Comb, Needle, and Haystacks: Balancing Push and Pull for Information Discovery
description
Transcript of Comb, Needle, and Haystacks: Balancing Push and Pull for Information Discovery
Comb, Needle, and Haystacks:Balancing Push and Pull for Information Discovery
Xin LiuComputer Science Dept.
University of California, Davis
Collaborators: Qingfeng Huang & Ying Zhang, PARC
11/4/2004 ACM Sensys 2
Objective
Simple, reliable, and efficient on-demand information discovery mechanisms
11/4/2004 ACM Sensys 3
Where are the tanks?
11/4/2004 ACM Sensys 4
Pull-based Strategy
11/4/2004 ACM Sensys 5
Pull-based Cont’d
11/4/2004 ACM Sensys 6
Push-based Strategy
11/4/2004 ACM Sensys 7
Comb-Needle Structure
11/4/2004 ACM Sensys 8
Related Work
D. Braginsky and D. Estrin, “Rumor routing algorithm for sensor networks”, WSNA, 2002.
J. Heidemann, F. Silva, and D. Estrin, “Matching data dissemination algorithms to application requirements”, SENSYS 2003.
ACQUIRE, IDSQ, SRT, GHT, DIMENSIONS, DIM, GRAB, gossip, flooding-based, agent-based, geo-routing, …
11/4/2004 ACM Sensys 9
Application Scenarios
On-demand information query Any node can be the query entry node Queries may be generated at anytime Events can happen anywhere and anytime Examples:
Firefighters query information in the field Surveillance
Sensor nodes know their locations
11/4/2004 ACM Sensys 10
When an Event Happens
Event
11/4/2004 ACM Sensys 11
When a Query is Generated
Event
Query
Event
11/4/2004 ACM Sensys 12
Tuning Comb-Needle
11/4/2004 ACM Sensys 13
The Spectrum of Push and Pull
Pull Push
Global pull +Local push
Global push +Local pull
Push & Pull
Inter-spike spacing increases
Reverse comb
Relative query frequency increases
11/4/2004 ACM Sensys 14
Reverse Comb
Query
Event
When query frequency > event frequency
11/4/2004 ACM Sensys 15
Mid-term Review
Basic idea: balancing push and pull
Preview: Reliability Random network An adaptive scheme
11/4/2004 ACM Sensys 16
Strategies for Improving Reliability
Local enhancement Interleaved mesh Routing update
Spatial diversity Correlated failures Enhance and balance query success rate at
different geo-locations
11/4/2004 ACM Sensys 17
Spatial Diversity
Query
xEvent
11/4/2004 ACM Sensys 18
Random Network
Constrained geographical flooding Needles and combs have certain widths
11/4/2004 ACM Sensys 19
Simulation
Simulator: Prowler
11/4/2004 ACM Sensys 20
11/4/2004 ACM Sensys 21
11/4/2004 ACM Sensys 22
Adaptive Scheme
Comb granularity depends on the query and event frequencies
Nodes estimate the query and event frequencies Important to match needle length and inter-spike
spacing Comb rotates
Load balancing Broadcast information of current inter-spike spacing
11/4/2004 ACM Sensys 23
Simulation
Regular grid Communication cost: hop counts No node failure Adaptive scheme
11/4/2004 ACM Sensys 24
Event & Query Frequencies
11/4/2004 ACM Sensys 25
Tracking the Ideal Inter-Spike Spacing
11/4/2004 ACM Sensys 26
Simulation Results
Gain depends on the query and event frequencies Even if needle length < inter-spike spacing, there is a
chance of success. Tradeoff between success ratio and cost
99.33% success ratio and 99.64% power consumption compared to the ideal case
11/4/2004 ACM Sensys 27
Summary
Adapt to system changes Can be applied in hierarchical structures
Pull Push
Global pull +Local push
Global push +Local pull
Push & Pull
Relative query frequency increases
11/4/2004 ACM Sensys 28
Future work
Further study on random networks Building a “comb-needle-like” structure
without location information Integrated with data aggregation and
compression Comprehensive models for communication
costs