Self-stabilizing sorting algorithms

A distributed system consists of a set of machines which do not share a global memory. Depending on the connectivity of the network, each machine gets a partial view of the global state. Transient failures in one area of the network may go unnoticed in other areas and may cause the system to go to an illegal global state. However, if the system were self-stabilizing, it would be guaranteed that regardless of the current state, the system would recover to a legal configuration in a finite number of moves; The traditional way of creating reliable systems is to make redundant components. Self-stabilization allows systems to be fault tolerant through software as well. This is an evolving paradigm in the design of robust distributed systems. The ability to recover spontaneously from an arbitrary state makes self-stabilizing systems immune to transient failures or perturbations in the system state such as changes in network topology; This thesis presents an O(nh) fault-tolerant distributed sorting algorithm for a tree network, where n is the number of nodes in the system, and h is the height of the tree. Fault-tolerance is achieved using Dijkstra\u27s paradigm of self-stabilization which is a method of non-masking fault-tolerance embedding the fault-tolerance within the algorithm. Varghese\u27s counter flushing method is used in order to achieve synchronization among processes in the system. In the distributed sorting problem each node is given a value and an id which are non-corruptible. The idea is to have each node take a specific value based on its id. The algorithm handles transient faults by weeding out false information in the system. Nodes can start with completely false information concerning the values and ids of the system yet the intended behavior is still achieved. Also, nodes are allowed to crash and re-enter the system later as well as allowing new nodes to enter the system

An immune system paradigm for the assurance of dependability of collaborative self-organizing systems

In collaborative self-organizing computing systems a complex task is performed by relatively simple autonomous agents that act without centralized control. Disruption of a task can be caused by agents that produce harmful outputs due to internal failures or due to maliciously introduced alterations of their functions. The probability of such harmful outputs is minimized by the application of a design principle called ”the immune system paradigm” that provides individual agents with an all-hardware fault tolerance infrastructure. The paradigm and its application are described in this paper.1st IFIP International Conference on Biologically Inspired Cooperative Computing - Biological Inspiration: Just a dream?Red de Universidades con Carreras en Informática (RedUNCI

An immune system paradigm for the assurance of dependability of collaborative self-organizing systems

In collaborative self-organizing computing systems a complex task is performed by relatively simple autonomous agents that act without centralized control. Disruption of a task can be caused by agents that produce harmful outputs due to internal failures or due to maliciously introduced alterations of their functions. The probability of such harmful outputs is minimized by the application of a design principle called ”the immune system paradigm” that provides individual agents with an all-hardware fault tolerance infrastructure. The paradigm and its application are described in this paper.1st IFIP International Conference on Biologically Inspired Cooperative Computing - Biological Inspiration: Just a dream?Red de Universidades con Carreras en Informática (RedUNCI

Wind turbine condition monitoring strategy through multiway PCA and multivariate inference

This article states a condition monitoring strategy for wind turbines using a statistical data-driven modeling approach by means of supervisory control and data acquisition (SCADA) data. Initially, a baseline data-based model is obtained from the healthy wind turbine by means of multiway principal component analysis (MPCA). Then, when the wind turbine is monitorized, new data is acquired and projected into the baseline MPCA model space. The acquired SCADA data are treated as a random process given the random nature of the turbulent wind. The objective is to decide if the multivariate distribution that is obtained from the wind turbine to be analyzed (healthy or not) is related to the baseline one. To achieve this goal, a test for the equality of population means is performed. Finally, the results of the test can determine that the hypothesis is rejected (and the wind turbine is faulty) or that there is no evidence to suggest that the two means are different, so the wind turbine can be considered as healthy. The methodology is evaluated on a wind turbine fault detection benchmark that uses a 5 MW high-fidelity wind turbine model and a set of eight realistic fault scenarios. It is noteworthy that the results, for the presented methodology, show that for a wide range of significance, a in [1%, 13%], the percentage of correct decisions is kept at 100%; thus it is a promising tool for real-time wind turbine condition monitoring.Peer ReviewedPostprint (published version

