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Balancing Push and Pull for
Efficient Information Discovery in
Large-Scale Sensor Networks
Xin Liu, Qingfeng Huang, Ying
Zhang
CS 6204 Adv Top. in Systems-Mob. Comp
Presentation By Morgan Yeh
1
Overview
Introduction
 Related Work
 Balancing ‘Push and Pull’
 Simulation Environment
 Research Results
 Conclusion

2
Introduction
Many emerging sensor network
applications involve the dissemination of
observed information to interested clients
 Propose a “comb-needle” query support
mechanism that integrates both push and
pull data dissemination and analyze its
performance in large-scale wireless
sensor networks

Introduction
The combing structure is dynamic
 Granularity adjusts dynamically based on
query and event frequencies to minimize
communication cost
 Combs are finer and needles shorter when
the query frequency is relatively low
compared to the event frequency, and vice
versa

Related Work

Reducing the number of potentially redundant
forwardings in the flooding process using
neighborhood topological information or via
probabilistic retransmissions
 Aims
to improve flooding efficiency
 Reducing the constant implicitly used in O(N), where
N is the number of nodes in the network. In contrast,
the comb-needle scheme achieves O(sqrt(N)) in the
best case by balancing push and pull
Related Work

Reducing the discovery/query cost by
taking into account application semantics
 Discovery
and dissemination protocols
 The scaling laws for structured and
unstructured information queries are studied
under storage and energy constraints
Related Work

Reducing the cost of search via efficient
distributed indexing schemes
 Distributed
indexing scheme called Semantic
Routing Tree (SRT), supporting queries from
a fixed node
 Distributed index for multidimensional data
(DIM), allowing queries to be issued from any
node
Related Work

Trajectory-based routing
 Develop
cross-shaped trajectories to
disseminate service information in the
network
 Focus : one alternative attempt to develop
cross-shaped trajectories
Balancing ‘Push and Pull’


Sensor nodes continuously gather information
and report to one or more sink nodes
Sensor network is considered as a distributed
database, where information can be extracted
only when needed
 Communications
of information that is not needed
result in a waste of resources and should be
minimized when possible
Balancing ‘Push and Pull’
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Support both mobile and stationary query nodes
Entry point can be anywhere in the network and
occurs at any time
Assume that the speed of the mobile node is
much smaller than that of communication in the
sense that disconnection does not happen
during a query process
Assume all nodes in the network have
information on their own locations
Balancing ‘Push and Pull’
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Assume that the network is ad hoc and uniform
in the sense that all nodes are equivalent and
the network does not have a built-in hierarchy
Assume sensor nodes are stationary
Use packet-hop as a metric to measure
communication efficiency and, thus, as an
indication of energy consumption
Assume that the size of a query packet is the
same as that of a data-duplication packet
Balancing ‘Push and Pull’
Balancing ‘Push and Pull’
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n nodes, located at (i,j), where 0 < i,j < n
Update, on the event, to (L – 1) of its vertical neighbors
and thus builds a vertical needle of length L


EXAMPLE : L = 5 & s = 3
Updates to nodes (i,j + 1), (i,j + 2) … (i,j + L/2) and (i,j – 1), (i,j –
2) … (i,j – L/2 +1)
Query is sent vertically from (i,j) to (n,j) and to (0,j)

Query is fanned out horizontally from nodes (i,j), (i +- s,j), (i +2s,j) …, where s is the interspike spacing or combing degree
Balancing ‘Push and Pull’
A lifetime parameter, τ (tau), can be
included in the query message to indicate
its time-window of interest
 CL = L – 1, the communication cost for
each query
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Balancing ‘Push and Pull’
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Query dissemination cost (cost to build
one vertical query line and multiple
horizontal query lines) :

Average query reply cost for each relevant
event is αn (alpha*n), where 0.5 <= α <= 1
is the distance factor reflecting the
positions of the query node
Balancing ‘Push and Pull’
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Average distance (averaging over all
locations of events) to node (0,0) :
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Total expected reply cost :
Balancing ‘Push and Pull’
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Total communication cost per query :
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L = s is required to guarantee that a query
meets all relevant event :
Balancing ‘Push and Pull’
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Minimum communication cost of combneedle :
Balancing ‘Push and Pull’
Balancing ‘Push and Pull’
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For Reverse Comb, the per-query
communication cost :
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The average distance is (s’ – 1)/2 :
Balancing ‘Push and Pull’
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Adaptive Comb-Needle
Strategy

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A query node can estimate
the value of fd based on the
number of replies it obtains.
The query node calculates s.
A data node uses its most
recent information on s to
synchronize the needle
length L with the comb width
s.
Balancing ‘Push and Pull’
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Fixed-Node Query
 Communication
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cost per unit time :
Binary Query
 Average
query cost per query :
Balancing ‘Push and Pull’
Compare comb with fixed-node query
Compare the width of regular
comb and sequential comb
Simulation Environment

Transmission model :
 Ptransmit
: signal strength at the transmitter
 Prec,ideal(d) : ideal received signal strength at
distance d
 α (alpha) and β (beta) are random variables
with normal distributions to σ (sigma) of N(0,
σα) & N(0,σβ)
Simulation Environment

Routing protocol in pseudocode :
Research Results
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Two separate tests are performed
 To
discover what is the best spacing for the comb for
a grid network with small random offset
 To show the robustness of the protocol with a varying
comb width w for a grid network with large random
offset

There exists a trade-off between the delivery
ratio and the communication cost
Research Results
Research Results
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Analysis of the comb-needle structure in a
regular grid network
 The
trajectory of query and event duplications
should cross each other to guarantee event
discovery
 The structure should adapt based on query
and event frequencies
Research Results
fq = 0.1, fe = 1
fq = 0.1, fe = 0.1
Research Results
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Full pull versus optimal comb-needle :
Research Results
Research Results
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In a network with a random topology, build
approximations of the combs and needles using
a Constrained Geographical Flooding
Use trajectory-based routing schemes to
develop trajectories for query and data
duplication
Query reliability is an important issue and is
inherent in all wireless sensor systems
Conclusion
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The comb-needle is not the only possible
structure
Cannot prove that comb-needle is an optimal
structure
Further work needed in discovering the optimal
structure for information dissemination and
discovery, in particular in random networks
Semi-conclusion : the shape of the optimal
structure may be determined by the particular
network topology
Conclusion

The comb-needle strategy :
a
simple yet efficient data discovery scheme for
supporting queries in large-scale sensor networks
 used as a substrate to study the benefit of balancing
push and pull in data gathering and dissemination in
large-scale wireless networks
 (including the reverse one) covers a spectrum of push
and pull strategies, with the pure push-based and
pure pull-based schemes in two extremes
Conclusion
Data aggregation and compression can be
integrated into the comb-needle strategy
to reduce the communication cost
 Optimal structures for information
dissemination and discovery, in particular
in random networks, are unknown

Questions or comments?