Cross-Layer Adaptive Feedback Scheduling of Wireless Control Systems

There is a trend towards using wireless technologies in networked control systems. However, the adverse properties of the radio channels make it difficult to design and implement control systems in wireless environments. To attack the uncertainty in available communication resources in wireless control systems closed over WLAN, a cross-layer adaptive feedback scheduling (CLAFS) scheme is developed, which takes advantage of the co-design of control and wireless communications. By exploiting cross-layer design, CLAFS adjusts the sampling periods of control systems at the application layer based on information about deadline miss ratio and transmission rate from the physical layer. Within the framework of feedback scheduling, the control performance is maximized through controlling the deadline miss ratio. Key design parameters of the feedback scheduler are adapted to dynamic changes in the channel condition. An event-driven invocation mechanism for the feedback scheduler is also developed. Simulation results show that the proposed approach is efficient in dealing with channel capacity variations and noise interference, thus providing an enabling technology for control over WLAN.Comment: 17 pages, 12 figures; Open Access at http://www.mdpi.org/sensors/papers/s8074265.pd

Improving Performance of Feedback-Based Real-Time Networks using Model Checking and Reinforcement Learning

Traditionally, automatic control techniques arose due to need for automation in mechanical systems. These techniques rely on robust mathematical modelling of physical systems with the goal to drive their behaviour to desired set-points. Decades of research have successfully automated, optimized, and ensured safety of a wide variety of mechanical systems. Recent advancement in digital technology has made computers pervasive into every facet of life. As such, there have been many recent attempts to incorporate control techniques into digital technology. This thesis investigates the intersection and co-application of control theory and computer science to evaluate and improve performance of time-critical systems. The thesis applies two different research areas, namely, model checking and reinforcement learning to design and evaluate two unique real-time networks in conjunction with control technologies. The first is a camera surveillance system with the goal of constrained resource allocation to self-adaptive cameras. The second is a dual-delay real-time communication network with the goal of safe packet routing with minimal delays.The camera surveillance system consists of self-adaptive cameras and a centralized manager, in which the cameras capture a stream of images and transmit them to a central manager over a shared constrained communication channel. The event-based manager allocates fractions of the shared bandwidth to all cameras in the network. The thesis provides guarantees on the behaviour of the camera surveillance network through model checking. Disturbances that arise during image capture due to variations in capture scenes are modelled using probabilistic and non-deterministic Markov Decision Processes (MDPs). The different properties of the camera network such as the number of frame drops and bandwidth reallocations are evaluated through formal verification.The second part of the thesis explores packet routing for real-time networks constructed with nodes and directed edges. Each edge in the network consists of two different delays, a worst-case delay that captures high load characteristics, and a typical delay that captures the current network load. Each node in the network takes safe routing decisions by considering delays already encountered and the amount of remaining time. The thesis applies reinforcement learning to route packets through the network with minimal delays while ensuring the total path delay from source to destination does not exceed the pre-determined deadline of the packet. The reinforcement learning algorithm explores new edges to find optimal routing paths while ensuring safety through a simple pre-processing algorithm. The thesis shows that it is possible to apply powerful reinforcement learning techniques to time-critical systems with expert knowledge about the system

Control and Embedded Computing: Survey of Research Directions

This paper provides a survey of the role of feedback control in embedded realtimesystems, presented in the context of a new EU/IST Network of Excellence, ARTIST2.The survey highlights recent research efforts and future research directions in the areasof codesign of computer-based control systems, implementation-aware embedded controlsystems, and control of real-time computing systems

Feedback and time are essential for the optimal control of computing systems

The performance, reliability, cost, size and energy usage of computing systems can be improved by one or more orders of magnitude by the systematic use of modern control and optimization methods. Computing systems rely on the use of feedback algorithms to schedule tasks, data and resources, but the models that are used to design these algorithms are validated using open-loop metrics. By using closed-loop metrics instead, such as the gap metric developed in the control community, it should be possible to develop improved scheduling algorithms and computing systems that have not been over-engineered. Furthermore, scheduling problems are most naturally formulated as constraint satisfaction or mathematical optimization problems, but these are seldom implemented using state of the art numerical methods, nor do they explicitly take into account the fact that the scheduling problem itself takes time to solve. This paper makes the case that recent results in real-time model predictive control, where optimization problems are solved in order to control a process that evolves in time, are likely to form the basis of scheduling algorithms of the future. We therefore outline some of the research problems and opportunities that could arise by explicitly considering feedback and time when designing optimal scheduling algorithms for computing systems

Discrete Event Simulation and Optimization Approaches for the Predictive Maintenance of Railway Infrastructure

This thesis is carried out within the PhD Course in Logistics and Transport at CIELI - Italian Centre of Excellence on Logistics, Transport and Infrastructures, University of Genoa. In this work, a discrete event simulation and optimization model is created to schedule the predictive maintenance activities. Nowadays, after a severe decrease of transport demand during the pandemic period, rail public transport is resuming a central role for both freight and passenger transport. To cope with this increase in demand, to maintain high safety standards and to avoid unnecessary costs, the idea is to switch to predictive maintenance strategy, intervening before an asset failure and when it has reached a certain state of degradation. The degradation and asset future conditions are predicted according to probabilistic models and maintenance deadlines are defined by applying a risk based approach. The problem is first formulated as a MILP (Mixed Integer Linear Programming) optimization problem and then transformed into a simulation-based optimization problem using the ExtendSim software. Different simulative models are created to take into account the stochastic nature of some variables in real processes. After the formal description of the models, some real-world applications are presented. Finally, considerations on the proposed approach are reported highlighting limits and challenges in predictive maintenance planning, such as lack of data and the stochastic and complex environment

An Intelligent Framework for Energy-Aware Mobile Computing Subject to Stochastic System Dynamics

abstract: User satisfaction is pivotal to the success of mobile applications. At the same time, it is imperative to maximize the energy efficiency of the mobile device to ensure optimal usage of the limited energy source available to mobile devices while maintaining the necessary levels of user satisfaction. However, this is complicated due to user interactions, numerous shared resources, and network conditions that produce substantial uncertainty to the mobile device's performance and power characteristics. In this dissertation, a new approach is presented to characterize and control mobile devices that accurately models these uncertainties. The proposed modeling framework is a completely data-driven approach to predicting power and performance. The approach makes no assumptions on the distributions of the underlying sources of uncertainty and is capable of predicting power and performance with over 93% accuracy. Using this data-driven prediction framework, a closed-loop solution to the DEM problem is derived to maximize the energy efficiency of the mobile device subject to various thermal, reliability and deadline constraints. The design of the controller imposes minimal operational overhead and is able to tune the performance and power prediction models to changing system conditions. The proposed controller is implemented on a real mobile platform, the Google Pixel smartphone, and demonstrates a 19% improvement in energy efficiency over the standard frequency governor implemented on all Android devices.Dissertation/ThesisDoctoral Dissertation Computer Engineering 201

Risk-Based Optimal Scheduling for the Predictive Maintenance of Railway Infrastructure

In this thesis a risk-based decision support system to schedule the predictive maintenance activities, is proposed. The model deals with the maintenance planning of a railway infrastructure in which the due-dates are defined via failure risk analysis.The novelty of the approach consists of the risk concept introduction in railway maintenance scheduling, according to ISO 55000 guidelines, thus implying that the maintenance priorities are based on asset criticality, determined taking into account the relevant failure probability, related to asset degradation conditions, and the consequent damages