Semantic clustering analysis for web service discovery and recognition in Internet of Things

Xu, Fang

doi:10.1007/s00500-021-06063-y

Cited by 2 publications

(1 citation statement)

References 43 publications

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“…• Service discovery: IoT service discovery and selection entail the thorough examination of services provided by IoT devices to pinpoint the most suitable options. This method takes into account the limitations inherent in IoT systems as well as the expectations of users, with the aim of improving the quality of service (QoS) [17].…”

Section: A Useful Concept Definitionsmentioning

confidence: 99%

A Review of Service Selection Strategies in Mobile IoT Networks

Mohamed Hadjkouider,

Kerrache,

Korichi

et al. 2024

IEEE Open J. Commun. Soc.

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With the proliferation of service-oriented architectures, the ubiquity of mobile networks, and an expanding spectrum of services available to users, the service selection process has become paramount in designing and implementing contemporary applications. Service selection strategies in mobile networks involve choosing the optimal services from many available options based on QoS (quality of service), bandwidth, and mobility, ultimately augmenting network efficiency and user experience. Nonetheless, particular challenges can impact the efficacy of these strategies. This paper presents a comprehensive survey of service selection strategies in mobile networks, transitioning from traditional approaches to emerging techniques, while also introducing a taxonomy classifying service selection methods based on criteria including mobile devices as service providers, architectural perspectives, computing devices allocation, evaluation metrics, and service selection methods. Additionally, We offer insights into the latest QoS-aware and energy-centric service selection methodologies, while addressing challenges related to battery life and energy efficiency, Quality of Service (QoS), security and privacy, as well as scalability and resource management. Additionally, we discuss prospective trajectories and future direction.

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Section: A Useful Concept Definitionsmentioning

confidence: 99%

A Review of Service Selection Strategies in Mobile IoT Networks

Mohamed Hadjkouider,

Kerrache,

Korichi

et al. 2024

IEEE Open J. Commun. Soc.

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Pattern Analysis in Marine Data Classification and Recognition

Wulff

2022

Advances in Library and Information Science

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With the advent of upcoming new patterns to handle (e.g., big data and semantic annotation), to encourage research to detect and identify objects, the development of the internet of things in the ocean requires the interconnection of all equipment (sensors) to observe the oceans. By serving as a common conceptualization among these tools, marine ontologies can lead to lower costs and better flexibility in marine data recognition and classification. To that end, marine pattern analysis literature (1991-2021) is used to create a sample network of records, comprising visual and textual features that can be annotated from video and image sequences, with the underwater parameters as the target of interest. The sample is split into ontological and machine learning (ML) datasets to build a prediction of the importance of data visualization techniques. The predicted suitability is strong with data classification that belongs to the machine learning dataset. However, the initial results from the study are encouraging, because ontologies' tools are proposed as automatic reasoning mechanisms.

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Semantic clustering analysis for web service discovery and recognition in Internet of Things

Cited by 2 publications

References 43 publications

A Review of Service Selection Strategies in Mobile IoT Networks

A Review of Service Selection Strategies in Mobile IoT Networks

Pattern Analysis in Marine Data Classification and Recognition

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