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

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.

References in corpus (3)

• Machine Learning in Wireless Sensor Networks: Algorithms, Strategies, and Applications
• Optimal Energy Allocation for Kalman Filtering over Packet Dropping Links with Imperfect Acknowledgments and Energy Harvesting Constraints
• Distributed and Centralized Hybrid CSMA/CA-TDMA Schemes for Single-Hop Wireless Networks

Cited by in corpus (2)

• A Survey of Multi-Objective Optimization in Wireless Sensor Networks: Metrics, Algorithms and Open Problems
• A Review on Internet of Things for Defense and Public Safety