Given a specific scenario for the border control problem, we propose a dynamic data-driven adaptation of the associated sensor network via embedded software agents which make sensor network control, adaptation and collaboration decisions based on the contextual information value of competing data provided by different multi-modal sensors. We further propose the use of influence diagrams to guide data-driven decision making in selecting the appropriate action or course of actions which maximize a given utility function by designing a sensor embedded software agent that uses an influence diagram to make decisions about whether to engage or not engage higher level sensors for accurately detecting human presence in the region. The overarching goal of the sensor system is to increase the probability of target detection and classification and reduce the rate of false alarms. The proposed decision support software agent is validated experimentally on a laboratory testbed for multiple border control scenarios.
Vehicle tracking can be done automatically based on data from a distributed sensor network. The determination of vehicle behavior must currently be done by humans. Behaviors of interest include searching, attacking and retreating. The purpose of this paper is to show an approach for the automatic interpretation of vehicle behaviors based on data from distributed sensor networks. The continuous dynamics of the sensor network are converted into symbolic dynamics by dividing its phase space into hypercubes and associating a symbol with each region. When the phase-space trajectory enters a region, its corresponding symbol is emitted into a symbol stream. Substrings from the stream are interpreted as a formal language defining the behavior of the vehicle. The formal language from the sensor network is compared to the languages associated with known behaviors of interest. Techniques for performing quantitative comparisons between formal languages are presented. The abstraction process is shown to be powerful enough to distinguish two simple behaviors of a robot based on data from a pressure sensitive floor.
KEYWORDS: Sensor networks, Pathology, Target detection, Control systems, Sensors, Device simulation, Environmental sensing, Systems modeling, Control systems design, Process control
Autonomous Sensor Networks have the potential for broad applicability to national security, intelligent transportation, industrial production and environmental and hazardous process control. Distributed sensors may be used for detecting bio-terrorist attacks, for contraband interdiction, border patrol, monitoring building safety and security, battlefield surveillance, or may be embedded in complex dynamic systems for enabling fault tolerant operations. In this paper we present algorithms and automation tools for constructing discrete event controllers for complex networked systems that restrict the dynamic behavior of the system according to given specifications. In our previous work we have modeled dynamic system as a discrete event automation whose open loop behavior is represented as a language L of strings generated with the alphabet 'Elipson' of all possible atomic events that cause state transitions in the network. The controlled behavior is represented by a sublanguage K, contained in L, that restricts the behavior of the system according to the specifications of the controller. We have developed the algebraic structure of controllable sublanguages as perfect right partial ideals that satisfy a precontrollability condition. In this paper we develop an iterative algorithm to take an ad hoc specification described using a natural language, and to formulate a complete specification that results in a controllable sublanguage. A supervisory controller modeled as an automaton that runs synchronously with the open loop system in the sense of Ramadge and Wonham is automatically generated to restrict the behavior of the open loop system to the controllable sublanguage. A battlefield surveillance scenario illustrates the iterative evolution of ad hoc specifications for controlling an autonomous sensor network and the generation of a controller that reconfigures the sensor network to dynamically adapt to environmental perturbations.
Conference Committee Involvement (1)
Advanced Signal Processing Algorithms, Architectures, and Implementations XIII
6 August 2003 | San Diego, California, United States
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