Delay Analysis in IoT Sensor Networks

Abstract

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.

A Network Design Scheme in Delay Sensitive Monitoring Services

“…Several types of research works have been studied on delay reduction in IoT services using multiple clouds via a wide-area network [12][13][14][15][16][17][18]. These works are based on the distributed allocation of functions at multiple clouds and focus on controlling the transmitting and processing delays.…”

“…The challenge of distributed processing in real-time IoT applications is using limited resources to control processing and queuing delays effectively. In [18], a two-stage tandem queuing model was developed to predict an average end-toend delay in two classes of networks, which are edge sensor networks (ESNs) and data center networks (DCNs). This effort can be assumed to predict queuing delays for conformance and monitoring for delay-sensitive IoT applications.…”

Self-Learning Pipeline for Low-Energy Resource-Constrained Devices

“…The overall application latency is typically dominated by the propagation delay between the edge and the data centers on the cloud. Another significant performance penalty factor is given by packet losses [21]. This, together with consideration about bandwidth, energy consumption, and privacy, suggests the importance of edge computing, thus keeping the computation as much as possible close to the information source [22].…”

Study of Generalized Phase Spectrum Time Delay Estimation Method for Source Positioning in Small Room Acoustic Environment

“…At present different approaches for TDE are known. A number of reviews have been devoted to the classification and systematization of TDE algorithms for numerous and diverse applications, in particular [ 8 , 12 , 13 , 14 , 15 ]. This paper compares well-known but seldom used TDE algorithms based on estimating the phase shift (GPS TDE) between signals.…”