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An experimental demonstration of directivities exceeding the fundamental Kildal limit, a phenomenon called superdirectivity, is provided for spherical high-index dielectric antennas with an electric dipole excitation. A directivity factor of about 10 with a total efficiency of more than 80% for an antenna having a size of a third of the wavelength was measured. High directivities are shown to be associated with constructive interference of particular electric and magnetic modes of an open spherical resonator. Both analytic solutions for a point dipole and a full-wave rigorous simulation for a realistic dipole antenna were employed for optimization and analysis, yielding an excellent agreement between experimentally measured and numerically predicted directivities. The use of high-index low-loss ceramics can significantly reduce the physical size of such antennas while maintaining their overall high radiation efficiency. Such antennas can be attractive for various high-frequency applications, such as antennas for the Internet of Things, smart city systems, 5G network systems, and others. The demonstrated concept can be scaled in frequency. [ABSTRACT FROM AUTHOR]

Accurate urban flow prediction (UFP) is crucial for a range of smart city applications such as traffic management, urban planning, and risk assessment. To capture the intrinsic characteristics of urban flow, recent efforts have utilized spatial and temporal graph neural networks to deal with the complex dependence between the traffic in adjacent areas. However, existing graph neural network based approaches suffer from several critical drawbacks, including improper graph representation of urban traffic data, lack of semantic correlation modeling among graph nodes, and coarse-grained exploitation of external factors. To address these issues, we propose DiffUFP, a novel probabilistic graph-based framework for UFP. DiffUFP consists of two key designs: (1) a semantic region dynamic extraction method that effectively captures the underlying traffic network topology, and (2) a conditional denoising score-based adjacency matrix generator that takes spatial, temporal, and external factors into account when constructing the adjacency matrix rather than simply concatenation in existing studies. Extensive experiments conducted on real-world datasets demonstrate the superiority of DiffUFP over the state-of-the-art UFP models and the effect of the two specific modules. [ABSTRACT FROM AUTHOR]

In December 2020, Singapore made global headlines by being the first country to approve the commercial sale of lab grown meat. This approval was part of an effort by the city state to intensify the development of novel foods on the island. This paper describes this recent growth of novel food companies in Singapore. Singapore's novel food policies ostensibly aim to create food security for its citizens. We argue that the embrace of the technology that surrounds novel foods also aligns with Singapore's vision of building a "smart nation." Although food is not a digital technology, the approaches to novel foods being taken in Singapore share many parallels with the rollout of digital technology. These parallels can be seen in the domains of standardization, regulation, intellectual property, data-gathering, and training. We anticipate that these shifts in procurement, regulation, consumption of foods, may ultimately challenge traditional food pathways and Singapore's food identity. [ABSTRACT FROM AUTHOR]

Nowadays, the Internet of Things (IoT) networks provide benefits to humans in numerous domains by empowering the projects of smart cities, healthcare, industrial enhancement and so forth. The IoT networks include nodes, which deliver the data to the destination. However, the network nodes' connectivity is affected by the nodes' removal caused due to the malicious attacks. The ideal plan is to construct a topology that maintains nodes' connectivity after the attacks and subsequently increases the network robustness. Therefore, for constructing a robust scale-free network, two different mechanisms are adopted in this paper. First, a Multi-Population Genetic Algorithm (MPGA) is used to deal with premature convergence in GA. Then, an entropy based mechanism is used, which replaces the worst solution of high entropy population with the best solution of low entropy population to improve the network robustness. Second, two types of Edge Swap Mechanisms (ESMs) are proposed. The Efficiency based Edge Swap Mechanism (EESM) selects the pair of edges with high efficiency. While the second ESM named as EESM-Assortativity, transforms the network topology into an onion-like structure to achieve maximum connectivity between similar degree network nodes. Further, Hill Climbing (HC) and Simulated Annealing (SA) methods are used for optimizing the network robustness. The simulation results show that the proposed MPGA Entropy has 9% better network robustness as compared to MPGA. Moreover, both the proposed ESMs effectively increase the network robustness with an average of 15% better robustness as compared to HC and SA. Furthermore, they increase the graph density as well as network's connectivity. [ABSTRACT FROM AUTHOR]

As a new pattern of urbanization, smart cities offer a set of initiatives to achieve a win-win situation for both environmental protection and economic development. This paper aims to explore the influence of smart city policy (SCP) on green technology innovation (GTI) by Chinese listed enterprises. Employing the difference-in-differences (DID) approach, the results show that SCP promotes enterprises' GTI. This finding remains valid after various robustness tests, which include the parallel trend test, PSM-DID, placebo test, the substitution of estimation model, replacement of dependent variable, and the exclusion of the impacts of other policies. Additional tests reveal that SCP improves GTI through government environmental subsidy, corporate environmental awareness, and research and development investments. Furthermore, the heterogeneity analysis suggests that our findings are more prominent in subsamples during the growth stage, in high-tech industries, and in eastern regions. Our research is vital to the urban ecological environment and high-quality economic development. [ABSTRACT FROM AUTHOR]

