KEYWORDS: Sensors, Reliability, Sensing systems, Data processing, Logic, Stereolithography, Data hiding, Improvised explosive devices, Systems modeling, Surgery
In modern coalition operations, decision makers must be capable of obtaining and fusing data from diverse
sources. The reliability of these sources can vary, and, in order to protect their interests, the data they provide
can be obfuscated. The trustworthiness of fused data depends on both the reliability of these sources and their
obfuscation strategy. Information consumers must determine how to evaluate trust in the presence of obfuscation,
while information providers must determine the appropriate level of obfuscation in order to ensure both that
they remain trusted, and do not reveal any private information. In this paper, through a coalition scenario, we
discuss and formalise trust and obfuscation in these contexts and the complex relationships between them.
KEYWORDS: Sensor networks, Sensors, Mathematical modeling, Network architectures, Data modeling, Wireless communications, Systems modeling, Chemical analysis, Relays, Data communications
In this paper, a novel mission-oriented sensor network architecture for military applications is proposed involving
multiple sensing missions with varying quality of information (QoI) requirements. A new concept of mission QoI
satisfaction index indicating the degree of satisfaction for any mission in the network is introduced. Furthermore,
the 5WH (why, when, where, what, who, how) principle on the operational context of information is extended
to capture the changes of QoI satisfaction indexes for mission admission and completion. These allow modeling
the whole network as a "black box". With system inputs including the QoI requirements of the existing and
newly arriving missions and output the QoI satisfaction index, the new concept of sensor network capacity is
introduced and mathematically described. The QoI-centric sensor network capacity is a key element of the
proposed architecture and aids controlling of admission of newly arriving missions in accordance with the QoI
needs of all (existing and newly admitted missions). Finally, the proposed architecture and its key design
parameters are illustrated through an example of a sensor network deployed for detecting the presence of a
hazardous, chemical material.
The output of a sensor network intended to detect events or objects generally comprises evidentiary reports of features in
the environment that may correspond to those phenomena. Signals from multiple sensors are commonly fused to
maximize fidelity of detection through for example synergy between different modes of detection, or simple
confirmation. We have previously demonstrated the ability to calculate the meaning of a location report as a probability
distribution over potential ground truths by using a stochastic process algebraic model compiled to a discrete-state,
continuous-time Markov chain, and performing a transient analysis which resembles the process of parameterizing a
Bayesian network. We introduce an approach to representing temporal fusion of multiple heterogeneous sensor
detections with different modalities and timing characteristics using a stochastic process algebra. This facilitates analysis
of probabilistic properties of the system, and inclusion of those properties into larger models. The formal models are
translated into continuous time Markov chains, which provide an important trade-off between the approximation of
timing information against complexity of analysis. This is vital to the investigation of analytic computation in real world
problems. We illustrate this with an example detection-oriented sensing service model emphasizing the impact of timing.
Detection probability and confidence is an essential aspect of the quality of information delivered by a sensing service.
The present work is part of an effort to develop a formal event detection calculus that captures the essence of sensor
information relating to events, such that features and dependencies can be exploited in re-usable, extendible
compositional models.
In a typical military application, a wireless sensor network will operate in diffcult and dynamic conditions.
Communication will be affected by local conditions, platform characteristics and power consumption constraints,
and sensors may be lost during an engagement. It is clearly of great importance to decision makers to know what
quality of information they can expect from a network in battlefield situations. We propose the development
of a supporting technology founded in formal modeling, using stochastic process algebras for the development
of quality of information measures. A simple example illustrates the central themes of outcome probability
distribution prediction, and time-dependency analysis.
KEYWORDS: Sensors, Signal to noise ratio, Signal detection, Telecommunications, Sensor networks, Reliability, Analytical research, Data processing, Detection theory, Sensing systems
In this paper, we present a set of attributes that are being proposed to characterize quality of information
(QoI) for sensor-enabled applications in a domain-agnostic manner. We then focus on two important of these
attributes, timeliness and data reliability, which capture the quality of detection processes with respect to how
fast and how accurately a detection is made. With special emphasis on transient phenomena, i.e., phenomena
of limited duration, using traditional Bayesian-based hypothesis testing techniques, we investigate the detection
of these phenomena and we analytically derive relationships that capture the QoI of a phenomenon detector as
a function of the duration of the observed phenomena and the rate with which observations of the phenomena
are collected.
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