Crowdsourcing and Human‐in‐the‐Loop forIoT

As cyberattacks grow more complex and frequent, organisations and nations face critical challenges in safeguarding their information systems and sensitive data. Recognising the limitations of traditional, solely human-centric defences, there is increasing agreement among practitioners and researchers on the need for a collaborative approach that integrates human intelligence with artificial intelligence (AI). This paper introduces the cybersecurity Augmented Intelligence Framework (cAIF), a conceptual framework designed to optimise human-AI teaming (HAIT) in cybersecurity. Augmented intelligence is about the role of AI enhancing rather than replacing human intelligence through a more harmonious working relationship. The methods followed consist of three phases. First, reviewing existing literature to identify foundational human–machine interaction (HMI) paradigms. A systematic review of papers from three databases led to a final selection of 20 analysis units. Second, the strengths and weaknesses of the identified paradigms for HAIT were evaluated. Lastly, outlining the core architectural components of the cAIF from the strengths of each paradigm. Six key HMI paradigms were identified: Human-in-the-loop (HITL), Human-out-of-the-loop (HOOTL), Human-on-the-loop (HOTL), Human-alongside-the-loop, Human-in-command, and Coactive Systems. Each paradigm offers unique strengths: for instance, HITL emphasises active and direct human intervention, while HOOTL supports full autonomy of AI operations. On the other hand, HOTL balances AI autonomy with human oversight. The analysed data suggests that strategically leveraging the strength of each paradigm allows for a hybrid intelligent framework comprising five core components: the Decision-Making Matrix, Paradigm Allocation Engine, Task-Specific Modules, Feedback and Learning System, and Interoperability Framework. The cAIF shows promise in enhancing human-AI collaboration, integrating human insights with AI capabilities to improve resilience and adaptability against evolving cyber threats. Future research should focus on empirically validating the cAIF in various cybersecurity domains, including healthcare and finance.

With the advent of ubiquitous sensors and Internet of Things (IoT) applications, research and development initiatives on smart cities are ramping up worldwide. It enables remote monitoring, management, and control of devices and the generation of fresh and actionable insight from huge quantities of real-time data. Real-time noise and emissions monitoring of vehicles remain indispensable in a smart city context. Effective management and control of noise and emissions of vehicles on the road are necessary and possible through analyzing lots of sensor data in real-time to take an actionable insight. To contribute to this, as part of an ongoing effort of the European Union project called “NEMO: Noise and Emissions Monitoring and Radical Mitigation”, in this paper, we present the design and development of an IoT-based real-time noise and emissions monitoring system for vehicles in a smart city context. Real-world sensor data of the vehicles in some European cities are collected during the pilot tests. We have developed a complete application for infrastructure managers and analysts to monitor the sensor data related to noise and emissions of vehicles in real-time. The data of the individual road vehicles and trains in selected EU cities and from trains on a track in the Netherlands are collected in the cloud and analyzed with artificial intelligence (AI) algorithms for classification such as high emitter, medium emitter, and normal emitters. We present the development of a complete software solution that can be integrated with existing intelligent transportation systems in smart cities. Finally, we report the initial vehicle classification results from the Rotterdam (Netherlands) pilot test as a representative example for the NEMO monitoring system.

With the pervasiveness of GPS-enabled smart devices and increased wireless communication technologies, Spatial Crowdsourcing (SC) has drawn increasing attention in assigning location-sensitive tasks to moving workers. In real-world scenarios, for the complex tasks, SC is more likely to assign each task to more than one worker, called Group Task Assignment (GTA), for the reason that an individual worker cannot complete the task well by herself. It is a challenging issue to assign worker groups the tasks that they are interested in and willing to perform. In this paper, we propose a novel framework for group task assignment based on worker groups’ preferences, which includes two components: Social Impact-based Preference Modeling (SIPM) and Preference-aware Group Task Assignment (PGTA). SIPM employs a Bipartite Graph Embedding Model (BGEM) and the attention mechanism to learn the social impact-based preferences of different worker groups on different task categories. PGTA utilizes an optimal task assignment algorithm based on the tree-decomposition technology to maximize the overall task assignments, in which we give higher priorities to the worker groups showing more interests in the tasks. Our empirical studies based on a real-world dataset verify the practicability of our proposed framework.

Social media have a huge impact on different areas of modern life. In smart city context, they can be used as a tool for enhancing dialogue between citizens and municipalities. Based on present research, empirical evidence is provided on insufficient use of social media capabilities. Relationship between smart cities and social media power is examined through study on the example of Varna municipality. An analysis of city government presence in social media was conducted. Through online questionnaire the citizens’ attitude towards smart city concept was studied. Suggestions for improving the communication between citizens and municipal administration of Varna are given.

