821 research outputs found
Finite-time average consensus in a Byzantine environment using Set-Valued Observers
This paper addresses the problem of consensus in the presence of Byzantine faults, modeled by an attacker injecting a perturbation in the state of the nodes of a network. It is firstly shown that Set-Valued Observers (SVOs) attain finite-time consensus, even in the case where the state estimates are not shared between nodes, at the expenses of requiring large horizons, thus rendering the computation problem intractable in the general case. A novel algorithm is therefore proposed that achieves finite-time consensus, even if the aforementioned requirement is dropped, by intersecting the set-valued state estimates of neighboring nodes, making it suitable for practical applications and enabling nodes to determine a stopping time. This is in contrast with the standard iterative solutions found in the literature, for which the algorithms typically converge asymptotically and without any guarantees regarding the maximum error of the final consensus value, under faulty environments. The algorithm suggested is evaluated in simulation, illustrating, in particular, the finite-time consensus property
Finite-time Average Consensus in a Byzantine Environment Using Set-Valued Observers
This paper addresses the problem of consensus in the presence of Byzantine faults, modeled by an attacker injecting a perturbation in the state of the nodes of a network. It is firstly shown that Set-Valued Observers (SVOs) attain finite-time consensus, even in the case where the state estimates are not shared between nodes, at the expenses of requiring large horizons, thus rendering the computation problem intractable in the general case. A novel algorithm is therefore proposed that achieves finite-time consensus, even if the aforementioned requirement is dropped, by intersecting the set-valued state estimates of neighboring nodes, making it suitable for practical applications and enabling nodes to determine a stopping time. This is in contrast with the standard iterative solutions found in the literature, for which the algorithms typically converge asymptotically and without any guarantees regarding the maximum error of the final consensus value, under faulty environments. The algorithm suggested is evaluated in simulation, illustrating, in particular, the finite-time consensus property
Finite-time Average Consensus in a Byzantine Environment Using Stochastic Set-Valued Observers
Compute the average of the initial states in finite-time. Guarantee fault detection and bounds on the maximum
possible deviation from an attack. Incorporate the transmissions stochastic information in the
fault detection mechanism
A general discrete-time method to achieve resilience in consensus algorithms
In this paper, we approach the problem of a set
of network agents reaching resilient consensus in the presence of a subset of attacked nodes. We devise a generalized
method, with polynomial time complexity, which receives as
input a discrete-time, synchronous-communication consensus
algorithm, a dynamic network of agents, and the maximum
number of attacked nodes. The distributed algorithm enables
each normal node to detect and discard the values of the
attacked agents while reaching the consensus of normal agents
for the input consensus algorithm. Hence, the proposed method
adds an extra layer of resilience to a given discrete-time and
synchronous-communication consensus algorithm. Finally, we
demonstrate the effectiveness of the method with experimental
results, showing some attack circumstances which we can
counter, where the state-of-the-art methods fail
Distributed Fault Detection Using Relative Information in Linear Multi-Agent Networks
This paper addresses the problem of fault detection in the context of a collection of agents performing a shared task and exchanging relative information over a communication network. We resort to techniques in the literature to construct a meaningful observable system and overcome the issue that the system of systems is not observable. A solution involving Set-Valued Observers (SVOs) is proposed to estimate the state in a distributed fashion and a proof of convergence of the estimates is given under mild assumptions. The performance of the proposed algorithm is assessed through simulations
Fault-tolerant Stochastic Distributed Systems
The present doctoral thesis discusses the design of fault-tolerant distributed systems, placing emphasis in addressing the case where the actions of the nodes or their interactions are stochastic. The main objective is to detect and identify faults to improve the resilience of distributed systems to crash-type faults, as well as detecting the presence of malicious nodes in pursuit of exploiting the network. The proposed analysis considers malicious agents and computational solutions to detect faults.
Crash-type faults, where the affected component ceases to perform its task, are tackled in this thesis by introducing stochastic decisions in deterministic distributed algorithms. Prime importance is placed on providing guarantees and rates of convergence for the steady-state solution. The scenarios of a social network (state-dependent example) and consensus (time- dependent example) are addressed, proving convergence. The proposed algorithms are capable of dealing with packet drops, delays, medium access competition, and, in particular, nodes failing and/or losing network connectivity.
The concept of Set-Valued Observers (SVOs) is used as a tool to detect faults in a worst-case scenario, i.e., when a malicious agent can select the most unfavorable sequence of communi- cations and inject a signal of arbitrary magnitude. For other types of faults, it is introduced the concept of Stochastic Set-Valued Observers (SSVOs) which produce a confidence set where the state is known to belong with at least a pre-specified probability. It is shown how, for an algorithm of consensus, it is possible to exploit the structure of the problem to reduce the computational complexity of the solution. The main result allows discarding interactions in the model that do not contribute to the produced estimates.
The main drawback of using classical SVOs for fault detection is their computational burden. By resorting to a left-coprime factorization for Linear Parameter-Varying (LPV) systems, it is shown how to reduce the computational complexity. By appropriately selecting the factorization, it is possible to consider detectable systems (i.e., unobservable systems where the unobservable component is stable). Such a result plays a key role in the domain of Cyber-Physical Systems (CPSs). These techniques are complemented with Event- and Self-triggered sampling strategies that enable fewer sensor updates. Moreover, the same triggering mechanisms can be used to make decisions of when to run the SVO routine or resort to over-approximations that temporarily compromise accuracy to gain in performance but maintaining the convergence characteristics of the set-valued estimates. A less stringent requirement for network resources that is vital to guarantee the applicability of SVO-based fault detection in the domain of Networked Control Systems (NCSs)
Fault Detection for Cyber-Physical Systems: Smart Grid case
The problem of fault detection and isolation in
cyber-physical systems is growing in importance following the
trend to have an ubiquitous presence of sensors and actuators
with network capabilities in power networks and other areas. In
this context, attacks to power systems or other vital components
providing basic needs might either present a serious threat or
at least cost a lot of resources. In this paper, we tackle the
problem of having an intruder corrupting a smart grid in two
different scenarios: a centralized detector for a portion of the
network and a fully distributed solution that only has limited
neighbor information. For both cases, differences in strategies
using Set-Valued Observers are discussed and theoretical results
regarding a bound on the maximum magnitude of the attacker’s
signal are provided. Performance is assessed through simulation,
illustrating, in particular, the detection time for various
types of faults in IEEE testbed scenarios
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