## 2 Related Work
During congestion it uses hop-by-hopcongestion control. But, SenTCP needs strict time synchronization between nodes, which is difficult for WSNs. In the Event-to-Sink Reliable Transport (ESRT) protocol [16], a sensor sets a congestion-notification (CN) bit in the packet header if its buffer is about full, which is like the \(\left(\text{RT}\right)^{2}\)protocol [17]. The sink periodically computes a new reporting rate (at which each source is supposed to report data) based on a reliability measurement, the received CN bits, and the previous reporting rate. It then broadcasts the new reporting rate to all data sources. Treating all sources equally is suboptimal. To remove all congestions, the reporting rate has to be set according to the worst hotspot in the network. In that case, the noncongested sources will be constrained by a conservative reporting rate. As the communication cost from different sources to the sink may be different and may change dynamically, and the contributions of packets from different sources are also different, it is necessary to bias the reporting rates of the sources. But, ESRT adjusts the report rate of sources in an undifferentiated manner. Based on these defects of ESRT, Price-Oriented Reliable Transport protocol (PORT) [18] employs node price, which is defined as the total number of transmission attempts across the network needed to achieve successful packet delivery from a node to the sink to measure the communication cost. At the same time, PORT dynamically feeds back the optimal reporting rate to each source according to the current contribution of the packets from each source and the node price of each source. In addition, other protocols [19-34] address congestion control from different angles too, but the related works proposed above adopt a passive approach to congestion adjustment. When congestion has occurred in the network, data source control or flow distribution is carried out passively through the feedback. At this point, a lot of useless information has been sent in the network, which results in wasted bandwidth and sensor energy. Naturally, The Active Congestion Control methods [35,36] have become particularly important in order to reduce energy overhead and improve bandwidth utilization ratios. Although the existing schemes play important roles to improve network performance, congestion control is still a challenging area in WSNs. In this paper, an active predictive method based on the research of other relevant protocols is demonstrated. The proposed scheme utilizes the priority of data sending to adjust the inputting and outputting rates of nodes, which prevents the occurrence of network congestion and ensures the timeliness and fairness of data transmission. The difference in our work from the aforementioned approaches mainly includes the following three aspects: (1) In order to actively predict and solve single-node congestion in networks, a single service window with the mixed queuing model M/M/1/m is applied to deal with the issue. (2) The predictive method of periodic flow is adopted to solve the congestion of local networks (grid). (3) The IEEE 802.11 protocol is revised to ensure fairness and timeliness of data transmission in the network by the design of cross-layer (according to the original priority and waiting time of data packets to make sending strategy).