Delay Analysis of an IOT Enabled Network Employing Different Network Control Methodologies

Abstract

newline vii newlineABSTRACT newlineIn modern Internet of Things (IoT) and Edge IoT environments, real-time control systems are progressively vulnerable to network-related issues such as communication delays, packet loss, and jitter. These disruptions can significantly impair system stability and performance, particularly in large-scale or latency-sensitive applications. This study provides a thorough comparative analysis of various networked control strategies, which include classical PID, Adaptive PID, State-Space PI, Robust and Adaptive State-Space Proportional controllers, and Distributed State-Space PI controllers with Adaptive Pole Placement. These strategies are implemented in both IoT and Edge IoT architectures. To enhance parameter adaptation and address dynamic communication uncertainties, advanced estimation techniques such as Recursive Least Squares (RLS), Extended Kalman Filter (EKF), and Unscented Kalman Filter (UKF) are incorporated. MATLAB-based simulations evaluate the performance of each controller concerning delay tolerance, jitter reduction, packet loss resilience, and CPU usage efficiency. newlineThis first work of thesis examines the effects of communication delays on closed-loop control performance when using classical PID controllers. Two design methodologies are utilized: the second method of Ziegler-Nichols tuning and the PID pole placement technique. These methodologies analyze delay tolerance and critical delay margins in IoT-based control loops. The control system is developed by considering network constraints on the sensor-to-controller and controller-to-actuator communication links. By employing the transfer function model of the physical process, a simulation-based approach is used to calculate the maximum allowable delay for each controller configuration. newlineA State-space approach utilizing a Pole-placement technique has been developed for an Edge-Internet of Things (IoT) environment that incorporates edge devices is the 2nd aspect of the thesis. This system aims to maintain consistent closed- loop performance despite unpredictable network time delays. The advantages of the proposed system, which employs event-driven sensors, include enhanced plant state prediction through the use of predictive controllers. Additionally, the system newlineviii newlinedemonstrates improved responsiveness when evaluating network parameters such as steady-state error, peak voltage, settling time, and packet loss. newlineWe have expanded our research by outlining the topology of the IoT-NCS framework. It then discusses various network control system architectures, including the Adaptive Proportional-Integral-Derivative (PID) controller, the Robust and Adaptive State-Space Proportional Controller, and the Distributed Proportional Integral (PI) controller. Additionally, the Adaptive Pole-Placement technique is considered. These Edge-IoT-based network control approaches are suggested for analyzing and mitigating delays in network configurations. newlineFinally, we present our fourth work, to address critical issues such as jitters, delays, and packet loss in Internet of Things (IoT) networks, this study explores the integration of Network Control Systems (NCS) with IoT. We outline the topology of the IoT-NCS framework and analyze the architecture of a Distributed Proportional Integral (PI) Controller using an Adaptive Pole-placement control method. This article introduces specialized control algorithms and compensation techniques, including an Unscented Kalman Filter (UKF) combined with an Event-Triggered Control (ETC) strategy and an adaptive control mechanism. This approach effectively manages time- varying parameter estimation, handles nonlinear systems, and compensates for delays. newlineWe carried out a thorough simulation of each proposed control methodology and compared our results with those from existing studies. The simulation results are presented in various visual and tabular formats, clearly demonstrating that our proposed methods significantly improve delay and packet loss performance compared to current methods. This improvement is evident across multiple performance metrics, including delay, packet loss, steady state error, settling time, stability, energy consumption, and scalability. newlineKeywords: Network control system, PID controller, IoT, edge-IoT, Distributed PI, state-space, time-delay, delay compensator, pole-placement, event driven sensor, stability, RLS, EKF, UKF, ETC, latency, jitter, packet loss, CPU usage, adaptive control