3,295 research outputs found
Personalized Resource Allocation in Wireless Networks: An AI-Enabled and Big Data-Driven Multi-Objective Optimization
The design and optimization of wireless networks have mostly been based on
strong mathematical and theoretical modeling. Nonetheless, as novel
applications emerge in the era of 5G and beyond, unprecedented levels of
complexity will be encountered in the design and optimization of the network.
As a result, the use of Artificial Intelligence (AI) is envisioned for wireless
network design and optimization due to the flexibility and adaptability it
offers in solving extremely complex problems in real-time. One of the main
future applications of AI is enabling user-level personalization for numerous
use cases. AI will revolutionize the way we interact with computers in which
computers will be able to sense commands and emotions from humans in a
non-intrusive manner, making the entire process transparent to users. By
leveraging this capability, and accelerated by the advances in computing
technologies, wireless networks can be redesigned to enable the personalization
of network services to the user level in real-time. While current wireless
networks are being optimized to achieve a predefined set of quality
requirements, the personalization technology advocated in this article is
supported by an intelligent big data-driven layer designed to micro-manage the
scarce network resources. This layer provides the intelligence required to
decide the necessary service quality that achieves the target satisfaction
level for each user. Due to its dynamic and flexible design, personalized
networks are expected to achieve unprecedented improvements in optimizing two
contradicting objectives in wireless networks: saving resources and improving
user satisfaction levels
The crowd as a cameraman : on-stage display of crowdsourced mobile video at large-scale events
Recording videos with smartphones at large-scale events such as concerts and festivals is very common nowadays. These videos register the atmosphere of the event as it is experienced by the crowd and offer a perspective that is hard to capture by the professional cameras installed throughout the venue. In this article, we present a framework to collect videos from smartphones in the public and blend these into a mosaic that can be readily mixed with professional camera footage and shown on displays during the event. The video upload is prioritized by matching requests of the event director with video metadata, while taking into account the available wireless network capacity. The proposed framework's main novelty is its scalability, supporting the real-time transmission, processing and display of videos recorded by hundreds of simultaneous users in ultra-dense Wi-Fi environments, as well as its proven integration in commercial production environments. The framework has been extensively validated in a controlled lab setting with up to 1 000 clients as well as in a field trial where 1 183 videos were collected from 135 participants recruited from an audience of 8 050 people. 90 % of those videos were uploaded within 6.8 minutes
Unleashing the Power of Edge-Cloud Generative AI in Mobile Networks: A Survey of AIGC Services
Artificial Intelligence-Generated Content (AIGC) is an automated method for
generating, manipulating, and modifying valuable and diverse data using AI
algorithms creatively. This survey paper focuses on the deployment of AIGC
applications, e.g., ChatGPT and Dall-E, at mobile edge networks, namely mobile
AIGC networks, that provide personalized and customized AIGC services in real
time while maintaining user privacy. We begin by introducing the background and
fundamentals of generative models and the lifecycle of AIGC services at mobile
AIGC networks, which includes data collection, training, finetuning, inference,
and product management. We then discuss the collaborative cloud-edge-mobile
infrastructure and technologies required to support AIGC services and enable
users to access AIGC at mobile edge networks. Furthermore, we explore
AIGCdriven creative applications and use cases for mobile AIGC networks.
Additionally, we discuss the implementation, security, and privacy challenges
of deploying mobile AIGC networks. Finally, we highlight some future research
directions and open issues for the full realization of mobile AIGC networks
Realizing an Efficient IoMT-Assisted Patient Diet Recommendation System Through Machine Learning Model
Recent studies have shown that robust diets recommended to patients by Dietician or an Artificial Intelligent automated medical diet based cloud system can increase longevity, protect against further disease, and improve the overall quality of life. However, medical personnel are yet to fully understand patient-dietician’s rationale of recommender system. This paper proposes a deep learning solution for health base medical dataset that automatically detects which food should be given to which patient base on the disease and other features like age, gender, weight, calories, protein, fat, sodium, fiber, cholesterol. This research framework is focused on implementing both machine and deep learning algorithms like, logistic regression, naive bayes, Recurrent Neural Network (RNN), Multilayer Perceptron (MLP), Gated Recurrent Units (GRU), and Long Short-Term Memory (LSTM). The medical dataset collected through the internet and hospitals consists of 30 patient’s data with 13 features of different diseases and 1000 products. Product section has 8 features set. The features of these IoMT data were analyzed and further encoded before applying deep and machine and learning-based protocols. The performance of various machine learning and deep learning techniques was carried and the result proves that LSTM technique performs better than other scheme with respect to forecasting accuracy, recall, precision, and -measures. We achieved 97.74% accuracy using LSTM deep learning model. Similarly 98% precision, 99% recall and -measure for allowed class is achieved, and for not-allowed class precision is 89%, recall score is 73% and Measure score is 80%
A Revolution of Personalized Healthcare: Enabling Human Digital Twin with Mobile AIGC
Mobile Artificial Intelligence-Generated Content (AIGC) technology refers to
the adoption of AI algorithms deployed at mobile edge networks to automate the
information creation process while fulfilling the requirements of end users.
Mobile AIGC has recently attracted phenomenal attentions and can be a key
enabling technology for an emerging application, called human digital twin
(HDT). HDT empowered by the mobile AIGC is expected to revolutionize the
personalized healthcare by generating rare disease data, modeling high-fidelity
digital twin, building versatile testbeds, and providing 24/7 customized
medical services. To promote the development of this new breed of paradigm, in
this article, we propose a system architecture of mobile AIGC-driven HDT and
highlight the corresponding design requirements and challenges. Moreover, we
illustrate two use cases, i.e., mobile AIGC-driven HDT in customized surgery
planning and personalized medication. In addition, we conduct an experimental
study to prove the effectiveness of the proposed mobile AIGC-driven HDT
solution, which shows a particular application in a virtual physical therapy
teaching platform. Finally, we conclude this article by briefly discussing
several open issues and future directions
- …