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Determining node duty cycle using Q-learning and linear regression for WSN
- Source :
- Frontiers of Computer Science. 15
- Publication Year :
- 2020
- Publisher :
- Springer Science and Business Media LLC, 2020.
-
Abstract
- Wireless sensor network (WSN) is effective for monitoring the target environment, which consists of a large number of sensor nodes of limited energy. An efficient medium access control (MAC) protocol is thus imperative to maximize the energy efficiency and performance of WSN. The most existing MAC protocols are based on the scheduling of sleep and active period of the nodes, and do not consider the relationship between the load condition and performance. In this paper a novel scheme is proposed to properly determine the duty cycle of the WSN nodes according to the load, which employs the Q-learning technique and function approximation with linear regression. This allows low-latency energy-efficient scheduling for a wide range of traffic conditions, and effectively overcomes the limitation of Q-learning with the problem of continuous state-action space. NS3 simulation reveals that the proposed scheme significantly improves the throughput, latency, and energy efficiency compared to the existing fully active scheme and S-MAC.
- Subjects :
- General Computer Science
business.industry
Computer science
ComputerSystemsOrganization_COMPUTER-COMMUNICATIONNETWORKS
Real-time computing
Q-learning
020207 software engineering
Access control
02 engineering and technology
Theoretical Computer Science
Scheduling (computing)
Function approximation
Duty cycle
0202 electrical engineering, electronic engineering, information engineering
020201 artificial intelligence & image processing
Latency (engineering)
business
Wireless sensor network
Efficient energy use
Subjects
Details
- ISSN :
- 20952236 and 20952228
- Volume :
- 15
- Database :
- OpenAIRE
- Journal :
- Frontiers of Computer Science
- Accession number :
- edsair.doi...........d21972f7df0d9554335ad1c017be0c4d