Smart city development is a complex, transdisciplinary challenge that requires adaptive resource use and context-aware decision-making practices to enhance human functionality and capabilities while respecting societal and environmental rights, and ethics. There is an urgent need for action in cities, particularly to (i) enhance the health and wellbeing of urban residents while ensuring inclusivity in urban development (e.g., through the intelligent design of public spaces, mobility, and transportation) and (ii) improve resilience and sustainability (e.g., through better disaster management, planning of city logistics, and waste management). This paper aims to explore how neuroscientific and neurotechnological solutions can contribute to the development of smart cities, as experts in various fields underline that real-time sensing designs and control algorithms inspired by the brain could help build and plan urban systems that are healthy, safe, inclusive, and resilient. Motivated by the potential interplay between societal challenges and these emerging technologies, we provide an overview of state-of-the-art research through a bibliometric analysis of neurochallenges within the context of smart cities using terms and data extracted from the Scopus database between 2018 and 2022. The results indicate that smart city research remains fragmented and technology-driven, relying heavily on internet of things (IoT) and artificial intelligence (AI)-based technologies. Mostly, it also lacks careful integration and adoption tailored to societal goals and human-centric concerns. In this context, the article explores key research streams and discusses how to create new synergies and complementarities in the challenge-technology intersection. We conclude that realizing the vision of smart cities at the nexus of neuroscience, technology, urban space, and society requires more than just technological progress. Integrating the human dimension alongside various technological tools and systems is crucial. This necessitates better interdisciplinary collaboration and co-production of knowledge toward a hybrid intelligence, where synergies of education and research, technological innovation, and societal innovation are genuinely built. We hope the insights from this analysis will help orient neurotechnological interventions on urban living and ensure they are more responsive to societal and environmental challenges as well as to legal and ethical concerns.

This book chapter explores the application of machine learning (ML) and deep learning (DL) algorithms for real-time data processing and decision-making within the context of smart cities. As urban environments evolve, the ability to harness vast amounts of data from diverse sourcessuch as sensors, IoT devices, and digital platformshas become essential for optimizing urban operations and improving quality of life. The chapter delves into the challenges and opportunities presented by these technologies, focusing on issues such as scalability, real-time performance, data quality, and the integration of heterogeneous systems. It also examines the critical role of model explainability, transparency, and ethical considerations in AI-driven decision-making processes. Moreover, the chapter discusses the importance of addressing uncertainty in smart city decision-making through advanced decision support systems, enabling cities to adapt dynamically to changing conditions. By investigating these themes, the chapter aims to provide comprehensive insights into the future of smart city systems powered by ML and DL, highlighting their potential to revolutionize urban management.

PurposeThe purpose of this paper is to advance knowledge of the transformative potential of big data on city-based transport models. The central question guiding this paper is: how could big data transform smart city transport operations? In answering this question the authors present initial results from a Markov study. However the authors also suggest caution in the transformation potential of big data and highlight the risks of city and organizational adoption. A theoretical framework is presented together with an associated scenario which guides the development of a Markov model.Design/methodology/approachA model with several scenarios is developed to explore a theoretical framework focussed on matching the transport demands (of people and freight mobility) with city transport service provision using big data. This model was designed to illustrate how sharing transport load (and capacity) in a smart city can improve efficiencies in meeting demand for city services.FindingsThis modelling study is an initial preliminary stage of the investigation in how big data could be used to redefine and enable new operational models. The study provides new understanding about load sharing and optimization in a smart city context. Basically the authors demonstrate how big data could be used to improve transport efficiency and lower externalities in a smart city. Further how improvement could take place by having a car free city environment, autonomous vehicles and shared resource capacity among providers.Research limitations/implicationsThe research relied on a Markov model and the numerical solution of its steady state probabilities vector to illustrate the transformation of transport operations management (OM) in the future city context. More in depth analysis and more discrete modelling are clearly needed to assist in the implementation of big data initiatives and facilitate new innovations in OM. The work complements and extends that of Setia and Patel (2013), who theoretically link together information system design to operation absorptive capacity capabilities.Practical implicationsThe study implies that transport operations would actually need to be re-organized so as to deal with lowering CO2 footprint. The logistic aspects could be seen as a move from individual firms optimizing their own transportation supply to a shared collaborative load and resourced system. Such ideas are radical changes driven by, or leading to more decentralized rather than having centralized transport solutions (Caplice, 2013).Social implicationsThe growth of cities and urban areas in the twenty-first century has put more pressure on resources and conditions of urban life. This paper is an initial first step in building theory, knowledge and critical understanding of the social implications being posed by the growth in cities and the role that big data and smart cities could play in developing a resilient and sustainable transport city system.Originality/valueDespite the importance of OM to big data implementation, for both practitioners and researchers, we have yet to see a systematic analysis of its implementation and its absorptive capacity contribution to building capabilities, at either city system or organizational levels. As such the Markov model makes a preliminary contribution to the literature integrating big data capabilities with OM capabilities and the resulting improvements in system absorptive capacity.

