In the early days, humans relied on their own and animal muscle power for most tasks. Later, they discovered how to harness the power of wind and water to power mills and other machines. The Industrial Revolution saw the rise of coal as the primary source of energy for industry, and later, oil became the dominant fuel for transportation and energy production [14,15]. In recent years, there has been a shift towards renewable energy sources, such as solar, wind, and hydropower, due to concerns about climate change and the environmental impact of fossil fuels. This shift has led to the development of new technologies to harness these forms of energy, such as solar panels and wind turbines, and a growing interest in energy storage solutions to increase the reliability of renewable energy. Overall, the evolution of energy has been driven by a combination of technological innovation, economic forces, and environmental concerns, and is likely to continue evolving as new forms of energy are discovered and developed. Figure 3 represents the evolution of energy systems and shows how each energy stage has built upon the previous one, with a focus on increasing sustainability, efficiency, and intelligence. It highlights the current focus on integrating advanced digital technologies into energy systems and the future vision of fully decentralized, integrated, and intelligent energy systems. The evolution of energy systems is divided into five stages:

3.1. Level 1 of the Energy Revolution

This stage encompasses the ancient practice of utilizing biomass, like wood and animal residue, to meet cooking and heating needs. This age-old energy paradigm sustained human societies for millennia until the advent of the Industrial Revolution. Despite its historical significance, it had limitations in terms of efficiency and environmental impact. The transition from this primal energy utilization to more advanced methods marked a pivotal juncture in human progress, highlighting the imperative for sustainable and innovative energy solutions to power our future.

3.2. Level 2 of the Energy Revolution

This stage began with a transformative phase, initiated by the exploration and utilization of fossil fuels—coal, oil, and natural gas. These resources played a transformative role, enabling unprecedented progress in the transportation and manufacturing sectors, which in turn propelled the rapid expansion of the global economy. The abundant and concentrated energy within these fuels revolutionized industries, driving technological innovation and urbanization on an unprecedented scale. However, this phase also brought forth environmental and sustainability challenges, exemplified by rising greenhouse gas emissions and ecological concerns.

3.3. Level 3 of the Energy Revolution

This stage involves the integration of renewable energy sources, such as wind and solar, into the energy mix. This shift is driven by concerns over climate change and the need for more sustainable energy sources. Renewable energy sources are becoming more competitive with fossil fuels, and technological advancements are making them more efficient and reliable. Innovations in energy storage, grid integration, and predictive analytics are bolstering their capacity to meet growing demands while minimizing environmental impact. As renewable energy gains traction, its synergy with advancing technology holds the promise of reshaping the global energy landscape, accelerating the transition to a sustainable and low-carbon future.

3.4. Level 4 of the Energy Revolution

The integration of advanced digital technologies into energy systems has the potential to significantly improve efficiency, reliability, and sustainability. This includes the deployment of smart grids, which use sensors and data analytics to optimize energy flows and improve grid resilience, and energy storage systems, which use batteries, hydrogen fuel cells, or other technologies to store excess energy for later use. The use of AI and machine learning also helps optimize energy use and reduce waste. These technologies empower systems to intelligently analyze vast datasets in real time, enabling predictive insights that enhance operational efficiency. From smart grids to industrial processes, AI-driven algorithms adapt to changing conditions, enabling precise control and responsive energy usage.

3.5. Level 5 of the Energy Revolution

The future vision for energy systems is a fully decentralized, integrated, and intelligent system where energy is generated and consumed locally, using a mix of renewable energy sources and advanced digital technologies. This stage envisions a system where buildings, vehicles, and other infrastructure are designed to generate and store their own energy, and where energy flows are optimized through intelligent systems that respond to real-time data.

Table 2. Key features and the development of each energy stage.

Stages of Energy Revolution	Key Features	Advantages	Limitations	Examples
Level 1	Traditional use of biomass for cooking and heating	− Easily accessible − Low-cost fuel source − Supports local economies	Reliance on biomass led to deforestation and air pollution.	Wood and animal waste
Level 2	Exploitation of fossil fuels for transportation and manufacturing	− High energy density − Established infrastructure − Global availability	Fossil fuels caused pollution, emissions, and resource depletion.	Coal, oil, and natural gas
Level 3	Integration of renewable energy sources for more sustainable energy systems	− Reduced environmental impact − Abundant and inexhaustible sources − Mitigates climate change	Nuclear energy posed safety risks, high costs, and waste disposal challenges.	Wind, solar, and hydroelectric
Level 4	Integration of advanced digital technologies for more efficient, reliable, and sustainable energy systems	− Enhanced grid resilience − Improved energy storage and management − Real-time monitoring and optimization	Centralized grids were vulnerable; renewables faced high costs and intermittency.	Smart grids, energy storage, and artificial intelligence
Level 5	Fully decentralized, integrated, and intelligent energy systems that optimize energy flows and reduce waste	− Optimal energy utilization − Reduced transmission losses − Improved energy security	Decentralized systems require complex integration, technological management, and evolving regulations.	Local renewable energy generation, intelligent infrastructure, and real-time data optimization

provides a quick overview of the key features and developments of each energy stage, highlighting the shift towards more sustainable, efficient, and intelligent energy systems over time. Overall, the evolution of energy systems reflects a shift towards more sustainable, efficient, and intelligent systems that meet the needs of society while minimizing the environmental impact.

4. Recent Advances in the Energy Sector

4.1. The Internet of Energy

The term ‘Internet of Energy’ (IoE) draws its inspiration from the IoT, which encompasses interconnected devices, extensive data analytics, and various communication modes such as people-to-people (P2P), machine-to-people (M2P), and machine-to-machine (M2M). Building upon the IoT concept, IoE is designed to provide critical information for the control and optimization of the power grid, with the ultimate goal of enhancing its self-sufficiency. Cisco’s assessments of value considerations have explored various public sector applications [78], as depicted in Figure 4. The IoE has introduced the possibility of low-energy consumption combined with increased internet connectivity, enabling minimized energy usage through smart, convenient, and controlled practices. Leveraging IoT technology, real-time applications in the energy sector now operate with minimal input and effort, facilitating large-scale digital transformation. This technology is particularly valuable for industrial processes that necessitate connectivity, safety, flexibility, accuracy, reliability, automation, and low latency.

However, without important updates, the information cannot be transferred efficiently, leading to energy wastage along the line. Incorporating IoE technology into the process facilitates the installation of smart grid technology in an efficient manner [79]. A sustainable smart city is an interconnected system of devices that enhances the convenience and security of its residents, ultimately leading to an improved quality of life. Energy management is a pivotal component of smart cities, with ongoing advancements aimed at ensuring efficient energy use, a fundamental requirement for sustainable urban living. Electric vehicles are recognized as an important component in addressing climate change as they contribute to the reduction in carbon emissions. Smart meters, used for monitoring and managing energy consumption, are already familiar to a large number of consumers worldwide.

