Real-Time and Energy-Efficient Routing for Industrial Wireless Sensor-Actuator Networks

With the emergence of industrial standards such as WirelessHART, process industries are adopting Wireless Sensor-Actuator Networks (WSANs) that enable sensors and actuators to communicate through low-power wireless mesh networks. Industrial monitoring and control applications require real-time communication among sensors, controllers and actuators within end-to-end deadlines. Deadline misses may lead to production inefficiency, equipment destruction to irreparable financial and environmental impacts. Moreover, due to the large geographic area and harsh conditions of many industrial plants, it is labor-intensive or dan- gerous to change batteries of field devices. It is therefore important to achieve long network lifetime with battery-powered devices. This dissertation tackles these challenges and make a series of contributions. (1) We present a new end-to-end delay analysis for feedback control loops whose transmissions are scheduled based on the Earliest Deadline First policy. (2) We propose a new real-time routing algorithm that increases the real-time capacity of WSANs by exploiting the insights of the delay analysis. (3) We develop an energy-efficient routing algorithm to improve the network lifetime while maintaining path diversity for reliable communication. (4) Finally, we design a distributed game-theoretic algorithm to allocate sensing applications with near-optimal quality of sensing

High Performance Wireless Sensor-Actuator Networks for Industrial Internet of Things

Wireless Sensor-Actuator Networks (WSANs) enable cost-effective communication for Industrial Internet of Things (IIoT). To achieve predictability and reliability demanded by industrial applications, industrial wireless standards (e.g., WirelessHART) incorporate a set of unique features such as a centralized management architecture, Time Slotted Channel Hopping (TSCH), and conservative channel selection. However, those features also incur significant degradation in performance, efficiency, and agility. To overcome these key limitations of existing industrial wireless technologies, this thesis work develops and empirically evaluates a suite of novel network protocols and algorithms. The primary contributions of this thesis are four-fold. (1) We first build an experimental testbed realizing key features of the WirelessHART protocol stack, and perform a series of empirical studies to uncover the limitations and potential improvements of existing network features. (2) We then investigate the impacts of the industrial WSAN protocol’s channel selection mechanism on routing and real-time performance, and present new channel and link selection strategies that improve route diversity and real-time performance. (3) To further enhance performance, we propose and design conservative channel reuse, a novel approach to support concurrent transmissions in a same wireless channel while maintaining a high degree of reliability. (4) Lastly, to address the limitation of the centralized architecture in handling network dynamics, we develop REACT, a Reliable, Efficient, and Adaptive Control Plane for centralized network management. REACT is designed to reduce the latency and energy cost of network reconfiguration by incorporating a reconfiguration planner to reduce a rescheduling cost, and an update engine providing efficient and reliable mechanisms to support schedule reconfiguration. All the network protocols and algorithms developed in this thesis have been empirically evaluated on the wireless testbed. This thesis represents a step toward next-generation IIoT for industrial automation that demands high-performance and agile wireless communication

Spectrum Management using Markov Decision Processes

Abstract: The advent of cognitive radio technology has enabled dramatically more options in the use of RF spectrum, allowing multiple transmitters to effectively share spectrum in ways that were previously unavailable (either due to technical limitations or regulatory restrictions). In this dissertation, we investigate approaches to managing RF spectrum use, with a focus on combining multiple control decisions in a mutually beneficial manner. Our approach to making spectrum management decisions is grounded in Markov decision theory, which has a rich formal foundation and is frequently used to guide decision making in other disciplines. Here, we develop a set of Markov Decision Processes (MDPs) that model the RF spectrum management problem (in various forms). These MDPs are then queried to provide guidance for management decisions, including the combination of both admission and modulation decisions. This results in control decisions that are optimal in expectation. To address the computational complexity inherent in computing these control decisions, we develop heuristic approaches that mimic the MDP\u27s decisions based upon patterns observed in the MDP decision space. These heuristics are shown to closely approximate the optimal results from the MDP. Finally, we empirically assess the appropriateness of using Markov decision theory for RF spectrum management by comparing our MDPs to a discrete-event simulation model that relaxes several of the modeling assumptions made in the development of the MDPs

Holistic Control for Cyber-Physical Systems

The Industrial Internet of Things (IIoT) are transforming industries through emerging technologies such as wireless networks, edge computing, and machine learning. However, IIoT technologies are not ready for control systems for industrial automation that demands control performance of physical processes, resiliency to both cyber and physical disturbances, and energy efficiency. To meet the challenges of IIoT-driven control, we propose holistic control as a cyber-physical system (CPS) approach to next-generation industrial automation systems. In contrast to traditional industrial automation systems where computing, communication, and control are managed in isolation, holistic control orchestrates the management of cyber platforms (networks and computing platforms) and physical plant control at run-time in an integrated architecture. Specifically, this dissertation research comprises the following primary components. Holistic wireless control: The core of holistic wireless control is a holistic controller comprising a plant controller and a network controller cooperating with each other. At run-time the holistic controller generates (1) control commands to the physical plant and (2) network reconfiguration commands to wireless networks based on both physical and network states. This part of dissertation research focused on the design and evaluation of holistic controllers exploiting a range of network reconfiguration strategies: (1) adapting transmission redundancy, (2) adapting sampling rates, (3) self-triggered control, and (4) dynamic transmission scheduling. Furthermore, we develop novel network reconfiguration protocols (NRP) as actuators to control network configurations in holistic control. Holistic edge control: This part of dissertation research explores edge computing as a multitier computing platform for holistic control. The proposed switching multi-tier control (SMC) dynamically switches controllers located on different computation platforms, thereby exploiting the trade-off between computation and communication in a multi-tier computing platform. We also design the stability switch between local and edge controllers under information loss from another perspective, based on co-design of edge and local controllers that are designed via a joint Lyapunov function. Real-time wireless cyber-physical simulators: To evaluate holistic control, we extend the Wireless Cyber-Physical Simulator (WCPS) to integrate simulated physical plants (in Simulink) with real wireless networks (WCPS-RT) and edge computing platforms (WCPS-EC). The real-time WCPS provides a holistic environment for CPS simulations that incorporate wireless dynamics that are challenging to simulate accurately, explore the impacts and trade-off of computation and communication of multi-tier platforms, and leverage simulation support for controllers and plants