With the proliferation of GPS-enabled smart devices and increased availability of wireless network, spatial crowdsourcing (SC) has been recently proposed as a framework to automatically request workers (i.e., smart device carriers) to perform location-sensitive tasks (e.g., taking scenic photos, reporting events). In this paper, we study a destination-aware task assignment problem that concerns the optimal strategy of assigning each task to proper worker such that the total number of completed tasks can be maximized whilst all workers can reach their destinations before deadlines after performing assigned tasks. Finding the global optimal assignment turns out to be an intractable problem since it does not imply optimal assignment for individual worker. Observing that the task assignment dependency only exists amongst subsets of workers, we utilize tree-decomposition technique to separate workers into independent clusters and develop an efficient depth-first search algorithm with progressive bounds to prune non-promising assignments. In order to make our proposed framework applicable to more scenarios, we further optimize the original framework by proposing strategies to reduce the overall travel cost and allow each task to be assigned to multiple workers. Extensive empirical studies verify that the proposed technique and optimization strategies perform effectively and settle the problem nicely.

Spatial Crowdsourcing (SC) is a popular outsourcing platform that assigns tasks to some individual workers, groups and communities through network. Commonly, the SC needs workers to upload their private information to untrust-worthy entities, which may cause a privacy breach. In this paper, we propose a framework for protecting privacy of workers participating in SC tasks. And we consider a complex scenario that the workers may have some different skills and a task may require a set of skills. What's more, in this framework, we propose an incentive mechanism which considered the feedbacks from task publishers and then adjust the skill level of workers. Experimental results based on synthetic datasets show the obvious improvement on completion quality and efficiency of our framework.

This paper introduces the main concepts of establishing a smart railway station under the context of smart city. In the recent years, cities are digitally developing to enhance the all aspects of the urban life including economic, social and environmental aspects. These three aspects are considered to be the main pillars of sustainable development of a smart city. A core element in the smart city development is the mobility. This might include the physical mobility of people or the economic mobility. Introducing the smartness concept in the transportation infrastructure will influence the population growth and business needs. Railway points of contact such as the stations that can offer excellent economics development, environmental performance, and punctuality for passengers are recognized as a fundamental component of urban development. Stations are always considered the interconnection between different transportation modes which had to be adapted to the urbanisation growth. The proposed work presents a state of the art review for rail smart station design and illustrates the key challenges that can be faced when thinking in establishment of smart railway station.

The context of smart cities should really be anchored onto two key attributes. First is the ability for a city to learn adaptively with the aid of machine learning (ML) or artificial intelligence (AI) and second is the ability for a city to sustain operations autonomously without any human intervention. While Internet of Things (IoT) is seen as an enabler by making all devices connect to a network that communicates with one another with minimal human interference; however, critical problems such as sewer management, health, parking woes, traffic congestion, pollution, waste management, and noise are not fully being addressed. In this paper, we discussed the most recent literature for smart city initiatives across the globe including the comprehensive Alcatel–Lucent study in 2011 and proposed an overarching autonomous learning city baseline and target architecture with specific functionalities for each layer.

The accelerating pace of urbanization has significantly complicated traffic management systems, leading to mounting challenges, such as persistent congestion, increased travel delays, and heightened environmental impacts. In response to these challenges, this study presents a novel deep learning framework designed to enhance short-term traffic flow prediction and support intelligent transportation systems within the context of smart cities. The proposed model integrates Gated Recurrent Units (GRUs) and Long Short-Term Memory (LSTM) networks, augmented by an attention mechanism that dynamically emphasizes relevant temporal patterns. The model was rigorously evaluated using the publicly available datasets and demonstrated substantial improvements over current state-of-the-art methods. Specifically, the proposed framework achieves a 3.75% reduction in the Mean Absolute Error (MAE), a 2.00% reduction in the Root Mean Squared Error (RMSE), and a 4.17% reduction in the Mean Absolute Percentage Error (MAPE) compared to the baseline models. The enhanced predictive accuracy and computational efficiency offer significant benefits for intelligent traffic control, dynamic route planning, and proactive congestion management, thereby contributing to the development of more sustainable and efficient urban mobility systems.