4.2. Energy 4.0/5.0

Energy 4.0/5.0, builds on the previous three industrial revolutions, with Industry 4.0 being the most recent, which focused on the automation and digitalization of manufacturing processes. The emergence of Energy 4.0 is a result of advanced technologies and the integration of the IoT into various industries. Meanwhile, hardware/software developers in the energy industry have been gaining experience in developing and incorporating industry-driven solutions with a focus on internal dependability and environmental safety. Digitalization provides an opportunity for Energy 4.0 companies to develop new business models and sustainable solutions for energy production and supply. With falling costs and exponential technological growth, they can capitalize on this opportunity. Consequently, there is increasing demand from consumers and organizations to address both the quantity and nature of energy consumption in all sectors and to adopt new and innovative solutions. While wind and solar, as new electricity sources, have demonstrated their maturity and reliability, there are still challenges to overcome. Therefore, the next step is for industry stakeholders to optimize these new forms of generation to align with consumer consumption patterns. According to [24], Energy 4.0 can be seen as a parallel progression to the Industrial Revolution, signifying a significant shift in the energy sector. As a result, the possibility of significant reductions in electricity consumption and greenhouse gas emissions has emerged. While there are concerns regarding security and legal aspects, remarkable examples of Energy 4.0 implementation have already been witnessed worldwide, highlighting the benefits of this substantial digitalization movement [25,26].

Energy 5.0 refers to the latest and most advanced technologies and strategies being implemented in the energy sector to increase efficiency, reduce costs, and minimize environmental impact. It incorporates many of the same principles and emerging technologies to optimize the production, distribution, and consumption of energy. The goal is to create a more sustainable and efficient energy system that meets the growing demand for energy while reducing greenhouse gas emissions and environmental impact. This includes the integration of renewable energy sources, such as solar and wind power, into the grid, the development of smart grids and energy storage systems, and the use of energy-efficient technologies in buildings, transportation, and industry. Overall, Energy 4.0/5.0 represents a major shift in the energy sector, with the potential to transform the way we generate, distribute, and consume energy, paving the way towards a more sustainable future.

4.3. Smart Grid

Smart grids, representing advanced iterations of traditional energy systems, constitute vital components of sustainable smart city planning [80], as illustrated in Figure 5. Through the integration of smart grids with buildings to optimize energy usage and generation, the next generation of intelligent energy systems and grids will facilitate efficient energy management within structures [81]. In practice, smart grids offer novel services to smart home residents by harnessing existing resources [82]. The deployment of smart meters empowers users with insights into and contributions to reducing carbon emissions, aiding them in making informed choices [83]. Furthermore, the management of peak demand empowers consumers to oversee energy-hungry appliances such as air conditioners, electric water heaters, pool pumps, and clothes dryers, allowing for more efficient control. With the increasing adoption of EVs, smart grids have the capability to identify and incorporate energy generated or stored by consumers. The smart grid is widely recognized as one of the most critical applications of the IoT. It depends heavily on the transmission, sensing, and analysis of data.

IoT technologies are integrated into electricity grids in various ways, enabling control, data collection, monitoring, and infrastructure development. Smart grids are designed to interact with various interconnected devices that can provide extensive data on the network’s status [84]. A cloud-based system efficiently handles the computation, real-time processing, and optimization of large data volumes in a cost-effective manner. The remaining requirements are fulfilled by utilizing IoT technologies [85,86] and edge resources while incorporating the cloud into a standardized architecture. The introduction of the smart grid has brought forth two new concepts in the electric system: microgrids and nanogrids. A microgrid is a system designed to power a small group of consumers (such as residential complexes, hospitals, data centers, and schools) using renewable energy sources like solar photovoltaics (PVs), wind power, micro-turbines, and fuel cells [87]. A microgrid is essentially a compact, self-contained power system that links local loads with distributed generation, typically having a power capacity ranging from 10 to 100 kW [88].

However, nanogrids are specifically designed for residential or commercial buildings, with a power output of up to 25 kW. Producing and distributing energy through intelligent energy systems helps to address major issues, such as energy losses in long-distance transportation [89], and their associated costs. Smart load and building automation play a crucial role in achieving this by improving energy efficiency, safety, and comfort on both residential and industrial scales [90,91,92]. In [93], the focus is on emerging urban difficulties and the study emphasizes the importance of developing intelligent grids.

4.4. Smart Grid to the IoE for Sustainable Smart Cities

The distinction between a smart grid and the IoE lies in their scope and functionality. A smart grid primarily concentrates on enhancing the electrical grid’s efficiency and reliability by incorporating advanced technologies and two-way communication systems. It serves as the backbone for optimizing energy distribution and facilitating the integration of renewable energy sources. On the other hand, the IoE takes a more comprehensive approach. It extends beyond the electrical grid, encompassing a broader spectrum of energy-related assets, including distributed energy resources, energy storage systems, and electric vehicle infrastructure. The IoE leverages data-driven optimization and interconnectedness to orchestrate these diverse components, enabling real-time monitoring and control to achieve the overarching goal of sustainable energy management in smart cities. Essentially, the IoE builds upon the foundation laid by smart grids, elevating the city’s capacity for holistic energy management and environmental sustainability to new heights.

The transition from a conventional smart grid to an IoE framework represents a transformative step toward achieving sustainable smart cities [94]. This evolution is rooted in the utilization of advanced digital technologies and interconnected systems to create a more integrated, efficient, and adaptable energy ecosystem. At the core of the IoE’s contribution to sustainability is its capability to seamlessly integrate an array of diverse energy resources. Moreover, the IoE encourages the decentralization of energy generation through microgrids and local energy communities [95]. These smaller-scale, localized energy networks enable communities to produce, store, and manage their energy independently. This not only enhances energy self-sufficiency but also fosters a sense of community engagement and responsibility for sustainable energy practices. Predictive maintenance and asset management are also integral components of the IoE’s sustainability contributions. By employing IoT sensors and data analytics, cities can monitor the condition of the energy infrastructure in real time [96]. This proactive approach to maintenance minimizes downtime, extends the lifespan of equipment, and reduces the consumption of resources needed for repairs and replacements. Incorporating electric vehicles (EVs) into the IoE further amplifies sustainability efforts. EVs can serve as mobile energy storage units, capable of contributing energy back to the grid during peak demand or drawing energy during off-peak hours [97]. This bidirectional energy flow enhances grid stability, optimizes renewable energy utilization, and positions EVs as essential components of a sustainable urban transportation system. Moreover, the IoT strengthens grid security by providing continuous monitoring for cybersecurity threats. Any anomalies can trigger immediate responses, safeguarding critical energy infrastructure. Ultimately, the transition from a smart grid to an IoE represents a comprehensive strategy to address sustainability challenges in smart cities. Through seamless integration, real-time analytics, demand response, decentralization, and holistic energy management, the IoE empowers cities to proactively shape their energy landscape and minimize environmental impact. This integration not only reduces carbon footprints but also sets the stage for greener, more sustainable urban living, creating a future where cities are resource-efficient and environmentally conscious.

5. Energy Management in Sustainable Smart Cities

There is a multitude of energy-saving technologies at our disposal, capable of efficiently cutting down on energy usage while maximizing the effective use of resources. Prominent research directions in the realm of energy-efficient IoE-driven smart cities include fine-tuning scheduling processes, integrating energy-efficient low-power transceivers, developing cognitive frameworks, and leveraging cloud computing technologies. These elements collectively form a crucial foundation for achieving energy efficiency in smart city environments [98,99,100,101], as depicted in Figure 6.

