Information assurance is a critical component of any organization's data network. Trustworthiness of the sensor data,
especially in the case of wireless sensor networks (WSNs), is an important metric for any application that requires
situational awareness. In a WSN, information packets are typically not encrypted and the nodes themselves could be
located in the open, leaving them susceptible to tampering and physical degradation. In order to develop a method to
assess trustworthiness in WSNs, we have utilized statistical trustworthiness metrics and have implemented an agentbased
simulation platform that can perform various trustworthiness measurement experiments for various WSN
operating scenarios. Different trust metrics are used against multiple vulnerabilities to detect anomalous behavior and
node failure as well as malicious attacks. The simulation platform simulates WSNs with various topologies, routing
algorithms, battery and power consumption models, and various types of attacks and defense mechanisms. Additionally,
we adopt information entropy based techniques to detect anomalous behavior. Finally, detection techniques are fused to
provide various metrics, and various trustworthiness metrics are fused to provide aggregate trustworthiness for the
purpose of situational awareness.
Persistent surveillance applications require unattended sensors deployed in remote regions to track and monitor some
physical stimulant of interest that can be modeled as output of time varying stochastic process. However, the accuracy or
the trustworthiness of the information received through a remote and unattended sensor and sensor network cannot be
readily assumed, since sensors may get disabled, corrupted, or even compromised, resulting in unreliable information.
The aim of this paper is to develop information theory based metric to determine sensor trustworthiness from the sensor
data in an uncertain and time varying stochastic environment. In this paper we show an information theory based
determination of sensor data trustworthiness using an adaptive stochastic reference sensor model that tracks the sensor
performance for the time varying physical feature, and provides a baseline model that is used to compare and analyze the
observed sensor output. We present an approach in which relative entropy is used for reference model adaptation and
determination of divergence of the sensor signal from the estimated reference baseline. We show that that KL-divergence
is a useful metric that can be successfully used in determination of sensor failures or sensor malice of various types.
The autonomous operations of intelligent unmanned aerial and space access vehicles demand fast online trajectory
computations, which rely heavily upon precise and expedited computation of aerodynamic coefficients. Traditional
methods use tabular data and linear interpolations, which are slow and, even worse, cannot produce smooth aerodynamic
functions that are highly demanded for trajectory computation. In this paper, we introduce neural network and PiecewiseSmooth Function based approaches to approximate these coefficients. Although in the past, neural networks have been
applied to aerodynamic coefficient modeling, they have not been considered for the purpose of trajectory design, which
generate large amounts of data during the flight envelope. In this paper, we present an efficient approach to reduce the
overwhelming amount of data requirements so that the training and testing of the proposed solutions are more
manageable and feasible. The preliminary testing results on the six aerodynamic coefficients show that the pitching
moment coefficient Cm and the axial force coefficient Ca are the most challenging to approximate, while the other four
coefficients are easily approximated. In this paper we have focused on improving approximation models for Cm with
promising results. In the future, we will continue our research on developing models for approximating Ca.
The advances in video surveillance technology have lead to the proliferation of surveillance video cameras for the
purposes of viewing areas of interest. Counter terrorism and surveillance applications require video forensics capabilities
like querying and searching video data for events, people or objects of interest. A human analyst may accurately spot a
suspicious activity in a small segment of video. However, due to the large volume of data collected in real-time video
surveillance, it is impractical for human analysts to watch or tag the entire video collected as this can lead to human
errors, lower throughput and inconsistencies in the level of scrutiny. In this paper, we introduce an ontology-based video
retrieval approach, which represents videos with object ontologies and event ontologies, and annotates videos
accordingly. We also describe a user-friendly interface for querying surveillance videos using event dictionaries. Our
approach leverages the capabilities of ontologies in specifying knowledge at different levels, and, in this way, provides
flexibility to a user while forming a query. It is also capable of detecting undefined events such as not previously
conceived abnormal events.
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