KEYWORDS: Process modeling, Social networks, Social network analysis, Data modeling, Stochastic processes, Network architectures, Sensors, Network security, Camouflage, Structural analysis
The detection and tracking of embedded malicious subnets in an active social network can be computationally
daunting due to the quantity of transactional data generated in the natural interaction of large numbers of actors
comprising a network. In addition, detection of illicit behavior may be further complicated by evasive strategies
designed to camouflage the activities of the covert subnet. In this work, we move beyond traditional static
methods of social network analysis to develop a set of dynamic process models which encode various modes of
behavior in active social networks. These models will serve as the basis for a new application of the Process
Query System (PQS) to the identification and tracking of covert dynamic processes in social networks. We
present a preliminary result from application of our technique in a real-world data stream— the Enron email
corpus.
This paper presents an automated decentralized surveillance system
for the problem of tracking multiple mobile ground targets with no
signature in a bounded area. The system consists of unmanned aerial
vehicles (UAVs) and unattended fixed ground sensors (UGSs) with limited
communication and detection range that are deployed in the area of
interest. Each component of the system (UAV and/or Sensor) is
completely autonomous and programmed to scan the area searching for
targets and share its knowledge with other components within
communication range. UAV scheduling of the areas to search is
stochastic and the characterizing probability distributions are
determined through hypotheses of consistent tracks of target
observations. Such hypotheses are formulated by a client subsystem
called Process Query System, which is queried with streams of
incoming observations of targets and stochastic models of their
kinematics. The purpose of this work is also to provide a quantitative
measure of the situational awareness of the monitoring system in
relation to the accuracy of the target models and the degree of
decentralization of the control.
This paper presents a fully automated and decentralized surveillance system for the problem of detecting and possibly tracking mobile unknown ground vehicles in a bounded area. The system consists ideally of unmanned aerial vehicles (UAVs) and unattended fixed sensors with limited communication and detection range that are deployed in the area of interest. Each component of the system (UAV and/or sensor) is completely autonomous and programmed to scan the area searching for targets and share its knowledge with other components within communication range. We assume that both UAVs and sensors have similar computing and sensing capabilities and differ only in their mobility (sensors are stationary while UAVs are mobile). Gathered information is reported to a base station (monitor) that computes an estimate of the global state of the system and quantifies the quality of the surveillance based on a measure of the uncertainty on the number and position of the targets overtime. The present solution has been achieved through a robotic implementation of a software simulation that was in turn realized under the principles of a novel top-down methodology for the design of provably performant agent-based control systems. In this paper we provide a description of our solution including the distributed algorithms that have been employed in the control of the UAV navigation and monitoring. Finally we show the results of an novel experimental performance analysis of our surveillance system.
KEYWORDS: Sensors, Sensor networks, Web services, Prototyping, Information fusion, Data processing, Signal processing, Detection and tracking algorithms, Process modeling, Environmental sensing
Recent advances in wireless communication and microelectronics have enabled the development of low-cost sensor devices leading to interest in large-scale sensor networks for military applications. Sensor networks consist of large numbers of networked sensors that can be dynamically deployed and used for tactical situational awareness. One critical challenge is how to dynamically integrate these sensor networks with information fusion processes to support real-time sensing, exploitation and decision-making in a rich tactical environment. In this paper, we describe our work on an extensible prototype to address the challenge. The prototype and its constituent technologies provide a proof-of-concept that demonstrates several fundamental new approaches for implementing next generation battlefield information systems. Many cutting-edge technologies are used to implement this system, including semantic web, web services, peer-to-peer network and content-based routing. This prototype system is able to dynamically integrate various distributed sensors and multi-level information fusion services into new applications and run them across a distributed network to support different mission goals. Agent technology plays a role in two fundamental ways: resources are described, located and tasked using semantic descriptions based on ontologies and semantic services; tracking, fusion and decision-making logic is implemented using agent objects and semantic descriptions as well.
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