Balancing Push and Pull for Efficient Information Discovery in LargeScale Sensor Networks

1
Balancing Push and Pull forEfficient Information
Discovery inLarge-Scale Sensor Networks

• Xin Liu, Qingfeng Huang, Ying Zhang
• CS 6204 Adv Top. in Systems-Mob. Comp
• Presentation By Morgan Yeh

2
Overview

• Introduction
• Related Work
• Balancing Push and Pull
• Simulation Environment
• Research Results
• Conclusion

2
3
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

4
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

5
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

6
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

7
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

8
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

9
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

10
Balancing Push and Pull

• 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

11
Balancing Push and Pull

• 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

12
Balancing Push and Pull
13
Balancing Push and Pull

• EXAMPLE L 5 s 3
• n nodes, located at (i,j), where 0 lt i,j lt n
• Update, on the event, to (L 1) of its vertical
  neighbors and thus builds a vertical needle of
  length L
• 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

14
Balancing Push and Pull

• A lifetime parameter, t (tau), can be included in
  the query message to indicate its time-window of
  interest
• CL L 1, the communication cost for each query

15
Balancing Push and Pull

• Query dissemination cost (cost to build one
  vertical query line and multiple horizontal query
  lines)

• Average query reply cost for each relevant event
  is an (alphan), where 0.5 lt a lt 1 is the
  distance factor reflecting the positions of the
  query node

16
Balancing Push and Pull

• Average distance (averaging over all locations of
  events) to node (0,0)

• Total expected reply cost

17
Balancing Push and Pull

• Total communication cost per query

• L s is required to guarantee that a query meets
  all relevant event

18
Balancing Push and Pull

• Minimum communication cost of comb-needle

19
Balancing Push and Pull
20
Balancing Push and Pull

• For Reverse Comb, the per-query communication
  cost

• The average distance is (s 1)/2

21
Balancing Push and Pull

• Adaptive Comb-Needle Strategy
• 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.

22
Balancing Push and Pull

• Fixed-Node Query
• Communication cost per unit time

• Binary Query
• Average query cost per query

23
Balancing Push and Pull
Compare comb with fixed-node query Compare the
width of regular comb and sequential comb
24
Simulation Environment

• Transmission model

• Ptransmit signal strength at the transmitter
• Prec,ideal(d) ideal received signal strength at
  distance d
• a (alpha) and ß (beta) are random variables with
  normal distributions to s (sigma) of N(0, sa)
  N(0,sß)

25
Simulation Environment

• Routing protocol in pseudocode

26
Research Results

• 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

27
Research Results
28
Research Results

• 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

29
Research Results
fq 0.1, fe 1 fq 0.1, fe 0.1
30
Research Results

• Full pull versus optimal comb-needle

31
Research Results
32
Research Results

• 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

33
Conclusion

• 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

34
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

35
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

36
Questions or comments?