Trends in Intelligent Communication Systems: Review of Standards, Major Research Projects, and Identification of Research Gaps

Koufos, Konstantinos; Haloui, Karim El; Dianati, Mehrdad; Higgins, Matthew D.; Elmirghani, Jaafar M. H.; Imran, Muhammad Ali; Tafazolli, Rahim

doi:10.3390/jsan10040060

Cited by 13 publications

(6 citation statements)

References 68 publications

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“…One of the most considerable advantages of BD, realistically, is predictive analysis. BID analytics tools can accurately predict possible outcomes, thereby allowing businesses and nonprofit organizations to make better decisions while simultaneously optimizing their operational efficiencies and appreciably reducing decision risks [65]. Therefore, the valuable information carefully extracted from BID analytics is rapidly transforming the principal media into an artificial ecosystem.…”

Section: Intelligent Communication Modelmentioning

confidence: 99%

Big Image Data 15Vs Model for Intelligent Data Ocean of Multimedia Things

Mahabadi,

Abdolazimi

2024

Preprint

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The classification of large image data using traditional methods has been based on changes in image gray features, extraction of edge and contour feature information, or conversion between image coordinate sets. However, due to the growth of new image size and big image data (BID) in real-time multimedia communication and future online big data (BD) applications, these methods have become increasingly complex and resulting in poor real-time performance. These complex methods suffer from complex algorithms, massive data communication, slow processing speed, unintelligent predictive modeling, weak data classification, limited accuracy, uncleaned usage data, combined destructive artificial and natural noise, exchanging data values over time, and exhausting updating data for accurate operation of media storage. In the era of the Internet of Multimedia Things (IoMT), most modern devices can accurately capture vast amounts of observational data in the form of valuable images, text, and acoustic recordings. This reliable data must be kept valid for local data processing at different times and for globally extracting knowledge from information at various data levels to address forthcoming challenges related to big image data. The challenges of secure communication require accurate, reliable, fast, and effective information gathering for local decision-making and global knowledge mining of BID to support cyber-physical systems for speedy data virtualization of smart city devices. Complex and uncontrollable problems persist on the outskirts of grids, fogs, and cloud networks regarding data cleansing and privacy protection for intelligent data ocean management. This study developed a new 15Vs model empirically to examine distributed big image data processing based on a modern BID connectivity approach. The model accurately extracts textured features from visible data images and overcomes a unique set of key challenges. The model introduces a strategic analyzer, uses intelligent local agents, and recruits clever global bots to provide a suitable platform for generous support of bot-oriented BD processing. This instantly forms a hierarchical data level for better data acquisition and cleansing, safe privacy protection, suitable information diffusion, and speedy knowledge extraction. Our study presents a novel definition of BID to address the ultimate challenges of data management in a high-risk environment for further big image data modeling research needs.

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Section: Intelligent Communication Modelmentioning

confidence: 99%

Big Image Data 15Vs Model for Intelligent Data Ocean of Multimedia Things

Mahabadi,

Abdolazimi

2024

Preprint

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“…The surge of cloud computing and communication network connects the related industries, unleashing the full potential of the industrial network orchestrated by machine learning and AI due to their capability to collect and generate large volume of network data. 3 Moreover, AI-based control algorithms, characterized by robust autonomous learning and the ability to handle complexity, have revitalized various control schemes and led to new research avenues. 4 Consequently, a very important technological orientation of industrial intelligence is the systematic study of intelligent control.…”

Section: Introductionmentioning

confidence: 99%

“…In recent years, there has been a trend toward implementing comprehensive intelligence in industrial production. The surge of cloud computing and communication network connects the related industries, unleashing the full potential of the industrial network orchestrated by machine learning and AI due to their capability to collect and generate large volume of network data 3 . Moreover, AI‐based control algorithms, characterized by robust autonomous learning and the ability to handle complexity, have revitalized various control schemes and led to new research avenues 4 .…”

Section: Introductionmentioning

confidence: 99%

Optimization control of the double‐capacity water tank‐level system using the deep deterministic policy gradient algorithm

Pei

2023

Engineering Reports

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Process control systems are subject to external factors such as changes in working conditions and perturbation interference, which can significantly affect the system's stability and overall performance. The application and promotion of intelligent control algorithms with self‐learning, self‐optimization, and self‐adaption characteristics have thus become a challenging yet meaningful research topic. In this article, we propose a novel approach that incorporates the deep deterministic policy gradient (DDPG) algorithm into the control of double‐capacity water tanklevel system. Specifically, we introduce a fully connected layer on the observer side of the critic network to enhance its expression capability and processing efficiency, allowing for the extraction of important features for water‐level control. Additionally, we optimize the node parameters of the neural network and use the RELU activation function to ensure the network's ability to continuously observe and learn from the external water tank environment while avoiding the issue of vanishing gradients. We enhance the system's feedback regulation ability by adding the PID controller output to the observer input based on the liquid level deviation and height. This integration with the DDPG control method effectively leverages the benefits of both, resulting in improved robustness and adaptability of the system. Experimental results show that our proposed model outperforms traditional control methods in terms of convergence, tracking, anti‐disturbance and robustness performances, highlighting its effectiveness in improving the stability and precision of double‐capacity water tank systems.

