Strategic Decision for Crowd-Sensing: An Approach based on Markov Decision Process

With the surging of smartphone sensing and wireless networking, Mobile Crowd Sensing (MCS) has become a promising paradigm for cross-space and large scale sensing. In the smart city context, MCS can be extremely useful in pulling crowd data for better monitoring resource demand and planning. Since these devices are portable it is carried by almost every citizen thus making it ubiquitous. However, portability comes at the cost of limited energy consumption. Thus, the problem is that, how frequently a smartphone would lend itself for crowd-sensing while striking a balance between energy and incentive. In order to solve this problem, we propose a Markov decision process formulation of the problem and solve it to identify an optimal strategy for crowd-sensing depending on remaining energy, recharging probability and current load. Results are taken in R for different real world scenarios.