Despite the impressive growth of smart city initiatives worldwide, an organizational theory of smart city has yet to be developed, and we lack models addressing the unprecedented organizational and management challenges that emerge in smart city contexts. Traditional models are often of little use, because smart cities pursue different goals than traditional organizations, are based on networked, cross-boundary activity systems, rely on distributed innovation processes, and imply adaptive policy-making. Complex combinations of factors may lead to vicious or virtuous cycles in smart city initiatives, but we know very little about how these factors may be identified and mapped. Based on an inductive study of a set of primary and secondary sources, we develop a framework for the configurational analysis of smart cities viewed as place-specific organizational fields. This framework identifies five key dimensions in the configurations of smart city fields; these five dimensions are mapped through five sub-frameworks, which can be used both separately as well as for an integrated analysis. Our contribution is conceived to support longitudinal studies, natural experiments and comparative analyses on smart city fields, and to improve our understanding of how different combinations of factors affect the capability of smart innovations to translate into city resilience, sustainability and quality of life. In addition, our results suggest that new forms of place-based entrepreneurship constitute the engine that allows for the dynamic collaboration between government, citizens and research centers in successful smart city organizational fields.

The deployment of fifth-generation (5G) wireless networks has already laid the ground-work for futuristic smart cities but along with this, it has also triggered the rapid growth of a wide range of applications, for example, the Internet of Everything (IoE), online gaming, extended/virtual reality (XR/VR), telemedicine, cloud computing, and others, which require ultra-low latency, ubiquitous coverage, higher data rates, extreme device density, ultra-high capacity, energy efficiency, and better reliability. Moreover, the predicted explosive surge in mobile traffic until 2030 along with envisioned potential use-cases/scenarios in a smart city context will far exceed the capabilities for which 5G was designed. Therefore, there is a need to harness the 6th Generation (6G) capabilities, which will not only meet the stringent requirements of smart megacities but can also open up a new range of potential applications. Other crucial concerns that need to be addressed are related to network security, data privacy, interoperability, the digital divide, and other integration issues. In this article, we examine current and emerging trends for the implementation of 6G in the smart city arena. Firstly, we give an inclusive and comprehensive review of potential 6th Generation (6G) mobile communication technologies that can find potential use in smart cities. The discussion of each technology also covers its potential benefits, challenges and future research direction. Secondly, we also explore promising smart city applications that will use these 6G technologies, such as, smart grids, smart healthcare, smart waste management, etc. In the conclusion part, we have also highlighted challenges and suggestions for possible future research directions. So, in a single paper, we have attempted to provide a wider perspective on 6G-enabled smart cities by including both the potential 6G technologies and their smart city applications. This paper will help readers gain a holistic view to ascertain the benefits, opportunities and applications that 6G technology can bring to meet the diverse, massive and futuristic requirements of smart cities.

PurposeThis study aims to explore the complex, multi-level institutional dynamics of smart city reforms and projects and their potential sustainability pressures on the implementation of a management accounting system in an Egyptian state-owned enterprise (SOE), which has a politically sensitive institutional character.Design/methodology/approachThis study adds to institutional management accounting research using a multi-level perspective of institutional dynamics in the smart city context. Data were collected from an interpretive case study of an Egyptian SOE that was under socio-political sustainability pressures to implement a smart electricity network project in New Minya city.FindingsSmart city projects have formed social and political sustainability pressures, which introduced the enterprise resource planning (ERP) network as a new management accounting system. A new (complex and multi-level) management accounting system was invented to reinvent the sustainable city as an “accounting city” (which appeared rhetorically as a “smart city”). “Smart” being the visibility and measurability of the sustainability performance of the collective body, which calls the city and its connectivity to different institutional levels brought out in a city network project for the ERP-enabled electricity distribution.Research limitations/implicationsThis study examines a single case study from a single smart city and identifies the accounting community’s need for multiple and comparative case studies to further analyse the potential impact of smart city reforms and projects on the sustainable implementation of management accounting systems.Practical implicationsCity policymakers and managers may benefit from the practical findings of this interpretive field-based case study in planning, implementing and monitoring smart city projects and objectives.Social implicationsIndividual and collective well-being may be enhanced through new management accounting forms of multi-level local governance and increased political, field and organisational sustainability.Originality/valueThis study provides important insights into the sustainability dynamics of management accounting in achieving smart city reforms. The achievement of sustainability management accounting systems has connected to multiple ERP roles at different institutional levels, which resulted in accommodating the socio-political objectives of smart city projects.