Project II Rule Optimizer for the Atlas Reactivity Engine CNT Dr. Sumi Helal Computer & Information Science & Engineering Department University of Florida, Gainesville, FL

Manuals & Downloads The Atlas Class Web Page – The Atlas Reactivity Engine –

Overall Architecture Atlas Middleware RE Optimizer Atlas Emulator Atlas Reactivity Engine Atlas Reactivity Engine Atlas Emulator Command Interpreter Command Interpreter

Atlas RE Engine An Event/Condition/Action paradigm A programming model for pervasive space A Command line interface to interact with the engine –View Basic Events & Actions. –Predefine: Events, Conditions and Actions –Define Rules –Accept commands & Provide results/feedback Engine interacts with the sensors /actuators through the Atlas middleware

EVENTS

Conditions

Rules Actions

Commands

Tokens & Delimiters

Optimization Frameworks Push/Pull Envelop Caching Evaluation Short Cuts Application Characteristics

Push / Pull Envelop Configuration of which sensors should participate, and if so, in which mode (push or pull, and if latter, at which frequency).

Example Caching: Time/Frequency Modifier

Evaluation Short Cuts Exploit Dominant Events – For a composite event whose sub-events are connected by logic +, some of its sub-events may have significantly higher probability of occurrence than others which makes them a dominant factor in determining the value of the composite event. ( Likewise, for composite events whose sub-events are connected by logic *, sub-events with lower probability of occurrence become dominant )

Project II Summary Form groups of 4 by no later than Friday Nov 5 noon. Understand all the components Study source code of RE Engine Develop the Optimizer –Think, formalize and create your algorithms –Implement Assess the success/failure of your Optimizer

Deliverables You will deliver source code with detailed documentation and about 5-10 pages report describing the following: Status of your project: what is completed and what not; what works and what not Your optimization ideas and strategies Formal description of your optimization algorithm Your experiment results Any additional features implemented and the rationale Conclusions