Design and Implementation of Switches in Network Simulator (ns2)

Zheng, Hongyun; Zhao, Yongqiang; Chen, Changjia

doi:10.1109/icicic.2006.65

Cited by 5 publications

(1 citation statement)

References 2 publications

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“…We perform experiments to evaluate the performance of the proposed RL based SDWSN prototype (SDWSN-RL) for environmental monitoring applications. The network simulator NS2 [21] is used to build the experiment environment. The parameter values used for the experiment setup are given in Table 1.…”

Section: Experiments Resultsmentioning

confidence: 99%

Energy-Efficient Monitoring in Software Defined Wireless Sensor Networks Using Reinforcement Learning: A Prototype

Huang¹,

Chu²,

Zhang³

et al. 2015

International Journal of Distributed Sensor Networks

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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.

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Section: Experiments Resultsmentioning

confidence: 99%