20,265 research outputs found
Gossip and Distributed Kalman Filtering: Weak Consensus under Weak Detectability
The paper presents the gossip interactive Kalman filter (GIKF) for
distributed Kalman filtering for networked systems and sensor networks, where
inter-sensor communication and observations occur at the same time-scale. The
communication among sensors is random; each sensor occasionally exchanges its
filtering state information with a neighbor depending on the availability of
the appropriate network link. We show that under a weak distributed
detectability condition:
1. the GIKF error process remains stochastically bounded, irrespective of the
instability properties of the random process dynamics; and
2. the network achieves \emph{weak consensus}, i.e., the conditional
estimation error covariance at a (uniformly) randomly selected sensor converges
in distribution to a unique invariant measure on the space of positive
semi-definite matrices (independent of the initial state.)
To prove these results, we interpret the filtered states (estimates and error
covariances) at each node in the GIKF as stochastic particles with local
interactions. We analyze the asymptotic properties of the error process by
studying as a random dynamical system the associated switched (random) Riccati
equation, the switching being dictated by a non-stationary Markov chain on the
network graph.Comment: Submitted to the IEEE Transactions, 30 pages
Information Flow for Security in Control Systems
This paper considers the development of information flow analyses to support
resilient design and active detection of adversaries in cyber physical systems
(CPS). The area of CPS security, though well studied, suffers from
fragmentation. In this paper, we consider control systems as an abstraction of
CPS. Here, we extend the notion of information flow analysis, a well
established set of methods developed in software security, to obtain a unified
framework that captures and extends system theoretic results in control system
security. In particular, we propose the Kullback Liebler (KL) divergence as a
causal measure of information flow, which quantifies the effect of adversarial
inputs on sensor outputs. We show that the proposed measure characterizes the
resilience of control systems to specific attack strategies by relating the KL
divergence to optimal detection techniques. We then relate information flows to
stealthy attack scenarios where an adversary can bypass detection. Finally,
this article examines active detection mechanisms where a defender
intelligently manipulates control inputs or the system itself in order to
elicit information flows from an attacker's malicious behavior. In all previous
cases, we demonstrate an ability to investigate and extend existing results by
utilizing the proposed information flow analyses
Distributed Linear Parameter Estimation: Asymptotically Efficient Adaptive Strategies
The paper considers the problem of distributed adaptive linear parameter
estimation in multi-agent inference networks. Local sensing model information
is only partially available at the agents and inter-agent communication is
assumed to be unpredictable. The paper develops a generic mixed time-scale
stochastic procedure consisting of simultaneous distributed learning and
estimation, in which the agents adaptively assess their relative observation
quality over time and fuse the innovations accordingly. Under rather weak
assumptions on the statistical model and the inter-agent communication, it is
shown that, by properly tuning the consensus potential with respect to the
innovation potential, the asymptotic information rate loss incurred in the
learning process may be made negligible. As such, it is shown that the agent
estimates are asymptotically efficient, in that their asymptotic covariance
coincides with that of a centralized estimator (the inverse of the centralized
Fisher information rate for Gaussian systems) with perfect global model
information and having access to all observations at all times. The proof
techniques are mainly based on convergence arguments for non-Markovian mixed
time scale stochastic approximation procedures. Several approximation results
developed in the process are of independent interest.Comment: Submitted to SIAM Journal on Control and Optimization journal.
Initial Submission: Sept. 2011. Revised: Aug. 201
Coverage Protocols for Wireless Sensor Networks: Review and Future Directions
The coverage problem in wireless sensor networks (WSNs) can be generally
defined as a measure of how effectively a network field is monitored by its
sensor nodes. This problem has attracted a lot of interest over the years and
as a result, many coverage protocols were proposed. In this survey, we first
propose a taxonomy for classifying coverage protocols in WSNs. Then, we
classify the coverage protocols into three categories (i.e. coverage aware
deployment protocols, sleep scheduling protocols for flat networks, and
cluster-based sleep scheduling protocols) based on the network stage where the
coverage is optimized. For each category, relevant protocols are thoroughly
reviewed and classified based on the adopted coverage techniques. Finally, we
discuss open issues (and recommend future directions to resolve them)
associated with the design of realistic coverage protocols. Issues such as
realistic sensing models, realistic energy consumption models, realistic
connectivity models and sensor localization are covered
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