Abstract
Wireless Sensor Networks (WSNs) consist of multiple sensor nodes, each of which has the ability to collect, receive and send data. However, irregular data sources can lead to severe network congestion. To solve this problem, the Proportional Integral Derivative (PID) controller is introduced into the congestion control mechanism to control the queue length of messages in nodes. By running the PID algorithm on cluster head nodes, the effective collection of sensor data is realized. In addition, a fuzzy control algorithm is proposed to solve the problems of slow parameter optimization, limited adaptive ability and poor optimization precision of traditional PID controller. However, the parameter selection of the fuzzy control algorithm relies too much on expert experience and has certain limitations. Therefore, this manuscript proposes the Cuckoo Fuzzy-PID Controller (CFPID), whose core idea is to apply the cuckoo search algorithm to optimize the fuzzy PID controller’s quantization factor and PID parameter increment. Simulation results show that in comparison with the existing methods, the instantaneous queue length and real-time packet loss rate of CFPID are better.
Highlights
Wireless Sensor Networks (WSNs) are one of the important technologies in recent times due to their widespread applications e.g., military, smart phones, disaster management, health care monitoring and other surveillance systems
Aiming at the defects of Proportional Integral Derivative (PID) Controller, we propose the Cuckoo Fuzzy-PID Controller (CFPID), whose core idea is to apply the Cuckoo search algorithm to optimize the fuzzy PID controller’s quantization factor and PID parameter increment
As can surpassed by CFPID, which shows the superiority of CFPID in terms of stability, and adaptive be seen from the Figure 11, the throughput of the method presented in this manuscript is improved by
Summary
Wireless Sensor Networks (WSNs) are one of the important technologies in recent times due to their widespread applications e.g., military, smart phones, disaster management, health care monitoring and other surveillance systems. In these widespread applications, WSNs may face many challenges, in which network congestion is the prominent one [1]. The deployment area and the number of nodes is generally very large in WSNs. there is no time limit for data acquisition and transmission, so possibly the network nodes may receive a large amount of data in an instant. Introduced a new type of neural networks controller based on (proportion, sum and differential) to improve the lack of fixed gain in single neuron adaptive PID.PSD.
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