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Routing and Dissemination in Wireless Sensor Networks

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e.g., 'Give me periodic reports about animal location in region A every t seconds' ... Event size: 64 bytes. Interest size: 36 bytes ... – PowerPoint PPT presentation

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Title: Routing and Dissemination in Wireless Sensor Networks


1
Routing and Dissemination in Wireless Sensor
Networks
  • Sandeep Gupta
  • Based on Slides by Huan and Junning U. Mass.

2
Outline
  • Motivation and Challenges
  • Basic Idea of Some Routing and Data Dissemination
    schemes in Sensor Networks

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 does not have 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
11
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., radar
  • Moving sensors, e.g., robotics

13
Directed Diffusion
  • A Scalable and Robust Communication Paradigm for
    Sensor Networks
  • C. Intanagonwiwat
  • R. Govindan
  • D. Estrin

14
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?

15
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

16
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

17
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
18
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

19
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)
20
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

21
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
22
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.

23
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.

24
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)

25
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

26
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

27
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)
28
Conclusions
  • Can leverage data processing/aggregation inside
    the network
  • Achieve desired global behavior through localized
    interactions
  • Empirically adapt to observed environment

29
SPIN Sensor Protocols for Information via
Negotiation
Paper Negotiation-Based Protocols for
Disseminating Information in Wireless Sensor
Networks Mobicom 99.
W.R.Heinzelman, J.Kulik, H.Balakrishnan
30
Conventional Approach
  • Flooding
  • Send to all neighbors
  • E.g., routing table updates

31
Resource Inefficiencies
  • Resource blindness

32
What is the optimum protocol?
  • Ideal
  • Shortest-path routes
  • Avoids overlap
  • Minimum energy
  • Need global topology information

33
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

34
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
35
SPIN-PP Example
A
B
36
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

37
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
38
Unlimited Energy Simulations
-- SPIN-PP -- Ideal -- Flooding
  • Flooding converges first
  • No queuing delays
  • SPIN-PP
  • Reduces energy by 70
  • No redundant DATA messages

39
Limited Energy Simulations
-- Ideal -- SPIN-EC -- SPIN-PP -- Flooding
  • SPIN-EC distributes additional 20 data

40
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.

41
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.

42
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.

43
TTDD A Two-tier Data Dissemination Model for
Large-scale Wireless Sensor Networks (Mobicom
2002)
  • Haiyun Luo
  • Fan Ye, Jerry Cheng
  • Songwu Lu, Lixia Zhang
  • UCLA CS Dept.

44
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

45
Sensor Network Model
Stimulus
Source
46
Mobile Sink
Excessive Power Consumption
Increased Wireless Transmission Collisions
State Maintenance Overhead
47
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

48
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

49
TTDD Basics
Dissemination Node
Data Announcement
Data
Query
Immediate Dissemination Node
50
TTDD Mobile Sinks
Dissemination Node
Trajectory Forwarding
Data Announcement
Immediate Dissemination Node
Data
Immediate Dissemination Node
51
TTDD Multiple Mobile Sinks
Dissemination Node
Trajectory Forwarding
Data Announcement
Immediate Dissemination Node
Data
52
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

53
Grid Maintenance
Dissemination Node
Immediate Dissemination Node
X
Data
54
Grid Maintenance (contd)
Dissemination Node
Immediate Dissemination Node
X
Data
55
Ns-2 Simulation
  • Metrics
  • Energy consumption, delay, success rate
  • Impacts of
  • Cell size
  • Number of sources and sinks
  • Sink mobility
  • Node failure rates

56
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

57
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
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