Directed Diffusion:A Scalable and Robust Communication

Paradigm for Sensor Networks

Motivation• Properties of Sensor Networks

– Data centric– No central authority– Resource constrained– Nodes are tied to physical locations– Nodes may not know the topology– Nodes are generally stationary

• How can we get data from the sensors?

Directed Diffusion• Data centric

– Individual nodes are unimportant• Request driven

– Sinks place requests as interests– Sources satisfying the interest can be found– Intermediate nodes route data toward sinks

• Localized repair and reinforcement• Multi-path delivery for multiple sources,

sinks, and queries

Motivating Example• Sensor nodes are monitoring

animals• Users are interested in receiving

data for all 4-legged creatures seen in a rectangle

• Users specify the data rate

Interest and Event Naming

• Query/interest:1. Type=four-legged animal2. Interval=20ms (event data rate)3. Duration=10 seconds (time to cache)4. Rect=[-100, 100, 200, 400]

• Reply:1. Type=four-legged animal2. Instance = elephant3. Location = [125, 220]4. Intensity = 0.65. Confidence = 0.856. Timestamp = 01:20:40

• Attribute-Value pairs, no advanced naming scheme

Directed Diffusion• Sinks broadcast interest to neighbors

– Initially specify a low data rate just to find sources for minimal energy consumptions

• Interests are cached by neighbors• Gradients are set up pointing back to

where interests came from • Once a source receives an interest, it

routes measurements along gradients

Interest Propagation• Flood interest• Constrained or Directional flooding based on location is

possible• Directional propagation based on previously cached data

Source

Sink

Interest

Gradient

Data Propagation• Multipath routing

– Consider each gradient’s link quality

Source

Sink

Gradient

Data

Reinforcement• Reinforce one of the neighbor after receiving

initial data.– Neighbor who consistently performs better than others– Neighbor from whom most events received

Source

Sink

Gradient

Data

Reinforcement

Negative Reinforcement• Explicitly degrade the path by re-sending interest with

lower data rate.• Time out: Without periodic reinforcement, a gradient

will be torn down

Source

Sink

Gradient

Data

Reinforcement

Summary of the protocol

Sampling & forwarding• Sensors match signature waveforms from codebook

against observations• Sensors match data against interest cache, compute

highest event rate request from all gradients, and (re) sample events at this rate

• Receiving node:– Find matching entry in interest cache

• If no match, silently drop– Check and update data cache (loop prevention,

aggregation)– Resend message along all the active gradients, adjusting

the frequency if necessary

Design Considerations

Evaluation• ns2 simulation• Modified 802.11 MAC for energy use calculation

– Idle time: 35mW– Receive: 395mw– Transmit: 660mw

• Baselines– Flooding – Omniscient multicast: A source multicast its event to

all sources using the shortest path multicast tree – Do not consider the tree construction cost

• Simulate node failures• No overload• Random node placement

– 50 to 250 nodes (increment by 50)– 50 nodes are deployed in 160m * 160m

• Increase the sensor field size to keep the density constant for a larger number of nodes

– 40m radio range

Metrics• Average dissipated energy

– Ratio of total energy expended per node to number of distinct events received at sink

– Measures average work budget• Average delay

– Average one-way latency between event transmission and reception at sink

– Measures temporal accuracy of location estimates• Both measured as functions of network size

Average Dissipated Energy

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DiffusionDiffusion

Omniscient MulticastOmniscient Multicast

FloodingFlooding

They claim diffusion can outperform omniscient multicast due toThey claim diffusion can outperform omniscient multicast due toiin-network processing & suppression. For example, multiple n-network processing & suppression. For example, multiple

sources can detect a four-legged animal in one area.sources can detect a four-legged animal in one area.

Impact of In-network Processing

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Diffusion With Diffusion With SuppressionSuppression

Diffusion Without Diffusion Without SuppressionSuppression

Impact of Negative Reinforcement

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Diffusion With Negative Diffusion With Negative ReinforcementReinforcement

Diffusion Without Diffusion Without Negative ReinforcementNegative Reinforcement

Reducing high-rate paths in steady state is criticalReducing high-rate paths in steady state is critical

Average Dissipated Energ y (80211.80211. energy m

odel)

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DiffusionDiffusion

Omniscient MulticastOmniscient MulticastFloodingFlooding

Standard 802.11 is dominated by idle energyStandard 802.11 is dominated by idle energy

Average energy and delay

• Average delay is misleading• Directed Diffusion is better than

Omniscient Multicast?– Why don’t they suppress messages in

Omniscient Multicast as done in Directed Diffusion?

– Topology has little path diversity

Failures• Dynamic failures

– 10-20% failure at any time• Each source sends different signals• <20% delay increase, fairly robust• Energy efficiency improves:

– Reinforcement maintains adequate number of high quality paths

– Shouldn’t it be done in the first place?

Analysis• Energy gains are dependent on 802.11

energy assumptions• Can the network always deliver at the

interest’s requested rate?• Can diffusion handle overloads?• Does reinforcement actually work?

Conclusions• Data-centric communication

between sources and sinks• Aggregation and duplicate

suppression • More thorough performance

evaluation is required

Extensions• One-phase pull

– Propagate interest– A receiving node pick the link that

delivered the interest first– Assumes the link bidirectionality

• Push diffusion– Sink does not flood interest– Source detecting events disseminate

exploratory data across the network– Sink having corresponding interest

reinforces one of the paths

TEEN (Threshold-sensitive Energy Efficient sensor Network protocol)

[IPDPS01]

• Push-based data centric protocol• Nodes immediately transmit a

sensed value exceeding the threshold to its cluster head that forwards the data to the sink

LEACH [HICSS00]• Proposed for continuous data gathering

protocol• Divide the network into clusters• Cluster head periodically collect &

aggregate/compress the data in the cluster using TDMA

• Periodically rotate cluster heads for load balancing

Discussions• Criteria to evaluate data-centric

routing protocols?– Or, what do we need to try to

optimize? Energy consumption? Data timeliness? Resilience? Confidence of event detection? Too many objectives already? Can we pick just one or two?

Questions?