Semi-Markov Decision Process-based Resource Allocation Strategy for Virtual Sensor Network

Ruyan WANG; Hongjuan LI; Dapeng WU; Hongxia LI

doi:10.11999/JEIT190016

Volume 41 Issue 12

Dec. 2019

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Article Navigation > Journal of Electronics & Information Technology > 2019 > 41(12): 3014-3021

Ruyan WANG, Hongjuan LI, Dapeng WU, Hongxia LI. Semi-Markov Decision Process-based Resource Allocation Strategy for Virtual Sensor Network[J]. Journal of Electronics & Information Technology, 2019, 41(12): 3014-3021. doi: 10.11999/JEIT190016

Citation:

Ruyan WANG, Hongjuan LI, Dapeng WU, Hongxia LI. Semi-Markov Decision Process-based Resource Allocation Strategy for Virtual Sensor Network[J]. Journal of Electronics & Information Technology, 2019, 41(12): 3014-3021. doi: 10.11999/JEIT190016

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Semi-Markov Decision Process-based Resource Allocation Strategy for Virtual Sensor Network

doi: 10.11999/JEIT190016

Ruyan WANG^{1, 2, 3},
Hongjuan LI^{1, 2, 3
,
,},
Dapeng WU^{1, 2, 3},
Hongxia LI⁴

1.
School of Communication and Information Engineering, Chongqing University of Posts and Telecommunications, Chongqing 400065, China
2.
Optical Communication and Network Key Laboratory of Chongqing, Chongqing 400065, China
3.
Key Laboratory of Ubiquitous Sensing and Networking in Chongqing, Chongqing 400065, China
4.
Chongqing Branch, China Unicom, Chongqing 401123, China

Funds: The National Natural Science Foundation of China (61871062, 61771082), The Chongqing Funded Project of Chongqing University Innovation Team Construction (CXTDX201601020)

Received Date: 2019-01-07
Rev Recd Date: 2019-04-16

Available Online: 2019-05-22

Publish Date: 2019-12-01

Abstract

Abstract

The close relationship between resource deployment and specific tasks in traditional Wireless Sensor Network(WSN) leads to low resource utilization and revenue. According to the dynamic changes of Virtual Sensor Network Request(VSNR), the resource allocation strategy based on Semi-Markov Decision Process(SMDP) is proposed in Virtual Sensor Network(VSN). Then, difining the state, action, and transition probability of the VSN, the expected reward is given by considering the energy and time to complete the VSNR, and the model-free reinforcement learning approach is used to maximize the long-term reward of the network resource provider. The numerical results show that the resource allocation strategy of this paper can effectively improve the revenue of the sensor network resource providers.
- Virtual Sensor Network(VSN),
- Resource allocation,
- Semi-Markov Decision Process(SMDP)

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References(16)

References

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