Combs, Needles, Haystacks: Balancing Push and Pull for Discovery in Large Scale Sensor Networks Xin...
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Combs, Needles, Haystacks: Balancing Push and Pull for Discovery in Large Scale Sensor Networks Xin Liu Department of Computer Science University of California Qingfeng Huang and Ying Zhang Palo Alto Research Center (PARC) Inc. SenSys 2004 Presenter ： Ruey-Chang Chang
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Transcript of Combs, Needles, Haystacks: Balancing Push and Pull for Discovery in Large Scale Sensor Networks Xin...
Combs, Needles, Haystacks: Balancing Push and Pull for Discovery in Large
Scale Sensor Networks
Xin LiuDepartment of Computer ScienceUniversity of California
Qingfeng Huang and Ying ZhangPalo Alto Research Center (PARC) Inc.
SenSys 2004
Presenter ： Ruey-Chang Chang
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Outline
Introduction The combing strategy Adaptive comb-needle strategy Simulation Conclusion
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Push-based
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Pull based
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Introduction
Push-pull strategies for discovery– Push-based
The push-based strategy is efficient when there are many sinks constantly in need of the information
A lot of broadcast bandwidth is wasted– Pull-based
The pull-based strategy is relative more efficient than the push-based strategy when the frequency of query is relatively low compared to the frequency of the interested event
– Hybrid (a comb-needle strategies) Combine the advantages of both push and pull strategies
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Hybrid (a comb-needle strategies)
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The combing strategy
In the comb-needle model– Each sensor node pushes its data to a certain
neighborhood and the query is disseminated only to a subset of the network
– The query process builds a routing structure dynamically that resembles a comb
– The sensor node push the data duplication structure like a needle
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The combing strategy
l
s
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The total cost per query
Cost of Query dissemination
Cost of Query response per each node
Cost of Query response
Cost of data push
fe:the arrival frequency of discovery queriesfq:the arrival frequency of relevant events
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The minimal cost
s=2l+1
l
s
sink
sensor
l
s
sink
sensor
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Routing protocol
Constrained Geographical Flooding– Whenever a new packet arrives, each node will decide if it
should rebroadcast the packet according to the geographical constraints W
W
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fqfe(Global-pull-local-push)
push
pullsink
sensor
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fq=fe
push
push
pull
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fqfe(Global-push-local-pull)
This paper focuses on fqfe
push
pull
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Adaptive comb-needle strategy
The query and event frequencies may be time-varying ,and thus a good query strategy should adapt to such change
fe:the arrival frequency of discovery queries
fq:the probability that a query is generated in a time slot
fd:the probability that a sensor node detects an event in a time slot
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Simulation1
fq/fe=0.1(fe= 1 packet/second,fq= 0.1p/s) l={0,1,2,3} s={1,3,5,7} Node:? Simulation:? Topology:grid
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Energy consumption(fq/fe=0.1)
L=1 S=3
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Energy consumption(fq/fe=1)
L=2 S=5
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Energy consumption
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Simulation2
3000 time slots 20*20 grid
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The query and the event frequency
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Adaptive comb vs. ideal comb
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Conclusion
The comb-needle model– A simple yet efficient data discovery scheme for
supporting queries– A substrate for study the benefit of balancing
push and pull in data gathering and dissemination– Covers a spectrum of the push and pull schemes– An adaptive comb-needle strategy for cases
where the utilization patterns and environmental activity frequency are time-frequency
