A Local Data Abstraction and Communication Paradigm for Pervasive Computing
Sanem Kabadayı and Christine Julien
[email protected] March 21, 2007
Overview
Motivation and Challenges Our Solution: Scenes Scene Model Evaluation Conclusion
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Introduction
Motivation: Potential to enhance human experience and simplify life Aware homes, intelligent construction sites, firstresponder deployments, battlefield scenarios Challenge: Highly dynamic operating contexts and vast amounts of raw information, need to limit the scope of interactions Solution: Declarative specification of the region of the network with which to interact
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Intelligent Construction Site
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Challenges
remote distributed sensing
pervasive computing
Sensor networks have almost exclusively been used in the former manner Applying sensor networks to pervasive computing
Locality of interactions Mobility-induced dynamics
Unpredictability of coordination 5
Our Solution: Scenes
Pervasive computing environments present highly dynamic operating contexts and vast amounts of raw information
Applications must be able to limit the scope of interactions
Scenes
Novel communication abstraction that allows application to limit scope of interactions to include only data that matches current needs Results not specific to sensor networks
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Scenes: Abstractions of Local Data
Programmer specifies three parameters for a scene:
Metric: property of network/environment defining connection cost Cost function: function that operates on network path (to support different cost functions for the same metric) Threshold: value path cost must satisfy for sensor to be member (to avoid flooding the entire network) For all j < k, cj THRESHOLD k
i ci = COST_FUNCTION(METRIC(pi, i)) 0
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Persistent (Continuous) and One-Time Queries
One-time queries
Return a single result from each scene member One location reading for each crane in the scene on a construction site
Persistent queries
Return periodic results from scene members Crane location readings every 30 seconds
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Scene Construction
For all j < k, cj THRESHOLD k
i 0
ci = COST_FUNCTION(METRIC(pi, i))
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An Example Scene
Construct a scene:
From sensors within 5m that can deliver data to host in less than 15 ms No messages are sent yet
Metric
Cost function
Metric value
Threshold
Distance component
SCENE_DISTANCE
SCENE_DFORMULA
Location of source
5m
Latency component
SCENE_LATENCY
SCENE_SUM
Latency on path so far
15ms
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An Example Scene
Volatile organic compound
VOC cloud
Organic chemical compounds that can vaporize under normal conditions May have adverse health effects
Query for gas cloud concentrations Metric
Cost function
Metric value
Threshold
Distance component
SCENE_DISTANCE
SCENE_DFORMULA
Location of source
5m
Latency component
SCENE_LATENCY
SCENE_SUM
Latency on path so far
15ms
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An Example Scene
Query for gas cloud concentrations > 85 ppm (parts per million) every second
VOC cloud
All the sensors in the scene receive the message Only scene members who can satisfy the query respond
Metric
Cost function
Metric value
Threshold
Distance component
SCENE_DISTANCE
SCENE_DFORMULA
Location of source
5m
Latency component
SCENE_LATENCY
SCENE_SUM
Latency on path so far
15ms
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An Example Scene
VOC cloud
Gas cloud moves, changing responses
Metric
Cost function
Metric value
Threshold
Distance component
SCENE_DISTANCE
SCENE_DFORMULA
Location of source
5m
Latency component
SCENE_LATENCY
SCENE_SUM
Latency on path so far
15ms
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An Example Scene
Client moves, changing scene membership
VOC cloud
Maintained by beacons
Sensors could move as well
Metric
Cost function
Metric value
Threshold
Distance component
SCENE_DISTANCE
SCENE_DFORMULA
Location of source
5m
Latency component
SCENE_LATENCY
SCENE_SUM
Latency on path so far
15ms
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An Example Scene
Metric value changes (e.g., latency increases) changing scene membership
VOC cloud
Maintained by beacons Latency increases on this link, disqualifying these nodes from the scene
Metric
Cost function
Metric value
Threshold
Distance component
SCENE_DISTANCE
SCENE_DFORMULA
Location of source
5m
Latency component
SCENE_LATENCY
SCENE_SUM
Latency on path so far
15ms
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Scene Feasibility Study
TOSSIM (Simulator for TinyOS) implementation of scene protocol Parameters
100 sensor nodes in a 200ft X 200ft square Single client device moves among sensors Topologies
Regular grid with 20ft internode spacing Uniform random placement
TOSSIM empirical radio model
Based on real Mica measurements in outdoor environment
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Scene Feasibility Study (cont.)
Variables
Client device speed: 0 mph, 2 mph, 4 mph (brisk walk), or 8 mph (slow moving vehicle) Scenes based on hop count metric (1, 2, 3 hops)
Implementation easily supports other types of scenes
Beacon interval set inversely proportional to speed
Empirically shown to provide stable scenes for varying mobility Requires use of global knowledge and is not how beacon intervals should be assigned
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Example Scene
Fixed radius (10 ft) radio model Scene threshold 3 hops Red LEDs turn on for scene members Blue circles represent nodes currently sending beacons
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Performance Metrics for Scenes
Average number of scene members
Number of messages sent per scene member
Measures performance of beacon assignment heuristic Number of scene members found should stay same as speed increases Measures sensor node's cost of participation in scene Correlated with battery dissipation for participating sensors
Number of messages sent per unit time
Measure scene scalability (i.e., for scenes of increasing sizes and increasing client mobility)
Measure of average activity in network Activity takes place only within scene 19
Numbers of Scene Members
Number of scene members independent of client speed Setting beacon interval directly proportional to client speed accurately keeps track of moving client
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Messages Sent per Scene Member
Beacon frequency set directly proportional to client speed
For 4mph, beacon every 0.5s; for 8mph, beacon every 0.25s
Linearity due mainly to increased beacons and nothing else
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Messages per Second of Scene Registration
Beacons sent more frequently as speed increases; more messages packed into a time interval As size increases, more nodes become members and beacon
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Conclusion
Communication protocol that enables opportunistic communication with the local immersive sensor environment for pervasive computing applications Future work Complete network performance evaluation that measures query response times and direct energy usage Real-world deployment on mixture of embedded and client devices Optimal beacon assignment
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Questions?
Thank you! Sanem Kabadayı
[email protected] http://mpc.ece.utexas.edu
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