2018/11/19 by Michal Kozlowski, Michał Kozłowski, Ryan McConville +8 · 12 citations
Computer Science · Engineering · Mathematics · #Artificial intelligence #Computer network #Computer science #Efficient energy use #Energy (signal processing) #Energy Harvesting in Wireless Networks #Energy consumption #Engineering #FOS: Computer and information sciences #FOS: Electrical engineering #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Machine learning #RSS #Real-time computing #Reinforcement Learning in Robotics #Reinforcement learning #Signal Processing (eess.SP) #Smart Grid Energy Management #Telecommunications #Wireless #Wireless sensor network #cs.LG #eess.SP #electronic engineering #information engineering #stat.ML
paper · pdf · doi:10.48550/arxiv.1812.02538
published in arXiv (Cornell University) (Cornell University) · This paper was accepted on 30/07/2018 and presented at the ECML-PKDD Workshop Green Data Mining 2018 on 14/09/2018
arxiv created 2018/11/19 · openalex publication_date 2018/11/19 · arxiv updated 2018/12/07 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/01
As sensor networks for health monitoring become more prevalent, so will the need to control their usage and consumption of energy. This paper presents a method which leverages the algorithm's performance and energy consumption. By utilising Reinforcement Learning (RL) techniques, we provide an adaptive framework, which continuously performs weak training in an energy-aware system. We motivate this using a realistic example of residential localisation based on Received Signal Strength (RSS). The method is cheap in terms of work-hours, calibration and energy usage. It achieves this by utilising other sensors available in the environment. These other sensors provide weak labels, which are then used to employ the State-Action-Reward-State-Action (SARSA) algorithm and train the model over time. Our approach is evaluated on a simulated localisation environment and validated on a widely available pervasive health dataset which facilitates realistic residential localisation using RSS. We show that our method is cheaper to implement and requires less effort, whilst at the same time providing a performance enhancement and energy savings over time.