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.
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