Fast Markov Decision Process for data collection in sensor networks

Abstract

We investigate the data collection problem in sensor networks. The network consists of a number of stationary sensors deployed at different sites for sensing and storing data locally. A mobile element moves from sites to sites to collect data from the sensors periodically. There are different costs associated with the mobile element moving from one site to another, and different rewards for obtaining data at different sensors. Furthermore, the costs and the rewards are assumed to change abruptly. The goal is to find a "fast" optimal movement pattern/policy of the mobile element that optimizes for the costs and rewards in non-stationary environments. We propose a novel optimization framework called Fast Markov Decision Process (FMDP) to solve this problem. The proposed FMDP framework extends the classical Markov Decision Process theory by incorporating the notion of mixing time that allows for the tradeoff between the optimality and the convergence rate to the optimality of a policy. Theoretical and simulation results are provided to verify the proposed approach.

Data Collection in Sensor Networks via the Novel Fast Markov Decision Process Framework

“…For queuing systems, we can find fast queuing policies to achieve a desired stationary distribution by minimizing the SLEM via convex relaxation [32]. In [33], fast policies of collecting data in sensor networks with mobile elements are obtained by including mixing time or SLEM as a trade-off term in the objective function of the formulated optimization problem. In [34], the mixing time is considered as a regularization term to accelerate the learning phase of reinforcement learning.…”