Routing and Data Dissemination

1
Routing and Data Dissemination

• Presented by
• Li, Huan
• Liu, Junning

2
Outline

• Motivation and Challenges
• Basic Idea of Three Routing and Data
  Dissemination schemes in Sensor Networks
• Some Thoughts on Comparison of the Data
  dissemination schemes

3
Differences with Current Networks

• Difficult to pay special attention to any
  individual node
• Collecting information within the specified
  region
• Collaboration between neighbors
• Sensors may be inaccessible
• embedded in physical structures.
• thrown into inhospitable terrain.

4
Differences with Current Networks

• Sensor networks deployed in very large ad hoc
  manner
• No static infrastructure
• They will suffer substantial changes as nodes
  fail
• battery exhaustion
• accidents
• new nodes are added.

5
Differences with Current Networks

• User and environmental demands also contribute to
  dynamics
• Nodes move
• Objects move
• Data-centric and application-centric
• Location aware
• Time aware

6
Overall Design of Sensor Networks

• One possible solution?
• Internet technology coupled with ad-hoc routing
  mechanism
• Each node has one IP address
• Each node can run applications and services
• Nodes establish an ad-hoc network amongst
  themselves when deployed
• Application instances running on each node can
  communicate with each other

7
Why Different and Difficult?

• A sensor node is not an identity (address)
• Content based and data centric
• Where are nodes whose temperatures will exceed
  more than 10 degrees for next 10 minutes?
• Tell me the location of the object ( with
  interest specification) every 100ms for 2
  minutes.

8
Why Different and Difficult?

• Multiple sensors collaborate to achieve one goal.
• Intermediate nodes can perform data aggregation
  and caching in addition to routing.
• where, when, how?

9
Why Different and Difficult?

• Not node-to-node packet switching, but
  node-to-node data propagation.
• High level tasks are needed
• At what speed and in what direction was that
  elephant traveling?
• Is it the time to order more inventory?

10
Challenges

• Energy-limited nodes
• Computation
• Aggregate data
• Suppress redundant routing information
• Communication
• Bandwidth-limited
• Energy-intensive

Goal Minimize energy dissipation
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Challenges

• Scalability ad-hoc deployment in large scale
• Fully distributed w/o global knowledge
• Large numbers of sources and sinks
• Robustness unexpected sensor node failures
• Dynamically Change no a-priori knowledge
• sink mobility
• target moving

12
Challenges

• Topology or geographically issue
• Time out-of-date data is not valuable
• Value of data is a function of time, location,
  and its real sensor data.
• Is there a need for some general techniques for
  different sensor applications?
• Small-chip based sensor nodes
• Large sensors, e.g., rada
• Moving sensors, e.g., robotics

13
SPIN The Goal
Broadcast with minimum energy
W.R.Heinzelman, J.Kulik, H.Balakrishnan
14
Conventional Approach

• Flooding
• Send to all neighbors
• E.g., routing table updates

15
Resource Inefficiencies

• Resource blindness

16
What is the optimum protocol?

• Ideal
• Shortest-path routes
• Avoids overlap
• Minimum energy
• Need global topology information

17
Two basic ideas

• Exchanging sensor data may be expensive, but
  exchanging data about sensor data may not be.
• Nodes need to monitor and adapt to changes in
  their own energy resources

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SPIN Family
Sensor Protocol for Information via Negotiation

• Data negotiation
• Meta-data (data naming)
• Application-level control
• Model ideal data paths
• SPIN messages
• ADV- advertise data
• REQ- request specific data
• DATA- requested data
• Resource management

ADV
A
B
REQ
A
B
DATA
A
B
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SPIN-PP Example
A
B
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SPIN on Point-to-Point Networks

• SPIN-PP
• 3-stage handshake protocol
• Advantages
• Simple
• Minimal start-up cost
• SPIN-EC
• SPIN-PP low-energy threshold
• Modifies behavior based on current energy
  resources

21
Test Network
25 Nodes
59 Edges
500 bytes
16 bytes
Average degree 4.7 neighbors
Network diameter 8 hops
Data
Antenna reach 10 meters
Meta-Data
22
Unlimited Energy Simulations
-- SPIN-PP -- Ideal -- Flooding

• Flooding converges first
• No queuing delays
• SPIN-PP
• Reduces energy by 70
• No redundant DATA messages

23
Limited Energy Simulations
-- Ideal -- SPIN-EC -- SPIN-PP -- Flooding

• SPIN-EC distributes additional 20 data

24
Conclusions

• Successfully use meta-data negotiation to solve
  the implosion, overlap problem of simple flooding
  and gossiping.
• Resource-adaptive enhancements
• Simple scheme, small communication overhead, but
  a performance close to the ideal situation.

