Comparing Apples and Oranges in IoT Context: A Deep Dive Into Methods for Comparing IoT Platforms

Mijuskovic, Adriana; Ullah, Ikram; Bemthuis, Rob; Meratnia, Nirvana; Havinga, Paul J.M.

doi:10.1109/jiot.2020.3016921

Cited by 28 publications

(15 citation statements)

References 43 publications

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“…This section presents the results for the SMMS, based on the analysis of the information extracted from the 50 selected publications [11]- [14], [19]- [64] and IoT platforms' documentation [65]-[69].…”

Section: Resultsmentioning

confidence: 99%

“…There are already several reviews, evaluations and comparisons for IoT platforms [10]- [14]. The work in [12] compare the most important cloud platforms related to IoT, according to some requirements and performance tests.…”

Section: Related Workmentioning

confidence: 99%

“…The work in [14] combine a set of non-functional and functional requirements together with a set of methods and statistical tests to propose a framework for comparing IoT platforms. Different from this study, the selection process uses K-means to cluster functional requirements and consequently, the platform, but not present the main software elements of the platforms.…”

Section: Related Workmentioning

confidence: 99%

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The Anatomy of IoT Platforms—A Systematic Multivocal Mapping Study

Barros

Neto

et al. 2022

IEEE Access

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Due to the large variety of Internet of Things (IoT) platforms, selecting the right one to implement an IoT solution is a tough task. To mitigate right selection by the developer, this paper presents a Systematic Multivocal Mapping Study on IoT platforms and its main software elements, to define their anatomy considering how they were studied by the market analysts and academia. By using a precise protocol defined on this work, it was possible to select 63 academic articles and industry reports that perform IoT platform descriptions, evaluations and comparisons. As results, this paper identified the most important IoT platforms are AWS IoT, Azure IoT, Watson IoT, PTC ThingWorx and Google IoT. Its main capabilities are Interoperability, Security & Privacy, Developer Support, Data Management, Device Management and Services Management. It was also defined an architectural model with the main platform components highlighted according to their relevance, the main communication models (Publish/Subscribe and REST APIs) and the common API that should be implemented by the IoT platforms.

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Section: Resultsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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The Anatomy of IoT Platforms—A Systematic Multivocal Mapping Study

Barros

Neto

et al. 2022

IEEE Access

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“…The one-column matrix formed as a result of this equation is called the composite distance matrix [CD] as shown in (12).…”

Section: : ð11þmentioning

confidence: 99%

A Secure IoT‐Based Cloud Platform Selection Using Entropy Distance Approach and Fuzzy Set Theory

Chakraborty

Jindal

Khosravi

et al. 2021

Wireless Communications and Mobile Computing

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With the growing emergence of the Internet connectivity in this era of Gen Z, several IoT solutions have come into existence for exchanging large scale of data securely, backed up by their own unique cloud service providers (CSPs). It has, therefore, generated the need for customers to decide the IoT cloud platform to suit their vivid and volatile demands in terms of attributes like security and privacy of data, performance efficiency, cost optimization, and other individualistic properties as per unique user. In spite of the existence of many software solutions for this decision-making problem, they have been proved to be inadequate considering the distinct attributes unique to individual user. This paper proposes a framework to represent the selection of IoT cloud platform as a MCDM problem, thereby providing a solution of optimal efficacy with a particular focus in user-specific priorities to create a unique solution for volatile user demands and agile market trends and needs using optimized distance-based approach (DBA) aided by Fuzzy Set Theory.

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“…In the current era of Internet of Things (IoT) revolution, the overwhelming applications of IoT are evolving in many domains [ 1 , 2 ]. The amount of data IoT devices generate is rapidly increasing [ 3 ].…”

Section: Introductionmentioning

confidence: 99%

Threat Modeling—How to Visualize Attacks on IOTA?

Ullah

Roode

Meratnia

et al. 2021

Sensors

Self Cite

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Internet of Things (IoT) has been deployed in a vast number of smart applications with the aim to bring ease and comfort into our lives. However, with the expansion of IoT applications, the number of security and privacy breaches has also increased, which brings into question the resilience of existing security and trust mechanisms. Furthermore, the contemporaneous centralized technology is posing significant challenges viz scalability, transparency and efficiency to wide range of IoT applications such as smart logistics, where millions of IoT devices need to be connected simultaneously. Alternatively, IOTA is a distributed ledger technology that offers resilient security and trust mechanisms and a decentralized architecture to overcome IoT impediments. IOTA has already been implemented in many applications and has clearly demonstrated its significance in real-world applications. Like any other technology, IOTA unfortunately also encounters security vulnerabilities. The purpose of this study is to explore and highlight security vulnerabilities of IOTA and simultaneously demonstrate the value of threat modeling in evaluating security vulnerabilities of distributed ledger technology. IOTA vulnerabilities are scrutinized in terms of feasibility and impact and we have also presented prevention techniques where applicable. To identify IOTA vulnerabilities, we have examined existing literature and online blogs. Literature available on this topic is very limited so far. As far as we know IOTA has barely been addressed in the traditional journals, conferences and books. In total we have identified six vulnerabilities. We used Common Vulnerability Scoring System (CVSS v3.0) to further categorize these vulnerabilities on the basis of their feasibility and impact.

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Comparing Apples and Oranges in IoT Context: A Deep Dive Into Methods for Comparing IoT Platforms

Cited by 28 publications

References 43 publications

The Anatomy of IoT Platforms—A Systematic Multivocal Mapping Study

The Anatomy of IoT Platforms—A Systematic Multivocal Mapping Study

A Secure IoT‐Based Cloud Platform Selection Using Entropy Distance Approach and Fuzzy Set Theory

Threat Modeling—How to Visualize Attacks on IOTA?

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