Exploring the Role of 6G Technology in Enhancing Quality of Experience for m-Health Multimedia Applications: A Comprehensive Survey

Nasralla, Moustafa M.; Khattak, Sohaib Bin Altaf; Rehman, Ikram Ur; Iqbal, Muddesar

doi:10.3390/s23135882

Cited by 26 publications

(17 citation statements)

References 166 publications

(215 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…6G networks are currently being researched and developed as the next evolution of mobile networks, with the expectation of providing unparalleled transmission speeds, ultra-low latency, and improved coverage [ 25 ]. These networks will incorporate cutting-edge technologies such as terahertz communication, ultra-massive MIMO, AI, machine learning (ML), quantum communication, millimeter, reconfigurable intelligent surfaces, etc.…”

Section: Technological Foundations Of 6g Network and Ai Revolutionmentioning

confidence: 99%

6G Networks and the AI Revolution—Exploring Technologies, Applications, and Emerging Challenges

Chataut,

Nankya,

Akl

2024

Sensors

View full text Add to dashboard Cite

In the rapidly evolving landscape of wireless communication, each successive generation of networks has achieved significant technological leaps, profoundly transforming the way we connect and interact. From the analog simplicity of 1G to the digital prowess of 5G, the journey of mobile networks has been marked by constant innovation and escalating demands for faster, more reliable, and more efficient communication systems. As 5G becomes a global reality, laying the foundation for an interconnected world, the quest for even more advanced networks leads us to the threshold of the sixth-generation (6G) era. This paper presents a hierarchical exploration of 6G networks, poised at the forefront of the next revolution in wireless technology. This study delves into the technological advancements that underpin the need for 6G, examining its key features, benefits, and key enabling technologies. We dissect the intricacies of cutting-edge innovations like terahertz communication, ultra-massive MIMO, artificial intelligence (AI), machine learning (ML), quantum communication, and reconfigurable intelligent surfaces. Through a meticulous analysis, we evaluate the strengths, weaknesses, and state-of-the-art research in these areas, offering a wider view of the current progress and potential applications of 6G networks. Central to our discussion is the transformative role of AI in shaping the future of 6G networks. By integrating AI and ML, 6G networks are expected to offer unprecedented capabilities, from enhanced mobile broadband to groundbreaking applications in areas like smart cities and autonomous systems. This integration heralds a new era of intelligent, self-optimizing networks that promise to redefine the parameters of connectivity and digital interaction. We also address critical challenges in the deployment of 6G, from technological hurdles to regulatory concerns, providing a holistic assessment of potential barriers. By highlighting the interplay between 6G and AI technologies, this study maps out the current landscape and lights the path forward in this rapidly evolving domain. This paper aims to be a cornerstone resource, providing essential insights, addressing unresolved research questions, and stimulating further investigation into the multifaceted realm of 6G networks. By highlighting the synergy between 6G and AI technologies, we aim to illuminate the path forward in this rapidly evolving field.

show abstract

Section: Technological Foundations Of 6g Network and Ai Revolutionmentioning

confidence: 99%

6G Networks and the AI Revolution—Exploring Technologies, Applications, and Emerging Challenges

Chataut,

Nankya,

Akl

2024

Sensors

View full text Add to dashboard Cite

show abstract

“…The multiple media objects have been accumulated over the web as massive multimedia resources that enabled the exploration of several different media types via advanced computing applications, i.e., digital libraries, social media platforms, knowledge-based systems, etc. [25]- [27]. The multimedia information resources enable access and interaction with multiple media objects [28].…”