In recent years, Internet of Things (IoT) systems have changed the way we live, work and do businesses in many areas, even those that until recently seemed unlikely. Some areas that can benefit from their application are, among others, healthcare, smart cities, industrial automation, smart agriculture, intelligent transportation systems, smart logistics, and emergency response. This research work proposes a novel alternative that allows the creation of IoT systems capable of making remote changes in devices' communication in a fast and agile manner. Our proposal gives way to some of the most common changes in communication made during the development and maintenance phase in IoT systems, like changing the destination of data transmission, sending the data to multiple destinations, and changing the frequency of sending data. Our solution, which is used in the programs, is loaded on the device. When the device starts, it connects to a configuration server in the background and listens for changes. The changes are sent to the configuration server using specified commands. When a change is detected, the command is processed, and the change in communication is applied without stopping the running program. We designed experiments to evaluate the complexity of the programs developed using our proposal and the actions needed to make a change. [ABSTRACT FROM AUTHOR]

This paper brings into dialogue recent critical scholarship on smart cities and geographies of the future by examining how city dwellers encounter normative visions of the future supplied by government actors under smart urbanisation. I focus specifically on the prosaic but significant ways in which people (re)interpret and (re)produce urban futures in and through their everyday affective and material engagements with digital technologies. Drawing on ethnographic fieldwork and semi‐structured interviews, I discuss the extent to which state‐promulgated imaginings of digital urban futures projected by electronic payment infrastructures are negotiated by urban inhabitants in Singapore, at both the levels of the individual and the collective. Although there is a tendency in Urban Studies to read the smart city as depoliticising, the findings in this paper suggest that urban dwellers' lived encounters with digital urban futures are characterised and bound up with politics. Furthermore, this paper casts attention on forms of negotiation that emerge not from grassroots movements and/or democratic activism that have so far attracted social and cultural geographers working on the future, but everyday lived practice around the digital. Such a fine‐grained, practice‐based approach productively foregrounds emancipatory potential for reworking and reimagining normative digital urban futures. Equally importantly, it takes seriously the diverse and uneven future‐making capacities of urban inhabitants in the digitally mediated city, contributing to ongoing projects that seek to develop a globally oriented alternative smart urban agenda for cities and urban spaces in the 21st century. This paper brings into dialogue recent critical scholarship on smart cities and geographies of the future by examining how city dwellers encounter normative visions of the future supplied by government actors under smart urbanisation. Drawing on ethnographic fieldwork and semi‐structured interviews, it discusses the extent to which state‐promulgated imaginings of digital urban futures projected by electronic payment infrastructures are negotiated by urban inhabitants in Singapore, at both the levels of the individual and the collective. [ABSTRACT FROM AUTHOR]

Jakarta is widely lauded by global consultancies, technology giants and critical scholars as an increasingly important 'smart' city. One of the reasons for this is the attempted use by state, corporate and individual actors of smart technologies, discourses and practices to solve the urban challenges of flooding, traffic and waste management in the context of climate change and urbanisation. In this paper, we examine the efforts of the Jakarta Smart City programme and office to govern these challenges. Where smart cities are often described as offering speed and real‐time efficiency, we instead find that the technologies and practices mobilised by the Jakarta Smart City programme govern by projecting, delaying and hypothesising into the future. We describe these governance strategies and outline three of their key forms: keeping track of objects and people, calculating and projecting state savings, and mundane watching but not acting. Rather than facilitating exacting and real‐time policy responses to address these urban problems, these practices instead produce temporal relations of delay that push action further into the future. Building on existing research about futuring and anticipation, and the tempos and rhythms of smart cities, our contribution is to highlight slowness and delay as a key mode of governing. [ABSTRACT FROM AUTHOR]

Summary: 5G and beyond (5GB) systems show a flexible characteristic to meet different user communications requirements in parallel. Multi‐numerology waveform design is an important part of this human centric characteristic, however, it is necessary to make multi‐numerology planning. In this paper, the integrated sensing and communications (ISAC) concept is used with machine learning (ML) techniques to develop an intelligent numerology control mechanism for 5GB. Thanks to the ISAC capabilities, an environment‐aware system is designed, and several useful sensing information (USI) that affect the wireless channel characteristic is utilized. It is assumed that the USI is obtained through 5GB smart city networks. At that point, synthetic data generation is performed to form a new dataset with USI‐based features. Additionally, a novel feature control algorithm is proposed in the paper. Lastly, simulation results are presented for different type of ML models including several ensemble methods. [ABSTRACT FROM AUTHOR]

A smart city is regarded as a new form of urban development proposed by humans to cope with urban problems. Smart cities are of great interest to theoretical circles and are favored by city governments. However, in China, research on common problems and solution strategies in the process of smart city construction is still insufficient. To address this insufficiency, we utilized Python language and the Selenium library to realize a web crawler and construct a smart city corpus background library. Then, the word2vec model was used for word vectorization to divide policy texts into phrases, and 132 policies from 85 cities above the prefecture level in China were analyzed using the Gaussian mixture model (GMM). The results showed many problems in the smart city construction process in China, including the homogenization of content, the wide topic coverage, the influence of higher-level governments, the mismatch with the local situation, and the neglect of relationship governance. Moreover, an efficiency-governance model was designed, and it held certain theoretical and practical guiding significance for further promoting the healthy and sustainable development of smart city construction in China. [ABSTRACT FROM AUTHOR]

