@Article{cmc.2020.011458,
AUTHOR = {Ting Xu, Ming Zhao, Xin Yao, Kun He},
TITLE = {An Adjust Duty Cycle Method for Optimized Congestion  Avoidance and Reducing Delay for WSNs},
JOURNAL = {Computers, Materials \& Continua},
VOLUME = {65},
YEAR = {2020},
NUMBER = {2},
PAGES = {1605--1624},
URL = {http://www.techscience.com/cmc/v65n2/39896},
ISSN = {1546-2226},
ABSTRACT = {With the expansion of the application range and network scale of wireless 
sensor networks in recent years, WSNs often generate data surges and delay queues 
during the transmission process, causing network paralysis, even resulting in local or 
global congestion. In this paper, a dynamically Adjusted Duty Cycle for Optimized 
Congestion based on a real-time Queue Length (ADCOC) scheme is proposed. In order 
to improve the resource utilization rate of network nodes, we carried out optimization 
analysis based on the theory and applied it to the adjustment of the node’s duty cycle 
strategy. Using this strategy to ensure that the network lifetime remains the same, can 
minimize system delay and maximize energy efficiency. Firstly, the problems of the 
existing RED algorithm are analyzed. We introduce the improved SIG-RED algorithm 
into the ADCOC mechanism. As the data traffic changes, the RED protocol cannot 
automatically adjust the duty cycle. A scheduler is added to the buffer area manager, 
referring to a weighted index of network congestion, which can quickly determine the 
status of network congestion. The value of the weighting coefficient W is adjusted by the 
Bayesian method. The scheduler preferably transmits severely urgent data, alleviating the 
memory load. Then we combined improved data fusion technology and information gain 
methods to adjust the duty cycle dynamically. By simulating the algorithm, it shows that 
it has faster convergence speed and smaller queue jitter. Finally, we combine the adjusted 
congestion weight and the duty cycle growth value to adjust the data processing rate 
capability in the real-time network by dynamically adjusting it to adapt to bursts of data 
streams. Thus, the frequency of congestion is reduced to ensure that the system has 
higher processing efficiency and good adaptability.},
DOI = {10.32604/cmc.2020.011458}
}