5.1. Scheduling Optimization

In a smart city that employs IoE-enabled scheduling optimization, the primary objective is to curtail overall energy consumption and electricity usage by efficiently managing the available resources. It is worth emphasizing that demand-side management (DSM) is a critical aspect of this process. DSM involves the control of residential electricity consumption to lower expenses by altering the system’s load profile. DSM encompasses two key elements: load shifting and energy conservation. Load shifting involves the practice of moving a customer’s electricity usage from peak-demand periods to off-peak hours. On the other hand, energy conservation focuses on reducing overall energy consumption through the adoption of efficient technologies, the promotion of energy-saving behaviors, and the encouragement of changes in people’s habits. When combined, these components play a pivotal role in achieving enhanced energy efficiency and the sustainable management of resources.

5.2. Low-Power Device Transceivers

Even though IoT devices in smart city applications often face power limitations, it is imperative to establish a low-power conceptual framework for effectively controlling energy consumption in IoE-driven smart cities. Many of the existing application protocols designed for IoT devices do not prioritize energy efficiency. In this context, the radio duty cycle emerges as a pivotal factor influencing the energy efficiency of IoT devices, prompting researchers to explore methods for minimizing radio duty cycles in the development of more energy-efficient IoT devices.

5.3. Cognitive Framework

Since IoT devices come in various forms and offer diverse services, predicting their behavior can be challenging. Consequently, it is of utmost importance for smart cities to integrate intelligence and cognitive methods into the entire IoT architecture. Ensuring that the framework possesses reasoning and learning capabilities is crucial for improving decision-making within the IoE network. In [98], a depiction is given of a cognitive management architecture that made decisions by considering the contextual information related to IoT devices.

5.4. Cloud Computing Technology

The emergence of cloud computing and storage has opened the door for IoT-enabled smart cities to offer energy-efficient solutions. To be more precise, adopting a cloud-based approach can enhance the effectiveness of data centers significantly, thanks to the substantial flexibility it provides.

6. IoT-Enabled Energy Harvesting in Smart Cities

Energy harvesting plays a pivotal role in driving energy efficiency and sustainability in smart cities. It involves capturing and converting ambient energy sources into usable electrical power to fuel IoT devices and sensors, reducing the reliance on conventional power sources. For instance, kinetic energy harvesting can power sensors embedded in roads, sidewalks, and public transport systems. These sensors collect data on traffic flow, pedestrian movement, and road conditions, enabling real-time traffic management, optimized public transportation, and reduced congestion. Solar energy harvesting, a well-known technique, is widely used to power streetlights, surveillance cameras, and environmental monitoring devices. These solar-powered assets enhance public safety and contribute to lower energy consumption and reduced carbon emissions. Another example is piezoelectric energy harvesting in public spaces. It can harness the vibrations caused by foot traffic or vehicular movement to generate electricity. Utilizing this harvested energy to charge smartphones at public charging stations not only alleviates the strain on the electrical grid but also delivers multiple tangible advantages for creating sustainable urban environments. Nevertheless, this approach necessitates a substantial deployment of sensors to ensure the precise collection of data across an extensive service area. Addressing the task of replacing the batteries in millions of devices presents a formidable challenge. To tackle this issue, the concept of the Internet of Energy Harvesting Things (IoEHT) has emerged as a potential solution [102,103]. Given these formidable challenges, IoT devices within smart cities must confront several hurdles, which are delineated below.

6.1. Receiver Design

The primary hurdle in radio-frequency-based energy harvesting centers around the development of energy harvesting circuits. These circuits are more sensitive compared to typical receivers, making them susceptible to fluctuations in energy transmission due to environmental conditions and mobile factors. Achieving optimal energy harvesting hinges on the creation of a dependable and effective circuit design. Consequently, circuit designers must integrate cutting-edge technologies to improve the efficiency of converting RF signals into direct current (DC) power.

6.2. Energy Optimization

The degree of unpredictability linked to energy production from ambient sources is greater when contrasted with the predictability associated with concentrated energy generation. This discrepancy arises from the fact that ambient sources rely on renewable energy, whereas focused energy generation employs designated sources with predefined positions tailored to the energy needs of IoT devices. To effectively assess the performance of energy harvesting systems in smart cities, it is imperative to create precise and comprehensive models of the energy arrival rate.

6.3. Energy Sources

IoT devices positioned at a considerable distance from energy sources can encounter difficulties when it comes to effectively gathering energy. As a result, devices located far from dedicated power sources may confront energy depletion issues, potentially shortening the network’s lifespan. While we cannot modify ambient energy sources, it is crucial to carefully plan the placement and quantity of dedicated energy sources when dealing with dedicated energy harvesting to mitigate these challenges.

6.4. Energy Scheduling

Utilizing assignment-based energy harvesting, where devices are scheduled to receive RF (radio frequency) power based on their energy requirements, can lead to reduced energy consumption. However, for this strategy to work effectively, it is crucial to maintain a certain level of coverage and provide adequate harvesting time. Consequently, energy transmitters need to be scheduled in a way that ensures a guaranteed coverage area and duration, promoting dedicated energy harvesting and enhancing overall energy efficiency.

6.5. Energy Routing

The multiple-path energy routing approach consists of gathering RF energy from various sources with the help of RF energy routers. This enables the distribution of energy to IoT devices through multiple routes. In energy routing, relay nodes are strategically positioned in proximity to IoT devices as part of the multi-hop energy transmission concept. Placing relay nodes near IoT devices reduces signal loss along the path, resulting in more efficient RF-to-DC (direct current) conversion.

In general, energy harvesting technologies, when integrated with the IoE, contribute significantly to sustainability in smart cities. They reduce energy costs, decrease the carbon footprint, and enhance the overall quality of urban life by enabling smart applications that improve traffic management, public safety, resource allocation, and environmental stewardship.

7. Green Energy Solutions

Green energy plays a vital role in environmental preservation by providing preferable alternatives to the detrimental impacts of fossil fuels. Green energy utilizes natural resources, is typically clean, renewable, and produces minimal to no greenhouse gas emissions. This reduced emission of greenhouse gases is better for the environment and the well-being of people and animals who breathe the air. Additionally, green energy sources benefit from a diversified and decentralized production model. Unlike centralized fossil fuel power plants that rely on a few large sources, renewable energy systems can be distributed across multiple locations. Green energy is also a cost-effective option to meet global energy demands.

In the future, green energy has the potential to displace fossil fuels, but this may require the use of a variety of production techniques. For instance, geothermal energy performs best in areas where this resource is easily accessible, while wind or solar energy may be more appropriate in other regions. Green energy is poised to play a significant role in shaping the future of global energy sources. It offers a superior alternative to many existing energy sources for several reasons. The increasing adoption of green energy sources offers a promising future for sustainable energy production, environmental protection, job creation, and economic viability. The fact remains that fossil fuels must be gradually replaced because they are unable to meet our long-term energy needs in an environmentally sustainable manner. It is feasible to create a completely sustainable future for our energy requirements without causing harm to the environment by developing a range of eco-friendly energy alternatives [104].

