A systematic survey of Internet of Things frameworks for smart city applications

Abadía, José Joaquín Peralta; Walther, Christian; Osman, Ammar; Smarsly, Kay

doi:10.1016/j.scs.2022.103949

Cited by 48 publications

(29 citation statements)

References 56 publications

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“…manufacturing systems and manufacturing resources. Defining standardized means of transmission, communication and application protocols define how the elements communicate data among each other, for example data transmission rate and communication range, ensuring real-time, secure, and scalable data transmission [24]. As with data collection, data may be transmitted at different frequencies, based on the requirements of the monitoring strategy, such as real-time data transmission or batch data transmission.…”

Section: Data Life Cyclementioning

confidence: 99%

Big Data Life Cycle in Shop-Floor–Trends and Challenges

et al. 2023

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Big data is defined as a large set of data that could be structured or unstructured. In manufacturing shop-floor, big data incorporates data collected at every stage of the production process. This includes data from machines, connecting devices, and even manufacturing operators. The large size of the data available on the manufacturing shop-floor presents a need for the establishment of tools and techniques along with associated best practices to leverage the advantage of data-driven performance improvement and optimization. There also exists a need for a better understanding of the approaches and techniques at various stages of the data life cycle. In the work carried out, the data life-cycle in shop-floor is studied with a focus on each of the components -Data sources, collection, transmission, storage, processing, and visualization. A narrative literature review driven by two research questions is provided to study trends and challenges in the field. The selection of papers is supported by an analysis of n-grams. Those are used to comprehensively characterize the main technological and methodological aspects and as starting point to discuss potential future research directions. A detailed review of the current trends in different data life cycle stages is provided. In the end, the discussion of the existing challenges is also presented.

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Section: Data Life Cyclementioning

confidence: 99%

Big Data Life Cycle in Shop-Floor–Trends and Challenges

et al. 2023

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“…The existing research acknowledges the emergence of IoT-based automation in non-residential buildings, but the influence of IoT-based systems on conventional building automation is apparently neglected. On the contrary, the recent upsurge of interest in the IoT has created a profusion of new device manufacturers, which develop their proprietary solutions, again resulting in increased heterogeneity (Abadia et al, 2022;Domingues et al, 2011;Sembroiz et al, 2018).…”

Section: The Four Technical Interoperability Approachesmentioning

confidence: 99%

“…A common IoT application layer standard, which would be comparable with the existing building automation standards, is seemingly unavailable (Curry et al, 2013;Minoli et al, 2017;Sembroiz et al, 2018). Often, IoT components are battery powered and/or they have a short service life, which increases maintenance costs and reduces reliability of systems (Abadia et al, 2022;Plageras et al, 2018). Communication between IoT devices and cloud services can be perceived as a parallel channel of communicatio n with existing BMSs, which increases complexity and can diminish the clarity on the share of responsibilities between technical systems and building automation in general.…”

Section: Introductionmentioning

confidence: 99%

Enhancing smartness and interoperability of building management systems in non-residential buildings

et al. 2023

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In smart and sustainable cities, smartness of buildings is expected to evolve. A smart building should enhance its users’ productivity and minimize its environmental impact. Although these objectives are seemingly common, the development of the smartness of buildings is actualized in the form of an incoherent combination of standards and solutions. The emergence of the IoT (Internet of Things) is expected to bring major improvements in the development of smartness, but at the same time, it may even worsen non-systematicity. Such inconsistency creates barriers to the commercialization of innovations and poses challenges to technical building management. Here, we clarify the situation between the conventional building management system approach and the IoT-based entrants. We analyze the different approaches to building automation systems’ interoperability by means of a literature review, professional interviews and smart readiness indicator impact evaluation. The study forms a general view based on the presented information and suggests approaches for specifying technical building systems, which are expected to improve their schematic clarity and reduce performance gaps of buildings. This study can be used as a practical tool by technical building managers, and it discusses issues needing consideration to the benefit of policymakers, industry and academia.

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“…The process of detecting possible jeopardies presented the concept of evaluating the intensity of digital fortification in an electronic media forum. These limits under their study can discourse the idea by developing an outline to classify various jeopardies in cyber security for edifice future engineering [25]. The small offices and home office security issues for industrial corporations presented a cyber-automated system for small and medium-sized enterprises (SMEs).…”

Section: Introductionmentioning

confidence: 99%

Adaptation issues of machine learning in safety digitization

Panigrahi

Barpanda

Anitha³

et al. 2023

IJEECS

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<span lang="EN-US">The internet community is the only set of irreplaceable spaces in today’s world and is used by millions for knowledge acquittance via the digital exchange between the landed gentry. The torrent of available e-contents in the Internet community attracts corporates and researchers to find the factual weightage of formed data. It is high time for digital diversification, which is the objective of using various learning-based machine learning (ML) systems for hands-on fortification. The main idea is to make stylistic communication more understandable. Here, the authors try to adapt the factual weightage procedure of formed data through the Internet community using machine learning schemes. Hence, the authors have chosen to emphasize cyber security, which is not well discussed and concerned with ethical contemplation from hackers' forums amidst internet communities. There are disparities in the continual growth of connotations, acronyms, spellings, and even technical jargon, which need periodic re-learning and their prototype implications through the proposed model.</span>

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A systematic survey of Internet of Things frameworks for smart city applications

Cited by 48 publications

References 56 publications

Big Data Life Cycle in Shop-Floor–Trends and Challenges

Big Data Life Cycle in Shop-Floor–Trends and Challenges

Enhancing smartness and interoperability of building management systems in non-residential buildings

Adaptation issues of machine learning in safety digitization

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