Urbanization is one of the biggest challenges in the world today. Sixty-eight percent of the world's population will reside in cities and urban areas by 2050. Transitioning to a smart city is the key to overcoming the urbanization challenges. This study aimed to develop a smart city rating system that determines the overall smartness score of Riyadh City. A mixed method approach was used: qualitative research involved interviews, whereas quantitative analysis utilized the fuzzy analytic hierarchy process (AHP) and multiple-criteria decision analysis (MCDA) to determine city smartness scores using main pillars and subfactors. The findings showed that Riyadh's smart city development is influenced by several factors. The smart economy carries a weight of 17.8% and a smartness score of 74.15%, while smart people holds a weight of 8.1% and a score of 59.79%. Culture diversity, education, and the Human Development Index emerge as the most dominant subfactors. Smart living carries a weight of 8.2% and a smartness score of 51.86%, with health and security being crucial aspects of human life. Smart governance carries a weight of 17.8% and a smartness score of 81.451%, with smart business services, transparency, open communication, and collaboration being the most important factors. Smart urban services hold a weight of 17.9% and a score of 43%, with transport, logistics, urban planning, infrastructure, smart parking, digital monitoring, and safety being the most dominant subfactors. Smart construction carries a weight of 3.9% and a smartness score of 43.83%, with sustainable houses and smart buildings being the most dominant subfactors. Smart mobility holds a weight of 18% and a smartness score of 52.34%, with congestion, accidents, transport efficiency, and a sustainable transportation system being the most dominant subfactors. Understanding the strengths and weaknesses of Riyadh smart city from the current research will help developers improve and maintain areas aligned with Saudi Vision 2030 while also providing a foundation for future research on similar cities or varying factors. [ABSTRACT FROM AUTHOR]

The city phenomenon is dynamic, complex, and has an abundance of data. City Information Modeling (CIM) and Digital Twins (DTs) are new data-driven approaches toward monitoring, controlling, and managing cities. The main purpose of this research is to introduce and raise the concept of CIM and DTs as a necessity in the smart city era for urban planning, design, and management and to implement a practical way to achieve these concepts in the Narmak neighborhood of Tehran City (Iran) as our case study. The primary tool is CityEngine software. A method was discovered to simulate cities where the existence and access to data are severely constrained. First, a Geographic Information System (GIS) database was assembled, then it was analyzed, and the relationships between urban data were determined. Then, programming in CityEngine generated a smart three-dimensional (3D) model with all the city's information. Finally, CIM and DTs led to the production of an integrated 3D database that is smart and interactive. [ABSTRACT FROM AUTHOR]

Smart Autonomous Vehicles (AVSs) are networks of Cyber-Physical Systems (CPSs) in which they wirelessly communicate with other CPSs sub-systems (e.g., smart -vehicles and smart-devices) to efficiently and securely plan safe travel. Due to unreliable wireless communication among them, such vehicles are an easy target of malware attacks that may compromise vehicles' autonomy, increase inter-vehicle communication latency, and drain vehicles' power. Such compromises may result in traffic congestion, threaten the safety of passengers, and can result in financial loss. Therefore, real-time detection of such attacks is key to the safe smart transportation and Intelligent Transport Systems (ITSs). Current approaches either employ static analysis or dynamic analysis techniques to detect such attacks. However, these approaches may not detect malware in real-time because of zero-day attacks and huge computational resources. Therefore, we introduce a hybrid approach that combines the strength of both analyses to efficiently detect malware for the privacy of smart-cities. [ABSTRACT FROM AUTHOR]

Internet of Things (IoTs) has been playing an important role in many sectors, such as smart cities, smart agriculture, smart healthcare, and smart manufacturing. However, IoT devices are highly vulnerable to cyber-attacks, which may result in security breaches and data leakages. To effectively prevent these attacks, a variety of machine learning-based network intrusion detection methods for IoT networks have been developed, which often rely on either feature extraction or feature selection techniques for reducing the dimension of input data before being fed into machine learning models. This aims to make the detection complexity low enough for real-time operations, which is particularly vital in any intrusion detection systems. This paper provides a comprehensive comparison between these two feature reduction methods of intrusion detection in terms of various performance metrics, namely, precision rate, recall rate, detection accuracy, as well as runtime complexity, in the presence of the modern UNSW-NB15 dataset as well as both binary and multiclass classification. For example, in general, the feature selection method not only provides better detection performance but also lower training and inference time compared to its feature extraction counterpart, especially when the number of reduced features K increases. However, the feature extraction method is much more reliable than its selection counterpart, particularly when K is very small, such as K = 4 . Additionally, feature extraction is less sensitive to changing the number of reduced features K than feature selection, and this holds true for both binary and multiclass classifications. Based on this comparison, we provide a useful guideline for selecting a suitable intrusion detection type for each specific scenario, as detailed in Table 14 at the end of Sect. 4. Note that such the comparison between feature selection and feature extraction over UNSW-NB15 as well as theoretical guideline have been overlooked in the literature. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) is revolutionising our everyday life by connecting a massive number of smart objects. IoT is considered a key enabler of multiple emerging applications in several areas such as manufacturing, agriculture, healthcare, automotive, smart city, etc. However, as it becomes increasingly prevalent in our life, IoT brings a number of challenging new security risks. The first issue is related to device authentication, especially with the massive number of connected devices with different types (sensors, vehicles, drones, etc.). This issue can be exploited by attackers to break into IoT networks. In addition, IoT devices generate a huge amount of confidential data that must be processed and transmitted independently without human intervention, which cannot be performed by traditional centralized security systems. Besides, the isolation imposed between different networks and the number of messages used for device's association are considered the main limitations in the two recent works. To overcome these limits, we design a novel decentralized scheme leveraging a hybridization of two main types of blockchain, local and public, which ensures authentication of things before establishing communication and exchanging information between devices. Our solution removes the isolation between different networks, contributes in reducing the number of messages required for the association phase, and solves the time delay problem imposed by the PoW consensus mechanism that is used in public blockchain as well. We adopt the Ethereum blockchain in its two versions local and public to implement our solution, and evaluate its performance as compared to the existing literature. The obtained results proved its effectiveness in terms of execution time and energy consumption which are the same metrics used in the most recent works. [ABSTRACT FROM AUTHOR]

