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

arXiv: https://arxiv.org/abs/1501.00644 · HTML: https://mlsanity.com/paper/1501.00644/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Jan 2015.

## Built on by (most cited first, 5 of 5)

- [Transfer Learning for Future Wireless Networks: A Comprehensive Survey](https://mlsanity.com/paper/2102.07572/) (2021)
- [A Comprehensive Review of Digital Twin -- Part 2: Roles of Uncertainty Quantification and Optimization, a Battery Digital Twin, and Perspectives](https://mlsanity.com/paper/2208.12904/) (2022)
- [Minimax Optimal Q Learning with Nearest Neighbors](https://mlsanity.com/paper/2308.01490/) (2023)
- [A distance function for stochastic matrices](https://mlsanity.com/paper/2410.12689/) (2024)
- [Optimistic Learning for Communication Networks](https://mlsanity.com/paper/2504.03499/) (2025)

## Abstract (arXiv)

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.