Integration of Smart City and Lifecycle Concepts for Enhanced Large-Scale Event Management

Hefnawy, Ahmed; Bouras, Abdelaziz; Cherifi, Chantal

doi:10.1007/978-3-319-33111-9_62

Cited by 6 publications

(7 citation statements)

References 8 publications

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“…As it was proposed in previous research in [12], [13] and [14], and in line with ambitions of many cities and states around the world, there is a need for a more holistic vision of smart city as a complete ecosystem. This paper carries on the proposed lifecycle approach to ensure systematic involvement and seamless flow of information between different stakeholders of the smart city ecosystem.…”

Section: Introductionmentioning

confidence: 85%

“…BOM is a hierarchical structure showing the components that make up the end item [14]. The end item in this case can be a smart city service system or a smart city SoS.…”

Section: Bill Of Materials (Bom)mentioning

confidence: 99%

“…Archiving and traceability requirements vary from one industry to another. Smart Object/ Service/ System data can be in various states, including in-work, in-process, in-review, released, as-designed, as-planned, as-built, as-installed, asmaintained, and as-operated [14].…”

Section: Object/ Service/ System Datamentioning

confidence: 99%

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Lifecycle Management in the Smart City Context: Smart Parking Use-Case

Hefnawy

Elhariri

Bouras

et al. 2016

IFIP Advances in Information and Communication Technology

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Abstract. Lifecycle management enables enterprises to manage their products, services and product-service bundles. IoT and CPS have made products and services smarter by closing the loop of data across different phases of lifecycle. Similarly, CPS and IoT empower cities with real-time data streams from heterogeneous objects. Yet, cities are smarter and more powerful when relevant data can be exchanged between different systems across different domains. From engineering perspective, smart city can be seen as a System of Systems composed of interrelated/ interdependent smart systems and objects. To better integrate people, processes, and systems in the smart city ecosystem, this paper discusses the use of Lifecycle Management in the smart city context. Considering the differences between ordinary and smart service systems, this paper seeks better understanding of lifecycle aspects in the smart city context. For better understanding, some of the discussed lifecycle aspects are demonstrated in a smart parking use-case.

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Section: Introductionmentioning

confidence: 85%

“…BOM is a hierarchical structure showing the components that make up the end item [14]. The end item in this case can be a smart city service system or a smart city SoS.…”

Section: Bill Of Materials (Bom)mentioning

confidence: 99%

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Lifecycle Management in the Smart City Context: Smart Parking Use-Case

Hefnawy

Elhariri

Bouras

et al. 2016

IFIP Advances in Information and Communication Technology

Self Cite

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“…The recent practical application is to analyze smart city planning in big cities such as Barcelona, Amsterdam, and Edinburgh. Hefnawy [13] et al combined a smart city and life cycle concept to create a suitable information and knowledge sharing platform in a smart city. It aims to solve the problem of unreasonable arrangement, lacking planning and internal coordination of large activities in the city, which can achieve the goal of organizational consistency and efficiency.…”

Section: Related Work a Smart Citiesmentioning

confidence: 99%

Urban Street Cleanliness Assessment Using Mobile Edge Computing and Deep Learning

et al. 2019

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During the process of smart city construction, city managers always spend a lot of energy and money for cleaning street garbage due to the random appearances of street garbage. Consequently, visual street cleanliness assessment is particularly important. However, the existing assessment approaches have some clear disadvantages, such as the collection of street garbage information is not automated and street cleanliness information is not real-time. To address these disadvantages, this paper proposes a novel urban street cleanliness assessment approach using mobile edge computing and deep learning. First, the highresolution cameras installed on vehicles collect the street images. Mobile edge servers are used to store and extract street image information temporarily. Second, these processed street data is transmitted to the cloud data center for analysis through city networks. At the same time, Faster Region-Convolutional Neural Network (Faster R-CNN) is used to identify the street garbage categories and count the number of garbage. Finally, the results are incorporated into the street cleanliness calculation framework to ultimately visualize the street cleanliness levels, which provides convenience for city managers to arrange clean-up personnel effectively. The overall approach is illustrated and visualized using the street images of Jiangning District in Nanjing, China. The practical application shows the feasibility and usability of the approach.INDEX TERMS Smart cities, street cleaning, garbage detection, deep learning, mobile edge computing.

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“…The proposed approach is based on decoupling information sources and sinks from real-time intelligence functions [5], as illustrated in Fig. 4.…”

Section: Lifecycle Approachmentioning

confidence: 99%

IoT for Smart City Services

Hefnawy¹,

Bouras

Cherifi³

2016

Proceedings of the International Conference on Internet of Things and Cloud Computing

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Internet of Things" (IoT) and "Smart City" are widely recognized to address the complexity of modern city operation. Concentration of population, scarcity of resources and environmental concerns are the main challenges that face city operators, and make ordinary service provisioning less efficient. In city environment, IoT sensors can be sources of real-time data; and, IoT actuators can execute real-time actions in the physical domain. IoT systems range from domain-specific to cross-sectoral systems where valuable data/ information flow across interconnected complex systems. Yet, to integrate domain-specific IoT systems into the complete vision of Smart City, as a System of Systems (SoS), there is a need to address heterogeneity of data sources, diversity of application domains and the big number of stakeholders across different phases of lifecycle. This paper suggests Service Lifecycle Management (SLM) concepts and Lifecycle Modeling Language (LML) to analyze, plan, specify, design, build and maintain IoT-enabled Smart City Service Systems.

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Integration of Smart City and Lifecycle Concepts for Enhanced Large-Scale Event Management

Cited by 6 publications

References 8 publications

Lifecycle Management in the Smart City Context: Smart Parking Use-Case

Lifecycle Management in the Smart City Context: Smart Parking Use-Case

Urban Street Cleanliness Assessment Using Mobile Edge Computing and Deep Learning

IoT for Smart City Services

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