<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Reinforcement Learning on Dr. Jane Smith</title><link>https://joaocarlos.github.io/Hugo-academia/tags/reinforcement-learning/</link><description>Recent content in Reinforcement Learning on Dr. Jane Smith</description><generator>Hugo</generator><language>en</language><lastBuildDate>Wed, 20 Sep 2023 00:00:00 +0000</lastBuildDate><atom:link href="https://joaocarlos.github.io/Hugo-academia/tags/reinforcement-learning/index.xml" rel="self" type="application/rss+xml"/><item><title>Adaptive Sensing for Smart Cities: A Reinforcement Learning Approach</title><link>https://joaocarlos.github.io/Hugo-academia/papers/adaptive-sensing-for-smart-cities-a-reinforcement-learning-approach/</link><pubDate>Wed, 20 Sep 2023 00:00:00 +0000</pubDate><guid>https://joaocarlos.github.io/Hugo-academia/papers/adaptive-sensing-for-smart-cities-a-reinforcement-learning-approach/</guid><description>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.</description></item></channel></rss>