Markov Decision Processes with Applications in Wireless Sensor Networks: A Survey
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 used 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.
Wireless sensor networks;Markov decision processes (MDPs);stochastic control;optimization methods;decision-making tools;multi-agent systems
Computer Sciences | Software Engineering
Software and Cyber-Physical Systems
Communications Surveys and Tutorials, IEEE Communications Society
Institute of Electrical and Electronics Engineers (IEEE)
ALSHEIKH, Abu Mohammad; HOANG, Dinh Thai; NIYATO, Dusit; and Hwee-Pink TAN.
Markov Decision Processes with Applications in Wireless Sensor Networks: A Survey. (2015). Communications Surveys and Tutorials, IEEE Communications Society. 17, (3), 1239-1267. Research Collection School Of Information Systems.
Available at: http://ink.library.smu.edu.sg/sis_research/2855
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