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AOM Architecture for WSNs

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Title: AOM Architecture for WSNs


1
Dart A Meta-Level Object-Oriented Framework
for Task-Specific Behavior Modeling by Domain
Experts
Reza Razavi University of LuxembourgLUXEMBOURG razavi_at_acm.org Jean-François Perrot Université Pierre et Marie Curie CNRS LIP6, Paris France jean-francois.perrot_at_lip6.fr Ralph Johnson University of Illinois at Urbana Champaign, Illinois USA johnson_at_cs.uiuc.edu
2
A Wireless Sensor Network
Connecting together a large number of small
computers with sensing and actuating
capabilities, to collectively and
cost-effectively solve problems, based on
real-time data.
3
Dynamic Macroprogramming of WSNs by End-users
(experts)
Query detect an animal
Requires specifying correlation of time series of
basic sensing data into high-level semantic data
Query photograph speeding vehicles
Query chart traffic patterns
Requires specifying coordination between tasks
Requires specifying a behavior repeated over time
4
Requirements summary
  • End-users
  • Expressing
  • Relatively easily
  • Complex behavior
  • DSML Programmer face diversity of application
    domains
  • Business-specific
  • Ontologies
  • Modeling constructs
  • Model execution strategies
  • DSML Programmers
  • Developing DSMLs for different application
    domains by
  • Reuse
  • Extension

5
Task-Specific Behavior Modeling B.A. Nardi
6
The Problem
  • Method for stepwise development of
  • Task-specific languages for behavior modeling
  • Satisfying both
  • End-users, and
  • Programmers requirements

7
Dart core concepts
Design decisions explained and motivated in the
paper.
8
Plot Histogram
For a Period of lt2 weeksgt Do ltT1gt
Illustration of behavior representation by Dart
9
Implementation
  • Research prototypes
  • Provides extension hooks
  • VisualWorks Smalltalk
  • Dyctalk
  • MiDyctalk
  • MetaclassTalk
  • MxDyctalk
  • Squeak and Seaside
  • Planned to be released as Open Source software

10
Reuse example Developing query engines for WSNs
Uses Seaside framework (http//www.seaside.st/)
and Squeak (http//www.squeak.org/)
11
Conclusion
Knowledge level
Intercession
Introspection
Operational level
Dynamically-composed behavior
Dart supports stepwise development of Adaptive
Object-Models
12
Current work
  • Reusing Dart to support End-user
    Macro-programming WSNs
  • Dart serves
  • Edition (GUI)
  • Representation
  • Verification
  • Optimization
  • Security
  • Transformation
  • Distributed, fine-grained, selective deployment
  • Concurrent execution

13
Thank you for your attention!
  • Questions?
  • Observations?

Please visit the Poster Session

Reza Razavi razavi_at_acm.org Autonomous Systems
Group University of Luxembourg
14
Outline of the talk
  • Context
  • Requirements
  • Problem
  • Solution
  • Conclusion and perspectives

15
Structure of queries
  • Finite directed acyclic graph
  • Recursive
  • Steps may hierarchically point to tasks
  • Reflective
  • Same set of concepts reused to extend the system

16
Semantics of queries
  • Parallel evaluation of contributions
  • Limited to their dependencies
  • Different execution semantics
  • Late
  • value binding
  • method binding
  • Comparable to spreadsheets

17
Role of the knowledge level
  • Comprises
  • Conceptual ontology
  • Behavioral ontology
  • Framework for specifying queries as a composition
    of services through mediation of concepts
  • Assumptions
  • Completeness of the service ontology
  • Acknowledgeability of the users in the domain
    covered by the ontologies
  • Low-level data, such as the sensor id, may be
    provided by users (in the process of being
    relaxed)
  • Keeps track of static and dynamic metadata.

18
Role of the operational level
  • The operational level comprises a set of mobile
    agents
  • The agents
  • Are defined dynamically
  • Execute concurrently
  • within the WSN, and
  • on a single node
  • Based on a formal model of computation
  • In order to be verifiable
  • ?Actors

19
EU requirements example 1
Query detect an animal
Requires specifying correlation of time series of
basic sensing data into high-level semantic data
20
EU requirements example 1
Query detect an animal
1
Requires specifying correlation of time series of
basic sensing data into high-level semantic data
Raw sensor data
T2 Identify Animal
21
EU requirements other examples
Query photograph speeding vehicles
Query chart traffic patterns
Requires specifying coordination between tasks
Requires specifying a behavior repeated over time
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