Abstract
Properly adjusting the transmission power of the wireless sensor nodes has shown to be an effective approach to reduce energy consumption and to improve the network reliability. I In this paper, an adaptive Node Degree adjustment based Power Control method for wireless sensor networks (NDPC) is proposed to dynamically adjust the transmission power of the nodes. In NDPC, each node is embedded with a fuzzy neural controller which is used to adjust the target node degree so as to control the communication range properly. The fuzzy neural controller consists of two Fuzzy Inference Systems (FIS). One adopts closed-loop feedback mechanism to adjust the target node degree according to the residual energy. The other adjusts the communication range by a self-learning neural network to control the transmission power based on the target node degree. Consequently, the actual energy consumption of the node is reduced while keeping the desired node degree. Several simulation experiments are conducted to evaluate the performance of NDPC, and the results show that NDPC can reduce the actual energy consumption as well as extend the network lifetime.
Highlights
Wireless Sensor Networks (WSNs) which consist of a certain number of tiny sensor nodes with integration of sensing and communication abilities have been widely investigated and deployed in a variety of application scenarios such as health-care, emergency response, environmental monitoring, and space exploration [1,2]
The neighbors’ locations are usually required which is not always available in sensor nodes, so localized algorithms without assumption that location information is needed have been proposed [11]. Intelligent control techniques such as fuzzy control is used for developing adaptation strategies on dynamics of WSNs and environment as well as constraints of the linear model, and the results show that these strategies can tolerate the uncertain interference and converge fast to keep the network stable, energy-efficient and communication-reliable [11,12,13]
The fuzzy neural controller designed above can be constructed with ANFIS, an adaptive fuzzy neural system tool in MATLAB, besides, evalfis is a function of the fuzzy inference system in MATLAB, representing FIS1 of Node Degree adjustment based Power Control (NDPC)
Summary
Wireless Sensor Networks (WSNs) which consist of a certain number of tiny sensor nodes with integration of sensing and communication abilities have been widely investigated and deployed in a variety of application scenarios such as health-care, emergency response, environmental monitoring, and space exploration [1,2]. Node degree has been widely used to adjust the transmission power because of its significant impact on signal inference, link reliability and latency times, and so on [7,8,9,10]. Intelligent control techniques such as fuzzy control is used for developing adaptation strategies on dynamics of WSNs and environment as well as constraints of the linear model, and the results show that these strategies can tolerate the uncertain interference and converge fast to keep the network stable, energy-efficient and communication-reliable [11,12,13]. A novel transmission power control approach based on self-adaptive fuzzy controller is proposed to dynamically adjust the transmission power of the nodes, which is called NDPC.
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