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“…Fortunately, NextG networks can meet these 31 requirements and support these applications with advanced 32 communication, computing, and Artificial Intelligence (AI) 33 technologies. AI is an extraordinary contributor among 34 them to innovative technologies in NextG networks [3]. 35 Intelligent Reflecting Surfaces (IRS) is one of those innovative technologies, in addition to Massive Multiple-Input Multiple-Output (MIMO) and millimeter wave, to improve the performance of NextG wireless networks in terms of data rate and channel capacity.…”

Section: Introductionmentioning

confidence: 99%

Security Hardening of Intelligent Reflecting Surfaces Against Adversarial Machine Learning Attacks

et al. 2022

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Next-generation communication networks, also known as NextG or 5G and beyond, are the future data transmission systems that aim to connect a large amount of Internet of Things (IoT) devices, systems, applications, and consumers at high-speed data transmission and low latency. Fortunately, NextG networks can achieve these goals with advanced telecommunication, computing, and Artificial Intelligence (AI) technologies in the last decades and support a wide range of new applications. Among advanced technologies, AI has a significant and unique contribution to achieving these goals for beamforming, channel estimation, and Intelligent Reflecting Surfaces (IRS) applications of 5G and beyond networks. However, the security threats and mitigation for AI-powered applications in NextG networks have not been investigated deeply in academia and industry due to being new and more complicated. This paper focuses on an AI-powered IRS implementation in NextG networks along with its vulnerability against adversarial machine learning attacks. This paper also proposes the defensive distillation mitigation method to defend and improve the robustness of the AI-powered IRS model, i.e., reduce the vulnerability. The results indicate that the defensive distillation mitigation method can significantly improve the robustness of AI-powered models and their performance under an adversarial attack. 15 16 ∇ x (x adv , y) + U( ) 245 -Add the gradient to the input data, x adv = x adv + 246 α × sign( ∇x ), 247 where is the budget, N is the number of iterations, and α is 248 the step size. PGD can generate stronger attacks than FGSM 249 and BIM. 250 4) MOMENTUM ITERATIVE METHOD (MIM) 251 MIM is a variant of the BIM adversarial attack, introducing 252 momentum and integrating it into iterative attacks [19]. It is 253 used to compute the gradient of the loss function with respect 254 to the input, x, and then the attacker creates the adversarial 255 example by adding the sign of the gradient to the input data. 256 The gradient sign is computed using the backpropagation 257 algorithm. The steps are summarized as follows: 258 • Initialize the adversarial example x adv = x and the 259 momentum, µ = 0 260 • Iterate i times, where i = 0, 1, 2, 3, . . . , N 261 -Compute the gradient of loss function, ∇ x (x adv , y) 262 -Update the momentum, µ = µ + η × ∇ x (x adv , y) 263 -Add random noise to the gradient, ∇x (x adv , y) = 264 ∇ x (x adv , y) + U( ) 265 -Add the gradient to the input data, x adv = x adv + 266 α × sign( ∇x ), 267 where is the budget, N is the number of iterations, η is the 268 momentum rate, and α is the step size. 269 Note that there are many types of adversarial attacks and 270 defenses. The existing defenses and adversarial attacks for 271 images can be applied to attack and defend on intelligent 272 reflecting surfaces and other fields [20], [21], [22], [23]. 273 The cleverly-designed adversarial examples can fool the deep 274 neural networks with high success rates on the test images. 275 The adversarial examples can also be transfer...

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Trends in Intelligent Communication Systems: Review of Standards, Major Research Projects, and Identification of Research Gaps

Cited by 13 publications

References 68 publications

Big Image Data 15Vs Model for Intelligent Data Ocean of Multimedia Things

Big Image Data 15Vs Model for Intelligent Data Ocean of Multimedia Things

Optimization control of the double‐capacity water tank‐level system using the deep deterministic policy gradient algorithm

Security Hardening of Intelligent Reflecting Surfaces Against Adversarial Machine Learning Attacks

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