IoT Platforms for the Internet of Production

Hoffmann, Jörg; Heimes, Pit; Senel, Semih

doi:10.1109/jiot.2018.2875594

Cited by 50 publications

(13 citation statements)

References 3 publications

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“…The international commercialized IoT platforms include the Google Cloud, Microsoft Azure, Ayla Agile, SAP Cloud, PTC Thing Worx, Amazon Web Service, etc. [27] that provides data aggregation, visualization and analytics services.…”

Section: Iot Platformsmentioning

confidence: 99%

“…The security features of the platform are based on multiple Non-Access Stratum (NAS) and Access Stratum (AS) ciphering and integrity algorithms existing between the UE and the eNB and MME respectively as inherited from LTE system. The security architecture as specified in the 3GPP Release 10 [31], indicates that the security configuration level depends on the UE capabilities [32] in addition to other requirements as described below: 1) Security Requirements: In [27], NB-IoT security relied on SNOW 3G encryption to provide data confidentiality and on ATR-128 for secure inter-mobile network data transfer. In our platform, the UE supports Secure Hash Algorithm (SHA), Message Digest 5 (MD5), Digital Encryption System (DES) and Advanced Encryption System (AES).…”

Section: B Nb-iot/lte-mmentioning

confidence: 99%

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Security Analysis of IoT Networks and Platforms

Ugwuanyi

Irvine

2020

2020 International Symposium on Networks, Computers and Communications (ISNCC)

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Section: Iot Platformsmentioning

confidence: 99%

Section: B Nb-iot/lte-mmentioning

confidence: 99%

Security Analysis of IoT Networks and Platforms

Ugwuanyi

Irvine

2020

2020 International Symposium on Networks, Computers and Communications (ISNCC)

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“…Many approaches and solutions have been introduced in the literature which address adaption layers and integration techniques to achieve protocol interoperability in heterogeneous IoT systems. Protocol interoperability has been implemented into the most popular IoT cloud-based services [3,4]. OneM2M is a standard defined to achieve a unique architecture for IoT technologies [5].…”

Section: Related Workmentioning

confidence: 99%

Message-Based Communication for Heterogeneous Internet of Things Systems

Oniga

Denis

Dadarlat

et al. 2020

Sensors

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The Internet of Things (IoT) domain presents a wide spectrum of technologies for building IoT applications. The requirements are varying from one application to another granting uniqueness to each IoT system. Each application demands custom implementations to achieve efficient, secure and cost-effective environments. They pose a set of properties that cannot be addressed by a single-based protocol IoT network. Such properties are achievable by designing a heterogeneous IoT system, which integrates diverse IoT protocols and provides a network management solution to efficiently manage the system components. This paper proposes an IoT message-based communication model applied atop the IoT protocols in order to achieve functional scalability and network management transparency agnostic to the employed communication protocol. The paper evaluates the proposed communication model and proves its functional scalability in a heterogeneous IoT system. The experimental assessment compares the payload size of the proposed system with respect to the LwM2M standard, a protocol designed specifically for IoT applications. In addition, the paper discusses the energy consumption introduced by the proposed model as well as the options available to reduce such impact.

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“…FDD identifies anomalies of critical equipment by analyzing and mining recorded data to deliver notifications to operators [6,7]. Recent embedded systems, wireless networks and cloud computing provide a strong technology push for the development of next generation FDD systems [3,4,8,9].…”

Section: Introductionmentioning

confidence: 99%

Fault Detection and Diagnosis Using Combined Autoencoder and Long Short-Term Memory Network

Park

Marco

Shin

et al. 2019

Sensors

141

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Fault detection and diagnosis is one of the most critical components of preventing accidents and ensuring the system safety of industrial processes. In this paper, we propose an integrated learning approach for jointly achieving fault detection and fault diagnosis of rare events in multivariate time series data. The proposed approach combines an autoencoder to detect a rare fault event and a long short-term memory (LSTM) network to classify different types of faults. The autoencoder is trained with offline normal data, which is then used as the anomaly detection. The predicted faulty data, captured by autoencoder, are put into the LSTM network to identify the types of faults. It basically combines the strong low-dimensional nonlinear representations of the autoencoder for the rare event detection and the strong time series learning ability of LSTM for the fault diagnosis. The proposed approach is compared with a deep convolutional neural network approach for fault detection and identification on the Tennessee Eastman process. Experimental results show that the combined approach accurately detects deviations from normal behaviour and identifies the types of faults within the useful time.

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IoT Platforms for the Internet of Production

Cited by 50 publications

References 3 publications

Security Analysis of IoT Networks and Platforms

Security Analysis of IoT Networks and Platforms

Message-Based Communication for Heterogeneous Internet of Things Systems

Fault Detection and Diagnosis Using Combined Autoencoder and Long Short-Term Memory Network

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