Smart power grids suffer from electricity theft cyber-attacks, where malicious consumers compromise their smart meters (SMs) to downscale the reported electricity consumption readings. This problem costs electric utility companies worldwide considerable financial burdens and threatens power grid stability. Therefore, several machine learning (ML)-based solutions have been proposed to detect electricity theft; however, they have limitations. First, most existing works employ supervised learning that requires the availability of labeled datasets of benign and malicious electricity usage samples. Unfortunately, this approach is not practical due to the scarcity of real malicious electricity usage samples. Moreover, training a supervised detector on specific cyberattack scenarios results in a robust detector against those attacks, but it might fail to detect new attack scenarios. Second, although a few works investigated anomaly detectors for electricity theft, none of the existing works addressed consumers' privacy. To address these limitations, in this paper, we propose a comprehensive federated learning (FL)-based deep anomaly detection framework tailored for practical, reliable, and privacy-preserving energy theft detection. In our proposed framework, consumers train local deep autoencoder-based detectors on their private electricity usage data and only share their trained detectors' parameters with an EUC aggregation server to iteratively build a global anomaly detector. Our extensive experimental results not only demonstrate the superior performance of our anomaly detector compared to the supervised detectors but also the capability of our proposed FL-based anomaly detector to accurately detect zero-day attacks of electricity theft while preserving consumers' privacy. [ABSTRACT FROM AUTHOR]

Waste management is one of the many major challenges faced by all urban cities around the world. With the increase in population, the current mechanisms for waste collection and disposal are under strain. The waste management problem is a global challenge that requires a collaborative effort from different stakeholders. Moreover, there is a need to develop technology-based solutions besides engaging the communities and establishing novel policies. While there are several challenges in waste management, the collection of waste using the current infrastructure is among the top challenges. Waste management suffers from issues such as a limited number of collection trucks, different types of household and industrial waste, and a low number of dumping points. The focus of this paper is on utilizing the available waste collection transportation capacity to efficiently dispose of the waste in a time-efficient manner while maximizing toxic waste disposal. A novel knapsack-based technique is proposed that fills the collection trucks with waste bins from different geographic locations by taking into account the amount of waste and toxicity in the bins using IoT sensors. Using the Knapsack technique, the collection trucks are loaded with waste bins up to their carrying capacity while maximizing their toxicity. The proposed model was implemented in MATLAB, and detailed simulation results show that the proposed technique outperforms other waste collection approaches. In particular, the amount of high-priority toxic waste collection was improved up to 47% using the proposed technique. Furthermore, the number of waste collection visits is reduced in the proposed scheme as compared to the conventional method, resulting in the recovery of the equipment cost in less than a year. [ABSTRACT FROM AUTHOR]

The arising of the Cyber–Physical Systems' vision and concepts drives technological evolution toward a new architectural design for the infrastructure of an environment referred to as a Smart Environment. This perspective alters the way systems within Smart City landscapes are conceived, designed, and ultimately realized. Modular architecture, resource-sharing techniques, and precise deployment approaches (such as microservices-oriented or reliant on the FaaS paradigm) serve as the cornerstones of a Smart City cognizant of multiple Cyber–Physical Systems composing it. This paper presents a framework integrating Digital Decisioning, encompassing the automated combination of human-derived knowledge and data-derived knowledge (e.g., business rules and machine learning), to enhance decision-making processes and application definition within the Smart City context. [ABSTRACT FROM AUTHOR]

The rapid advancement toward smart cities has accelerated the adoption of various Internet of Things (IoT) devices for underground applications, including agriculture, which aims to enhance sustainability by reducing the use of vital resources such as water and maximizing production. On-farm IoT devices with above-ground wireless nodes are vulnerable to damage and data loss due to heavy machinery movement, animal grazing, and pests. To mitigate these risks, wireless Underground Sensor Networks (WUSNs) are proposed, where devices are buried underground. However, implementing WUSNs faces challenges due to soil heterogeneity and the need for low-power, small-size, and long-range communication technology. While existing radio frequency (RF)-based solutions are impeded by substantial signal attenuation and low coverage, acoustic wave-based WUSNs have the potential to overcome these impediments. This paper is the first attempt to review acoustic propagation models to discern a suitable model for the advancement of acoustic WUSNs tailored to the agricultural context. Our findings indicate the Kelvin–Voigt model as a suitable framework for estimating signal attenuation, which has been verified through alignment with documented outcomes from experimental studies conducted in agricultural settings. By leveraging data from various soil types, this research underscores the feasibility of acoustic signal-based WUSNs. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic has made it more difficult and expensive for medium-sized enterprises (SMEs) to finance. In this context, relying on the network platform, smart supply chain finance effectively solves financing problems for small and SMEs. However, in the development of smart supply chain finance, there are still some problems such as unstable willingness of SMEs to participate in financing, difficulty in determining the optimal development mode of platform-based core enterprises and lack of appropriate regulatory measures. Based on whether the network platform can use its own capitals for lending, this study introduces two smart supply chain financial models (the dominant and cooperation models of platform-based core enterprises) to solve the above problems. In this study, we construct two evolutionary game models: the tripartite model, including government, platform-based core enterprises, and SMEs, and the quadrilateral model, including government, financial institutions, platform-based core enterprises, and SMEs. This study presents the evolution and stability strategies of each participant under different modes. In addition, we discuss the willingness of platforms to choose different modes and corresponding government supervision measures. This study offers several important conclusions. (1) Core enterprises that do not have the conditions to build a highly intelligent platform choose the cooperation model; otherwise, they will preferentially choose the dominant mode. (2) Under the dominant mode, the stable development of smart supply chain finance must rely on strict government supervision. (3) By adjusting the scope of tax rates and subsidies, the government can control the trend of mutual transformation of the two modes, so that the dominant mode and the cooperative mode can develop in a balanced way in the market. [ABSTRACT FROM AUTHOR]