Immunity-Based Accommodation of Aircraft Subsystem Failures

This thesis presents the design, development, and flight-simulation testing of an artificial immune system (AIS) based approach for accommodation of different aircraft subsystem failures.;Failure accommodation is considered as part of a complex integrated AIS scheme that contains four major components: failure detection, identification, evaluation, and accommodation. The accommodation part consists of providing compensatory commands to the aircraft under specific abnormal conditions based on previous experience. In this research effort, the possibility of building an AIS allowing the extraction of pilot commands is investigated.;The proposed approach is based on structuring the self (nominal conditions) and the non-self (abnormal conditions) within the AIS paradigm, as sets of artificial memory cells (mimicking behavior of T-cells, B-cells, and antibodies) consisting of measurement strings, over pre-defined time windows. Each string is a set of features values at each sample time of the flight including pilot inputs, system states, and other variables. The accommodation algorithm relies on identifying the memory cell that is the most similar to the in-coming measurements. Once the best match is found, control commands corresponding to this match will be extracted from the memory and used for control purposes.;The proposed methodology is illustrated through simulation of simple maneuvers at nominal flight conditions, different actuators, and sensor failure conditions. Data for development and demonstration have been collected from West Virginia University 6-degrees-of-freedom motion-based flight simulator. The aircraft model used for this research represents a supersonic fighter which includes model following adaptive control laws based on non-linear dynamic inversion and artificial neural network augmentation.;The simulation results demonstrate the possibility of extracting pilot compensatory commands from the self/non-self structure and the capability of the AIS paradigm to address the problem of accommodating actuator and sensor malfunctions as a part of a comprehensive and integrated framework along with abnormal condition detection, identification, and evaluation

Application of an AIS to the problem of through life health management of remotely piloted aircraft

The operation of RPAS includes a cognitive problem for the operators(Pilots, maintainers, ,managers, and the wider organization) to effectively maintain their situational awareness of the aircraft and predict its health state. This has a large impact on their ability to successfully identify faults and manage systems during operations. To overcome these system deficiencies an asset health management system that integrates more cognitive abilities to aid situational awareness could prove beneficial. This paper outlines an artificial immune system (AIS) approach that could meet these challenges and an experimental method within which to evaluate it

Data-Driven Architecture to Increase Resilience In Multi-Agent Coordinated Missions

The rise in the use of Multi-Agent Systems (MASs) in unpredictable and changing environments has created the need for intelligent algorithms to increase their autonomy, safety and performance in the event of disturbances and threats. MASs are attractive for their flexibility, which also makes them prone to threats that may result from hardware failures (actuators, sensors, onboard computer, power source) and operational abnormal conditions (weather, GPS denied location, cyber-attacks). This dissertation presents research on a bio-inspired approach for resilience augmentation in MASs in the presence of disturbances and threats such as communication link and stealthy zero-dynamics attacks. An adaptive bio-inspired architecture is developed for distributed consensus algorithms to increase fault-tolerance in a network of multiple high-order nonlinear systems under directed fixed topologies. In similarity with the natural organisms’ ability to recognize and remember specific pathogens to generate its immunity, the immunity-based architecture consists of a Distributed Model-Reference Adaptive Control (DMRAC) with an Artificial Immune System (AIS) adaptation law integrated within a consensus protocol. Feedback linearization is used to modify the high-order nonlinear model into four decoupled linear subsystems. A stability proof of the adaptation law is conducted using Lyapunov methods and Jordan decomposition. The DMRAC is proven to be stable in the presence of external time-varying bounded disturbances and the tracking error trajectories are shown to be bounded. The effectiveness of the proposed architecture is examined through numerical simulations. The proposed controller successfully ensures that consensus is achieved among all agents while the adaptive law v simultaneously rejects the disturbances in the agent and its neighbors. The architecture also includes a health management system to detect faulty agents within the global network. Further numerical simulations successfully test and show that the Global Health Monitoring (GHM) does effectively detect faults within the network