1. Directed Diffusion for Wireless Sensor Networking
By Chalermek Intanagonwiwat, Ramesh Govindan,
Deborah Estrin, John Heidemann, and Fabio Silva
Presented by: Jin Sun
1. Sensor Network – 100s or 1000s of sensor nodes deployed in a geographical region to sense an event.
2. Sensor Networks provide a high-level description of the event being sensed.
3. Used in harsh inhospitable environments, environmental control in offices, robot control and guidance in automatic manufacturing environments etc
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CS240 Presentation

2. Outline Introduction The problem Directed Diffusion Concepts
Simulation Results
Summary
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3. Introduction A region requires event-monitoring
Deploy sensors forming a distributed network
Wireless networking
Energy-limited nodes
On event, sensed and/or processed information delivered to the inquiring destination
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4. The Problem Where should the data be stored? Event
A sensor field
Where should the data be stored?
How should queries be routed to the stored data?
How should queries for sensor networks be expressed?
Where and how should aggregation be performed?
Event
Sensor sources
Directed
Diffusion
How many people cross Linden St. every day?
(eg.) How much poisonous gas in the atmosphere?
We can expect that with the development in processors, memory and radio technology, small and cheap sensor node will be capable of wireless communication and significant computation
Several problem here:
1. Where should the data be stored?
2. How should queries be routed to the stored data?
3. How should queries for sensor networks be expressed?
4.Where and how should aggregation be performed?
Sensor sink
On event, sensed and/or processed information delivered to the inquiring destination
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CS240 Presentation

5. Directed Diffusion Initial Goals:
Propose an application-aware paradigm to facilitate efficient aggregation, and delivery of sensed data to inquiring destination
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6. Directed Diffusion-how it works
Low data rate
Sink
“How many vehicles do you observe in the southeast quadrant?”
High data rate
Source
aggregation point
Additional source
1. Data Centric Routing
2. Query–Response Model
A Query (Interest) is broadcasted by a node (sink). This reaches the sensor nodes (sources) which can satisfy the query. This sets up exploratory gradients.
3. Once data is available, the sources sends data back to the sink via a sequence of local interaction.
Robust, efficient data distribution in sensor networks
name data (not nodes), use physicality
diffuse requests and responses across network
optimize path with gradient-based feedback
additional data can be processed and aggregated within the network
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CS240 Presentation

7. Directed Diffusion Data Naming Interests and Gradient Data Propagation
Reinforcement
Path establishment
Path failure / recovery
Loop elimination
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8. Data Naming Data reply Expressing an Interest
Using attribute-value pairs
E.g.,
Data reply
Type = Wheeled vehicle // detect vehicle location
Interval = 20 ms // send events every 20ms Duration = 10 s // Send for next 10 s
Field = [x1, y1, x2, y2] // from sensors in this area
Reply need to modified
Type = Wheeled vehicle // type of vehicle seen
Instance = truck // instance of this type
Intensity = 0.6 // signal amplitude measure
Confidence = 0.85 // confidence in the match
Timestamp = 01:20:34 // event generation time
Field = [x1, y1, x2, y2] // from sensors in this area
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9. Directed Diffusion Data Naming Interests and Gradient Data Propagation
Reinforcement
Path establishment
Path failure / recovery
Loop elimination
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10. Interest Propagation
Sink
Sources
Interest
Inquirer (sink) broadcasts exploratory interest, i1
Intended to discover routes between source and sink
Neighbors update interest-cache and forwards i1
No way of knowing differentiating new interests from repeated
Periodically purged
Host must reissue interest
No information about sink
Gradient table
Rate per neighbor
Timestamp
Expiration
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11. Gradient Establishment
Routed Data
Sink
Gradient
Gradient for i1 set up to upstream neighbor
No source routes
Gradient – a weighted reverse link
Low gradient  Few packets per unit time needed
Periodically purged
Host must reissue interest
No information about sink
Gradient table
Rate per neighbor
Timestamp
Expiration
02/08/2005
CS240 Presentation