Cities today are key sites for the operation of global digital marketplaces. It is on the curbsides and at the intersections of cities where technology companies and digital platforms gain access to valuable urban data to be used in the delivery of data-driven services. In this context, urban data ownership and control have become a central policy arena for smart city governance. This article argues that, given the increased policy activism by city governments, there is an urgent need to better understand the key goals and instruments deployed by cities to resist corporate control of urban data. It first reviews the treatment of the topic by different strands in the literature on smart city governance and then uses the 'New Data Deal' programme launched by the city of Barcelona to draw empirical data from one of the author's involvement leading the programme, interviews with actors involved in the programme as well as from key policy and evaluation documents. By studying the design and implementation of Barcelona's 'New Data Deal', an early mover and leading reference in the academic and policy debates, the article presents the key successes, limitations and tensions faced by a city government trying to regain access and control over urban data, including a reflection on the role that city governments can play in shaping a global agenda around improved data governance. [ABSTRACT FROM AUTHOR]

In today's world, the Internet of Things (IoT) plays a major role to interconnect all the devices and improve the overall Quality of Life (QoL) for people. The main concern among IoT systems revolve around three pillars namely security, confidentiality, and privacy owing to the sensitive nature of the data being transmitted and processed byIoT devices. Traditional cryptographic approaches address these concerns by ensuring the authenticity and confidentiality of IoT systems. However, the majority of IoT devices are resource-constrained, which implies that they operate under significant resource constraints such as limited computational power, constrained battery life, physical compactness, and restricted memory capacity. To this end, Lightweight cryptography (LWC) offers methods specifically designed to accommodate the limitations of resource-constrained IoT devices. This work establishes the role of light weight cryptography for such resource constrained IoT networks in terms of security perspectives. In this work, we explore the security vulnerabilities of IoT systems and the associated lightweight cryptographic methods highlighting four components namely lightweight block ciphers, lightweight stream ciphers, hash functions, and Elliptic Curve Cryptography. The work further discusses the role of LWC and reviews the recent advancements in different sectors of IoT such as smart city, industries, healthcare, smart grids, and agriculture. Finally, several open research directions are highlighted in order to guide future LWC and IoT researchers. [ABSTRACT FROM AUTHOR]

The use of AI has progressed rapidly in recent years, and the notion of smartness is commonplace. However, while the topic of smart cities has received considerable research attention, there has been a lack of discussion about the use of smart technologies in prisons. While, especially in Eastern countries, AI in law enforcement is mainly used for security and control purposes (in Hong Kong, intelligent CCTV can detect unusual behaviour, and wristbands are used to collect data on prisoners), in Northern Europe the trend is to build smart prisons where AI is used as a rehabilitation tool (in Finland, Virtual Reality is employed to help rehabilitation). Adopting a comparative approach, the aim of this article is to demonstrate that AI is a transformative technology that is provoking structural changes in criminal law enforcement – especially in prisons – and that these changes express two opposing tendencies, that could transform the essence of penalties. [ABSTRACT FROM AUTHOR]

The public library serves as the point of convergence and the vector for information. However, the role of public libraries in promoting and contributing to the SDGs has been largely overlooked. This paper examines the strategic role public libraries can play in promoting SDGs and proffers key strategies for public libraries in promoting sustainable development. It is concluded in the paper that the public library that serves as the knowledge repository of the public owes the duty to ensure the dissemination of relevant information to educate, inform and mobilize the public toward collaborative efforts to save the growing cities. [ABSTRACT FROM AUTHOR]

This article explores the intertwining connections among artificial intelligence, machine learning, digital transformation, and computational sustainability, detailing how these elements jointly empower citizens within a smart city framework. As technological advancement accelerates, smart cities harness these innovations to improve residents' quality of life. Artificial intelligence and machine learning act as data analysis powerhouses, making urban living more personalized, efficient, and automated, and are pivotal in managing complex urban infrastructures, anticipating societal requirements, and averting potential crises. Digital transformation transforms city operations by weaving digital technology into every facet of urban life, enhancing value delivery to citizens. Computational sustainability, a fundamental goal for smart cities, harnesses artificial intelligence, machine learning, and digital resources to forge more environmentally responsible cities, minimize ecological impact, and nurture sustainable development. The synergy of these technologies empowers residents to make well-informed choices, actively engage in their communities, and adopt sustainable lifestyles. This discussion illuminates the mechanisms and implications of these interconnections for future urban existence, ultimately focusing on empowering citizens in smart cities. [ABSTRACT FROM AUTHOR]

