875 research outputs found
Fault detection and isolation of malicious nodes in MIMO Multi-hop Control Networks
A MIMO Multi-hop Control Network (MCN) consists of a MIMO LTI system where
the communication between sensors, actuators and computational units is
supported by a (wireless) multi-hop communication network, and data flow is
performed using scheduling and routing of sensing and actuation data. We
provide necessary and sufficient conditions on the plant dynamics and on the
communication protocol configuration such that the Fault Detection and
Isolation (FDI) problem of failures and malicious attacks to communication
nodes can be solved.Comment: 6 page
Consensus Computation in Unreliable Networks: A System Theoretic Approach
This work addresses the problem of ensuring trustworthy computation in a
linear consensus network. A solution to this problem is relevant for several
tasks in multi-agent systems including motion coordination, clock
synchronization, and cooperative estimation. In a linear consensus network, we
allow for the presence of misbehaving agents, whose behavior deviate from the
nominal consensus evolution. We model misbehaviors as unknown and unmeasurable
inputs affecting the network, and we cast the misbehavior detection and
identification problem into an unknown-input system theoretic framework. We
consider two extreme cases of misbehaving agents, namely faulty (non-colluding)
and malicious (Byzantine) agents. First, we characterize the set of inputs that
allow misbehaving agents to affect the consensus network while remaining
undetected and/or unidentified from certain observing agents. Second, we
provide worst-case bounds for the number of concurrent faulty or malicious
agents that can be detected and identified. Precisely, the consensus network
needs to be 2k+1 (resp. k+1) connected for k malicious (resp. faulty) agents to
be generically detectable and identifiable by every well behaving agent. Third,
we quantify the effect of undetectable inputs on the final consensus value.
Fourth, we design three algorithms to detect and identify misbehaving agents.
The first and the second algorithm apply fault detection techniques, and
affords complete detection and identification if global knowledge of the
network is available to each agent, at a high computational cost. The third
algorithm is designed to exploit the presence in the network of weakly
interconnected subparts, and provides local detection and identification of
misbehaving agents whose behavior deviates more than a threshold, which is
quantified in terms of the interconnection structure
Failure detection and identification in linear time-invariant systems
Bibliography: p. 38-40.NASA Langley Research Center grant NAG1-126 Air Force Office of Scientific Research grant AFOSR-82-0258by Mohammad-Ali Massoumnia, George C. Verghese, Alan S. Willsky
A MLMVN WITH ARBITRARY COMPLEX-VALUED INPUTS AND A HYBRID TESTABILITY APPROACH FOR THE EXTRACTION OF LUMPED MODELS USING FRA
A procedure for the identification of lumped models of distributed parameter electromagnetic systems is presented in this paper. A Frequency Response Analysis (FRA) of the
device to be modeled is performed, executing repeated measurements or intensive simulations. The method can be used to extract the values of the components. The fundamental
brick of this architecture is a multi-valued neuron (MVN), used in a multilayer neural network (MLMVN); the neuron is modified in order to use arbitrary complex-valued inputs,
which represent the frequency response of the device. It is shown that this modification
requires just a slight change in the MLMVN learning algorithm. The method is tested
over three completely different examples to clearly explain its generality
Termination Detection of Local Computations
Contrary to the sequential world, the processes involved in a distributed
system do not necessarily know when a computation is globally finished. This
paper investigates the problem of the detection of the termination of local
computations. We define four types of termination detection: no detection,
detection of the local termination, detection by a distributed observer,
detection of the global termination. We give a complete characterisation
(except in the local termination detection case where a partial one is given)
for each of this termination detection and show that they define a strict
hierarchy. These results emphasise the difference between computability of a
distributed task and termination detection. Furthermore, these
characterisations encompass all standard criteria that are usually formulated :
topological restriction (tree, rings, or triangu- lated networks ...),
topological knowledge (size, diameter ...), and local knowledge to distinguish
nodes (identities, sense of direction). These results are now presented as
corollaries of generalising theorems. As a very special and important case, the
techniques are also applied to the election problem. Though given in the model
of local computations, these results can give qualitative insight for similar
results in other standard models. The necessary conditions involve graphs
covering and quasi-covering; the sufficient conditions (constructive local
computations) are based upon an enumeration algorithm of Mazurkiewicz and a
stable properties detection algorithm of Szymanski, Shi and Prywes
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