https://joaocarlos.github.io/Hugo-academia/tags/environmental-monitoring/Recent content in Environmental Monitoring on Dr. Jane SmithHugoenThu, 10 Mar 2022 00:00:00 +0000https://joaocarlos.github.io/Hugo-academia/papers/edge-computing-for-real-time-environmental-monitoring/Thu, 10 Mar 2022 00:00:00 +0000https://joaocarlos.github.io/Hugo-academia/papers/edge-computing-for-real-time-environmental-monitoring/Abstract Real-time environmental monitoring requires processing large volumes of sensor data with minimal latency. This paper presents an edge computing architecture that performs local data processing and anomaly detection, reducing cloud bandwidth requirements by 80% while enabling sub-second response times for critical events. Citation @article{smith2022edge, title = {Edge Computing for Real-Time Environmental Monitoring}, author = {Smith, Jane and Chen, Robert and Wang, Lisa}, journal = {IEEE Internet of Things Journal}, year = {2022}, doi = {10.https://joaocarlos.github.io/Hugo-academia/projects/smart-urban-monitoring-network/Mon, 01 Jan 0001 00:00:00 +0000https://joaocarlos.github.io/Hugo-academia/projects/smart-urban-monitoring-network/Project Overview The Smart Urban Monitoring Network (SUMN) is a large-scale research project aimed at deploying and managing a city-wide network of environmental sensors. The project combines cutting-edge IoT hardware with advanced machine learning algorithms to provide real-time insights into urban environmental conditions. Objectives Deploy 1,000+ environmental sensors across San Francisco Develop adaptive sensing algorithms to optimize data collection Create predictive models for air quality and noise pollution Provide open data access to researchers and city planners Key Results Successfully deployed 500 sensors in pilot phase Achieved 99.