Keeping users empowered in a cloudy Internet of Things

Gerdes, Stefanie; Bormann, Carsten; Bergmann, Olaf

doi:10.1016/b978-0-12-801595-7.00011-2

Cited by 2 publications

(3 citation statements)

References 5 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Gerdes et al [20] tackle the problem of authorization and authentication for devices with constrained computational power. The authors divide IoT devices into the categories "constrained" and "less-constrained" based on resource availability and allow less-constrained devices to perform some authorization functions on behalf of the constrained devices.…”

Section: Authorizationmentioning

confidence: 99%

“…At the other end of the spectrum, the technologies used in some proposals specifically preclude centralization. For instance, methods which rely on the creators of data to specify security rules, or which grant access selectively, do not operate with a central server [20,35,37,39,48,55]. Blockchain-based access rule verification [33,40] also can not be centralized, and the same applies to extensions of the ABAC system which rely on peer devices to confirm an entity's attributes over the network [50].…”

Section: Distribution Versus Centralizationmentioning

confidence: 99%

“…Given these differences in capability, the adaptation of existing M2M security technologies [21,30,34,43,55] works well for IoT scenarios where a user is not required. Proposals exist for M2M authentication even with low-resource devices [20,44,45].…”

Section: User Versus Device-centrismmentioning

confidence: 99%

See 2 more Smart Citations

Survey of Authentication and Authorization for the Internet of Things

Trnka

Černý

Stickney

2018

Security and Communication Networks

View full text Add to dashboard Cite

The Internet of Things is currently getting significant interest from the scientific community. Academia and industry are both focused on moving ahead in attempts to enhance usability, maintainability, and security through standardization and development of best practices. We focus on security because of its impact as one of the most limiting factors to wider Internet of Things adoption. Numerous research areas exist in the security domain, ranging from cryptography to network security to identity management. This paper provides a survey of existing research applicable to the Internet of Things environment at the application layer in the areas of identity management, authentication, and authorization. We survey and analyze more than 200 articles, categorize them, and present current trends in the Internet of Things security domain.

show abstract

Section: Authorizationmentioning

confidence: 99%

Section: Distribution Versus Centralizationmentioning

confidence: 99%

See 1 more Smart Citation

Survey of Authentication and Authorization for the Internet of Things

Trnka

Černý

Stickney

2018

Security and Communication Networks

View full text Add to dashboard Cite

show abstract

Neural Network for Low-Memory IoT Devices and MNIST Image Recognition Using Kernels Based on Logistic Map

Velichko

2020

Electronics

View full text Add to dashboard Cite

This study presents a neural network which uses filters based on logistic mapping (LogNNet). LogNNet has a feedforward network structure, but possesses the properties of reservoir neural networks. The input weight matrix, set by a recurrent logistic mapping, forms the kernels that transform the input space to the higher-dimensional feature space. The most effective recognition of a handwritten digit from MNIST-10 occurs under chaotic behavior of the logistic map. The correlation of classification accuracy with the value of the Lyapunov exponent was obtained. An advantage of LogNNet implementation on IoT devices is the significant savings in memory used. At the same time, LogNNet has a simple algorithm and performance indicators comparable to those of the best resource-efficient algorithms available at the moment. The presented network architecture uses an array of weights with a total memory size from 1 to 29 kB and achieves a classification accuracy of 80.3–96.3%. Memory is saved due to the processor, which sequentially calculates the required weight coefficients during the network operation using the analytical equation of the logistic mapping. The proposed neural network can be used in implementations of artificial intelligence based on constrained devices with limited memory, which are integral blocks for creating ambient intelligence in modern IoT environments. From a research perspective, LogNNet can contribute to the understanding of the fundamental issues of the influence of chaos on the behavior of reservoir-type neural networks.

show abstract

Keeping users empowered in a cloudy Internet of Things

Cited by 2 publications

References 5 publications

Survey of Authentication and Authorization for the Internet of Things

Survey of Authentication and Authorization for the Internet of Things

Neural Network for Low-Memory IoT Devices and MNIST Image Recognition Using Kernels Based on Logistic Map

Contact Info

Product

Resources

About