1. Routing and Data Dissemination
Vinay Singh
Dongseo University

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. Goal: Minimize energy dissipation
Challenges
Energy-limited nodes
Computation
Aggregate data
Suppress redundant routing information
Communication
Bandwidth-limited
Energy-intensive
Goal: Minimize energy dissipation

11. Broadcast with minimum energy W.R.Heinzelman, J.Kulik, H.Balakrishnan
SPIN: The Goal
Broadcast with minimum energy
W.R.Heinzelman, J.Kulik, H.Balakrishnan

12. Conventional ApproachB D E F G
Flooding
Send to all neighbors
E.g., routing table updates

13. Resource Inefficiencies
Implosion A B C D
(a) A B C
(r,s)
(q,r) q s r
Data overlap
Resource blindness

14. What is the optimum protocol?B D E F
“Ideal”
Shortest-path routes
Avoids overlap
Minimum energy
Need global topology information G

15. 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

16. 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

17. SPIN-PP Example:
DATA
ADV A
REQ
DATA
ADV
REQ B

18. 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

19. Test Network 16 bytes 500 bytes 25 Nodes 59 Edges
Average degree = 4.7 neighbors
Network diameter = 8 hops
Data
Antenna reach = 10 meters
Meta-Data

20. Unlimited Energy Simulations
-- SPIN-PP
-- Ideal
-- Flooding
Flooding converges first
No queuing delays
SPIN-PP
Reduces energy by 70%
No redundant DATA messages

21. Limited Energy Simulations
-- Ideal
-- SPIN-EC
-- SPIN-PP
-- Flooding
SPIN-EC distributes additional 20% data

22. 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.

23. 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.

24. Limitations
The SPIN EC (Energy Constrained) version’s 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.

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

26. 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?
As we repeatedly emphasize that diffusion is application-aware, before we go on illustrating the concept of diffusion, we need to come up with an example application first.
And our example is remote surveillance application. In this application, users are interested to know animal locations in a specific region. As long as the users are still interested in such information, the report will be sent to them every t seconds.

27. 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

28. 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

29. 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:
Node data
Type =four-legged animal
Instance = elephant
Location = [125, 220]
Confidence = 0.85
Time = 02:10:35
Reply
Request
Interest ( Task ) Description
Type = four-legged animal
Interval = 20 ms
Duration = 1 minute
Location = [-100, -100; 200, 400]

30. 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

31. Setting Up Gradient Interest = Interrogation
Source
Neighbor’s choices :
1. Flooding
2. Geographic routing
3. Cache data to direct interests
Sink
- Introduce sink and source
- The user requests for low-data-rate events
- Interest is sent and propagated. (the black arrow)
- Gradients are set up. (the blue arrow)
- Events are sent along the gradients. (the red arrow)
- The thin red arrow show a low data rate.
- The user reinforces the best path in terms of delay (the green arrow)
- The thick red arrow shows a high data rate.
- Suppose the middle node fail.
- There is no high-data-rate events received.
- The sink reinforces a low-data-rate path to recover from the node failure.
Interest = Interrogation
Gradient = Who is interested
(data rate , duration, direction)

32. 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

33. 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
- Introduce sink and source
- The user requests for low-data-rate events
- Interest is sent and propagated. (the black arrow)
- Gradients are set up. (the blue arrow)
- Events are sent along the gradients. (the red arrow)
- The thin red arrow show a low data rate.
- The user reinforces the best path in terms of delay (the green arrow)
- The thick red arrow shows a high data rate.
- Suppose the middle node fail.
- There is no high-data-rate events received.
- The sink reinforces a low-data-rate path to recover from the node failure.
Low rate event
Reinforcement = Increased interest

34. 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.
However, what we show you is just an example of local rules that can be used. There are several choices of local rules to use. Each of them lead to a different global behavior.
In our example, we use flooding for interest propagation. But there are several sophisticated rules that can be used. For example, interest propagation that is based on cached information or GPS.
There are also several choices of gradients. For our example, we use data-rate gradients. But probabilistic gradients are also possible.
We may also deliver data using only a single path or multiple paths. Deterministically or probabilistically, Redundant or distinct, and so on.
Our reinforcement is based on observed delay. We can also reinforce based on observed losses, delay variances, or energy level.

35. 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.

36. 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)
To evaluate diffusion, we compare with 2 idealized schemes. Flooding and omniscient multicast.
For omniscient multicast, the multicast tree is centrally computed from the simulator. No overhead is assigned.
We use 3 metrics for our evaluation
To show energy efficiency, we measure the average dissipated energy per distinct event received.
We also measure the delay so that we can imply the temporal accuracy of information.
The event delivery ratio is measure to compare their robustness

37. Diffusion Simulation Details
Simulator: ns-2
Network Size: 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
We evaluated our diffusion on several network size ranging from 50 to 250 nodes with constant density.
We use IEEE as our MAC but we are not quite happy with the choice at all. The main reason is that the energy consumption during idle intervals is too much, comparable to energy consumption in reception.
So, we have modify the energy model such that it mimics a realistic sensor radio. In this energy model, the energy consumption during idle intervals is only 10% of energy consumption in reception.

38. 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
Here are parameters we used in our simulation.
There are 5 sources and 5 sinks.
Event size is very small.
In our simulated scenario, all sources will detect the same animal so they send the same location estimates.

39. Average Dissipated Energy
0.018
0.016
Flooding
0.014
0.012
0.01
Average Dissipated Energy
(Joules/Node/Received Event)
0.008
Omniscient Multicast
0.006
Diffusion
0.004
0.002
- The X axis is network size
- The Y axis is dissipated energy
- As expected, flooding performed the worst.
- Diffusion can outperform omniscient multicast because they can perform application-level duplicate suppression while the other can’t. 50
100
150
200
250
300
Network Size
Diffusion can outperform flooding and even omniscient multicast.
(suppress duplicate location estimates)

40. Conclusions
Can leverage data processing/aggregation inside the network
Achieve desired global behavior through localized interactions
Empirically adapt to observed environment
The main challenges on the design of our data dissemination system are dynamics.
There are several types of dynamics that our system need to deal with.
For example, nodes may fail or move out of range. Deploying such sensor networks in a jungle, animals may walk by, become obstacles, play with the devices, and completely change the network topology.
And even worse, after deploy networks for a while, you may need to re-task the network to do something else.
It will be a pain to manually re-configure the huge number of devices for the new task.
So, it is very important that our mechanism can adapt automatically to various types of dynamics.

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

42. 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
No.2
Reasonable
local flooding+ reasonable aggregation
OK:
Four neighbor, Constant
IP Multicast
Low: b/c heavy machinery, ‘big’ node
Most complex

43. Thank you!