Characterizing Internet of Things Systems through Taxonomies: A Systematic Mapping Study

Alkhabbas, Fahed; Spalazzese, Romina; Davidsson, Paul

doi:10.1016/j.iot.2019.100084

Cited by 26 publications

(36 citation statements)

References 37 publications

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“…Several authors have proposed taxonomies for dealing with IoE and IoT systems in the following distinct approaches: technology and architecture design [ 24 , 35 , 36 , 37 , 38 , 39 , 40 ], sensors’ capabilities [ 26 , 41 , 42 , 43 , 44 , 45 ], and observation context issues [ 25 , 27 , 28 , 46 , 47 , 48 , 49 , 50 , 51 ]. However, still, due to the considerable heterogeneity of actual IoT devices, these taxonomies have approach limitations, mostly restricted to enabling technology and infrastructure.…”

Section: Related Workmentioning

confidence: 99%

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Internet of Everything (IoE) Taxonomies: A Survey and a Novel Knowledge-Based Taxonomy

Costa

Oliveira

Souza

2021

Sensors

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The paradigm of the Internet of everything (IoE) is advancing toward enriching people’s lives by adding value to the Internet of things (IoT), with connections among people, processes, data, and things. This paper provides a survey of the literature on IoE research, highlighting concerns in terms of intelligence services and knowledge creation. The significant contributions of this study are as follows: (1) a systematic literature review of IoE taxonomies (including IoT); (2) development of a taxonomy to guide the identification of critical knowledge in IoE applications, an in-depth classification of IoE enablers (sensors and actuators); (3) validation of the defined taxonomy with 50 IoE applications; and (4) identification of issues and challenges in existing IoE applications (using the defined taxonomy) with regard to insights about knowledge processes. To the best of our knowledge, and taking into consideration the 76 other taxonomies compared, this present work represents the most comprehensive taxonomy that provides the orchestration of intelligence in network connections concerning knowledge processes, type of IoE enablers, observation characteristics, and technological capabilities in IoE applications.

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Section: Related Workmentioning

confidence: 99%

“…Several taxonomies for IoT [ 24 , 25 , 26 , 27 , 28 , 29 , 30 ] and IoE [ 6 , 13 , 15 , 17 , 31 , 32 ], have been proposed. However, there are challenges concerning the ranking and managing of knowledge processes in IoE applications.…”

Section: Introductionmentioning

confidence: 99%

Internet of Everything (IoE) Taxonomies: A Survey and a Novel Knowledge-Based Taxonomy

Costa

Oliveira

Souza

2021

Sensors

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“…The Internet of Things (IoT)-based systems are increasingly penetrating all business industries, in which the main characteristic of these systems is the heterogeneity of their components and technologies. Huge numbers of diverse independent devices, such as embedded objects, actuators and sensors, are continuously connected, leading to an enormous scale of components [1]. Such systems usually include a data generator layer, which aggregates data from all connected devices.…”

Section: Introductionmentioning

confidence: 99%

“…Many limitations have been explored for testing IoT systems, such as the inability to handle the dynamicity of connected components [6], diversity of network protocols and technologies [7], real time complexity and scalability of systems that manage their connectivity [8]. In addition, the systems' interoperability should be frequently verified by checking the consistency of services provided by different sensors [1], as well as the transmitted data between receivers and suppliers should be verified against the confidentiality of networks and the safety of users' data [9].…”

Section: Introductionmentioning

confidence: 99%

A Framework for Continuous Regression and Integration Testing in IoT Systems Based on Deep Learning and Search-Based Techniques

et al. 2020

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Tremendous systems are rapidly evolving based on the trendy Internet of Things (IoT) in various domains. Different technologies are used for communication between the massive connected devices through all layers of the IoT system, causing many security and performance issues. Regression and integration testing are considered repeatedly, in which the vast costs and efforts associated with the frequent execution of these inflated test suites hinder the adequate testing of such systems. This necessitates the focus on exploring innovative scalable testing approaches for large test suites in IoT-based systems. In this paper, a scalable framework for continuous integration and regression testing in IoT-based systems (IoT-CIRTF) is proposed, based on IoT-related criteria for test case prioritization and selection. The framework utilizes search-based techniques to provide an optimized prioritized set of test cases to select from. The selection is based on a trained prediction model for IoT standard components using supervised deep learning algorithms to continuously ensure the overall reliability of IoT-based systems. The experiments are held on two GSM datasets. The experimental results achieved prioritization accuracy up to 90% and 92% for regression testing and integration testing respectively. This provides an enhanced and efficient framework for continuous testing of IoT-based systems, as per IoT-related criteria for the prioritization and selection purposes.

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“…Every day more and more objects such as sensors, actuators, appliances, wireless grids, and automated devices are connected to the internet [1]. Such objects are commonly used to measure, diagnose, control, and optimize resource consumption and in the decision-making process towards costeffectiveness.…”

Section: Introductionmentioning

confidence: 99%

Sustainable Engineering and Internet of Things (IoT): trends and perspectives

García-Pérez¹,

Mesa²

2020

Proceedings of the 18th LACCEI International Multi-Conference for Engineering, Education, and Technology: Engineering, Integrat

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Internet of Things-IoT appeared in the forecast of engineering three decades ago. Since then, a vast range of applications has been developed in industrial, urban, and rural matters, where one of the most relevant issues has been sustainability. This paper presents a retrospective analysis of the most relevant trends in sustainability regarding IoT and its applications, considering evolution trends and tangible results from the last decade (2011-2020). When the concept of sustainability in engineering gained importance. The study provides useful insights into the future development of IoT in human life and industrial activities as well as research opportunities in terms of sustainability based on IoT approaches.

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Characterizing Internet of Things Systems through Taxonomies: A Systematic Mapping Study

Cited by 26 publications

References 37 publications

Internet of Everything (IoE) Taxonomies: A Survey and a Novel Knowledge-Based Taxonomy

Internet of Everything (IoE) Taxonomies: A Survey and a Novel Knowledge-Based Taxonomy

A Framework for Continuous Regression and Integration Testing in IoT Systems Based on Deep Learning and Search-Based Techniques

Sustainable Engineering and Internet of Things (IoT): trends and perspectives

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