https://joaocarlos.github.io/Hugo-academia/tags/iot/Recent content in IoT on Dr. Jane SmithHugoenWed, 20 Sep 2023 00:00:00 +0000https://joaocarlos.github.io/Hugo-academia/papers/adaptive-sensing-for-smart-cities-a-reinforcement-learning-approach/Wed, 20 Sep 2023 00:00:00 +0000https://joaocarlos.github.io/Hugo-academia/papers/adaptive-sensing-for-smart-cities-a-reinforcement-learning-approach/Abstract Urban sensing networks face the challenge of balancing data quality with energy consumption. We propose an adaptive sensing framework that uses reinforcement learning to dynamically adjust sampling rates based on environmental conditions and application requirements. Our system reduces energy consumption by 60% while maintaining 95% of the data quality compared to fixed-rate sampling approaches. Citation @inproceedings{smith2023adaptive, title = {Adaptive Sensing for Smart Cities: A Reinforcement Learning Approach}, author = {Smith, Jane and Garcia, Maria}, booktitle = {Proceedings of the ACM Conference on Embedded Networked Sensor Systems (SenSys)}, year = {2023}, doi = {10.https://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.