Fog Computing Platforms for Smart City Applications: A Survey

Silva, Thiago Pereira da; Batista, Thaı́s; Lopes, Frederico; Neto, Aluizio Rocha; Delicato, Flávia Coimbra; Pires, Paulo F.; Rocha, Atslands Rego da

doi:10.1145/3488585

Cited by 14 publications

(3 citation statements)

References 67 publications

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“…Smart city infrastructures are inherently heterogeneous and deal with dynamic data flows, making centralized platforms inadequate due to scalability and latency challenges [52]. The growing shift towards distributed data processing allocates tasks across interconnected tiers, each vital for timely, efficient, and secure data processing in urban environments [53]. Figure 1 provides a conceptual overview of the distributed data processing and management within smart city middleware.…”

Section: B Distributed Data Processing and Managementmentioning

confidence: 99%

Smart City Middleware: A Survey and a Conceptual Framework

Goumopoulos

2024

IEEE Access

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Smart city middleware serves as a foundational tool in the evolution of urban digitalization, acting as an intermediary software layer that simplifies the development, deployment, and management of applications tailored for smart urban environments. However, the development of effective middleware for smart cities is challenging. The present research embarks on a comprehensive exploration of the smart city middleware landscape, unraveling the intricacies of its development and the challenges faced therein. Rooted in the assessment of 20 distinct middleware solutions, our study highlights the pivotal technologies, features and functionalities that are imperative for a middleware to effectively support a city's digital transformation. The functional and non-functional requirements form the nucleus of our evaluation. We also explore the architectural styles pivotal to middleware development and the programming paradigms shaping smart city application development. Our study highlights challenges in using middleware for smart city applications, such as interoperability, scalability, security amidst big data, context management, reliability, quality of service, energy efficiency, and compliance with technological standards and regulations. Based on the detailed analysis, we propose a conceptual framework for smart city middleware, shaped by the challenges and requirements identified in existing literature and middleware solutions. This framework is designed to reflect the diverse demands and complexities of urban digital transformation, and guide smart city middleware development accordingly. As a result, this research stands as a reference study for software developers, urban planners, and researchers, outlining the current state and future directions in the domain of smart city middleware.

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Section: B Distributed Data Processing and Managementmentioning

confidence: 99%

Smart City Middleware: A Survey and a Conceptual Framework

Goumopoulos

2024

IEEE Access

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“…Many researchers define smart cities as a large‐scale, multidimensional, and multisource integration of diverse resources (e.g., software, sensors, smart objects) from social, physical, and IT infrastructure to improve the quality of regular city services (e.g., safety, education, transportation) and users lives 23 . Smart services are arranged into six dimensions, as asserted in Reference 24: smart economy, smart people, smart governance, smart mobility, smart environment, and smart living. Each dimension can be covered by several service providers, which have their own information networks .…”

Section: Related Workmentioning

confidence: 99%

Cross‐network service recommendation in smart cities

Mezni,

Sellami,

Al‐Rasheed

et al. 2024

Concurrency and Computation

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SummaryNowadays, Internet of Things, artificial intelligence, cloud computing, and other revolutionary technologies (e.g., edge and fog computing) have become the pillar of smart cities. These latter make users' lives easier, thanks to a wide variety of smart services offered in different dimensions (e.g., smart living, smart mobility, smart economy, smart governance). However, the rapid adoption of smart services by users and the full servicelization of several cities around the world is faced with two major issues: the lack of knowledge regarding smart services' capacities (e.g., features, contextual requirements, pricing models, privacy policies, provisioning terms, etc.), and the lack of unified rating and quantification of smart services' QoS behavior. Indeed, interested users often exploit traditional search tools (e.g., Web search engines, social networks) to find and rate the needed services. This behavior has scattered the smart services' usage data (e.g., users contexts, ratings) across multiple providers platforms, which makes the search task beyond the capacity of users and, even, other service providers. Although recommender systems are a natural solution to exempt users from exploring the huge space of the offered smart services, current recommendation approaches for smart city environments are unable to deliver correct recommendations. In fact, they have been initially designed to single‐network settings (a single service repository), while smart services' consumers often are involved in multiple provider platforms. To the best of our knowledge, there exists no approach that treated smart service recommendation across multiple information networks. Therefore, the goal of this paper is to propose a cross‐network recommender system for smart cities. We first model the multiplex network of smart services' providers as a multirelational fuzzy lattice family thanks to fuzzy relational concept analysis (fuzzy RCA), which is a powerful mathematical method for data analysis and clustering. We also use the concept of anchor users to connect providers networks via the users involved in more than one provider platform. Guided by anchors' cross‐network relations, we compute the similarity between users and we define algorithms for exploring the smart services' information network, i.e. lattice family. Extensive experiments have proved the effectiveness of cross‐network recommendation and the quality of produced recommendations, compared to state‐of‐the‐art single‐network recommendation.

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“…Consequently, FogC and Edge Computing have emerged as viable computing paradigms for designing, implementing, deploying, and controlling such systems/applications. These paradigms bring computing resources closer to the IoT/device plane so that the primary computation can be done locally [65,66]. Each computing paradigm offers particular assistance based on the requirements of the application at hand.…”

Section: Additional Challengesmentioning

confidence: 99%

Networking Architectures and Protocols for IoT Applications in Smart Cities: Recent Developments and Perspectives

et al. 2023

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Numerous municipalities employ the smart city model in large cities to improve the quality of life of their residents, utilize local resources efficiently, and save operating expenses. This model incorporates many heterogeneous technologies such as Cyber-Physical Systems (CPS), Wireless Sensor Networks (WSNs), and Cloud Computing (ClCom). However, effective networking and communication protocols are required to provide the essential harmonization and control of the many system mechanisms to achieve these crucial goals. The networking requirements and characteristics of smart city applications (SCAs) are identified in this study, as well as the networking protocols that can be utilized to serve the diverse data traffic flows that are required between the dissimilar mechanisms. Additionally, we show examples of the networking designs of a few smart city systems, such as smart transport, smart building, smart home, smart grid, smart water, pipeline monitoring, and control systems.

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Fog Computing Platforms for Smart City Applications: A Survey

Cited by 14 publications

References 67 publications

Smart City Middleware: A Survey and a Conceptual Framework

Smart City Middleware: A Survey and a Conceptual Framework

Cross‐network service recommendation in smart cities

Networking Architectures and Protocols for IoT Applications in Smart Cities: Recent Developments and Perspectives

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