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Found 60 papers, 7 papers with code
A Blockchain-based Reliable Federated Meta-learning for Metaverse: A Dual Game Framework
A blockchain-based cooperative coalition formation game is crafted, grounded on a reputation metric, user similarity, and incentives.
CosmoCLIP: Generalizing Large Vision-Language Models for Astronomical Imaging
Existing vision-text contrastive learning models enhance representation transferability and support zero-shot prediction by matching paired image and caption embeddings while pushing unrelated pairs apart.
AstroSpy: On detecting Fake Images in Astronomy via Joint Image-Spectral Representations
The prevalence of AI-generated imagery has raised concerns about the authenticity of astronomical images, especially with advanced text-to-image models like Stable Diffusion producing highly realistic synthetic samples.
Multi-UAV Multi-RIS QoS-Aware Aerial Communication Systems using DRL and PSO
Our system model considers a UAV swarm that navigates an area, providing wireless communication to ground users with RIS support to improve the coverage of the UAVs.
Efficient Prompting for LLM-based Generative Internet of Things
Large language models (LLMs) have demonstrated remarkable capacities on various tasks, and integrating the capacities of LLMs into the Internet of Things (IoT) applications has drawn much research attention recently.
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Can We Enhance the Quality of Mobile Crowdsensing Data Without Ground Truth?
Given sensing data submitted by MUs, PRBTD can eliminate the data with heavy noise and identify malicious MUs with high accuracy.
FLARE up your data: Diffusion-based Augmentation Method in Astronomical Imaging
The intersection of Astronomy and AI encounters significant challenges related to issues such as noisy backgrounds, lower resolution (LR), and the intricate process of filtering and archiving images from advanced telescopes like the James Webb.
On-Demand Model and Client Deployment in Federated Learning with Deep Reinforcement Learning
In Federated Learning (FL), the limited accessibility of data from diverse locations and user types poses a significant challenge due to restricted user participation.
Enhancing Mutual Trustworthiness in Federated Learning for Data-Rich Smart Cities
Federated learning is a promising collaborative and privacy-preserving machine learning approach in data-rich smart cities.
Trust Driven On-Demand Scheme for Client Deployment in Federated Learning
In our research, we are motivated to integrate a trust element into the client selection and model deployment processes within our system architecture.
From Generative AI to Generative Internet of Things: Fundamentals, Framework, and Outlooks
Generative Artificial Intelligence (GAI) possesses the capabilities of generating realistic data and facilitating advanced decision-making.
Beyond Reality: The Pivotal Role of Generative AI in the Metaverse
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.
Towards Ubiquitous Semantic Metaverse: Challenges, Approaches, and Opportunities
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.
Motion Comfort Optimization for Autonomous Vehicles: Concepts, Methods, and Techniques
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.
The Metaverse: Survey, Trends, Novel Pipeline Ecosystem & Future Directions
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.
Sparsity-Aware Intelligent Massive Random Access Control in Open RAN: A Reinforcement Learning Based Approach
Massive random access of devices in the emerging Open Radio Access Network (O-RAN) brings great challenge to the access control and management.
Semi-decentralized Inference in Heterogeneous Graph Neural Networks for Traffic Demand Forecasting: An Edge-Computing Approach
This approach minimizes inter-cloudlet communication thereby alleviating the communication overhead of the decentralized approach while promoting scalability due to cloudlet-level decentralization.
An Incremental Gray-box Physical Adversarial Attack on Neural Network Training
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.
Warmup and Transfer Knowledge-Based Federated Learning Approach for IoT Continuous Authentication
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.
ON-DEMAND-FL: A Dynamic and Efficient Multi-Criteria Federated Learning Client Deployment Scheme
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.
FedMint: Intelligent Bilateral Client Selection in Federated Learning with Newcomer IoT Devices
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.
Energy Pricing in P2P Energy Systems Using Reinforcement Learning
The microgrid considers consumers, prosumers, the service provider, and a community battery.
RL-DistPrivacy: Privacy-Aware Distributed Deep Inference for low latency IoT systems
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.
A Practical Cross-Device Federated Learning Framework over 5G Networks
The concept of federated learning (FL) was first proposed by Google in 2016.
Artificial Intelligence for 6G Networks: Technology Advancement and Standardization
With the deployment of 5G networks, standards organizations have started working on the design phase for sixth-generation (6G) networks.
PerFED-GAN: Personalized Federated Learning via Generative Adversarial 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.
Cross-Silo Heterogeneous Model Federated Multitask Learning
CoFED is a federated learning method that is compatible with heterogeneous models, tasks, and training processes.
Exploring Deep Reinforcement Learning-Assisted Federated Learning for Online Resource Allocation in Privacy-Persevering EdgeIoT
Federated learning (FL) has been increasingly considered to preserve data training privacy from eavesdropping attacks in mobile edge computing-based Internet of Thing (EdgeIoT).
