Energy-Efficient Monitoring in Software Defined Wireless Sensor Networks Using Reinforcement Learning: A Prototype
Software defined wireless networks (SDWNs) present an innovative framework for virtualized network control and flexible architecture design of wireless sensor networks (WSNs). However, the decoupled control and data planes and the logically centralized control in SDWNs may cause high energy consumpt...
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| Format: | Article |
| Language: | English |
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Wiley
2015-10-01
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| Series: | International Journal of Distributed Sensor Networks |
| Online Access: | https://doi.org/10.1155/2015/360428 |
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| _version_ | 1849704403937787904 |
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| author | Ru Huang Xiaoli Chu Jie Zhang Yu Hen Hu |
| author_facet | Ru Huang Xiaoli Chu Jie Zhang Yu Hen Hu |
| author_sort | Ru Huang |
| collection | DOAJ |
| description | Software defined wireless networks (SDWNs) present an innovative framework for virtualized network control and flexible architecture design of wireless sensor networks (WSNs). However, the decoupled control and data planes and the logically centralized control in SDWNs may cause high energy consumption and resource waste during system operation, hindering their application in WSNs. In this paper, we propose a software defined WSN (SDWSN) prototype to improve the energy efficiency and adaptability of WSNs for environmental monitoring applications, taking into account the constraints of WSNs in terms of energy, radio resources, and computational capabilities, and the value redundancy and distributed nature of data flows in periodic transmissions for monitoring applications. Particularly, we design a reinforcement learning based mechanism to perform value-redundancy filtering and load-balancing routing according to the values and distribution of data flows, respectively, in order to improve the energy efficiency and self-adaptability to environmental changes for WSNs. The optimal matching rules in flow table are designed to curb the control signaling overhead and balance the distribution of data flows for achieving in-network fusion in data plane with guaranteed quality of service (QoS). Experiment results show that the proposed SDWSN prototype can effectively improve the energy efficiency and self-adaptability of environmental monitoring WSNs with QoS. |
| format | Article |
| id | doaj-art-03d4dcfea0f44c9fba4feb4364789e72 |
| institution | DOAJ |
| issn | 1550-1477 |
| language | English |
| publishDate | 2015-10-01 |
| publisher | Wiley |
| record_format | Article |
| series | International Journal of Distributed Sensor Networks |
| spelling | doaj-art-03d4dcfea0f44c9fba4feb4364789e722025-08-20T03:16:46ZengWileyInternational Journal of Distributed Sensor Networks1550-14772015-10-011110.1155/2015/360428360428Energy-Efficient Monitoring in Software Defined Wireless Sensor Networks Using Reinforcement Learning: A PrototypeRu Huang0Xiaoli Chu1Jie Zhang2Yu Hen Hu3 School of Information Science & Engineering, East China University of Science and Technology, Shanghai 200237, China Department of Electronic and Electrical Engineering, University of Sheffield, Sheffield S1 3JD, UK Department of Electronic and Electrical Engineering, University of Sheffield, Sheffield S1 3JD, UK Department of Electrical and Computer Engineering, University of Wisconsin-Madison, Madison, WI 53705, USASoftware defined wireless networks (SDWNs) present an innovative framework for virtualized network control and flexible architecture design of wireless sensor networks (WSNs). However, the decoupled control and data planes and the logically centralized control in SDWNs may cause high energy consumption and resource waste during system operation, hindering their application in WSNs. In this paper, we propose a software defined WSN (SDWSN) prototype to improve the energy efficiency and adaptability of WSNs for environmental monitoring applications, taking into account the constraints of WSNs in terms of energy, radio resources, and computational capabilities, and the value redundancy and distributed nature of data flows in periodic transmissions for monitoring applications. Particularly, we design a reinforcement learning based mechanism to perform value-redundancy filtering and load-balancing routing according to the values and distribution of data flows, respectively, in order to improve the energy efficiency and self-adaptability to environmental changes for WSNs. The optimal matching rules in flow table are designed to curb the control signaling overhead and balance the distribution of data flows for achieving in-network fusion in data plane with guaranteed quality of service (QoS). Experiment results show that the proposed SDWSN prototype can effectively improve the energy efficiency and self-adaptability of environmental monitoring WSNs with QoS.https://doi.org/10.1155/2015/360428 |
| spellingShingle | Ru Huang Xiaoli Chu Jie Zhang Yu Hen Hu Energy-Efficient Monitoring in Software Defined Wireless Sensor Networks Using Reinforcement Learning: A Prototype International Journal of Distributed Sensor Networks |
| title | Energy-Efficient Monitoring in Software Defined Wireless Sensor Networks Using Reinforcement Learning: A Prototype |
| title_full | Energy-Efficient Monitoring in Software Defined Wireless Sensor Networks Using Reinforcement Learning: A Prototype |
| title_fullStr | Energy-Efficient Monitoring in Software Defined Wireless Sensor Networks Using Reinforcement Learning: A Prototype |
| title_full_unstemmed | Energy-Efficient Monitoring in Software Defined Wireless Sensor Networks Using Reinforcement Learning: A Prototype |
| title_short | Energy-Efficient Monitoring in Software Defined Wireless Sensor Networks Using Reinforcement Learning: A Prototype |
| title_sort | energy efficient monitoring in software defined wireless sensor networks using reinforcement learning a prototype |
| url | https://doi.org/10.1155/2015/360428 |
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