12. Directed Diffusion Data Naming Interests and Gradient Data Propagation
Reinforcement
Path establishment
Path failure / recovery
Loop elimination
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13. Event-data propagation
Event e1 occurs, matches i1 in sensor cache
e1 identified based on waveform pattern matching
Interest reply diffused down gradient (unicast)
Diffusion initially exploratory (low packet-rate)
Cache filters suppress previously seen data
Problem of bidirectional gradient avoided
1. A node that matches an interest generates reply at desired rate
2. If node receives a reply, it searches interest cache
3. Forwards along given route(s) if found
Drops otherwise
4. Loop prevention and otherwise
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14. Directed Diffusion Data Naming Interests and Gradient Data Propagation
Reinforcement
Path establishment
Path failure / recovery
Loop elimination
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15. Reinforcement
Reinforced gradient
Event
Reinforced gradient D B
From exploratory gradients, reinforce optimal path for high-rate data download  Unicast
By requesting higher-rate-i1 on the optimal path
Exploratory gradients still exist – useful for faults
A sensor field
Sink A C
2 .Reinforcement period: multiple paths are reduced and now still certain redundancy is permitted
3. Message cache can be used to avoid loop
Source can reissue the same request with a higher rate
"Draw down" higher quality data from a particular neighbor
Other node reacts when receiving
"Outflow" increased, must reinforce another node to increase "inflow"
Selects and empirically low delay path
Such as the first neighbor that sent it the reply
Poor metric, switches a lot – wasteful
Need a metric that in consistent and doesn’t switch paths unless sure beneficial
Perhaps neighbor that usually returns the fastest
Neighbor with most power
Best metric unknown, probably application dependent
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16. Path Failure / Recovery
Link failure detected by reduced rate, data loss
Choose next best link (i.e., compare links based on infrequent exploratory downloads)
Negatively reinforce lossy link
Either send i1 with base (exploratory) data rate
Or, allow neighbor’s cache to expire over time
Link A-M lossy
A reinforces B
B reinforces C …
D need not
A negative reinforces M
M negative reinforces D
Event D M
Src A C B
Sink
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17. Loop Elimination P Q
M gets same data from both D and P, but P always delivers late due to looping
M negatively-reinforces (nr) P, P nr Q, Q nr M
Loop {M  Q  P} eliminated
Conservative nr useful for fault resilience D M A
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18. Simulation Results Compare directed diffusion to Key metrics: flooding
Omniscient multicast
Key metrics:
Average dissipated energy
per node energy dissipation / # events seen by sinks
Average packet delay
latency of event transmission to reception at sink
Distinct event delivery
# of distinct events received / # of events originally sent
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19. Average Dissipated Energy
flooding
Multicast
Diffusion
In-network aggragation reduces DD redundancy
- Flooding is poor because of multiple paths from source to sink
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20. Delay flooding Diffusion Multicast DD finds least delay paths
- Floof]ding incurs latency due to high MAC contention, colission
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21. Event Delivery Ratio under node failures
0 %
10%
20%
Delivery ration degrades with more nodes failures
- Graceful degradation indicate efficient negative reinforcement
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22. Summary Main Contributions Description of new networking paradigm
Interests, gradients, reinforcement
Benefits of in-network processing
Aggregation and nested-queries
Works with multiple sources and sinks
Can perform local repair
Reinforce another path if a node dies
1. Works with multiple sources and sinks
2. Can perform local repair
3. Reinforce another path if a node dies
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23. Summary (cont’d) Disadvantages
Design doesn’t deal with congestion or loss
Periodic broadcasts of interest reduces network lifetime
Nodes within range of human operator may die quickly
1. Periodic broadcasts of interest reduces network lifetime
2. Nodes within range of human operator may die quickly.
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24. Thank You!
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