8. Proposed Quantum IoT Architecture for Smart Cities

A new conceptual framework for an energy-based quantum IoT network architecture is proposed as shown in Figure 7, consisting of four layers, each with distinct roles within the network. These layers play vital roles in enabling sustainable smart city operations, as described below.

8.1. Physical Layer

The quantum network layer encompasses all quantum hardware, optical fibers, and IoT devices within the network. This layer retains the existing structure of the IoT network layer. Devices transmit regular bits to the gateway in the usual manner, and then the gateway forwards these bits to the quantum server. At the quantum network layer, these bits are further converted into qubits. The timing and synchronization within the quantum network layer are managed by the physical layer hardware.

Sensors: Sensors are commonly acknowledged as a crucial element in IoT technologies due to their capacity to gather and transmit real-time data [105,106]. In the realm of IoT, energy-related sensors are indispensable and serve a dual purpose: they help manage costs and enhance energy conservation efforts. Temperature sensors play a significant role in influencing both energy generation and consumption by detecting and monitoring temperature. Humidity sensors are extensively used in the energy industry, including wind energy production, to gauge the moisture levels and relative humidity of the environment. Similarly, light sensors are used in both residential and commercial appliances to detect ambient light levels. They enable the automatic control of lighting systems to conserve energy [107]. Passive infrared sensors, often known as motion sensors, monitor object movement by sensing the infrared light radiation emitted within a specific environment. In the energy sector, they have proven essential in reducing building energy consumption.

Actuators: Actuators can be distinguished from sensors in their function as they receive electrical inputs and transform them into specific types of motion. In the energy sector, various types of actuators are utilized for different purposes. For instance, pneumatic actuators are commonly used as the ultimate control components in power plant operations. Furthermore, actuators play a role in energy conservation by optimizing operations such as opening portals, tightening wind turbine brakes, and controlling movements in solar tracking panels. For instance, [108] demonstrates the advantages of utilizing remote sensors and actuators in IoT-based autonomous intelligent processes. The suggested solution effectively reduces energy consumption in devices operating within IoT systems.

8.2. Quantum Network Layer

The quantum network layer plays a crucial role in managing the transmission of both quantum and traditional data across the internet and local networks. This layer includes a specialized quantum device responsible for overseeing all activities related to quantum data and converting between regular bits and quantum bits. The existing hardware in this layer functions like a gateway, similar to a router, enabling the two-way transmission of regular data between the physical layer and the quantum server in the quantum network layer. Wireless communication technology is a vital component of the IoT operational mechanism. It allows IoT devices to communicate end-to-end by connecting sensor devices to IoT gateways. The evolution of wireless frameworks is influenced by various wireless standards. For example, many renewable energy sources, like wind and solar power plants, are often situated in extremely remote areas. As a result, it is quite difficult to ensure reliable IoT communication in those places. The deployment of IoT frameworks in these locations requires the selection of appropriate communication technologies that ensure a reliable and uninterrupted link for efficient real-time data transfer. Wi-Fi, Bluetooth, Zigbee, Long Range (LoRa), Sigfox, LTE-M, and BLE are among the various communication technologies available. In the near future, LPWAN technologies are anticipated to provide energy-efficient alternatives when compared to Wi-Fi technologies. In [109], it is shown how these emerging LPWAN technologies make it possible to create dependable, uncomplicated, low-power, and highly efficient solutions for managing energy over the long term. Compared to alternative technologies, LPWAN technologies consume less energy and entail lower installation and operating expenses [110]. Another important technology is Zigbee, which is utilized for establishing secure network connectivity. It shares similarities with BLE in terms of being simple to install, inexpensive, sharing data at a low rate, and providing reliable networks for low-power devices [111]. Zigbee finds its primary application in IoT devices for various purposes, including lighting systems, smart grids, home automation systems, and industrial automation. On the other hand, when it comes to long-range communication technologies, options such as long-term evolution for machine-type communications (LTE-M), Sigfox, Narrowband IoT, and LoRa are commonly used. LoRa is a communication technology used in IoT applications, offering low-power and affordable connectivity solutions with a range that can extend up to approximately 50 km [112]. Numerous research studies, including those referenced as [113,114,115], have explored how these technologies can be applied in smart homes and industrial HVAC systems. When comparing Sigfox and LoRa, they exhibit similarities in aspects like energy usage, installation expenses, and coverage range. Nevertheless, it is worth noting that Sigfox has significantly slower data transfer rates compared to LoRa. On the other hand, NB-IoT stands out as a cost-effective choice with extended battery life and the capability to update its battery. Satellite communication technology has innovated to support low data rate applications in machine-to-machine (M2M) communication [116]. Satellite technology is suitable for providing backup connectivity to IoT devices in remote locations. Recent research has introduced a method for incorporating satellite communication technology into a smart grid, as well as solar and wind turbine power generation systems [117,118].

8.3. Quantum Teleportation Layer

The quantum transportation layer relies on quantum repeaters as its core elements, tasked with preserving qubit states and facilitating their passage to the next stage. Quantum repeaters serve as crucial tools to counteract qubit decoherence and the loss of entanglement during their transmission across extended distances. By generating entanglement between local qubits, storing them in quantum memory, and swapping entanglement with remote qubits using transmitted classical information, these repeaters extend the reach of entanglement while mitigating the effects of environmental interference. This vital function ensures the viability of secure and efficient quantum communication over substantial network spans, marking a key advancement in quantum information science and technology.

8.4. Application Layer

The application layer acts as the intermediary between the client and the network, establishing the client’s connection. Within this layer, qubits play a pivotal role in enabling bidirectional communication, allowing clients to transmit and receive commands to and from the subsequent layer. This interface empowers clients to harness the capabilities of the network, both classical and quantum, for diverse applications. By serving as a conduit for instructions and data exchange, the application layer bridges the gap between users and the intricate quantum architecture, facilitating the seamless integration of quantum technology into real-world applications.

9. Enabling Technologies for the Energy Sector

A significant amount of energy is wasted during consumption, and the adoption of efficient energy monitoring and smart energy solutions helps to reduce energy demand. Smart energy systems not only decrease energy demand but also enhance energy efficiency. Therefore, it is crucial to implement smart solutions that utilize emerging technologies to reduce energy demand and improve energy management systems. Essentially, smart energy solutions are necessary to decrease energy consumption and increase energy efficiency. Research studies have indicated that disruptive technologies play a crucial role in advancing efficient energy management systems. Among these technologies, the IoT has demonstrated significant potential in facilitating the development of present and future energy management systems [119]. It monitors energy performance and real-time energy consumption at any point in the supply chain, thereby enhancing awareness about energy usage. In simpler terms, the IoT is a vital tool in developing an efficient energy management system by constantly monitoring and providing insights into energy consumption patterns. Emerging technologies such as the IoT, AI, big data, computing technologies, and blockchain are seen as major enablers in the energy sector, as shown in Figure 8.

