IoT security issues in the context of Edge Computing: A Systematic Review of Literature

Torres-Carrion, Hernan; Solano-Chamba, Cecivel; Narvaez-Guillen, Cristian; Cueva-Hurtado, Mario Enrique

doi:10.23919/cisti54924.2022.9820159

Cited by 2 publications

(2 citation statements)

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“…Their study delves into the evolution of these paradigms, highlighting the role of fog, edge, mist, and dew computing in addressing challenges like bandwidth limitation, high response time, and resource demands of latencysensitive applications. Torres-Carrión et al (2022) focus on the security issues in the context of edge computing, particularly within IoT environments. Their systematic literature review identifies various security problems that are prevalent in edge computing scenarios, emphasizing the need for in-depth security measures in these new computing paradigms.…”

Section: In-depth Analysis Of Edge Computing Technologiesmentioning

confidence: 99%

“…These tools play a crucial role in analyzing and optimizing the performance of fog and edge computing systems. Torres-Carrión et al (2022) highlight that the decentralized architecture and geographical distribution of edge computing contribute to the occurrence of security attacks. This insight is critical for developing robust security frameworks that can protect data and maintain user privacy in edge computing environments.…”

Section: In-depth Analysis Of Edge Computing Technologiesmentioning

confidence: 99%

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Innovation in Data Storage Technologies: From Cloud Computing to Edge Computing

Odunayo Josephine Akindote,

Abimbola Oluwatoyin Adegbite,

Samuel Onimisi Dawodu

et al. 2023

Comput. sci. IT res. j.

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In an era where data is the new gold, understanding the evolution and future trajectory of data storage technologies is crucial. This paper delves into the transformative journey from traditional storage methods to contemporary paradigms like cloud and edge computing, underpinned by the burgeoning influence of Big Data, IoT, AI, and machine learning. The study's aim is to provide a comprehensive analysis of these technologies, assessing their development, efficacy, and the challenges they face in meeting the escalating demands of data storage. The methodology employed is a meticulous synthesis of literature reviews, case studies, and comparative analyses. This approach facilitates an in-depth exploration of the historical evolution of data storage, the paradigm shifts from cloud to edge computing, and the interplay between technological advancements and user demands. The study also scrutinizes the security concerns inherent in these technologies and identifies strategic directions for future research. Key findings reveal that while cloud computing has revolutionized data storage with its scalability and flexibility, edge computing emerges as a vital solution to latency and bandwidth limitations. The integration of AI and machine learning is identified as a pivotal factor in enhancing the efficiency and intelligence of data storage systems. However, this integration presents unique challenges, necessitating innovative solutions. Conclusively, the study recommends a continued focus on innovation in data storage technologies, emphasizing the development of integrated, secure, and efficient solutions. Future research should particularly explore the potential of AI and machine learning in overcoming current limitations. The paper's scope encompasses a comprehensive overview of the current state and future potential of data storage technologies, making it a valuable resource for researchers, technologists, and policymakers in the field. Keywords: Data Storage Technologies, Cloud Computing, Edge Computing, Big Data, Internet of Things (IoT), Artificial Intelligence (AI).

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Section: In-depth Analysis Of Edge Computing Technologiesmentioning

confidence: 99%

Section: In-depth Analysis Of Edge Computing Technologiesmentioning

confidence: 99%