02/08/2005CS240 Presentation 1 Directed Diffusion for Wireless Sensor Networking By Chalermek...
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Transcript of 02/08/2005CS240 Presentation 1 Directed Diffusion for Wireless Sensor Networking By Chalermek...
02/08/2005 CS240 Presentation 1
Directed Diffusion
for Wireless Sensor Networking
By Chalermek Intanagonwiwat, Ramesh Govindan,
Deborah Estrin, John Heidemann, and Fabio Silva
Presented by: Jin Sun
CS240 Presentation 202/08/2005
Outline
Introduction The problem Directed Diffusion Concepts Simulation Results Summary
CS240 Presentation 302/08/2005
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
CS240 Presentation 402/08/2005
The Problem 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
Event
Sensor sources
Sensor sink
Directed Diffusion
A sensor field
On event, sensed and/or processed information delivered to the inquiring destination
CS240 Presentation 502/08/2005
Directed Diffusion
Initial Goals:Propose an application-aware paradigm to
facilitate efficient aggregation, and delivery of sensed data to inquiring destination
CS240 Presentation 602/08/2005
Directed Diffusion-how it works
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
“How many vehicles do you observe in the southeast quadrant?”
Source
Sink
aggregation point Additional source
Low data rate
High data rate
CS240 Presentation 702/08/2005
Directed Diffusion
Data Naming Interests and Gradient Data Propagation Reinforcement
Path establishmentPath failure / recoveryLoop elimination
CS240 Presentation 802/08/2005
Data Naming Expressing an Interest
Using attribute-value pairsE.g.,
Data replyUsing attribute-value pairsE.g.,
Type = Wheeled vehicle // detect vehicle locationInterval = 20 ms // send events every 20ms Duration = 10 s // Send for next 10 sField = [x1, y1, x2, y2] // from sensors in this area
Type = Wheeled vehicle // type of vehicle seenInstance = truck // instance of this typeIntensity = 0.6 // signal amplitude measureConfidence = 0.85 // confidence in the matchTimestamp = 01:20:34 // event generation timeField = [x1, y1, x2, y2] // from sensors in this area
CS240 Presentation 902/08/2005
Directed Diffusion
Data Naming Interests and Gradient Data Propagation Reinforcement
Path establishmentPath failure / recoveryLoop elimination
CS240 Presentation 1002/08/2005
Interest Propagation
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
Sink Sink
Sources Interest
CS240 Presentation 1102/08/2005
Gradient EstablishmentRouted Data
Sink SinkGradient
Gradient for i1 set up to upstream neighbor No source routes Gradient – a weighted reverse link Low gradient Few packets per unit time needed
CS240 Presentation 1202/08/2005
Directed Diffusion
Data Naming Interests and Gradient Data Propagation Reinforcement
Path establishmentPath failure / recoveryLoop elimination
CS240 Presentation 1302/08/2005
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
CS240 Presentation 1402/08/2005
Directed Diffusion
Data Naming Interests and Gradient Data Propagation Reinforcement
Path establishmentPath failure / recoveryLoop elimination
CS240 Presentation 1502/08/2005
Reinforcement
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
EventEvent
Sink AA sensor field
Reinforced gradient
Reinforced gradient
B
C
D
CS240 Presentation 1602/08/2005
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
EventEvent
Sink
Src AC
B
MD
Link A-M lossyA reinforces BB reinforces C …D need notA negative reinforces MM negative reinforces D
CS240 Presentation 1702/08/2005
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
Loop Elimination
A
QP
D M
CS240 Presentation 1802/08/2005
Simulation Results
Compare directed diffusion to 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
CS240 Presentation 1902/08/2005
Average Dissipated Energy
flooding
DiffusionMulticast
In-network aggragation reduces DD redundancy- Flooding is poor because of multiple paths from source to sink
CS240 Presentation 2002/08/2005
Delay
flooding
Diffusion
Multicast
DD finds least delay paths
- Floof]ding incurs latency due to high MAC contention, colission
CS240 Presentation 2102/08/2005
Event Delivery Ratio under node failures
0 %
10%20%
Delivery ration degrades with more nodes failures- Graceful degradation indicate efficient negative reinforcement
CS240 Presentation 2202/08/2005
Summary
Main ContributionsDescription of new networking paradigm
Interests, gradients, reinforcement Benefits of in-network processing Aggregation and nested-queries
Works with multiple sources and sinksCan perform local repairReinforce another path if a node dies
CS240 Presentation 2302/08/2005
Summary (cont’d)
DisadvantagesDesign doesn’t deal with congestion or lossPeriodic broadcasts of interest reduces
network lifetimeNodes within range of human operator may
die quickly
02/08/2005 CS240 Presentation 24
Thank You!