A Fairness-Based Adaptive Explicit Congestion Control Protocol in Microburst Traffic Scenarios

During Unmanned Aerial Vehicle (UAV) missions, sudden weather changes like strong winds can destabilize UAV flight, making it essential to quickly transmit weather information to improve safety and efficiency. However, these abrupt data surges can lead to network congestion, affecting transmission timeliness and reliability. Current congestion control schemes often prioritize network efficiency but overlook user fairness. This paper introduces an Adaptive Explicit Congestion Control algorithm based on Fairness (AECCF) for handling Microburst traffic, balancing congestion management with fair resource distribution. The AECCF algorithm works as follows: First, the router node monitors its outgoing packet queue and assesses local link congestion state using a double delay threshold. It then calculates and attaches congestion probability, congestion state, and sojourn time to packets, enabling complete congestion feedback to downstream nodes. In cases of burst traffic, the router labels the traffic as heavily congested and activates flow shaping, transferring some packets to a cache queue for staggered forwarding. The consumer then adjusts its interest packet rate based on congestion markings and fair bandwidth allocation, preventing throughput loss from over-adjustment and maintaining network stability and fairness. Finally, the router forwards interest packets based on the proportion of consumer request prefixes on available interfaces, improving data transmission efficiency under sudden traffic. Simulations on ndnSIM show that AECCF achieves higher throughput and fairness in different scenarios compared to existing methods. For dumbbell networks, AECCF reaches 18.64 Mbps throughput with a fairness index of 0.97, and in complex network scenarios, the throughput is 24.9% higher than that of the PCON protocol, and the fairness index is 0.86.