25
Future work

• Consider the cost of not only communicating data,
  but also synthesizing data, make it more
  realistic resource-adaptation protocols.
• Queuing delay, loss-prone nature of wireless
  channels can be incorporated and experimented.

26
Limitations

• The SPIN EC(Energy Constrained) versions
  strategy may be too simple.
• There should be a topology dependant strategy,
  e.g. a narrow bridge connecting two connected
  component should be more energy conservative.
• The ideal criteria used to compare with SPIN is
  ideal in terms of data dissemination rate, so
  really not ideal anymore when energy or other
  resources are limited, need a new goal function.

27
Directed Diffusion

• A Scalable and Robust Communication Paradigm for
  Sensor Networks
• C. Intanagonwiwat
• R. Govindan
• D. Estrin

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Application Example Remote Surveillance

• e.g., Give me periodic reports about animal
  location in region A every t seconds
• Tell me in what direction that vehicle in region
  Y is moving?

29
Basic Idea

• In-network data processing (e.g., aggregation,
  caching)
• Distributed algorithms using localized
  interactions
• Application-aware communication primitives
• expressed in terms of named data

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Elements of Directed Diffusion

• Naming
• Data is named using attribute-value pairs
• Interests
• A node requests data by sending interests for
  named data
• Gradients
• Gradients is set up within the network designed
  to draw events, i.e. data matching the
  interest.
• Reinforcement
• Sink reinforces particular neighbors to draw
  higher quality ( higher data rate) events

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Naming

• Content based naming
• Tasks are named by a list of attribute value
  pairs
• Task description specifies an interest for data
  matching the attributes
• Animal tracking

Request
Interest ( Task ) Description Type four-legged
animal Interval 20 ms Duration 1
minute Location -100, -100 200, 400
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Interest

• The sink periodically broadcasts interest
  messages to each of its neighbors
• Every node maintains an interest cache
• Each item corresponds to a distinct interest
• No information about the sink
• Interest aggregation identical type, completely
  overlap rectangle attributes
• Each entry in the cache has several fields
• Timestamp last received matching interest
• Several gradients data rate, duration, direction

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Setting Up Gradient
Source
Neighbors choices 1. Flooding 2. Geographic
routing 3. Cache data to direct interests
Sink
Interest Interrogation
Gradient Who is interested (data rate ,
duration, direction)
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Data Propagation

• Sensor node computes the highest requested event
  rate among all its outgoing gradients
• When a node receives a data
• Find a matching interest entry in its cache
• Examine the gradient list, send out data by rate
• Cache keeps track of recent seen data items (loop
  prevention)
• Data message is unicast individually to the
  relevant neighbors

35
Reinforcing the Best Path
Source
The neighbor reinforces a path 1. At least one
neighbor 2. Choose the one from whom it first
received the latest event (low delay) 3. Choose
all neighbors from which new events were
recently received
Sink
Low rate event
Reinforcement Increased interest
36
Local Behavior Choices

• For propagating interests
• In the example, flood
• More sophisticated behaviors possible e.g. based
  on cached information, GPS

• For setting up gradients
• data-rate gradients are set up towards neighbors
  who send an interest.
• Others possible probabilistic gradients, energy
  gradients, etc.

37
Local Behavior Choices

• For data transmission
• Multi-path delivery with selective quality along
  different paths
• probabilistic forwarding
• single-path delivery, etc.
• For reinforcement
• reinforce paths based on observed delays
• losses, variances etc.

38
Initial simulation study of diffusion

• Key metric
• Average Dissipated Energy per event delivered
• indicates energy efficiency and network lifetime
• Compare diffusion to
• flooding
• centrally computed tree (omniscient multicast)

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Diffusion Simulation Details

• Simulator ns-2
• Network Size 50-250 Nodes
• Transmission Range 40m
• Constant Density 1.95x10-3 nodes/m2 (9.8 nodes
  in radius)
• MAC Modified Contention-based MAC
• Energy Model Mimic a realistic sensor radio
  Pottie 2000
• 660 mW in transmission, 395 mW in reception, and
  35 mw in idle

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Diffusion Simulation

• Surveillance application
• 5 sources are randomly selected within a 70m x
  70m corner in the field
• 5 sinks are randomly selected across the field
• High data rate is 2 events/sec
• Low data rate is 0.02 events/sec
• Event size 64 bytes
• Interest size 36 bytes
• All sources send the same location estimate for
  base experiments

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Average Dissipated Energy
0.018
0.016
Flooding
0.014
0.012
0.01
0.008
(Joules/Node/Received Event)
Omniscient Multicast
Average Dissipated Energy
0.006
Diffusion
0.004
0.002
0
0
50
100
150
200
250
300
Network Size
Diffusion can outperform flooding and even
omniscient multicast. (suppress duplicate
location estimates)
42
Conclusions

• Can leverage data processing/aggregation inside
  the network
• Achieve desired global behavior through localized
  interactions
• Empirically adapt to observed environment

43
Comments

• Primary concern is energy
• Simulations only
• Only use five sources and five sinks
• How to exam scalability?
• ???