Despite over a decade of development, smart cities remain at a stage of visions and expectations. There is a dualist attitude in the smart cities literature, whereby the research either studies the content of visions or compares the visions with the innovation outcomes. What expectations actually do in a smart city innovation process is often neglected. This article addressed this gap by adopting an ethnographic approach and applying conceptual tools from the sociology of expectations and strategic niche management to study a UK smart city project – the Open Programmable Harbour City (OPHC). This study enriches the literature on smart city vision by showing that expectation played four complex and paradoxical roles in making the OPHC: (1) It tidied up the messy past and prescribed a future. (2) It provided limited coordination for innovation activities. (3) It helped to attract attentions to the OPHC, but it also caused disappointments. (4) It provided protection for the negative experimental results, but the overprotection also impeded innovation. This article concludes with four valuable lessons about expectation management for smart city innovators and some fresh theoretical insights for the sociology of expectations and strategic niche management. [ABSTRACT FROM AUTHOR]

Urban areas are undergoing significant changes with the rise of smart cities, with technology transforming how cities develop through enhanced connectivity and data-driven services. However, these advancements also bring new challenges, especially in dealing with urban emergencies that can disrupt city life and infrastructure. The emergency management systems have become crucial elements for enabling cities to better handle urban emergencies, although ensuring the reliability and detectability of such system remains critical. This article introduces a new method to perform reliability and detectability assessments. By using Fault Tree Markov chain models, this article evaluates their performance under extreme conditions, providing valuable insights for designing and operating urban emergency systems. These analyses fill a gap in the existing research, offering a comprehensive understanding of emergency management systems functionality in complex urban settings. [ABSTRACT FROM AUTHOR]

With the rapid development of the Internet of Things, the Internet, and artificial intelligence, people's consumption patterns have undergone major changes. Consumers' consumption channels are no longer monolithic. A new retail model was born. Additionally, how to improve the revenue of the new retail supply chain and the scientific and reasonable distribution of revenue is particularly important. On this background, the concept of new retail is defined, and the principle of supply chain synergy is analysed. Stackelberg game is used to analyse and determine the optimal selling and wholesale prices of retailers and suppliers in the supply chain. The defects of the traditional supply chain model and the necessity of optimization are further analysed and demonstrated. Because of the traditional retail supply chain, new retail channels are introduced, and the second‐order supply chain is established. New retail channels are added to the supply chain, and the profitability before and after the addition is analysed. Stackelberg game and Shapley value method are used for modelling. Finally, Matlab is used for numerical analysis. The results show that adding new retail channels to the traditional supply chain dominated by suppliers will increase the market share of products. The market share f is positively correlated with the profits of suppliers and retailers. Under the multi‐channel retail model, product demand and supply chain profits are higher than a single channel. The Shapley value method can enable supply chain members to obtain corresponding benefits on their input, thereby achieving a win–win situation for retail supply chain members. [ABSTRACT FROM AUTHOR]

The emergence of Distributed Ledger Technologies (DLT) in the past decade has challenged our imagination to discover new, innovative and disruptive solutions to problems in domains ranging from finance and healthcare to supply chain and Smart Cities. However, the enormous energy consumption that has been observed in some of the most successful DLT applications raises the question of their long term sustainability. This article reviews the standardization efforts of the International Telecommunications Union (ITU) to provide guidelines to regulators and policy makers for making informed decisions on the applicability and sustainability of DLT architectures from the point of view of energy consumption. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) has emerged as an inevitable part of human life, that includes online learning, smart homes, smart cars, smart grids, smart cities, agriculture, and e-healthcare. It allows us to operate them 24/7 from anywhere. These smart IoT devices streamline our daily lives by automating everything around us. Several security issues have arisen with the continuous growth of non-secure IoT devices. Distributed Denial of Service (DDoS) attack is one of the most prominent security threats to Internet-based services and IoT platforms. It has the potential to break down the victim's server or network by transferring an immense amount of irrelevant traffic from the pool of compromised IoT devices. In this article, we present: (i) A comprehensive cyberattacks taxonomy for IoT platforms, (ii) Systematically demonstrate IoT technology: evolution, applications, and challenges, (iv) Systematic review of existing machine learning (ML) and deep learning (DL)-based detection approaches for large-scale IoT traffic-based DDoS attacks, (v) Characterize publicly available IoT-traffic-specific datasets, and (vi) Discuss various open research issues with possible solutions for detecting IoT traffic-based DDoS attacks, including future directions. [ABSTRACT FROM AUTHOR]

The potential of Internet of Things and wireless sensor networks technologies can be used to build a picture of a future intelligent surveillance system. Because of the small size of the sensor nodes and their ability to transmit data remotely, they can be deployed at locations that are difficult or impossible to access. Wireless multimedia sensor network represents a distinct subdomain within the broader scope of wireless sensor networks. It has found diverse applications in the context of future smart cities, particularly in areas such as healthcare monitoring, home automation, transportation systems, and vehicular networks. A wireless multimedia sensor network is a resource-constrained network in which the nodes are small battery-powered devices. In addition, sending a large amount of collected data by wireless multimedia sensor network across the Internet of Things network imposes important challenges in terms of bandwidth, storage, processing, energy consumption, and wireless multimedia sensor network lifespan. One of the solutions to these kinds of problems is data transmission reduction. Therefore, this systematic literature review will review various techniques used in data transmission reduction, ranging from data transmission redundancy reduction to machine learning algorithms. Additionally, this review investigates the range of applications and the challenges encountered within the domain of wireless multimedia sensor networks. This work can serve as a basic strategy and a road map for scholars interested in data reduction techniques for intelligent surveillance systems using WMSN in Internet of Things networks. [ABSTRACT FROM AUTHOR]

