Secure hierarchical fog computing-based architecture for industry 5.0 using an attribute-based encryption scheme

Shruti, .; Rani, Shalli; Srivastava, Gautam

doi:10.1016/j.eswa.2023.121180

Cited by 37 publications

(6 citation statements)

References 33 publications

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“…By processing data at the edge, organizations can reduce latency, minimize data transmission costs, and improve operational efficiency. This approach also enables real-time decision-making and response to events without relying on centralized data centers, enhancing overall system performance (Rani and Srivastava, 2024). Maintaining data privacy and security is paramount in edge computing environments.…”

Section: Operational Efficiency and Data Securitymentioning

confidence: 99%

Reviewing the Transformational Impact of Edge Computing on Real-Time Data Processing and Analytics

Oluwole Temidayo Modupe,

Aanuoluwapo Ayodeji Otitoola,

Oluwatayo Jacob Oladapo

et al. 2024

Comput. sci. IT res. j.

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Edge computing has emerged as a pivotal paradigm shift in the realm of data processing and analytics, revolutionizing the way organizations handle real-time data. This review presents a comprehensive review of the transformational impact of edge computing on real-time data processing and analytics. Firstly, the review delves into the fundamental concepts of edge computing, elucidating its architectural framework and highlighting its distinct advantages over traditional cloud-centric approaches. By distributing computational resources closer to data sources, edge computing mitigates latency issues and enhances responsiveness, thereby enabling real-time data processing at the edge. Furthermore, this review explores how edge computing facilitates the seamless integration of analytics capabilities into edge devices, empowering organizations to derive actionable insights at the source of data generation. Leveraging advanced analytics algorithms, such as machine learning and artificial intelligence, edge computing enables autonomous decision-making and predictive analytics in real time, fostering innovation across diverse industry verticals. Moreover, the review examines the transformative implications of edge computing on various sectors, including healthcare, manufacturing, transportation, and smart cities. By enabling localized data processing and analytics, edge computing enhances operational efficiency, ensures data privacy and security, and unlocks new opportunities for business optimization and value creation. This review underscores the profound impact of edge computing on real-time data processing and analytics, revolutionizing the way organizations harness data to drive informed decision-making and gain competitive advantage in today's dynamic business landscape. As edge computing continues to evolve, its transformative potential is poised to redefine the future of data-driven innovation and digital transformation. Keywords: Edge, Computing, Analytics, Data, Impact, Review.

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Section: Operational Efficiency and Data Securitymentioning

confidence: 99%

Reviewing the Transformational Impact of Edge Computing on Real-Time Data Processing and Analytics

Oluwole Temidayo Modupe,

Aanuoluwapo Ayodeji Otitoola,

Oluwatayo Jacob Oladapo

et al. 2024

Comput. sci. IT res. j.

View full text Add to dashboard Cite

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“…Fog computing is an architectural paradigm that extends cloud computing by distributing some processing, storage, and control functions closer to the network edge. A fog computing architecture for IoT consists of: [10] Edge Layer: This consists of the endpoint IoT devices as well as edge nodes with computing/storage capabilities. Data analytics and filtering occur at this layer to reduce upstream data traffic.…”

Section: Fog Computing Architecturementioning

confidence: 99%

Internet of Things: Architecture, Technologies, Applications, and Challenges

Abdulazeez,

Nawar,

Hassan

et al. 2024

KJCS

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A subset of cutting-edge information technology is the Internet of things (IoT). IoT refers to a network of physical objects with sensors attached that are linked to the Internet via LAN and WAN networking techniques. It is now commonly used to sense the environment and gather data in a variety of settings, including smart cities, healthcare, intelligent transportation, smart homes, and other structures. The IoT network architecture, core technology, and significant applications were outlined in this overview. The sensing layer, transport layer, and application layer are separated in the IoT network architecture. The essential technologies are embedded systems, network connectivity, sensor, and radio frequency identification (RFID) technology. IoT implementation in logistics still faces challenges despite the potential advantages. The utilization of technology in the IoT context is a topic with many open studies, which are also examined in this paper.

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“…The next industrial revolution is propelled by these technologies, which facilitate sustainable practices, process optimization, and intelligent decision-making. The fundamental tenets of Industry 5.0 are in line with AI algorithms, which maximize production and efficiency while reducing resource consumption thanks to Big Data insights [21]- [26].…”

Section: Goals Of the Researchmentioning

confidence: 99%

Sustainability Measures: An Experimental Analysis of AI and Big Data Insights in Industry 5.0

Ivanovich Vatin,

Singh Negi,

Sahithi Yellanki

et al. 2024

BIO Web Conf.

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In the context of Industry 5.0, this empirical research investigates the concrete effects of artificial intelligence (AI) and big data insights on sustainability metrics. Real-world data analysis shows that during a two-year period, there was a 10% rise in the energy used by solar panels, a 6.7% increase in the energy consumed by wind turbines, and a 6.7% drop in the energy consumed by the grid. Paper trash output was reduced by 14% and plastic waste by 24% as a consequence of waste reduction initiatives. Product quality was maintained by AI-driven quality control, with quality ratings ranging from 89 to 94. Moreover, there was a 6% decrease in carbon emissions from industry, 3.1% from transportation, and 4.6% from energy production. These results highlight how AI and Big Data may revolutionize Industry 5.0 by promoting environmental responsibility, waste reduction, energy efficiency, sustainability, and high-quality products.

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Secure hierarchical fog computing-based architecture for industry 5.0 using an attribute-based encryption scheme

Cited by 37 publications

References 33 publications

Reviewing the Transformational Impact of Edge Computing on Real-Time Data Processing and Analytics

Reviewing the Transformational Impact of Edge Computing on Real-Time Data Processing and Analytics

Internet of Things: Architecture, Technologies, Applications, and Challenges

Sustainability Measures: An Experimental Analysis of AI and Big Data Insights in Industry 5.0

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