A Novel Fuzzy PID Congestion Control Model Based on Cuckoo Search in WSNs

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.

A Novel Congestion Control Scheme using Firefly Algorithm Optimized Fuzzy-PID Controller in Wireless Sensor Network

“…This result, instead, ignores the issue of low accuracy in PID controllers for high-demand control environments. In [10][11][12][13], researchers have introduced various PID controller improvement approaches, yet these still need to be improved when used with WSNs. This study discovered that low adaptability and low calculation accuracy are issues that must be considered when applying PID controllers to control congestion in WSNs.…”

Survey on: A variety of AQM algorithm schemas and intelligent techniques developed for congestion control

“…The FL takes a crisp value as input (for example, x value) then transforms it into a fuzzy set by splitting it into different stages as in Table 1 to optimize decisions [49]. The fuzzy proportional integral derivative (FPID) controller [50], [51] was designed to optimize the PID-AQM algorithm parameters, and these parameters are three constant gain K p , K i and K d . Where the FL adjusted these parameters to achieve optimal control on congestion when a different type of service flows used over a network.…”

Soft Computing-Based Congestion Control Schemes in Wireless Sensor Networks: Research Issues and Challenges

“…Fuzzy logic-based congestion control can be considered as one of the latest approaches to control congestion. Some well-known fuzzy logic-based congestion control schemes are summarized as follows [14]- [28], [43], [57]:…”

“…Swarm intelligence is suggested to mitigate congestion by mimicking the collective behavior of swarms where swarms are low-intelligence interacting agents which are organized in small societies [35]. Some well-known swarm intelligence-based congestion control schemes are summarized as follows [36][37][38][39][40][41][42][43]:…”

“…In [43], queue size is controlled using PID controller and the effective sensor data collection is realized by applying the PID algorithm on cluster head nodes. Moreover, the problems concerning PID controller, i.e., the limited adaptive ability, slow parameter optimization, and poor optimization precision are rectified using a fuzzy control scheme.…”