Related papers: Federated Learning in the Sky: Aerial-Ground Air Quality Sensing Framework with UAV Swarms

Federated Learning in the Sky: Aerial-Ground Air Quality Sensing Framework with UAV Swarms

URL: http://arxiv.org/abs/2007.12004v1
Date: Thu, 23 Jul 2020 13:32:47 GMT
Title: Federated Learning in the Sky: Aerial-Ground Air Quality Sensing Framework with UAV Swarms
Authors: Yi Liu, Jiangtian Nie, Xuandi Li, Syed Hassan Ahmed, Wei Yang Bryan Lim, Chunyan Miao
Abstract summary: Air quality significantly affects human health, it is increasingly important to accurately and timely predict the Air Quality Index (AQI) This paper proposes a new federated learning-based aerial-ground air quality sensing framework for fine-grained 3D air quality monitoring and forecasting. For ground sensing systems, we propose a Graph Convolutional neural network-based Long Short-Term Memory (GC-LSTM) model to achieve accurate, real-time and future AQI inference.
Score: 53.38353133198842
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Abstract: Due to air quality significantly affects human health, it is becoming increasingly important to accurately and timely predict the Air Quality Index (AQI). To this end, this paper proposes a new federated learning-based aerial-ground air quality sensing framework for fine-grained 3D air quality monitoring and forecasting. Specifically, in the air, this framework leverages a light-weight Dense-MobileNet model to achieve energy-efficient end-to-end learning from haze features of haze images taken by Unmanned Aerial Vehicles (UAVs) for predicting AQI scale distribution. Furthermore, the Federated Learning Framework not only allows various organizations or institutions to collaboratively learn a well-trained global model to monitor AQI without compromising privacy, but also expands the scope of UAV swarms monitoring. For ground sensing systems, we propose a Graph Convolutional neural network-based Long Short-Term Memory (GC-LSTM) model to achieve accurate, real-time and future AQI inference. The GC-LSTM model utilizes the topological structure of the ground monitoring station to capture the spatio-temporal correlation of historical observation data, which helps the aerial-ground sensing system to achieve accurate AQI inference. Through extensive case studies on a real-world dataset, numerical results show that the proposed framework can achieve accurate and energy-efficient AQI sensing without compromising the privacy of raw data.

Related papers

Developing Gridded Emission Inventory from High-Resolution Satellite Object Detection for Improved Air Quality Forecasts [1.4238093681454425]
This study presents an innovative approach to creating a dynamic, AI based emission inventory system for use with the Weather Research and Forecasting model coupled with Chemistry (WRF Chem) The system offers unprecedented temporal and spatial resolution in emission estimates, facilitating more accurate short term air quality forecasts and deeper insights into urban emission dynamics. Future work will focus on expanding the system's capabilities to non vehicular sources and further improving detection accuracy in challenging environmental conditions.
arXiv Detail & Related papers (2024-10-14T01:32:45Z)
SFANet: Spatial-Frequency Attention Network for Weather Forecasting [54.470205739015434]
Weather forecasting plays a critical role in various sectors, driving decision-making and risk management. Traditional methods often struggle to capture the complex dynamics of meteorological systems. We propose a novel framework designed to address these challenges and enhance the accuracy of weather prediction.
arXiv Detail & Related papers (2024-05-29T08:00:15Z)
Observation-Guided Meteorological Field Downscaling at Station Scale: A Benchmark and a New Method [66.80344502790231]
We extend meteorological downscaling to arbitrary scattered station scales and establish a new benchmark and dataset. Inspired by data assimilation techniques, we integrate observational data into the downscaling process, providing multi-scale observational priors. Our proposed method outperforms other specially designed baseline models on multiple surface variables.
arXiv Detail & Related papers (2024-01-22T14:02:56Z)
Towards an end-to-end artificial intelligence driven global weather forecasting system [57.5191940978886]
We present an AI-based data assimilation model, i.e., Adas, for global weather variables. We demonstrate that Adas can assimilate global observations to produce high-quality analysis, enabling the system operate stably for long term. We are the first to apply the methods to real-world scenarios, which is more challenging and has considerable practical application potential.
arXiv Detail & Related papers (2023-12-18T09:05:28Z)
Over-the-Air Federated Learning and Optimization [52.5188988624998]
We focus on Federated learning (FL) via edge-the-air computation (AirComp) We describe the convergence of AirComp-based FedAvg (AirFedAvg) algorithms under both convex and non- convex settings. For different types of local updates that can be transmitted by edge devices (i.e., model, gradient, model difference), we reveal that transmitting in AirFedAvg may cause an aggregation error. In addition, we consider more practical signal processing schemes to improve the communication efficiency and extend the convergence analysis to different forms of model aggregation error caused by these signal processing schemes.
arXiv Detail & Related papers (2023-10-16T05:49:28Z)
GreenEyes: An Air Quality Evaluating Model based on WaveNet [11.513011576336744]
We propose a deep neural network model, which consists of a WaveNet-based backbone block for learning representations of sequences and an LSTM with a Temporal Attention module. We show our model can effectively predict the air quality level of the next timestamp given any segment of the air quality data from the data set.
arXiv Detail & Related papers (2022-12-08T10:28:57Z)
AirSPEC: An IoT-empowered Air Quality Monitoring System integrated with a Machine Learning Framework to Detect and Predict defined Air Quality parameters [0.0]
A novel Internet of Things framework is proposed which is easily implementable, semantically distributive, and empowered by a machine learning model. The proposed system is equipped with a NodeRED dashboard which processes, visualizes, and stores the primary sensor data. The dashboard is integrated with a machine-learning model to obtain temporal and geo-spatial air quality predictions.
arXiv Detail & Related papers (2021-11-28T12:13:30Z)
Joint Air Quality and Weather Prediction Based on Multi-Adversarial Spatiotemporal Networks [44.34236994440102]
We propose the Multi-versaadrial recurrent Graph Neural Networks (MasterGNN) for joint air quality and weather predictions. Specifically, we first propose a recurrent graph neural network to model heterogeneous autotemporalcorrelation among air quality and weather monitoring stations.
arXiv Detail & Related papers (2020-12-30T04:42:03Z)
Adaptive machine learning strategies for network calibration of IoT smart air quality monitoring devices [1.957338076370071]
Low cost chemical microsensors array have shown capable to provide relatively accurate air pollutant quantitative estimations. Their accuracy have shown limited in long term field deployments due to negative influence of several technological issues. In this work, we address this non stationary framework with adaptive learning strategies in order to prolong the validity of multisensors calibration models.
arXiv Detail & Related papers (2020-03-24T10:26:51Z)
Federated Learning in the Sky: Joint Power Allocation and Scheduling with UAV Swarms [98.78553146823829]
Unmanned aerial vehicle (UAV) swarms must exploit machine learning (ML) in order to execute various tasks. In this paper, a novel framework is proposed to implement distributed learning (FL) algorithms within a UAV swarm.
arXiv Detail & Related papers (2020-02-19T14:04:01Z)

This list is automatically generated from the titles and abstracts of the papers in this site.