论文标题
用异步时间敏感的周期性流量来表征物联网网络
Characterizing IoT Networks with Asynchronous Time-Sensitive Periodic Traffic
论文作者
论文摘要
本文开发了一种新型的时空模型，用于具有异步定期交通和硬包截止日期的大型物联网网络。使用静态标记的泊松双极点过程来对物联网设备的空间位置进行建模，其中标记模仿了不同设备在不同设备的交通税周期的相对时间。在每个设备上，吸收的马尔可夫链被用来捕获数据包的时间演变，直到成功交付或截止日期到期。数据包的时间演变是根据Aloha传输/向后状态定义的。从网络的角度来看，传输成功概率的元分布用于表征共存设备之间的相互干扰。为此，网络性能的特征是会议/缺少交付截止日期和传输延迟的概率。结果在传输成功和延迟方面揭示了严格的数据包期限的违反直觉的出色表现。
This paper develops a novel spatiotemporal model for large-scale IoT networks with asynchronous periodic traffic and hard-packet deadlines. A static marked Poisson bipolar point process is utilized to model the spatial locations of the IoT devices, where the marks mimic the relative time-offsets of traffic duty cycles at different devices. At each device, an absorbing Markov chain is utilized to capture the temporal evolution of packets from generation until either successful delivery or deadline expiry. The temporal evolution of packets is defined in terms of the Aloha transmission/backoff states. From the network perspective, the meta distribution of the transmission success probability is used to characterize the mutual interference among of the coexisting devices. To this end, the network performance is characterized in terms of the probabilities of meeting/missing the delivery deadlines and transmission latency. The results unveil counter-intuitive superior performance of strict packet deadlines in terms of transmission success and latency.
