Making Smart Homes Smarter: Optimizing Energy Consumption with Human in
  the Loop
  Rapid advancements in the Internet of Things (IoT) have facilitated more
efficient deployment of smart environment solutions for specific user
requirement. With the increase in the number of IoT devices, it has become
difficult for the user to control or operate every individual smart device into
achieving some desired goal like optimized power consumption, scheduled
appliance running time, etc. Furthermore, existing solutions to automatically
adapt the IoT devices are not capable enough to incorporate the user behavior.
This paper presents a novel approach to accurately configure IoT devices while
achieving the twin objectives of energy optimization along with conforming to
user preferences. Our work comprises of unsupervised clustering of devices'
data to find the states of operation for each device, followed by
probabilistically analyzing user behavior to determine their preferred states.
Eventually, we deploy an online reinforcement learning (RL) agent to find the
best device settings automatically. Results for three different smart homes'
data-sets show the effectiveness of our methodology. To the best of our
knowledge, this is the first time that a practical approach has been adopted to
achieve the above mentioned objectives without any human interaction within the
system.
