most citedACES -- Automatic Configuration of Energy Harvesting Sensors with Reinforcement Learning

50 citations · 75 across the 4 of their papers we have counts for

collaborators

5 papers

eess.SY2019★ 50 cited

ACES -- Automatic Configuration of Energy Harvesting Sensors with Reinforcement Learning

Francesco Fraternali, Bharathan Balaji, Yuvraj Agarwal +1

Internet of Things forms the backbone of modern building applications. Wireless sensors are being increasingly adopted for their flexibility and reduced cost of deployment. However…

cs.LG2019

Resource-Efficient Computing in Wearable Systems

Mahdi Pedram, Mahsan Rofouei, Francesco Fraternali +2

We propose two optimization techniques to minimize memory usage and computation while meeting system timing constraints for real-time classification in wearable systems. Our method…

cs.NI2019

Towards Large-Scale Autonomous Wireless Sensor Networks

Francesco Fraternali

Wireless Sensor Networks (WSNs) have the goal of gathering data from the environment. The advent of the Internet of Things (IoT) drastically changed WSN's vision that, as never bef…

cs.NI2018

Pible: Battery-Free Mote for Perpetual Indoor BLE Applications

Francesco Fraternali, Bharathan Balaji, Yuvraj Agarwal +2

Smart building applications require a large-scale deployment of sensors distributed across the environment. Recent innovations in smart environments are driven by wireless networke…

cs.LG2018★ 25 cited

Scaling Configuration of Energy Harvesting Sensors with Reinforcement Learning

Francesco Fraternali, Bharathan Balaji, Rajesh Gupta

With the advent of the Internet of Things (IoT), an increasing number of energy harvesting methods are being used to supplement or supplant battery based sensors. Energy harvesting…