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Found 27 papers, 2 papers with code
Meta Reinforcement Learning for Strategic IoT Deployments Coverage in Disaster-Response UAV Swarms
Our simulation results prove that our introduced approach is better than the three state-of-the-art algorithms in providing coverage to strategic locations with fast convergence.
Intelligent DRL-Based Adaptive Region of Interest for Delay-sensitive Telemedicine Applications
Telemedicine applications have recently received substantial potential and interest, especially after the COVID-19 pandemic.
Zero-touch realization of Pervasive Artificial Intelligence-as-a-service in 6G networks
In this context, we introduce a novel platform architecture to deploy a zero-touch PAI-as-a-Service (PAIaaS) in 6G networks supported by a blockchain-based smart system.
Adaptive ResNet Architecture for Distributed Inference in Resource-Constrained IoT Systems
Hence, for dynamic systems, it is necessary to have a resilient DNN with an adaptive architecture that can downsize as per the available resources.
LLHR: Low Latency and High Reliability CNN Distributed Inference for Resource-Constrained UAV Swarms
Our system model deals with real-time requests, aiming to find the optimal transmission power that guarantees higher reliability and low latency.
Physical Layer Security in Satellite Communication: State-of-the-art and Open Problems
Satellite communications emerged as a promising extension to terrestrial networks in future 6G network research due to their extensive coverage in remote areas and ability to support the increasing traffic rate and heterogeneous networks.
Deep Reinforcement Learning for Trajectory Path Planning and Distributed Inference in Resource-Constrained UAV Swarms
The deployment flexibility and maneuverability of Unmanned Aerial Vehicles (UAVs) increased their adoption in various applications, such as wildfire tracking, border monitoring, etc.
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.
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.
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.
Federated Learning for UAV Swarms Under Class Imbalance and Power Consumption Constraints
The usage of unmanned aerial vehicles (UAVs) in civil and military applications continues to increase due to the numerous advantages that they provide over conventional approaches.
Client Selection Approach in Support of Clustered Federated Learning over Wireless Edge Networks
Extensive experiments show that the proposed approach lowers the training time and accelerates the convergence rate by up to 50% while imbuing each client with a specialized model that is fit for its local data distribution.
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.
Emotion Recognition for Healthcare Surveillance Systems Using Neural Networks: A Survey
Recognizing the patient's emotions using deep learning techniques has attracted significant attention recently due to technological advancements.
Efficient Real-Time Image Recognition Using Collaborative Swarm of UAVs and Convolutional Networks
We formulate the model as an optimization problem that minimizes the latency between acquiring images and making the final decisions.
Fine-Grained Data Selection for Improved Energy Efficiency of Federated Edge Learning
Specifically, we consider a problem that aims to find the optimal user's resources, including the fine-grained selection of relevant training samples, bandwidth, transmission power, beamforming weights, and processing speed with the goal of minimizing the total energy consumption given a deadline constraint on the communication rounds of FEEL.
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.
Threshold-Based Data Exclusion Approach for Energy-Efficient Federated Edge Learning
Then, the problem is formulated as joint energy minimization and resource allocation optimization problem to obtain the optimal local computation time and the optimal transmission time that minimize the total energy consumption considering the worker's energy budget, available bandwidth, channel states, beamforming, and local CPU speed.
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.
Compress or Interfere?
Rapid evolution of wireless medical devices and network technologies has fostered the growth of remote monitoring systems.
Cybersecurity for Industrial Control Systems: A Survey
In this work, we have a close look at the shift of the ICS from stand-alone systems to cloud-based environments.
Cryptography and Security Networking and Internet Architecture Systems and Control Systems and Control
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.
Feasibility of Supervised Machine Learning for Cloud Security
As a result of this, researchers prefer to generate datasets for training and testing purpose in the simulated or closed experimental environments which may lack comprehensiveness.
When A Small Leak Sinks A Great Ship: Deanonymizing Tor Hidden Service Users Through Bitcoin Transactions Analysis
We investigate the feasibility of deanonymizing users of Tor hidden services who rely on Bitcoin as a payment method by exploiting public information leaked from online social networks, the Blockchain, and onion websites.
Cryptography and Security
