A Functional Architecture for 6G Special-Purpose Industrial IoT Networks

Mahmood, Nurul Huda; Berardinelli, Gilberto; Khatib, Emil J.; Hashemi, Ramin; Lima, Carlos de

doi:10.1109/tii.2022.3182988

Cited by 17 publications

(4 citation statements)

References 41 publications

(79 reference statements)

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“…Several research papers have discussed a number of applications expected to use 6G network. Among the papers that have presented these applications are [22][23][24][25]. Among the expected applications to use the 6G network areindustrial networks, smart healthcare, ecological remote sensing monitoring, holographiccommunication, internet of things, smart railway networks and autonomous vehicles.…”

Section: Applications Of 6gmentioning

confidence: 99%

Robust and Sophisticated Combined-Layered Security: Secured 6G Mobile Network Considerations

Sr,

El-Bendary,

Eltokhy

et al. 2024

Preprint

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Deep learning, machine learning and artificial intelligence have been proposed for enhancing the multimedia processing and security as well as the advanced security tools for 6G networks. The vision of 6G networks refers to linking undersea, terrestrial, and space networks together. This vision involves transferring a massive amount of data over the network. The data hiding based on the deep learning is considered compared to the traditional steganography tools in 6G network, it requires robust and flexible/combined multi- levels of security. This paper presents the proposed vision of 6G security. This paper focuses on security levels on 6G network. The paper proposes a multi-level security system that secures the data without affecting it. The multi-level security system consists of three security levels, which are two encryption techniques and a data hiding technique. The paper carried out several simulation experiments using multi dataset (Mathwork, Yolov8 and others) to evaluate the proposed scenarios and find integration of these techniques that provides the best security performance without affecting the data. The best simulation experiments that provided the best data security performance were the integration between 2D Logistic map, SVD, and Baker Map, respectively. The proposed steganography performs better than the recent published related works and compared with the deep learning based steganography. The proposed combined system provided the better simulation results for image security. The simulation results indicated a perfect match between the original message and the decryption original message after applying the system. The results also indicated that there was no effect on the data and no loss of data.

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Section: Applications Of 6gmentioning

confidence: 99%

Robust and Sophisticated Combined-Layered Security: Secured 6G Mobile Network Considerations

Sr,

El-Bendary,

Eltokhy

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

Section: 2applications Of 6gmentioning

confidence: 99%

Flexible Multi-Layered Security System: Secure 6G Mobile Network Considerations

Sr,

El-Bendary,

Eltokhy

et al. 2023

Preprint

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The vision of 6G networks refers to linking undersea, terrestrial, and space networks together. This vision involves transferring a massive amount of data over the network. 6G network requires very high levels of security. This paper presents the proposed vision of 6G security. This paper focuses on security levels on 6G network. The paper proposes a multi-level security system that secures the data without affecting it. The multi-level security system consists of three security levels, which are two encryption techniques and a data hiding technique. The paper carried outmany simulation experiments to find the best integration of these techniques that provides the best security performance without affecting the data. The best simulation experiments that provided the best data security performance were the integration between 2D Logistic map, SVD, and Baker Map, respectively. This system provided the best simulation results for data security. The simulation results indicated a perfect match between the original message and the decryption original message after applying the system. The results also indicated that there was no effect on the data and no loss of data.

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“…The author R. Hashemi would like to acknowledge the support of the Nokia Scholarship Foundation. they are characterized by a large number of use-cases and applications [4], [5]. An underlying commonality among these diverse applications is that the wireless industrial automation connectivity solutions envisioned in Industry 4.0 (initialized in 5G) [6] will leverage cloud computing and machine learning throughout the manufacturing process.…”

Section: Introductionmentioning

confidence: 99%

Deep Reinforcement Learning for Practical Phase-Shift Optimization in RIS-Aided MISO URLLC Systems

Hashemi

Ali

Mahmood

2023

IEEE Internet Things J.

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We study the joint active/passive beamforming and channel blocklength (CBL) allocation in a non-ideal reconfigurable intelligent surface (RIS)-aided ultra-reliable and lowlatency communication (URLLC) system. The considered scenario is a finite blocklength (FBL) regime and the problem is solved by leveraging a deep reinforcement learning (DRL) algorithm named twin-delayed deep deterministic policy gradient (TD3). First, assuming an industrial automation system, the signal-to-interference-plus-noise ratio and achievable rate in the FBL regime are identified for each actuator. Next, the joint active/passive beamforming and CBL optimization problem is formulated where the objective is to maximize the total achievable FBL rate in all actuators, subject to non-linear amplitude response at the RIS elements, BS transmit power budget and total available CBL. Since the formulated problem is highly nonconvex and non-linear, we resort to employing an actor-critic policy gradient DRL algorithm based on TD3. The considered method relies on interacting RIS with the industrial automation environment by taking actions which are the phase shifts at the RIS elements, CBL variables, and BS beamforming to maximize the expected observed reward, i.e., the total FBL rate. We assess the performance loss of the system when the RIS is non-ideal, i.e., with non-linear amplitude response, and compare it with ideal RIS without impairments. The numerical results show that optimizing the RIS phase shifts, BS beamforming, and CBL variables via the TD3 method with deterministic policy outperforms conventional methods and it is highly beneficial for improving the network total FBL rate considering finite CBL size.

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A Functional Architecture for 6G Special-Purpose Industrial IoT Networks

Cited by 17 publications

References 41 publications

Robust and Sophisticated Combined-Layered Security: Secured 6G Mobile Network Considerations

Robust and Sophisticated Combined-Layered Security: Secured 6G Mobile Network Considerations

Flexible Multi-Layered Security System: Secure 6G Mobile Network Considerations

Deep Reinforcement Learning for Practical Phase-Shift Optimization in RIS-Aided MISO URLLC Systems

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