This paper uses the panel data of 245 cities in China from 2005 to 2020 to build an ambient air pollution index to measure ambient air quality. Taking China's national smart city pilot as a quasiexperiment, the impact of smart city construction on urban ambient air quality and its impact path is tested by using the multi-period double difference method. The research results show that the construction of smart cities has significantly improved the urban ambient air quality, and with the improvement of the level of smart city construction, the degree of improvement in urban ambient air quality shows a positive fluctuation trend. The mechanism test results show that the construction of smart cities improves urban ambient air quality by enhancing the level of technological innovation and marketization in the city. Heterogeneity testing shows that the ambient air quality improvement effects of smart city construction vary depending on the size of the city's population and the geographical location of the city. Furthermore, the moderating effect test shows that financial autonomy and industrial structure upgrading have a positive moderating effect on the improvement of ambient air quality in smart cities, while local government environmental regulation intensity has a reverse moderating effect. [ABSTRACT FROM AUTHOR]

Urban digital autonomy refers to the autonomous innovation capability of cities in the field of digital technology, which directly determines whether cities can achieve high-quality development in the urban digital economy. Using Chinese prefecture-level data from 2009 to 2018, this paper takes the smart city policy as a quasi-natural experiment to analyze the impact of the smart city policy on digital autonomy of cities empirically. The research results show that the smart city policy has significantly improved the digital autonomy of cities, which is 28.25% higher than that of other cities. After a series of robustness tests, such as the placebo test and propensity score matching DID (PSM-DID) test and using different samples, this research conclusion still holds. Heterogeneity shows that the promotion effect of the smart city policy on digital autonomy of cities is greater in eastern regions, cities with higher rank and larger population size. The research of influence mechanism shows that the smart city policy can promote digital autonomy of cities through the effect of digital human capital and digital material capital. Finally, this paper puts forward policy suggestions from the perspectives of developing digital industries, promoting the construction of smart cities, increasing the cultivation of digital talents, and paying attention to fairness and efficiency, so as to promote the digital autonomy of cities. [ABSTRACT FROM AUTHOR]

Community-level interventions through technological updates and connectedness are quite popular within smart city visions. These interventions, under the collective label of smart and connected communities (SCC), promise to increase technology access, services, and the sense of entrepreneurship and organization at the community level. This paper addresses the lack of academic research investigating SCC with regard to its merits and linkages to the debate on smart cities. It reviews the academic literature on conceptions, constituents, and enablers of SCC. It highlights SCC as a concept with more issues and complexity than conventional smart city projects, particularly with regard to soft or human-related factors. While SCC is associated with diverse objectives, there are some basic elements of SCC projects such as a common direction, digitalization, optimization, better services, and participation. This paper also presents a range of critical factors and enablers based on previous studies. These factors include the features of communities and services as well as collaborative and institutional mechanisms. This paper shows the importance of the planning and design tasks in initiating SCC interventions. When designing successful and context-specific SCC projects, it is important to address the contextual environment of SCC through an informed SCC project design. This is particularly relevant for (ill-defined) communities with unconducive institutional context or no cooperation legacies. The success of SCC often depends on engagement and change management within communities as well as the development of context-specific, and often iterative, project designs. [ABSTRACT FROM AUTHOR]

The Internet of Medical Things (IoMT) has recently become the norm in medical operations and emergency situations. A physician or a medical personnel is interested in accessing the patients' data from remote locations using IoMT. The patients' medical management system should allow their doctors and family members to have access to the data, especially in emergency situations. Deploying sensor nodes and managing the large amount of medical data generated by these sensors require a system that provides protection against known security threats and a robust mechanism to recover when subjected to attacks. The proposed scheme that provides mutual authentication between user(s) and power generation nodes, and also mitigate against attacks by validating the identity during the authentication phase. Moreover, the proposed scheme, called Grid‐Based Authentication, uses the existing smart grid infrastructure for communications, which is a model of integrated infrastructure in a smart city. Furthermore, the simulation results show that our proposed scheme yields better performance in terms of average throughput, end to end delay, and computation and communication costs compared with other state‐of‐the‐art approaches. [ABSTRACT FROM AUTHOR]

Summary: The advancement in technology and the adoption of innovative developments have made our lives easier. Every industry, from agriculture to transport, has experienced this advancement. The entire transportation system is in the process of becoming intelligent. Smart mobility of autonomous vehicles is an important part of the upcoming smart cities. The autonomous vehicle provides functionalities for safety and convenience. It has been an emerging field with a lot of attention and still under advancement and requires a thorough study and solutions for potential challenges and opportunities. Despite impressive advantages, it has some security vulnerabilities which could lead to life‐threatening consequences. Though they could prevent road accidents by eliminating human errors from the driving process, they also create the possibility of cyber‐physical attacks on the vehicular controlling system. For this, many researchers have focused on the security attacks and defenses in autonomous vehicular systems. A 360‐degree research has been undertaken to understand the concept of autonomous vehicles entirely, such as history, social impacts, difficulties and challenges, current product developments, and security aspects. Also, a vehicular architecture is proposed by integrating various technologies such as 5G, SDN, cloud, and so forth, its possible security attacks are mentioned. [ABSTRACT FROM AUTHOR]

