Advanced Machine Learning for Enterprise IoT Modeling

Deepa, N.; Prabadevi, B.

doi:10.1007/978-3-030-44407-5_5

Cited by 4 publications

(2 citation statements)

References 38 publications

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“…Humans and autonomous devices may cooperate beyond their physical vicinity using digital technology–decision‐making benefits from increased sharing and access to enterprise knowledge‐base. In addition, a surge of digital services, processes, economy, and goods push knowledge‐based decision‐making for digital Enterprise Architectures (EA) and Expert Systems (ES), including consumer devices, social networks, and intelligent transportation (Moulahi et al (2023), Deepa and Prabadevi (2020)). A plethora of physical devices are interconnected in current enterprise architectures through wireless Internet data transfer providing a global communications environment.…”

Section: Introductionmentioning

confidence: 99%

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Machine learning and internet of things applications in enterprise architectures: Solutions, challenges, and open issues

Rehman,

Tariq,

Moqurrab

et al. 2023

Expert Systems

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SummaryThe rapid growth of the Internet of Things (IoT) has led to its widespread adoption in various industries, enabling enhanced productivity and efficient services. Integrating IoT systems with existing enterprise application systems has become common practice. However, this integration necessitates reevaluating and reworking current Enterprise Architecture (EA) models and Expert Systems (ES) to accommodate IoT and cloud technologies. Enterprises must adopt a multifaceted view and automate various aspects, including operations, data management, and technology infrastructure. Machine Learning (ML) is a powerful IoT and smart automation tool within EA. Despite its potential, a need for dedicated work focuses on ML applications for IoT services and systems. With IoT being a significant field, analyzing IoT‐generated data and IoT‐based networks is crucial. Many studies have explored how ML can solve specific IoT‐related challenges. These mutually reinforcing technologies allow IoT applications to leverage sensor data for ML model improvement, leading to enhanced IoT operations and practices. Furthermore, ML techniques empower IoT systems with knowledge and enable suspicious activity detection in smart systems and objects. This survey paper conducts a comprehensive study on the role of ML in IoT applications, particularly in the domains of automation and security. It provides an in‐depth analysis of the state‐of‐the‐art ML approaches within the context of IoT, highlighting their contributions, challenges, and potential applications.

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Section: Introductionmentioning

confidence: 99%

“…transportation (Moulahi et al (2023), Deepa and Prabadevi (2020)). A plethora of physical devices are interconnected in current enterprise architectures through wireless Internet data transfer providing a global communications environment.…”

mentioning

confidence: 99%