9.1. The 6G Internet of Things

The IoT is a rapidly emerging technology that leverages the power of the internet to connect physical devices or ‘things’ together [120]. By utilizing sensors and communication networks, these devices provide valuable information and services to users. For instance, the IoT is used to intelligently regulate and manage the energy consumption of buildings, leading to a significant reduction in energy costs [121]. In simpler terms, the IoT is a technology that connects devices and allows them to communicate with each other, enabling smarter and more efficient energy management systems. The combination of the IoT and 5G networks has already started transforming the energy sector by enabling new levels of connectivity, automation, and efficiency [122]. However, as we move towards the future, the next generation of wireless communication technology, 6G, is expected to bring about even greater changes and opportunities in the energy sector [123]. One of the most significant areas where the 6G-IoT could have a major impact on the energy sector is in the development of smart grids. Smart grids are electricity networks that utilize advanced technologies, such as sensors, communication networks, and data analytics, to more efficiently manage the generation, distribution, and consumption of energy. With 6G-IoT, smart grids could become even more intelligent and responsive, enabling better management of renewable energy sources, more precise load balancing, and greater integration of electric vehicles and energy storage systems.

Another area where the 6G-IoT could make a significant difference is in the development of smart buildings. Smart buildings employ IoT sensors and automation systems to optimize energy use, reduce waste, and improve occupant comfort. With the 6G-IoT, smart buildings could become even more sophisticated, allowing for more precise control over heating, cooling, lighting, and other energy-consuming systems. Additionally, the 6G-IoT could play a role in the development of energy-efficient industrial processes. By utilizing sensors, data analytics, and automation, industries could optimize energy use, reduce waste, and improve productivity. Furthermore, the 6G-IoT could enable more efficient and reliable monitoring of critical infrastructure, such as pipelines and power grids, reducing the risk of failures and enhancing safety.

The Internet of Vehicles (IoVs) is another rapidly growing area of technology in the energy sector that involves the integration of vehicles with IoT devices and networks, enabling intelligent and connected transportation systems [124]. With the emergence of 6G networks, there are several energy-efficient applications that could be developed for the IoV. One such application is intelligent traffic management [125]. By utilizing sensors and data analytics, real-time identification of traffic patterns and congestion allows for more efficient traffic flow and reduced energy consumption. This can be achieved through the use of 6G-connected traffic lights, which adjust their timings based on the number of vehicles on the road and overall traffic flow. Another potential application is predictive maintenance for vehicles. By utilizing IoT sensors to monitor the health of vehicles, maintenance needs can be predicted before they become a problem, reducing the likelihood of breakdowns and improving vehicle efficiency. This includes monitoring the condition of the engine, brakes, and other critical components, as well as analyzing driving patterns to identify potential issues.

The 6G-IoT also enables more efficient route planning for vehicles. By using real-time traffic data, weather information, and other factors, vehicles can be routed on the most energy-efficient routes, reducing fuel consumption and emissions. These benefits are particularly useful for fleet management and logistics companies, where route optimization has a significant impact on energy costs and environmental footprint. Finally, the 6G-IoT could be utilized to enable more efficient and safe autonomous driving. By incorporating advanced sensors and communication technologies, vehicles can communicate with each other and with surrounding infrastructure, facilitating faster and more efficient decision-making [126]. These capabilities include automated lane changes, collision avoidance, and predictive braking, all of which enhance safety and energy efficiency on the road. In summary, the 6G-IoT has significant potential to bring about transformative changes and opportunities in the energy sector [127]. As the technology continues to evolve, we anticipate the emergence of new applications and use cases that will create fresh prospects for businesses, consumers, and governments alike.

9.2. Artificial Intelligence/Machine Learning

Artificial intelligence is increasingly being utilized in the energy sector to optimize energy systems, reduce costs, and improve efficiency [128], particularly in the context of sustainable smart cities. AI algorithms analyze data from sensors in energy infrastructure to predict maintenance needs before failures occur, thereby reducing downtime and maintenance costs [129]. AI is also used to analyze historical energy usage data and forecast future energy demand, enabling energy companies to optimize energy generation and distribution to meet demand. AI technologies aid energy companies in managing renewable energy sources [130], such as wind and solar, by predicting fluctuations in energy production and adjusting energy storage and distribution accordingly. In addition, AI collects data from energy systems to identify inefficiencies and suggest improvements, such as adjusting temperature set points or optimizing lighting schedules, to reduce energy consumption. Furthermore, AI can analyze market data and weather forecasts to predict energy prices and optimize energy trading strategies. Overall, AI has the potential to revolutionize the energy sector by enhancing efficiency, reducing costs, and promoting the use of renewable energy sources.

The integration of AI and ML with the IoE offers powerful capabilities for optimizing energy management in smart cities [131]. This combination enhances the efficiency, responsiveness, and sustainability of energy systems by leveraging data-driven insights and automation. By harnessing the power of data-driven insights and automation, this convergence offers a comprehensive and dynamic solution for optimizing energy utilization in urban environments. At its core, AI/ML with IoE revolutionizes energy management by collecting and analyzing real-time data from a myriad of interconnected devices, ranging from smart meters and sensors to renewable energy sources and electric vehicles. Through advanced algorithms, ML processes this influx of data to discern intricate patterns, anomalies, and correlations that enable informed decision-making [132]. Furthermore, AI/ML plays a pivotal role in orchestrating optimized energy distribution, seamlessly integrating factors such as real-time demand, the availability of renewable sources, and grid stability [133]. This orchestration leads to efficient energy routing and consumption, reducing wastage and bolstering sustainability efforts. In the realm of demand response, AI/ML drives automation by forecasting demand peaks and orchestrating intelligent load-shedding or load-shifting strategies. Through seamless communication with IoE-connected appliances and devices, this automated response ensures an equilibrium between energy supply and consumption. Additionally, AI/ML augments the management of energy storage systems by dynamically optimizing charging and discharging cycles. By factoring in variables such as electricity prices and grid conditions, these algorithms enhance the efficiency of energy storage utilization. The implications of AI/ML and the IoE reach beyond immediate energy management benefits [134]. They contribute to informed urban planning by providing insights into energy consumption patterns, which in turn influence decisions about infrastructure development and city layouts. In essence, the integration of AI/ML with the IoE in smart cities enhances energy management by providing real-time insights, automation, and optimization. This synergy empowers cities to make informed decisions, reduce energy wastage, increase efficiency, and advance their sustainability goals.

9.3. Cloud/Fog/Edge Computing

The energy sector can leverage the vast amounts of data generated to improve energy efficiency, reduce consumption, and drive development. However, these data are often collected from multiple sources and consist of large volumes of raw data, making it essential to use sophisticated computing techniques to sort and classify relevant and irrelevant data sets from big data [135]. Cloud and fog computing are two significant computing mechanisms available to process and handle big data. In simpler terms, to make the most of the data generated in the energy sector, it is important to use advanced computing techniques like cloud and fog computing to effectively process and analyze large volumes of data and extract relevant insights. Cloud computing is a technology that relies on the advancements and integration of various computer technologies such as fast microprocessors, large memory capacities, high-speed networks, and robust system architectures. It is built on the foundation of the TCP/IP (Transmission Control Protocol/Internet Protocol) protocol, which forms the backbone of the Internet.

Cloud computing is a complex system comprised of five layers: clients, applications, platform, infrastructure, and servers. It combines hardware, software, and services layers, along with Internet and communication protocols, to enable sophisticated processing and analysis of big data generated by IoT devices. Users access the service layer through the Internet, and access mechanisms are implemented to ensure user privacy. However, the cloud servers are located in large data centers that may be far from the user’s physical location. Many organizations are adopting cloud services for various benefits. These include minimizing hardware expenses, efficiently storing large volumes of data, and establishing secure, multi-layered architectures. These cloud architectures can be accessed from multiple geographical locations [136].

