no code implementations • 28 Jul 2023 • Vinay Chamola, Gaurang Bansal, Tridib Kumar Das, Vikas Hassija, Naga Siva Sai Reddy, Jiacheng Wang, Sherali Zeadally, Amir Hussain, F. Richard Yu, Mohsen Guizani, Dusit Niyato
This paper offers a comprehensive exploration of how generative AI technologies are shaping the Metaverse, transforming it into a dynamic, immersive, and interactive virtual world.
In recent years, ubiquitous semantic Metaverse has been studied to revolutionize immersive cyber-virtual experiences for augmented reality (AR) and virtual reality (VR) users, which leverages advanced semantic understanding and representation to enable seamless, context-aware interactions within mixed-reality environments.
We also discuss the technical details related to the automatic driving comfort system, the response time of the AV driver, the comfort level of the AV, motion sickness, and related optimization technologies.
no code implementations • 18 Apr 2023 • Hani Sami, Ahmad Hammoud, Mouhamad Arafeh, Mohamad Wazzeh, Sarhad Arisdakessian, Mario Chahoud, Osama Wehbi, Mohamad Ajaj, Azzam Mourad, Hadi Otrok, Omar Abdel Wahab, Rabeb Mizouni, Jamal Bentahar, Chamseddine Talhi, Zbigniew Dziong, Ernesto Damiani, Mohsen Guizani
To address these issues, we present in this survey a novel multi-layered pipeline ecosystem composed of (1) the Metaverse computing, networking, communications and hardware infrastructure, (2) environment digitization, and (3) user interactions.
Massive random access of devices in the emerging Open Radio Access Network (O-RAN) brings great challenge to the access control and management.
This approach minimizes inter-cloudlet communication thereby alleviating the communication overhead of the decentralized approach while promoting scalability due to cloudlet-level decentralization.
Finally, the attack effectiveness property was concluded from the fact that it was able to flip the sign of the loss gradient in the conducted experiments to become positive, which indicated noisy and unstable training.
Most of the literature focuses on training machine learning for the user by transmitting their data to an external server, subject to private user data exposure to threats.
In this paper, we address the aforementioned limitations by introducing an On-Demand-FL, a client deployment approach for FL, offering more volume and heterogeneity of data in the learning process.
Our solution involves the design of: (1) preference functions for the client IoT devices and federated servers to allow them to rank each other according to several factors such as accuracy and price, (2) intelligent matching algorithms that take into account the preferences of both parties in their design, and (3) bootstrapping technique that capitalizes on the collaboration of multiple federated servers in order to assign initial accuracy value for the newly connected IoT devices.
The microgrid considers consumers, prosumers, the service provider, and a community battery.
In this paper, we present an approach that targets the security of collaborative deep inference via re-thinking the distribution strategy, without sacrificing the model performance.
The concept of federated learning (FL) was first proposed by Google in 2016.
With the deployment of 5G networks, standards organizations have started working on the design phase for sixth-generation (6G) networks.
Due to the differences of clients, a single global model may not perform well on all clients, so the personalized federated learning method, which trains a personalized model for each client that better suits its individual needs, becomes a research hotspot.
CoFED is a federated learning method that is compatible with heterogeneous models, tasks, and training processes.
Federated learning (FL) has been increasingly considered to preserve data training privacy from eavesdropping attacks in mobile edge computing-based Internet of Thing (EdgeIoT).
This paper proposes a bandwidth aware multi domain virtual network embedding algorithm (BA-VNE).
Therefore, it is crucial to motivate edge devices to become learners and offer their computing resources, and either offer their private data or receive the needed data from the orchestrator and participate in the training process of a learning task.
The scenario results showed a decrease of 522. 2 kW of active power when compared to original consumption over a 200-hours period.
The heterogeneity in edge devices' capabilities will require the joint optimization of the learners-orchestrator association and task allocation.
