Paper Info
Title
Pricing and Selection of Channels for Remote State Estimation Using a Stackelberg Game Framework
Abstract
We consider the communication channel pricing and selection problem in a networked control system. To encompass the sequentialized nature of the decision-making process, we use game theory and formulate a Stackelberg game framework, where the server first determines the channel pricing strategy, and the clients then make channel selection decisions. Both single-server-single-client (SSSC) scenario and single-server-multi-client (SSMC) scenario are discussed. The existence of an optimal stationary and deterministic policy for the clients is proved. We show that for the SSSC scenario, the server's optimal pricing strategy in terms of maximizing revenue is to ensure that the client uses the good channel all the time. For the SSMC scenario, it is assumed that the channel price remains invariant. As a consequence, each client has an optimal policy with threshold structure. Some properties of the optimal policy pair for both scenarios are obtained. Simulation results confirm the structure and properties of both the server and clients’ optimal strategies.
Year
DOI
Venue
2019
10.1109/TSIPN.2019.2932684
IEEE Transactions on Signal and Information Processing over Networks
Keywords
Field
DocType
Pricing,Servers,Games,Channel estimation,Intelligent sensors,Communication networks
Revenue,Mathematical optimization,Telecommunications network,Networked control system,Computer science,Intelligent sensor,Server,Communication channel,Game theory,Stackelberg competition
Journal
Volume
Issue
ISSN
5
4
2373-776X
Citations 
PageRank 
References 
1
0.35
0
Authors
4
Name
Order
Citations
PageRank
Yuqing Ni132.07
Alex S. Leong220816.53
Daniel E. Quevedo31393100.60
Ling Shi41717107.86