Despite the benefits of cloud computing, such as improved data computation and analytics, it has limitations, including potential delays in accessing servers and bandwidth issues. In simpler terms, cloud computing combines hardware, software, and services to process and analyze large datasets. While it offers numerous advantages, such as cost-effective data processing and the efficient handling of big data, it also faces challenges like connectivity issues. Additionally, cloud computing offers fast data processing capacity, making it a more efficient solution for handling big data in smart cities. In the energy sector, another technology, fog computing, plays a crucial role in enabling smart grids. Smart grids are modern electrical grids that incorporate advanced communication and information technologies to enhance efficiency and reliability. Fog computing helps the energy sector manage the substantial volumes of data generated by smart grids, including energy consumption patterns, weather data, and electricity production levels. It also improves the reliability of the energy grid by enabling faster response times to outages and other emergencies. Fog computing has great potential to transform the energy sector by promoting efficient and sustainable energy production and distribution while enhancing the resilience and reliability of energy grids.

Edge computing is becoming increasingly important in the energy sector as it helps improve the efficiency and reliability of energy systems. It involves processing data closer to where they are generated or consumed, rather than sending them to a centralized data center. By analyzing these data in real time, potential issues can be identified before they become critical, allowing for the planning of maintenance activities to avoid downtime. This real-time data analysis enables informed decisions about energy generation, consumption, and storage, leading to cost savings and improved efficiency. Balancing the load across the grid can prevent blackouts, reduce energy waste, and enhance reliability. In summary, edge computing has the potential to transform the energy sector by enhancing efficiency, reliability, and security. As energy equipment and devices generate more data, the role of edge computing in managing and optimizing energy systems will continue to grow.

9.4. Quantum Computing

Quantum computing has the potential to significantly reduce energy consumption and carbon emissions compared to traditional supercomputers and data centers due to its ability to perform complex calculations using less energy. Quantum processors operate at extremely low temperatures and are superconducting, meaning they have no resistance and do not produce heat. Hybrid computing, which combines quantum and classical computing, offers a promising solution for reducing energy usage and costs while addressing complex problems. Nevertheless, realizing the full potential of hybrid computing requires further research and development. To accurately estimate the energy demand of quantum computers, advanced machine learning techniques and data analytics are being employed to predict energy consumption from both renewable and non-renewable sources [137]. This effort aims to develop more energy-efficient quantum data centers for the better utilization of energy.

9.5. Big Data

IoT technologies are generating vast amounts of data, known as big data, which are being used to make important decisions about business operations and processes. As smart cities become more reliant on ICT, and as smart metering expands, there is a growing focus on the processing and analysis of big data. Researchers are investigating methods such as AI, machine learning, deep learning, and Q-learning to analyze these data. Big data analysis is becoming increasingly important not only for understanding historic trends but also for developing future energy plans for smart cities through forecasting. For example, district heating systems exhibit different energy response patterns depending on the building, making accurate thermal load forecasting that takes these differences into account necessary [138]. The ability to effectively analyze big data is critical for smart cities to optimize their energy usage and plan for a sustainable future. The use of advanced analysis methods, such as AI and machine learning, can help identify patterns in large datasets that would be difficult or impossible to detect using traditional methods.

9.6. Blockchain

Blockchain technology is a secure, transparent, and decentralized way of storing and sharing information. It consists of a chain of blocks containing transaction data and is not controlled by a central authority. This technology is being adopted by various industries, including the energy, healthcare, and automotive industries, due to its advantages, such as transparency, security, and immutability. In the energy sector, blockchain is being used for various purposes, such as managing renewable energy certificates, facilitating peer-to-peer electricity trading, and managing electric vehicle charging [139]. The use of blockchain technology in the energy sector is expected to improve efficiency, reduce costs, and integrate distributed energy sources, leading to a promising future for blockchain in this industry.

10. Security Concerns in the IoE for a Sustainable Smart City

Security in the IoE is a critical consideration for ensuring the sustainable development of smart cities [140,141,142,143,144,145]. As energy systems become more interconnected and reliant on digital technologies, they also become more vulnerable to cyberattacks and other security threats. Some key aspects to consider when addressing security in the IoE for a sustainable smart city are presented here, as depicted in Figure 9.

