  Millions of sensors, cameras, meters, and other edge devices are deployed in
networks to collect and analyse data. In many cases, such devices are powered
only by Energy Harvesting(EH) and have limited energy available to analyse
acquired data. When edge infrastructure is available, a device has a choice: to
perform analysis locally or offload the task to other resource-rich devices
such as cloudlet servers. However, such a choice carries a price in terms of
consumed energy and accuracy. On the one hand, transmitting raw data can result
in a higher energy cost in comparison to the required energy to process data
locally. On the other hand, performing data analytics on servers can improve
the task's accuracy. Additionally, due to the correlation between information
sent by multiple devices, accuracy might not be affected if some edge devices
decide to neither process nor send data and preserve energy instead. For such a
scenario, we propose a Deep Reinforcement Learning (DRL) based solution capable
of learning and adapting the policy to the time-varying energy arrival due to
EH patterns. We leverage two datasets, one to model energy an EH device can
collect and the other to model the correlation between cameras. Furthermore, we
compare the proposed solution performance to three baseline policies. Our
results show that we can increase accuracy by 15% in comparison to conventional
approaches while preventing outages.