44
TTDD A Two-tier Data Dissemination Model for
Large-scale Wireless Sensor Networks

• Haiyun Luo
• Fan Ye, Jerry Cheng
• Songwu Lu, Lixia Zhang
• UCLA CS Dept.

45
Assumptions

• Fixed source and sensor nodes, mobile or
  stationary sinks
• nodes densely applied in large field
• Position-aware nodes, sinks not necessarily
• Once a stimulus appears, sensors surrounding it
  collectively process signal, one becomes the
  source to generate the data report

46
Sensor Network Model
Stimulus
Source
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Mobile Sink
Excessive Power Consumption
Increased Wireless Transmission Collisions
State Maintenance Overhead
48
Goal, Idea

• Efficient and scalable data dissemination from
  multiple sources to multiple, mobile sinks
• Two-tier forwarding model
• Source proactively builds a grid structure
• Localize impact of sink mobility on data
  forwarding
• A small set of sensor node maintains forwarding
  state

49
Grid setup

• Source proactively divide the plane into aXa
  square cells, with itself at one of the crossing
  point of the grid.
• The source calculates the locations of its four
  neighboring dissemination points
• The source sends a data-announcement message to
  reach these neighbors using greedy geographical
  forwarding
• The node serving the point called dissemination
  node
• This continues

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TTDD Basics
Dissemination Node
Data Announcement
Data
Query
Immediate Dissemination Node
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TTDD Mobile Sinks
Dissemination Node
Trajectory Forwarding
Data Announcement
Immediate Dissemination Node
Data
Immediate Dissemination Node
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TTDD Multiple Mobile Sinks
Dissemination Node
Trajectory Forwarding
Data Announcement
Immediate Dissemination Node
Data
53
Grid Maintenance

• Issues
• Efficiency
• Handle unexpected dissemination node failures
• Solutions
• Source sets the Grid Lifetime in Data
  Announcement
• DN replication each DN recruits several sensor
  nodes from its one-hop neighbor, replicates the
  location of the upstream DN
• DN failure detected and replaced on-demand by
  on-going query and data flows

54
Grid Maintenance
Dissemination Node
Immediate Dissemination Node
X
Data
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Grid Maintenance (contd)
Dissemination Node
Immediate Dissemination Node
X
Data
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Ns-2 Simulation

• Metrics
• Energy consumption, delay, success rate
• Impacts of
• Cell size
• Number of sources and sinks
• Sink mobility
• Node failure rates

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Conclusions

• TTDD two-tier data dissemination Model
• Exploit sensor nodes being stationary and
  location-aware
• Construct maintain a grid structure with low
  overhead
• First Infrastructure-approach in semi-stationary
  sensor networks
• Efficiency effectiveness in supporting mobile
  sinks
• Proactive sources
• Localize sink mobility impact

58
Limitations and Future work

• Knowledge of cell size
• Greedy geographical routing failures, it is not
  clear how the greedy geographical routing works
  in terms of the neighbors range, which may lead
  to a problem of finding two dissemination node
  for one
• Mobile stimulus
• Mobile sensor node
• Sink mobility speed limited speed
• Data aggregation

59
Comparison of routing algorithms
Attributes Algo. Data Efficiency Energy Efficiency (data/energy ratio) State complexity
Flooding Fastest Low b/c Implosion Small, upstream
Gossiping Slowest No. 7 Lowest Random walk None
Rumor Routing Very slow No. 6 Very low Some
SPIN Very Fast Higher than above, SPIN-EC close to ideal Data- neighbor pairs
Directed Diffusion Quite Fast No. 3 Higher than TTDD global flooding strong aggregation Complex Neighbor X Interest
TTDD Very Fast No.2 Reasonable local flooding reasonable aggregation OK Four neighbor, Constant
IP Multicast Fastest Low b/c heavy machinery, big node Most complex
60
Discussions

• Source initiated or Sink initiated? Why?

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Discussion (con)

• Should we build more infrastructure or not,
  whats the trade off?

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The End

• Thank you!