Data for: Leveraging Deep Learning Towards Gamification In Human-Centric Cyber-Physical Systems
Description
Our experimental environment is comprised of residential housing single room apartments on the Nanyang Technological University campus. We designed a social game such that all single room dorm occupants could freely view their daily room’s resource usage with a convenient interface. In each dorm room we have installed two Internet of Things (IoT) sensors — one close to the desk light and another near the ceiling fan. With the deployment of IoT sensors dorm occupants can monitor in real-time their room’s lighting system (desk and ceiling light usage) and HVAC (ceiling fan and aircon usage) with a refresh interval of up to 1 second. Dorm occupants are rewarded with points based on how energy efficient their daily usage is in comparison to their past usage before the social game was deployed. The past usage data that serves as our baseline is gathered by monitoring occupant energy usage for approximately one month before the introduction of the game for each semester. Using this prior data, we have calculated a weekday and weekend baseline for each of an occupant’s resources. We bucket data in weekdays and weekends so as to maintain fairness for occupants who have alternative schedules of occupancy (e.g. those who tend to stay at their dorm room over the weekends versus weekdays). We employ a lottery mechanism consisting of several gift cards awarded on a bi-weekly basis to incentivize occupants; occupants with more points are more likely to win the lottery.