Markov Decision Processes with Applications in Wireless Sensor Networks: A Survey

TL;DR

This survey reviews how Markov decision processes (MDPs) are applied to develop adaptive, robust algorithms for wireless sensor networks, addressing challenges like resource management, coverage, and security.

Contribution

It provides a comprehensive overview of MDP applications in WSNs, comparing solution methods and guiding future research in adaptive decision-making.

Findings

MDPs effectively model WSN decision problems.

Various solution methods are compared and analyzed.

MDP-based algorithms improve adaptability and robustness.

Abstract

Wireless sensor networks (WSNs) consist of autonomous and resource-limited devices. The devices cooperate to monitor one or more physical phenomena within an area of interest. WSNs operate as stochastic systems because of randomness in the monitored environments. For long service time and low maintenance cost, WSNs require adaptive and robust methods to address data exchange, topology formulation, resource and power optimization, sensing coverage and object detection, and security challenges. In these problems, sensor nodes are to make optimized decisions from a set of accessible strategies to achieve design goals. This survey reviews numerous applications of the Markov decision process (MDP) framework, a powerful decision-making tool to develop adaptive algorithms and protocols for WSNs. Furthermore, various solution methods are discussed and compared to serve as a guide for using MDPs in WSNs.