IoV Scenario: Implementation of a Bandwidth Aware Algorithm in Wireless Network Communication Mode
This paper proposes a bandwidth aware multi domain virtual network embedding algorithm (BA-VNE).
Motivating Learners in Multi-Orchestrator Mobile Edge Learning: A Stackelberg Game Approach
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.
Internet of Behavior (IoB) and Explainable AI Systems for Influencing IoT Behavior
The scenario results showed a decrease of 522. 2 kW of active power when compared to original consumption over a 200-hours period.
Energy-Efficient Multi-Orchestrator Mobile Edge Learning
The heterogeneity in edge devices' capabilities will require the joint optimization of the learners-orchestrator association and task allocation.
Reinforcement Learning for Intelligent Healthcare Systems: A Comprehensive Survey
After that, we provide a deep literature review for the applications of RL in I-health systems.
Communication-Efficient Hierarchical Federated Learning for IoT Heterogeneous Systems with Imbalanced Data
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.
An Intelligent Resource Reservation for Crowdsourced Live Video Streaming Applications in Geo-Distributed Cloud Environment
Crowdsourced live video streaming (livecast) services such as Facebook Live, YouNow, Douyu and Twitch are gaining more momentum recently.
Distributed CNN Inference on Resource-Constrained UAVs for Surveillance Systems: Design and Optimization
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.
Pervasive AI for IoT applications: A Survey on Resource-efficient Distributed Artificial Intelligence
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.
The Duo of Artificial Intelligence and Big Data for Industry 4.0: Review of Applications, Techniques, Challenges, and Future Research Directions
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.
Green IoT using UAVs in B5G Networks: A Review of Applications and Strategies
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.
Edge Intelligence for Empowering IoT-based Healthcare Systems
The objective of this article is to highlight the benefits of the adoption of edge intelligent technology, along with AI in smart healthcare systems.
Implicit Feedback-based Group Recommender System for Internet of Thing Applications
With the prevalence of Internet of Things (IoT)-based social media applications, the distance among people has been greatly shortened.
Analysis and Optimal Edge Assignment For Hierarchical Federated Learning on Non-IID Data
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.
Budgeted Online Selection of Candidate IoT Clients to Participate in Federated Learning
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.
Performance Analysis and Optimization for the MAC Protocol in UAV-based IoT Network
A modified three-dimensional Markov chain model adopting the quitting probability and cluster division is developed for the performance analysis.
Information Theory Information Theory
Optimization-driven Machine Learning for Intelligent Reflecting Surfaces Assisted Wireless Networks
Due to the large size of scattering elements, the passive beamforming is typically challenged by the high computational complexity and inexact channel information.
Compress or Interfere?
Rapid evolution of wireless medical devices and network technologies has fostered the growth of remote monitoring systems.
Reliable Federated Learning for Mobile Networks
Based on this metric, a reliable worker selection scheme is proposed for federated learning tasks.
Cryptography and Security
QoE-Aware Resource Allocation for Crowdsourced Live Streaming: A Machine Learning Approach
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.
Self-Selective Correlation Ship Tracking Method for Smart Ocean System
In recent years, with the development of the marine industry, navigation environment becomes more complicated.
Super-Resolution of Brain MRI Images using Overcomplete Dictionaries and Nonlocal Similarity
The sparsity and self-similarity of the image blocks are taken as the constraints.
Interest-Related Item Similarity Model Based on Multimodal Data for Top-N Recommendation
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.
Adversarial Samples on Android Malware Detection Systems for IoT Systems
An automated testing framework is needed to help these learning-based malware detection systems for IoT devices perform security analysis.
Design Challenges of Multi-UAV Systems in Cyber-Physical Applications: A Comprehensive Survey, and Future Directions
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.
Semi-supervised Deep Reinforcement Learning in Support of IoT and Smart City Services
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.
A Survey of Machine and Deep Learning Methods for Internet of Things (IoT) Security
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.
Reinforcement Learning based QoS/QoE-aware Service Function Chaining in Software-Driven 5G Slices
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.
FPAN: Fine-grained and Progressive Attention Localization Network for Data Retrieval
The Localization of the target object for data retrieval is a key issue in the Intelligent and Connected Transportation Systems (ICTS).
Deep Learning for IoT Big Data and Streaming Analytics: A Survey
The potential of using emerging DL techniques for IoT data analytics are then discussed, and its promises and challenges are introduced.
When Traffic Flow Prediction Meets Wireless Big Data Analytics
Traffic congestion is one of the most serious problems in a city, which can be predicted in advance by analyzing traffic flow patterns.
VANETs Meet Autonomous Vehicles: A Multimodal 3D Environment Learning Approach
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).
