Orchestrating Heterogeneous Devices and AI Services as Virtual Sensors for Secure Cloud-Based IoT Applications

Alberternst, Sebastian; Anisimov, Alexander; Antakli, André; Duppe, Benjamin; Hoffmann, Hilko; Meiser, Michael; Muaz, Muhammad; Spieldenner, Daniel; Zinnikus, Ingo

doi:10.3390/s21227509

Cited by 8 publications

(3 citation statements)

References 25 publications

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“…Moreover, other aspects that, although not unique to IoTSs, are essential in this type of system, such as the incorporation of artificial intelligence (AI) techniques to help the system in decision-making and to be context-aware, i.e., it reacts appropriately according to the context or the existing conditions in the environment [ 39 , 42 ]. Therefore, and if we also consider the heterogeneity of the components and application domains of IoTSs [ 76 , 77 ], it is obvious to conclude that professionals from different areas should be part of their development team in order to carry out the activities included in the different stages of its development, either to act throughout the whole life cycle or to carry out specific tasks [ 78 ].…”

Section: Methodologies Designed For the Development Of Iotssmentioning

confidence: 99%

Agile Methodologies Applied to the Development of Internet of Things (IoT)-Based Systems: A Review

Guerrero-Ulloa

Rodríguez-Domínguez

Hornos

2023

Sensors

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Throughout the evolution of software systems, empirical methodologies have been used in their development process, even in the Internet of Things (IoT) paradigm, to develop IoT-based systems (IoTS). In this paper, we review the fundamentals included in the manifesto for agile software development, especially in the Scrum methodology, to determine its use and role in IoTS development. Initially, 4303 documents were retrieved, a number that was reduced to 186 after applying automatic filters and by the relevance of their titles. After analysing their contents, only 60 documents were considered. Of these, 38 documents present the development of an IoTS using some methodology, 8 present methodologies focused on the construction of IoTS software, and 14 present methodologies close to the systems life cycle (SLC). Finally, only one methodology can be considered SLC-compliant. Out of 38 papers presenting the development of some IoTS following a methodology for traditional information systems (ISs), 42.1% have used Scrum as the only methodology, while 10.5% have used Scrum combined with other methodologies, such as eXtreme Programming (XP), Kanban and Rapid Prototyping. In the analysis presented herein, the existing methodologies for developing IoTSs have been grouped according to the different approaches on which they are based, such as agile, modelling, and service oriented. This study also analyses whether the different proposals consider the standard stages of the development process or not: planning and requirements gathering, solution analysis, solution design, solution coding and unit testing (construction), integration and testing (implementation), and operation and maintenance. In addition, we include a review of the automated frameworks, platforms, and tools used in the methodologies analysed to improve the development of IoTSs and the design of their underlying architectures. To conclude, the main contribution of this work is a review for IoTS researchers and developers regarding existing methodologies, frameworks, platforms, tools, and guidelines for the development of IoTSs, with a deep analysis framed within international standards dictated for this purpose.

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Section: Methodologies Designed For the Development Of Iotssmentioning

confidence: 99%

Agile Methodologies Applied to the Development of Internet of Things (IoT)-Based Systems: A Review

Guerrero-Ulloa

Rodríguez-Domínguez

Hornos

2023

Sensors

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“…Cloud platform (Topic 2) addresses the use of sensors and cloud servers for data collection. In particular, this topic covers sensing technologies necessary for supporting efficient data collection (Kwon and Seo 2022) and frameworks for virtual sensor configurations to collect data via multiple devices (Alberternst et al 2021). Learning technology (Topic 3) addresses the machine learning algorithms can be used in various AI service areas (e.g., smart city service and healthcare service), such as deep learning models for image classification and segmentation (Lee et al 2022b;Tseng et al 2021) and federated learning models for privacy protection of AI services (Rodríguez-Barroso et al 2020).…”

Section: Key Topics (12) In the Ai Service Literaturementioning

confidence: 99%

Connecting artificial intelligence to value creation in services: mechanism and implications

Kim

2023

Serv Bus

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Artificial intelligence (AI) is transforming services by providing personalized solutions, enhancing customer experience, and reducing operational costs. To tackle the challenges posed by the extensive and diverse literature on AI services, a comprehensive review was conducted using text mining techniques on journal articles. Twelve key research topics were identified, and the enabler–interface–business framework was developed. In addition, a value creation mechanism for AI services consisting of 6Cs (i.e., connection, collection, and computation, communication, control, and co-creation) was proposed. The study provides a complete overview of AI services, facilitating academic discussion and industrial transformation.

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“…To sum up, the research on Federated Learning mainly focuses on privacy protection [14][15][16][17] and incentive mechanism [18][19][20][21]. At present, the application of Federated Learning in market segments is also gradually developing [22][23][24]. However, there are few examples of literature on the application of Federated Learning to forecasting [25][26][27][28][29]; especially, the application of Federated Learning to the sustainable development research of e-commerce enterprise demand forecasting has not yet been found.…”

Section: Federated Learningmentioning

confidence: 99%

Demand Forecasting of E-Commerce Enterprises Based on Horizontal Federated Learning from the Perspective of Sustainable Development

Cui

Yang

et al. 2021

Sustainability

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Public health emergencies have brought great challenges to the stability of the e-commerce supply chain. Demand forecasting is a key driver for the sound development of e-commerce enterprises. To prevent the potential privacy leakage of e-commerce enterprises in the process of demand forecasting using multi-party data, and to improve the accuracy of demand forecasting models, we propose an e-commerce enterprise demand forecasting method based on Horizontal Federated Learning and ConvLSTM, from the perspective of sustainable development. First, in view of the shortcomings of traditional RNN and LSTM demand forecasting models, which cannot handle multi-dimensional time-series problems, we propose a demand forecasting model based on ConvLSTM. Secondly, to address the problem that data cannot be directly shared and exchanged between e-commerce enterprises of the same type, the goal of demand information sharing modeling is realized indirectly through Horizontal Federated Learning. Experimental results on a large number of real data sets show that, compared with benchmark experiments, our proposed method can improve the accuracy of e-commerce enterprise demand forecasting models while avoiding privacy data leakage, and the bullwhip effect value is closer to 1. Therefore, we effectively alleviate the bullwhip effect of the entire supply chain system in demand forecasting, and promote the sustainable development of e-commerce companies.

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Orchestrating Heterogeneous Devices and AI Services as Virtual Sensors for Secure Cloud-Based IoT Applications

Cited by 8 publications

References 25 publications

Agile Methodologies Applied to the Development of Internet of Things (IoT)-Based Systems: A Review

Agile Methodologies Applied to the Development of Internet of Things (IoT)-Based Systems: A Review

Connecting artificial intelligence to value creation in services: mechanism and implications

Demand Forecasting of E-Commerce Enterprises Based on Horizontal Federated Learning from the Perspective of Sustainable Development

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