After that, we provide a deep literature review for the applications of RL in I-health systems.
Federated learning (FL) is a distributed learning methodology that allows multiple nodes to cooperatively train a deep learning model, without the need to share their local data.
Crowdsourced live video streaming (livecast) services such as Facebook Live, YouNow, Douyu and Twitch are gaining more momentum recently.
Unmanned Aerial Vehicles (UAVs) have attracted great interest in the last few years owing to their ability to cover large areas and access difficult and hazardous target zones, which is not the case of traditional systems relying on direct observations obtained from fixed cameras and sensors.
The confluence of pervasive computing and artificial intelligence, Pervasive AI, expanded the role of ubiquitous IoT systems from mainly data collection to executing distributed computations with a promising alternative to centralized learning, presenting various challenges.
In this paper, we provide a comprehensive overview of different aspects of AI and Big Data in Industry 4. 0 with a particular focus on key applications, techniques, the concepts involved, key enabling technologies, challenges, and research perspective towards deployment of Industry 5. 0.
Due to a drone's capability to fly closer to IoT, UAV technology plays a vital role in greening IoT by transmitting collected data to achieve a sustainable, reliable, eco-friendly Industry 4. 0.
The objective of this article is to highlight the benefits of the adoption of edge intelligent technology, along with AI in smart healthcare systems.
With the prevalence of Internet of Things (IoT)-based social media applications, the distance among people has been greatly shortened.
Distributed learning algorithms aim to leverage distributed and diverse data stored at users' devices to learn a global phenomena by performing training amongst participating devices and periodically aggregating their local models' parameters into a global model.
In this work, we solve the problem of optimizing accuracy in stateful FL with a budgeted number of candidate clients by selecting the best candidate clients in terms of test accuracy to participate in the training process.
A modified three-dimensional Markov chain model adopting the quitting probability and cluster division is developed for the performance analysis.
Information Theory Information Theory
Due to the large size of scattering elements, the passive beamforming is typically challenged by the high computational complexity and inexact channel information.
Based on this metric, a reliable worker selection scheme is proposed for federated learning tasks.
Cryptography and Security
This can be achieved by advocating a geo-distributed cloud infrastructure to allocate the multimedia resources as close as possible to viewers, in order to minimize the access delay and video stalls.
In recent years, with the development of the marine industry, navigation environment becomes more complicated.
Specifically, the Multimodal IRIS model consists of three modules, i. e., multimodal feature learning module, the Interest-Related Network (IRN) module and item similarity recommendation module.
The sparsity and self-similarity of the image blocks are taken as the constraints.
An automated testing framework is needed to help these learning-based malware detection systems for IoT devices perform security analysis.
We present and propose state-of-the-art algorithms to address design challenges with both quantitative and qualitative methods and map these challenges with important CPS applications to draw insightful conclusions on the challenges of each application.
In this paper, we propose a semi-supervised deep reinforcement learning model that fits smart city applications as it consumes both labeled and unlabeled data to improve the performance and accuracy of the learning agent.
Consequently, ML/DL methods are important in transforming the security of IoT systems from merely facilitating secure communication between devices to security-based intelligence systems.
With the ever growing diversity of devices and applications that will be connected to 5G networks, flexible and agile service orchestration with acknowledged QoE that satisfies end-user's functional and QoS requirements is necessary.
The Localization of the target object for data retrieval is a key issue in the Intelligent and Connected Transportation Systems (ICTS).
The potential of using emerging DL techniques for IoT data analytics are then discussed, and its promises and challenges are introduced.
Traffic congestion is one of the most serious problems in a city, which can be predicted in advance by analyzing traffic flow patterns.
In this paper, we design a multimodal framework for object detection, recognition and mapping based on the fusion of stereo camera frames, point cloud Velodyne Lidar scans, and Vehicle-to-Vehicle (V2V) Basic Safety Messages (BSMs) exchanged using Dedicated Short Range Communication (DSRC).