• Data Security and Privacy: Protecting the data generated and transmitted within the IoE ecosystem is essential. Failing to protect these data can lead to significant breaches of privacy and potential misuse. Encryption, secure authentication, and access controls must be implemented to ensure that sensitive information remains confidential and is only accessible by authorized parties. Threats to data security and privacy include cyberattacks aiming to steal or manipulate data, eavesdropping on data transmissions, and unauthorized access to IoT devices. Vulnerabilities that expose data to these threats often stem from poorly configured or outdated IoT devices and insufficient data encryption. Inadequate authentication mechanisms and unpatched software can also open doors to attackers. Implementing robust encryption techniques to safeguard data both in transit and at rest could be a possible solution. Secure and unique authentication methods, such as biometrics or two-factor authentication, should be applied to prevent unauthorized access. Regular security audits and software updates are necessary to patch vulnerabilities promptly. Furthermore, strong access controls and data anonymization practices can help protect individual privacy while still allowing for valuable data analysis.
• Network Security: The communication networks connecting energy devices and systems must be robust and resilient against cyber threats. Implementing firewalls, intrusion detection and prevention systems, and regular security audits can help safeguard against unauthorized access and attacks. Network security is critical to protect the communication networks that interconnect energy devices and systems within the IoE. Potential threats include Distributed Denial-of-Service (DDoS) attacks, which can disrupt critical services, and malware that can compromise network integrity. Vulnerabilities often result from unpatched network equipment and weak authentication protocols. The solutions encompass deploying robust firewalls to filter malicious traffic, intrusion detection and prevention systems to spot and thwart threats in real time, and conducting regular security audits to identify and rectify vulnerabilities promptly. This multi-layered approach ensures the resilience of IoE communication networks against cyber threats.
• Device Security: All connected devices within the IoE, such as smart meters, sensors, and controllers, should be designed with security in mind. This includes regular software updates, secure boot mechanisms, and the ability to isolate compromised devices from the network. Device security is paramount to ensure the integrity of all connected devices within the IoE ecosystem. Potential threats encompass device tampering, unauthorized access, and malware infections. Vulnerabilities may arise from unpatched software or weak authentication measures on devices. The solutions involve designing devices with built-in security features, enabling regular software updates to patch vulnerabilities, implementing secure boot mechanisms to ensure device integrity, and providing mechanisms to isolate compromised devices from the network swiftly. These measures collectively fortify the security posture of IoE devices, minimizing the risk of breaches and ensuring the continued functionality of the overall system.
• Authentication and Authorization: Strong authentication mechanisms, such as multi-factor authentication, should be used to ensure that only authorized individuals or systems can access and control the energy infrastructure. Authentication and authorization mechanisms are pivotal in safeguarding the energy infrastructure within the IoE. Potential threats encompass unauthorized access to critical systems, while vulnerabilities may arise from weak authentication protocols or misconfigurations. The solutions involve implementing robust multi-factor authentication methods, ensuring that only authorized individuals or systems can access and control the energy infrastructure. This approach fortifies the IoE ecosystem against unauthorized access, reducing the risk of security breaches and ensuring the integrity of energy management systems in smart cities.
• Anomaly Detection and Response: Implementing advanced monitoring and anomaly detection systems can help identify unusual behavior patterns indicative of potential security breaches. Automated response mechanisms can then be triggered to mitigate threats in real time. In the realm of anomaly detection and response, potential threats include unrecognized intrusions or malicious activities that can compromise the security of energy systems. Vulnerabilities might stem from inadequate monitoring or response mechanisms. To tackle these challenges, it is essential to implement advanced monitoring systems that can swiftly identify unusual patterns. When anomalies are detected, automated response mechanisms can promptly engage to neutralize threats in real time, bolstering the overall security posture of the IoE ecosystem in smart cities.
• Regulatory Compliance: Adherence to relevant cybersecurity regulations and standards is crucial. Depending on the region, there may be specific guidelines that dictate the minimum security requirements for energy systems. Regulatory compliance is vital to ensure that energy systems in smart cities meet cybersecurity standards and regulations. Threats might arise from non-compliance, which could result in legal penalties and security breaches. To address these issues, organizations must stay informed about regional regulations and standards. Implementing proactive compliance measures, regular audits, and assessments can help in both identifying vulnerabilities and ensuring adherence to the necessary cybersecurity protocols, safeguarding energy systems from potential threats.
• Cybersecurity Training: Personnel responsible for managing and maintaining IoE systems should receive regular cybersecurity training to stay updated on the latest threats and best practices. Cybersecurity training is critical in enhancing the human element of security within IoE systems. Threats often exploit human vulnerabilities, such as falling victim to phishing attacks or making errors in security configurations. To mitigate these risks, organizations should invest in comprehensive cybersecurity training programs for their staff. This includes raising awareness about common threats, providing guidance on best practices, and conducting simulated exercises to prepare personnel for potential security incidents. These measures can significantly reduce human-related vulnerabilities and enhance the overall security posture of IoE systems in sustainable smart cities.
• Supply Chain Security: It is essential to ensure that the components and software used in IoE devices and systems are sourced from reputable suppliers and are free from vulnerabilities. Supply chain security plays a pivotal role in safeguarding IoE ecosystems. Threats can infiltrate through compromised components or software in the supply chain. To address this, organizations should establish rigorous supply chain security measures. This includes vetting suppliers for security practices, monitoring the integrity of components throughout their lifecycle, and regularly updating devices and software to patch known vulnerabilities. By ensuring the integrity of the supply chain, potential threats can be significantly mitigated, enhancing the overall security of IoE systems in sustainable smart cities.
• Disaster Recovery and Business Continuity: Developing robust disaster recovery plans ensures the rapid restoration of critical energy services in the event of a cyberattack or other disruptions. Disaster recovery and business continuity are essential aspects of IoE security. Threats like cyberattacks or natural disasters can disrupt critical energy services. To mitigate these risks, organizations should establish comprehensive disaster recovery and business continuity plans. These plans should include regular data backups, geographically dispersed data storage, and rapid response protocols. By preparing for worst-case scenarios, organizations can ensure minimal downtime and swift recovery from disruptions, enhancing the overall resilience of energy systems in sustainable smart cities.
• Public Awareness: Educating the public about the benefits and risks of the IoE can help individuals take appropriate measures to protect their own devices and data, contributing to overall system security. Public awareness plays a vital role in strengthening IoE security. Threats such as cyberattacks often exploit vulnerabilities in individual devices. To address this, comprehensive public awareness campaigns should be conducted to educate individuals about the importance of device security, password hygiene, and safe online practices. This empowers users to take an active role in securing their devices, reducing the likelihood of device-level vulnerabilities that could be exploited to compromise the overall security of the IoE ecosystem in smart cities.
• Collaboration and Information Sharing: It is important to establish partnerships and collaborations with relevant stakeholders, including government agencies, private sector organizations, and cybersecurity experts, to share information and collectively address emerging threats. Collaboration and information sharing are pivotal in bolstering IoE security. Emerging threats, especially in the rapidly evolving technology landscape, require collective efforts. Smart cities must establish partnerships with government agencies, private sector organizations, and cybersecurity experts to facilitate threat intelligence sharing. By pooling resources and knowledge, these collaborations can swiftly respond to emerging threats, adapt security measures, and collectively fortify the resilience of the IoE in ensuring the sustainable development of smart cities.
• Future-Proofing: As technology evolves, steps should be taken to ensure that the IoE infrastructure is designed to accommodate future security challenges and can be easily updated to incorporate new security measures. Future-proofing the IoE infrastructure against evolving security challenges is paramount. Continual updates and adaptations must be integrated into the system design. To address this, smart cities should strategically plan for the future by ensuring their IoE infrastructure is scalable and capable of accommodating advanced security measures. Regular security audits and upgrades should be conducted to keep pace with emerging threats, ensuring that the IoE remains robust and secure, supporting the sustainable development of smart cities.

By addressing these aspects, a smart city can develop a secure and resilient Internet of Energy that contributes to its sustainability goals while minimizing the risks associated with cyber threats. This approach not only enhances the resilience of energy systems but also contributes to the overarching goals of sustainability and improved urban living.

11. Current Research Challenges and Future Research Directions

With the rise in consumer demand, effective energy management has become an essential global necessity. The looming threats of global warming and climate change highlight the urgency of the situation, as increased energy consumption leads to the release of more greenhouse gases [146]. As the number of IoT devices continues to surge, it is crucial to prioritize energy management for these devices, especially in the realization of smart cities [147]. Efficient energy management holds the key to significantly reducing energy consumption and mitigating its environmental impact. To delve deeper into future research directions, it is imperative to consider specific challenges and areas that merit further investigation. For instance, researchers could explore advanced technologies and methodologies for enhancing energy efficiency in various sectors, such as transportation, urban planning, and industrial processes. Moreover, understanding consumer behaviors and preferences regarding energy usage and conservation can provide insights into designing effective energy management strategies. Addressing the integration of renewable energy sources into smart city grids, optimizing energy storage solutions, and developing predictive analytics for demand forecasting are also promising research avenues [148]. Additionally, investigating the socio-economic and policy-related aspects of sustainable energy management in smart cities is essential for fostering holistic solutions. The following examples illustrate how efficient management can significantly reduce energy consumption, as depicted in Figure 10.

11.1. Home Appliances/Domestic Services/Rural and Suburban Areas

Household appliances, domestic services, and rural/suburban areas are among the highest consumers of energy. Demand management encompasses aspects like lighting, temperature control, and thermal management, enabling the customization of energy consumption in residential units. On the other hand, intelligent activity management ensures the efficient and effective management of energy.