Opportunistic Networks (OppNets) are characterized by intermittently connected nodes with fluctuating performance. Their dynamic topology, caused by node movement, activation, and deactivation, often relies on controlled flooding for routing, leading to significant resource consumption and network congestion. To address this challenge, we propose the Adaptive Clustering-based Routing Protocol (ACRP). This ACRP protocol uses the common member-based adaptive dynamic clustering approach to produce optimal clusters, and the OppNet is converted into a TCP/IP network. This protocol adaptively creates dynamic clusters in order to facilitate the routing by converting the network from a disjointed to a connected network. This strategy creates a persistent connection between nodes, resulting in more effective routing and enhanced network performance. It should be noted that ACRP is scalable and applicable to a variety of applications and scenarios, including smart cities, disaster management, military networks, and distant places with inadequate infrastructure. Simulation findings demonstrate that the ACRP protocol outperforms alternative clustering approaches such as kRop, QoS-OLSR, LBC, and CBVRP. The analysis of the ACRP approach reveals that it can boost packet delivery by 28% and improve average end-to-end, throughput, hop count, and reachability metrics by 42%, 45%, 44%, and 80%, respectively. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) is emerging as an impactful feature of the life, planning and governance of 21st-century cities. Once confined to the realm of science fiction and small-scale technological experiments, AI is now all around us, in the shape of urban artificial intelligences including autonomous cars, robots, city brains and urban software agents. The aim of this article is to critically examine the nature of urbanism in the emergent age of AI. More specifically, we shed light on how urban AI is impacting the development of cities, and argue that an urbanism influenced by AI, which we term AI urbanism, differs in theory and practice from smart urbanism. In the future, the rise of a post-smart urbanism driven by AI has the potential to form autonomous cities that transcend, theoretically and empirically, traditional smart cities. The article compares common practices and understandings of smart urbanism with emerging forms of urban living, urban governance and urban planning influenced by AI. It critically discusses the limitations and potential pitfalls of AI urbanism and offers conceptual tools and a vocabulary to understand the urbanity of AI and its impact on present and future cities. [ABSTRACT FROM AUTHOR]

Urban heritage is often tasked with the burden of mediating city history as well as its imagined futures. In Asian contexts, these imagined futures have often been encoded as 'smart' urbanity while historicity is wielded as soft power. Past studies have discussed this in the context of Ahmedabad, a significant urban centre in the region of Gujarat, India, whose urban heritage has served to promote the city's image. This article suggests that it is the digital archive, the Gandhi Heritage Portal, that potentially offers ways of disrupting the mediation of the city through heritage. With ethnographic interviews, observations, and secondary literature on the histories of the city, this article attempts a close discursive reading of the spatial, material, and ideological implications of an Ashram 'doing' technology through the Gandhi Heritage Portal. The article suggests that the making of the digital archive offered opportunities to dislocate heritage from neoliberal aspirations of the city. [ABSTRACT FROM AUTHOR]

Due to population growth, climate change, and the urban heat island effect, heat exposure is becoming an important issue faced by urban built environments. Heat exposure assessment is a prerequisite for mitigation measures to reduce the impact of heat exposure. However, there is limited research on urban heat exposure assessment approaches that provides fine-scale spatiotemporal heat exposure information, integrated with meteorological status and human collective exposure as they move about in cities, to enable proactive heat exposure mitigation measures. Smart city digital twins (SCDTs) provide a new potential avenue for addressing this gap, enabling fine spatiotemporal scales, human-infrastructure interaction modeling, and predictive and decision support capabilities. This study aims to develop and test an SCDT for collective urban heat exposure assessment and forecasting. Meteorological sensors and computer vision techniques were implemented in Columbus, Georgia, to acquire temperature, humidity, and passersby count data. These data were then integrated into a collective temperature humidity index. A time-series prediction model and a crowd simulation were employed to predict future short-term heat exposures based on the data accumulated by this SCDT and to support heat exposure mitigation efforts. The results demonstrate the potential of SCDT to enhance public safety by providing city officials with a tool for discovering, predicting, and, ultimately, mitigating community exposure to extreme heat. [ABSTRACT FROM AUTHOR]

This document is a summary of a special issue of the journal Sensors titled "Artificial Intelligence Methods for Smart Cities." The special issue focuses on the use of artificial intelligence (AI) and the Internet of Things (IoT) in smart cities. The articles in the special issue cover a range of topics, including anomaly detection, traffic management, public safety, surveillance, and indoor surveillance. The articles highlight the potential benefits of AI and IoT in improving urban systems and addressing key urban challenges. The special issue also acknowledges the challenges and ethical considerations associated with the widespread adoption of these technologies. [Extracted from the article]

In this paper, the design and research of a sensor-based personal air-quality monitoring device are presented, which is retrofitted into different personal protective face masks. Due to its small size and low power consumption, the device can be integrated into and applied in practical urban usage. We present our research and the development of the sensor node based on a BME680-type environmental sensor cluster with a wireless IoT (Internet of Things)-capable central unit and overall low power consumption. The integration of the sensor node was investigated with traditional medical masks and a professional FFP2-type mask. The filtering efficiency after embedding was validated with a head model and a particle counter. We found that the professional mask withstood the embedding without losing the protective filtering aspect. We compared the inner and outer sensor data and investigated the temperature, pressure, humidity, and AQI (Air Quality Index) relations with possible sensor data-fusion options. The novelty is increased with the dual-sensor layout (inward and outward). It was found that efficient respiration monitoring is achievable with the device. With the analysis of the recorded data, characteristic signals were identified in an urban environment, enabling urban altimetry and urban zone detection. The results promote smart city concepts and help in endeavors related to SDGs (Sustainable Development Goals) 3 and 11. [ABSTRACT FROM AUTHOR]