Section: A Multimedia Resourcesmentioning

confidence: 99%

Sampling Fingerprints From Multimedia Content Resource Clusters

Rashid,

Naseer,

Khan

et al. 2023

IEEE Access

View full text Add to dashboard Cite

Nowadays, the growth of multimedia content over the web is exponential. The fingerprints are inconspicuously embedded in multimedia content. The fingerprints can be exploited to trace divergent information from multimedia resources. Sampling fingerprints, particularly from multimedia resources, is challenging since they are complex, heterogeneous, and diverse. This research proposed an approach to sample fingerprints from multimedia resources. Our approach partitions the multimedia content space into converged clusters using variations of Canberra distance and identifies the most diverged samples using Kullback-Leibler (KL) divergence. The resultant clusters represent the information belonging to particular concepts and the diverged samples within the clusters represent multimedia fingerprints. The fingerprint sampling process is leveraged using unsupervised learning algorithms, instantiated across various multimedia descriptors, and tested over standard multimedia datasets. The average results obtained over various standard visual and acoustic datasets reveal 80%, 77%, and 78% accuracy, precision, and recall, respectively, surpassing most of the existing baseline clustering methods such as K-Means, Mean-Shift, and DBSCAN. Furthermore, the rigorousness of the proposed algorithm clustering is evaluated using the internal clustering stability silhouette coefficient and the fingerprint diversity scores. The results unveil a maximum of 94% diversity score. The proposed variation of Canberra distance and KL divergence provides the most stable performance (SD=0.02) and creates promising implications in future multimedia retrieval, summarization, and exploration activities.

show abstract

“…Researchers have developed several CNN-based models for various purposes, including applications in photovoltaics [49], crowd estimation [50], deep learning in big data [51], classification and detection [2,52,53], medical data/healthcare [54,55], renewable energy [56], energy consumption [57], IoT-based smart cities [58][59][60], and fire recognition [61][62][63][64]. Examples of these architectures include AlexNet, SqueezeNet, GoogleNet, Mo-bileNet, etc.…”

Section: Feature Extraction and Encodingmentioning

confidence: 99%

Efficient Fire Detection with E-EFNet: A Lightweight Deep Learning-Based Approach for Edge Devices

Farman,

Nasralla,

Khattak

et al. 2023

Applied Sciences

Self Cite

View full text Add to dashboard Cite

Fire detection employing vision sensors has drawn significant attention within the computer vision community, primarily due to its practicality and utility. Previous research predominantly relied on basic color features, a methodology that has since been surpassed by adopting deep learning models for enhanced accuracy. Nevertheless, the persistence of false alarms and increased computational demands remains challenging. Furthermore, contemporary feed-forward neural networks face difficulties stemming from their initialization and weight allocation processes, often resulting in vanishing-gradient issues that hinder convergence. This investigation recognizes the considerable challenges and introduces the cost-effective Encoded EfficientNet (E-EFNet) model. This model demonstrates exceptional proficiency in fire recognition while concurrently mitigating the incidence of false alarms. E-EFNet leverages the lightweight EfficientNetB0 as a foundational feature extractor, augmented by a series of stacked autoencoders for refined feature extraction before the final classification phase. In contrast to conventional linear connections, E-EFNet adopts dense connections, significantly enhancing its effectiveness in identifying fire-related scenes. We employ a randomized weight initialization strategy to mitigate the vexing problem of vanishing gradients and expedite convergence. Comprehensive evaluation against contemporary state-of-the-art benchmarks reaffirms E-EFNet’s superior recognition capabilities. The proposed model outperformed state-of-the-art approaches in accuracy over the Foggia and Yar datasets by achieving a higher accuracy of 0.31 and 0.40, respectively, and its adaptability for efficient inferencing on edge devices. Our study thoroughly assesses various deep models before ultimately selecting E-EFNet as the optimal solution for these pressing challenges in fire detection.

show abstract

Exploring the Role of 6G Technology in Enhancing Quality of Experience for m-Health Multimedia Applications: A Comprehensive Survey

Cited by 26 publications

References 166 publications

6G Networks and the AI Revolution—Exploring Technologies, Applications, and Emerging Challenges

6G Networks and the AI Revolution—Exploring Technologies, Applications, and Emerging Challenges

Sampling Fingerprints From Multimedia Content Resource Clusters

Efficient Fire Detection with E-EFNet: A Lightweight Deep Learning-Based Approach for Edge Devices

Contact Info

Product

Resources

About