11.2. E-Healthcare

In the context of electronic healthcare (e-healthcare) in a smart city, several energy-related challenges must be addressed. The most significant among these challenges include power management, energy efficiency, battery life, connectivity, and data security. E-healthcare services rely on energy for various operations, including server operation, powering medical devices, and data transmission. Effective power management is crucial to ensure energy efficiency and prevent system overload or downtime. With the growing demand for e-healthcare services, efficient energy use becomes paramount to reduce costs and minimize the carbon footprint. This can be achieved through the adoption of renewable energy sources, energy-saving devices, and robust power management systems. Many e-healthcare devices, such as wearables and implants, depend on batteries for power. Extending battery life is essential to enable continuous device usage without frequent replacements. Furthermore, e-healthcare services require a dependable and stable network connection for seamless data transmission between medical devices, healthcare providers, and patients. Maintaining an uninterrupted network is vital for delivering high-quality e-healthcare services. Lastly, e-healthcare systems handle sensitive data, including personal health information. Protecting these data from cyber threats and breaches is critical to safeguard patient privacy and prevent potential legal and financial liabilities. Addressing these energy-related challenges in e-healthcare necessitates close collaboration between healthcare providers, technology developers, and energy providers. Through collective efforts, sustainable and energy-efficient e-healthcare solutions can be developed, ultimately enhancing patient outcomes and improving their overall quality of life.

11.3. E-Education

E-education, or electronic education, is a rapidly growing field within smart cities. It presents several energy-related challenges that need to be addressed for effective implementation. Some of these challenges include the energy consumption of devices, internet connectivity, energy-efficient infrastructure, and sustainable energy sources. E-education relies heavily on devices such as laptops, tablets, and smartphones. These devices, while essential for digital learning, tend to consume significant amounts of energy and can quickly drain batteries. To ensure uninterrupted learning, educational institutions, including schools and universities, must provide an adequate number of power outlets and charging stations within classrooms. Furthermore, a reliable and stable internet connection is paramount for e-education. Disruptions in internet connectivity due to power outages or supply fluctuations can disrupt the learning process. Smart cities need to invest in backup power systems, such as generators or battery backups, to ensure consistent and uninterrupted internet access for students and educators. In addition to addressing device and connectivity challenges, smart cities should focus on developing energy-efficient infrastructures for educational institutions. Implementing smart building technologies and energy-efficient lighting systems can significantly reduce energy consumption within these facilities. Not only does this lead to cost savings, but it also helps reduce greenhouse gas emissions, contributing to a more sustainable learning environment. Lastly, embracing sustainable energy sources, such as solar or wind power, in schools and universities can further reduce the carbon footprint associated with e-education. By harnessing renewable energy, educational institutions can lower their energy costs while aligning with environmental sustainability goals.

11.4. Industrial Applications

A system utilizing the IoT can enhance decision-making regarding food availability, while intelligent transportation methods improve the efficiency of food distribution. IoT devices typically operate on batteries and possess limited storage capacity. These constraints related to sensors pose challenges when aiming to establish IoT systems with extended network lifespans. Therefore, it becomes crucial to design an optimized, energy-efficient architecture that maximizes the utilization of these limited sensor resources, ultimately reducing overall energy consumption and ensuring a consistent quality of service.

11.5. Intelligent Transportation Systems

Intelligent transportation encompasses a wide range of aspects, from public transit services to daily commutes in private vehicles, all of which consume a significant amount of energy and contribute to urban pollution. Technologies that harness the power of the IoT and the IoE, such as those used in transportation planning, traffic engineering, and smart parking solutions, play a crucial role in decreasing energy usage and lowering the emissions of greenhouse gases.

11.6. Terahertz Communication

In smart cities, the IoE faces a significant challenge related to the limited availability of radio spectrum, particularly with the growing demand for faster data speeds. Traditional methods aimed at improving data speed, such as reducing the amount of signaling overhead, increasing bandwidth, or enhancing spectral efficiency, have been beneficial. However, these approaches alone may not suffice to meet future data traffic requirements. To tackle this issue, emerging technologies like terahertz (THz) transmission play a crucial role. Utilizing the THz spectrum, which covers frequencies ranging from 0.3 to 10 THz, becomes essential to achieve the higher peak data rates that the IoE in smart cities demands.

11.7. Policies, Security, and Standardization

The IoE offers numerous advantages, such as versatile technology platforms. However, reaping these benefits calls for efficient coordination and collaboration among different technologies, networks, and entities. Both producers and consumers face difficulties related to connections, policies, security, and standardization. To surmount the IoE’s obstacles, it is crucial to prioritize achieving more effective energy generation and ensuring a consistent supply from all sources. Furthermore, endeavoring to lower energy development and production expenses throughout the industry will boost energy accessibility for consumers.

Creating sustainable smart cities necessitates the seamless integration of their energy infrastructure. To address forthcoming research challenges in these urban hubs, a pivotal component is real-time energy monitoring, which can be effectively facilitated through the application of AI technology. Moreover, the pivotal role played by intelligent energy management technologies in optimizing an organization’s energy performance underscores the need for the development of robust big data analytics solutions. Furthermore, the core concept of sustainable smart cities revolves around integrated energy network technologies. The burgeoning concept of the IoE is gaining momentum with advancements in cutting-edge technologies.

The implementation of IoT-based integrated energy networks in the development of sustainable smart cities leverages a spectrum of emerging technologies. The ultimate objective is to devise a system that is universally accessible and practical across diverse infrastructures worldwide. This collective effort is poised to pave the way for the tangible realization of sustainable smart cities in the not-so-distant future. In conclusion, future research directions should concentrate on addressing the multifaceted challenges of energy management within smart cities. This includes a strong focus on technological innovation, understanding and influencing behavioral aspects, establishing robust policy frameworks, and fostering interdisciplinary collaboration. By diving deep into these specific domains, researchers can significantly contribute to the advancement of sustainable and efficient energy utilization within the dynamic landscape of smart cities.

12. Conclusions

The studies highlighted in this review underscore the integration of advanced technologies and economic principles within smart grid systems, aimed at efficiently managing energy resources. This paper extensively addresses the pivotal role of energy management in establishing sustainable smart cities, optimizing energy consumption, enhancing generation and storage efficiency, and leveraging data-driven strategies to reduce waste and emissions. The study encompasses a range of energy-efficient urban technologies, including harvesting, optimization, scheduling, and routing, all culminating in a comprehensive conceptual framework for energy-driven sustainable smart cities. Notably, the study acknowledges the significance of energy management in IoE-based sustainable smart cities through the classification of approaches, such as scheduling optimization, low-power device transceivers, cognitive frameworks, and cloud computing. It also emphasizes the fundamental importance of smart grids in smart city development, particularly focusing on micro/nanogrids. Furthermore, the exploration of the intricacies of energy harvesting, encompassing receiver design, optimization, sources, scheduling, and routing, contributes to the thoroughness of the analysis in this paper. This study’s multi-domain perspective introduces a new conceptual framework and enabling technologies grounded in energy management principles. Lastly, by outlining research challenges and future directions, the paper provides valuable insights for the advancement of energy management in IoE-enabled sustainable smart cities. To continue this pioneering work, future research should prioritize real-world implementation, data analytics, scalability, interoperability, and collaboration with policymakers to drive the transformation of cities into more sustainable and energy-efficient urban environments. In summary, our comprehensive investigation strives to advance both theoretical understanding and practical implementation, thereby fostering energy-efficient practices and sustainable development in the realm of smart urban environments.