Department of Computer Science, Kent State University, Kent, OH 44242, USA.
Department of Computer Science, Taif University, Taif 26571, Saudi Arabia.
Sensors (Basel). 2021 Jun 4;21(11):3876. doi: 10.3390/s21113876.
Internet of Things (IoT) devices, particularly those used for sensor networks, are often latency-sensitive devices. The topology of the sensor network largely depends on the overall system application. Various configurations include linear, star, hierarchical and mesh in 2D or 3D deployments. Other applications include underwater communication with high attenuation of radio waves, disaster relief networks, rural networking, environmental monitoring networks, and vehicular networks. These networks all share the same characteristics, including link latency, latency variation (jitter), and tail latency. Achieving a predictable performance is critical for many interactive and latency-sensitive applications. In this paper, a two-stage tandem queuing model is developed to estimate the average end-to-end latency and predict the latency variation in closed forms. This model also provides a feedback mechanism to investigate other major performance metrics, such as utilization, and the optimal number of computing units needed in a single cluster. The model is applied for two classes of networks, namely, Edge Sensor Networks (ESNs) and Data Center Networks (DCNs). While the proposed model is theoretically derived from a queuing-based model, the simulation results of various network topologies and under different traffic conditions prove the accuracy of our model.
物联网（IoT）设备，特别是用于传感器网络的设备，通常对延迟很敏感。传感器网络的拓扑结构在很大程度上取决于整个系统的应用。各种配置包括线性、星型、分层和网状结构，可在 2D 或 3D 部署中使用。其他应用包括具有高电波衰减的水下通信、救灾网络、农村网络、环境监测网络和车载网络。这些网络都具有相同的特点，包括链路延迟、延迟变化（抖动）和尾部延迟。对于许多交互式和对延迟敏感的应用程序来说，实现可预测的性能至关重要。在本文中，提出了一种两阶段串联排队模型，以封闭形式估计平均端到端延迟并预测延迟变化。该模型还提供了一种反馈机制，用于研究其他主要性能指标，如利用率和单个集群中所需的计算单元的最佳数量。该模型适用于两类网络，即边缘传感器网络（ESN）和数据中心网络（DCN）。虽然所提出的模型是从基于排队的模型理论上推导出来的，但各种网络拓扑结构和不同流量条件下的仿真结果证明了我们模型的准确性。
