密集QoS网络中活动感知的自适应EDCA算法
QoS Categories Activeness-Aware Adaptive EDCA Algorithm for Dense IoT Networks
摘要：
IEEE 802.11网络在支持和部署物联网（IoT）方面发挥着重要作用。物联网的实现取决于网络处理大量站点和传输的能力，以及支持服务质量（QoS）的能力。IEEE 802.11网络通过应用具有静态参数的增强型分布式信道接入（EDCA）来启用QoS，而不管现有网络容量或QoS的哪个接入类别（AC）已经是活动的。我们在本文中的目标是提高802.11网络中的上行链路接入的效率，因此，我们提出了一种算法称为QoS类别活动感知自适应EDCA算法（QCAAAE），适应竞争窗口（CW）的大小，仲裁帧间间隔数（AIFSN）值取决于相关的站（STA）的数量，并考虑每个AC的存在。对于不同的业务场景，仿真结果证实了所提出的算法在吞吐量（平均增加23%）和重传尝试率（平均减少47%）方面的优越性，考虑到可接受的延迟敏感的延迟服务。
英文摘要：
IEEE 802.11 networks have a great role to play in supporting and deploying of the Internet of Things (IoT). The realization of IoT depends on the ability of the network to handle a massive number of stations and transmissions, and to support Quality of Service (QoS). IEEE 802.11 networks enable the QoS by applying the Enhanced Distributed Channel Access (EDCA) with static parameters regardless of existing network capacity or which Access Category (AC) of QoS is already active. Our objective in this paper is to improve the efficiency of the uplink access in 802.11 networks; therefore we proposed an algorithm called QoS Categories Activeness-Aware Adaptive EDCA Algorithm (QCAAAE) which adapts Contention Window (CW) size, and Arbitration Inter-Frame Space Number (AIFSN) values depending on the number of associated Stations (STAs) and considering the presence of each AC. For different traffic scenarios, the simulation results confirm the outperformance of the proposed algorithm in terms of throughput (increased on average 23%) and retransmission attempts rate (decreased on average 47%) considering acceptable delay for sensitive delay services.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
