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In the smart city paradigm, the deployment of Internet of Things (IoT) services and solutions requires extensive communication and computing resources to place and process IoT applications in real time, which consumes a lot of energy and increases operational costs. Usually, IoT applications are placed in the cloud to provide high-quality services...
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An incessant technological evolution is daily introduced. This can't be stopped and the scene is continuously enriched. Men are always trying to simplify their everyday life. One try to take advantage of the possibilities and facilities daily offered thanks to new modes of communication. Compared to former communication modes, means and techniques...
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... Moreover, the vision of smart cities extends beyond the boundaries of city limits. Collaboration between neighboring municipalities and even countries is crucial for creating interconnected networks that optimize resource sharing, transportation, and disaster response (Aldossary, 2023). The smart city paradigm extends its reach to create smart regions and interconnected ecosystems, fostering a harmonious coexistence that transcends traditional geopolitical boundaries. ...
... As we embark on this exploration, it is clear that smart cities have the potential to redefine not only urban living but also the very essence of citizenship and community engagement. The influx of data and technology challenges us to redefine privacy norms and develop innovative strategies for ensuring that the benefits of the smart city are distributed equitably to all segments of society (Aldossary, 2023). The journey into the world of smart cities requires holistic thinking, collaboration, and an unwavering commitment to the well-being of citizens and the planet. ...
... Smart transportation systems are a cornerstone of smart cities. This requirement encompasses real-time traffic management systems that optimize traffic flow, reduce congestion, and minimize travel times (Aldossary, 2023). Integrated public transportation networks and digital platforms for route planning and payment enhance mobility. ...
The concept of a Smart City Ecosystem has garnered significant attention due to its potential to revolutionize urban living through advanced technologies and data-driven solutions. This chapter explores the multifaceted dimensions of this ecosystem by delving into its requirements, architecture, applications, security considerations, and the open research questions it present. In the realm of requirements, it highlights the intricate balance between technological innovation, social inclusivity, economic sustainability, and environmental preservation that smart cities demand. It navigates these requirements’ challenges, shedding light on the trade-offs and synergies with which city planners and stakeholders must grapple. The chapter provides a comprehensive overview of the foundational structure of a smart city ecosystem. It dissects the intricate web of interconnected components, layers, and communication infrastructures that underpin its functionality. The architecture’s complexity is unraveled by examining the integration of diverse technologies and data sources. The diverse applications enabled by the smart city ecosystem are categorized across sectors such as transportation, energy, healthcare, and more. These applications are explored in detail, demonstrating their potential to enhance efficiency, sustainability, and citizen well-being. Real-world examples are presented, showcasing the transformative impact of these innovations. Security is critical in this ecosystem, as interconnectivity introduces new vulnerabilities. The chapter discusses the intricate web of security challenges encompassing data privacy, cybersecurity, and potential threats. Strategies and best practices for safeguarding smart city systems are highlighted, underscoring the paramount importance of secure design and operation. Then, it raises Open Research Questions that underscore the dynamic nature of the smart city landscape. It identifies areas where further exploration is needed, including the integration of emerging technologies, ethical considerations, and the societal impacts of data-centric urban environments. These questions beckon researchers and practitioners to actively shape the trajectory of smart city development.
... Fog devices are heterogeneous, dynamic, and resource-constrained, and the applications send requests/tasks for processing to these nodes [24]. Firstly, the resources of fog nodes are compared with the computational needs of these tasks, and if the resources are sufficient to process these tasks they should be allocated to that node and executed. ...
Fog computing is a novel idea that extends cloud computing by offering services like processing, storage, analysis, and networking on fog devices closer to IoT devices. Numerous fog devices are required to process the ever-growing amount of data generated by IoT applications. The heterogeneous tasks from various IoT applications compete for a limited number of resources of these devices. The process of assigning this set of tasks to different available fog nodes according to QoS requirements for processing is resource scheduling. Resource scheduling aims to optimize resource utilization and performance metrics however, the dynamic nature of the Fog environment, resource-constrained, and heterogeneity in fog devices make resource scheduling a complex issue. This research presents the design and implementation of a multi-objective optimization-based resource scheduling algorithm using Modified Particle Swarm Optimization (MPSO) that addresses the application module placement and task allocation issues. This two-step MPSO-based resource scheduling model finds the optimal fog node to place each application module and assigns appropriate tasks to the most optimal fog nodes for execution. The proposed model unlocks the full potential of fog resources along with maximization of overall system performance in terms of optimization of cost, latency, energy consumption, and network usage. The simulation results indicate that using MPSO energy consumption is reduced by 53.94% and 43.58% as compared to First Come First Serve (FCFS) and Particle Swarm Optimization (PSO), respectively. The loop delay, network usage and cost using MPSO are reduced by 40.3%, 67.69% and 90.01% respectively, as compared to PSO algorithm.
... AI is used for object detection in the telecom sector, presented in Gao et al. (2020) study. IoT and AI are also being combined to optimize various services within smart cities, as presented in Aldossary (2023). ...
... UAV, FOG, AND CLOUD LAYERS (COMPUTING AND NETWORKING VARIABLES)[37] Parameter Description N Collection of various nodes within the architecture of the UAV-fog-cloud system.Server at cloud level.Set of nodes at fog level.A number of nodes in UAV layer.s and d Source and destination within the architecture of the UAV-fog-cloud system. ...
Unmanned Aerial Vehicles (UAVs) are used in various applications, including crowd management, crime prevention, accident detection, and rescue operations. However, since UAVs perform their tasks independently, some UAV applications are dynamic and geographically distributed, which may require extensive real-time processing capabilities. Thus, processing UAV data locally can be challenging due to their limited computing capabilities. To overcome such limitations, fog and cloud computing can facilitate UAV application development by providing additional resource capacities when needed. Despite this, designing sophisticated and efficient UAV task offloading strategies that collaborate with fog and cloud technologies considering their service latency and energy consumption, is rarely addressed in the literature. Therefore, a collaborative offloading strategy for UAV applications is presented in this work, leveraging fog and cloud computing advantages and capabilities. This approach aims to minimize UAVs’ service latency and energy consumption, as well as provide the required resources and services in real time. In addition, task offloading decisions are formulated using the Mixed-Integer Linear Programming (MILP) model to reduce the energy consumption of the entire UAV-fog-cloud system by optimizing the allocation of computation resources and communication requested by each UAV. The simulation results demonstrate that the proposed strategy can significantly reduce UAV service latency by 15.38%, 35.29%, and 59.26%, as well as decrease overall energy consumption (including processing and networking) by 3.3%, 7.37%, and 12% when compared to alternative standalone strategies (namely UAV, fog, and cloud).
... In the healthcare sector, computing layers (e.g., edge, fog, and cloud layers) provide different computing capabilities such as storage, processing, and communication links over the internet to fulfill IoMT application requirements. Therefore, an integrated edge-fog-cloud architecture offers scalable data analytics and trustworthy solutions to overcome IoMT application challenges (e.g., the problem of reducing service execution time and energy consumption of IoMT applications) [13]. A number of studies in the literature [10,11,[14][15][16] have highlighted the importance of edge, fog, and cloud computing in terms of optimizing the placement of IoMT applications, considering several performance metrics such as energy consumption, service latency, resource usage, and security [17,18]. ...
