Document Type

Technical Report

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Technical Report Number



A Distributed Constraint Satisfaction Problem (DCSP) aims to find consistent assignments of values to a set of variables distributed on multiple nodes. Despite its simple definition, DCSPs can model a broad variety of traditional artificial intelligence problems. Furthermore, many problems found in emerging sensor-actuator networks can be formalized to DCSPs. However, due to the platform limitations of networked embedded systems such as sensor-actuators networks, building real-world applications for solving DCSPs not only requires the improved DCSP algorithms but also novel system approaches. This thesis first develops a performance-driven middleware framework for solving DCSP problems. Then the prototype system built with the framework is used to evaluate the performance of special-purpose middleware called nORB that was designed for a Boeing experimental sensor-actuator platform. To validate the design of nORB, various experiments are performed to compare the performance of nORB with other existing DOC middleware platforms. In investigating the problems revealed by the empirical results, we explored various optimization techniques for nORB. The resulting performance of nORB has been improved significantly and is comparable with the high-performance middleware TAO with a decrease in overall footprint.


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