Adaptive Power Management in Solar Energy Harvesting Sensor Node Using Reinforcement Learning

2017 ◽  
Vol 16 (5s) ◽  
pp. 1-21 ◽  
Author(s):  
Shaswot Shresthamali ◽  
Masaaki Kondo ◽  
Hiroshi Nakamura
2012 ◽  
Vol 29 (2) ◽  
pp. 76-82 ◽  
Author(s):  
Kah‐Yoong Chan ◽  
Hee‐Joe Phoon ◽  
Chee‐Pun Ooi ◽  
Wai‐Leong Pang ◽  
Sew‐Kin Wong

ACTA IMEKO ◽  
2021 ◽  
Vol 10 (2) ◽  
pp. 111
Author(s):  
Tommaso Addabbo ◽  
Ada Fort ◽  
Matteo Intravaia ◽  
Marco Mugnaini ◽  
Lorenzo Parri ◽  
...  

<p>The aim of this paper is to discuss the characterisation of a solar energy harvesting system to be integrated in a wireless sensor node, to be deployed on means of transport to pervasively collect measurements of Particulate Matter (PM) concentration in urban areas. The sensor node is based on the use of low-cost PM sensors and exploits LoRaWAN connectivity to remotely transfer the collected data. The node also integrates GPS localisation features, that allow to associate the measured values with the geographical coordinates of the sampling site. In particular, the system is provided with an innovative, small-scale, solar-based powering solution that allows its energy self-sufficiency and then its functioning without the need for a connection to the power grid. Tests concerning the energy production of the solar cell were performed in order to optimise the functioning of the sensor node: satisfactory results were achieved in terms of number of samplings per hour. Finally, field tests were carried out with the integrated environmental monitoring device proving its effectiveness.</p>


2019 ◽  
Vol 18 (4) ◽  
pp. 1-26 ◽  
Author(s):  
Rehan Ahmed ◽  
Bernhard Buchli ◽  
Stefan Draskovic ◽  
Lukas Sigrist ◽  
Pratyush Kumar ◽  
...  

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