Designing Energy Efficient Strategies Using Markov Decision Process for Crowd-Sensing Applications

In mobile crowd-sensing, smartphone users take part in sensing and then share the data to the server (cloud) and get an incentive. These data can be utilized for providing better services to improve quality of life. Batteries used in smartphones constrain the usability of these devices for longer charge cycles. Hence, maintaining a balance between energy consumption due to crowd-sensing application and that due to the current computational load on the device is the need of the hour. Consequently, in this paper, we formulate strategies applying Markov Decision Process (MDP) by which a smart handheld would crowd-sense while keeping the device active for a longer period of time. MDP used here helps to decide when a device would lend itself to crowd-sense considering the remaining energy of the device, it’s recharging probability, current computational load,and the incentive it receives. In this work, we have considered indoor localization as an example of a smartphone based crowd sensing application. The strategies found by solving MDP formulation are implemented for a smartphone application for crowd-sensed indoor localization. We have experimented using 5 smart handheld devices for different use cases. Our scheme is found to perform better than the state-of-the-art works.