https://joaocarlos.github.io/Hugo-academia/tags/smart-cities/Recent content in Smart Cities on Dr. Jane SmithHugoenSat, 15 Jun 2024 00:00:00 +0000https://joaocarlos.github.io/Hugo-academia/papers/deep-learning-for-urban-traffic-prediction/Sat, 15 Jun 2024 00:00:00 +0000https://joaocarlos.github.io/Hugo-academia/papers/deep-learning-for-urban-traffic-prediction/Abstract We present a novel deep learning architecture for predicting urban traffic patterns using multi-modal sensor data. Our approach combines convolutional neural networks with attention mechanisms to capture both spatial and temporal dependencies in traffic flow. Experiments on real-world datasets from three major cities demonstrate that our method outperforms existing approaches by 15% in prediction accuracy while reducing computational requirements by 40%. Citation @article{smith2024traffic, title = {Deep Learning for Urban Traffic Prediction}, author = {Smith, Jane and Doe, John and Johnson, Alice}, journal = {IEEE Transactions on Intelligent Transportation Systems}, year = {2024}, doi = {10.https://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/books/ml-smart-cities/Fri, 15 Sep 2023 00:00:00 +0000https://joaocarlos.github.io/Hugo-academia/books/ml-smart-cities/Description This comprehensive textbook provides a thorough introduction to machine learning techniques and their applications in smart city environments. Written for graduate students and practitioners, the book covers both theoretical foundations and practical implementations. Table of Contents Introduction to Smart Cities Data Collection and Sensor Networks Machine Learning Fundamentals Deep Learning for Urban Data Reinforcement Learning for Control Systems Privacy and Security Considerations Case Studies and Applications Future Directions Reviews “An essential resource for anyone working at the intersection of AI and urban computing.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.