Data interplay: A model to optimize data usage in the Internet of Things

Andrade, Leandro; Santana, Cleber Jorge Lira de; Alencar, Brenno de Mello; Silva, Claudio Jr.; Prazeres, Cássio

doi:10.1002/spe.3193

Cited by 4 publications

(3 citation statements)

References 39 publications

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“…As part of our future work, we aim to further develop our PDS-based solution [83] to address privacy issues in the context of Fog of Things [84] data [85]. We advocate for a user-centric approach, and assert that it can mitigate various privacy issues through an approach that enhances users' control while improving transparency, awareness, and trust.…”

Section: Discussionmentioning

confidence: 99%

A Systematic Review on Privacy-Aware IoT Personal Data Stores

Pinto,

Donta,

Dustdar

et al. 2024

Sensors

Self Cite

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Data from the Internet of Things (IoT) enables the design of new business models and services that improve user experience and satisfaction. These data serve as important information sources for many domains, including disaster management, biosurveillance, smart cities, and smart health, among others. However, this scenario involves the collection of personal data, raising new challenges related to data privacy protection. Therefore, we aim to provide state-of-the-art information regarding privacy issues in the context of IoT, with a particular focus on findings that utilize the Personal Data Store (PDS) as a viable solution for these concerns. To achieve this, we conduct a systematic mapping review to identify, evaluate, and interpret the relevant literature on privacy issues and PDS-based solutions in the IoT context. Our analysis is guided by three well-defined research questions, and we systematically selected 49 studies published until 2023 from an initial pool of 176 papers. We analyze and discuss the most common privacy issues highlighted by the authors and position the role of PDS technologies as a solution to privacy issues in the IoT context. As a result, our findings reveal that only a small number of works (approximately 20%) were dedicated to presenting solutions for privacy issues. Most works (almost 82%) were published between 2018 and 2023, demonstrating an increased interest in the theme in recent years. Additionally, only two works used PDS-based solutions to deal with privacy issues in the IoT context.

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Section: Discussionmentioning

confidence: 99%

A Systematic Review on Privacy-Aware IoT Personal Data Stores

Pinto,

Donta,

Dustdar

et al. 2024

Sensors

Self Cite

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“…Related to finding CPSs, current research tries to manage the high volume of data produced by the CPSs. For instance, in Reference 23, the authors propose a model to improve the performance of collecting, analyzing, and using the data produced by IoT devices. Our proposal advances the research of the data space domain by the mean of facilitating searching for similar devices and processing large amounts of devices.…”

Section: Literature Reviewmentioning

confidence: 99%

A data model for enabling deep learning practices on discovery services of cyber‐physical systems

Llopis,

Fernández‐García,

Criado

et al. 2024

Softw Pract Exp

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The W3C Web of Things (WoT) is a leading technology that facilitates dynamic information management in the Internet of Things (IoT). In most IoT scenarios, devices and their associated information change continuously, generating a large amount of data. Hence, to correctly use the information and the data generated by different devices, a new perspective of managing and ensuring data quality is recommended. Applying Data Science techniques to create the data model can help to manage and ensure data quality by creating a common schema that can be reused in future projects, as well as producing recommendations to facilitate Service Discovery. In addition, due to the dynamic devices that change over time or under specific circumstances, the data model created must be sufficiently abstract to add new instances and to support new requirements that devices should incorporate. The use of models helps to raise the abstraction level, adapting it to the continuous changes of devices by defining instances associated with the data model. This paper proposes two data models: one for Cyber‐Physical Systems (CPS) to define device information fetched by a Discovery Service, and another for applying Deep Learning in natural language problems through a Transformer approach. The latter matches user queries in natural language sentences with WoT devices or services. These data models expand the Thing Description model to help find similar CPSs by giving a confidence level to each CPS based on features such as security and the number of times the device was accessed. The results show how the proposed models support the search process of CPSs in syntactic and natural language searches. Furthermore, the four levels of the FAIR principles are validated for the proposed data models, thus ensuring the data's transparency, reproducibility, and reusability.

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“…Their methodology employed traditional machine learning algorithms to predict query performance, enhancing efficiency by reordering query plans. However, this technique fell short of fully harnessing the capabilities inherent in DL network models [ 4 ]. In a complementary endeavor, Vijayalakshmi and Karthika (2023) delved deeper into the optimization of IoT database queries, introducing a genetic algorithm-based approach.…”

Section: Recent Related Workmentioning

confidence: 99%

The analysis of the internet of things database query and optimization using deep learning network model

2024

PLoS ONE

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To explore the application effect of the deep learning (DL) network model in the Internet of Things (IoT) database query and optimization. This study first analyzes the architecture of IoT database queries, then explores the DL network model, and finally optimizes the DL network model through optimization strategies. The advantages of the optimized model in this study are verified through experiments. Experimental results show that the optimized model has higher efficiency than other models in the model training and parameter optimization stages. Especially when the data volume is 2000, the model training time and parameter optimization time of the optimized model are remarkably lower than that of the traditional model. In terms of resource consumption, the Central Processing Unit and Graphics Processing Unit usage and memory usage of all models have increased as the data volume rises. However, the optimized model exhibits better performance on energy consumption. In throughput analysis, the optimized model can maintain high transaction numbers and data volumes per second when handling large data requests, especially at 4000 data volumes, and its peak time processing capacity exceeds that of other models. Regarding latency, although the latency of all models increases with data volume, the optimized model performs better in database query response time and data processing latency. The results of this study not only reveal the optimized model’s superior performance in processing IoT database queries and their optimization but also provide a valuable reference for IoT data processing and DL model optimization. These findings help to promote the application of DL technology in the IoT field, especially in the need to deal with large-scale data and require efficient processing scenarios, and offer a vital reference for the research and practice in related fields.

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Data interplay: A model to optimize data usage in the Internet of Things

Cited by 4 publications

References 39 publications

A Systematic Review on Privacy-Aware IoT Personal Data Stores

A Systematic Review on Privacy-Aware IoT Personal Data Stores

A data model for enabling deep learning practices on discovery services of cyber‐physical systems

The analysis of the internet of things database query and optimization using deep learning network model

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