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Routing

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Data-Centric Routing

Paradigm shift from accessing data from individual nodes to accessing “relevant” data. Data within certain region, Data on events, Collective data processing, e.g., “What’s the

average temperature of a region?”, “How many animals cross this path?”, “Is there an intruder in the area?”.

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Challenges

Energy-limited nodes. Computation.

Aggregate data. Suppress redundant routing information.

Communication. Bandwidth-limited. Energy-intensive.

Goal: Minimize energy dissipation

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Challenges

Scalability: arbitrarily large scale ad-hoc deployment. Fully distributed w/o global knowledge. Large numbers of sources and sinks.

Robustness: unexpected sensor node failures.

Dynamics: Topology changes (e.g., mobility, failures, etc.) Target mobility.

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Directed Diffusion

Intanagonwiwat et al., ACM Mobicom 2000.

One of the first data centric routing paradigms.

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Application Example: Remote Surveillance

““ Give me periodic report Give me periodic reportss about animal lo about animal lo cation in region A every t seconds” cation in region A every t seconds”..

Tell me in what direction that vehicle in Tell me in what direction that vehicle in region Y is moving?region Y is moving?

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Basic Idea

Simple attribute-based naming as fundamental building block.

Requests for information (interests) and relevant data (reports) are described as sets of value-attribute pairs.

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NamingNaming

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:

Interest ( Task ) DescriptionType = four-legged animalInterval = 20 msDuration = 1 minuteLocation = [-100, -100; 200, 400]

RequestRequest

Node dataType =four-legged animalInstance = elephantLocation = [125, 220]Confidence = 0.85Time = 02:10:35

ReplyReply

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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 are set up within the network designed

toward the sink to “draw” events, i.e. data matching interest.

Reinforcement Sink reinforces particular neighbors to draw higher

quality ( higher data rate) events.

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Basic Algorithm Sink floods interest.(interest may be

periodically repeated).

Every node caches interest while valid, and creates local gradient towards neighboring nodes from which it heard interest.

Sources with relevant data starts sending it according to local gradients.

When sink starts receiving data, it reinforces one or some of the paths, pruning the rest.

Negative reinforcements can be used for adjusting to changing consitions.

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Source

Sink

Interest = Interrogation

Gradient = Who is interested(data rate , duration, direction)

Example

Neighbor’s choices :1. Flooding 2. Geographic routing3. Cache data to direct interests

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Data Propagation

Sensor node computes the highest requested event rate among all its outgoing gradients.

When a node receives 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.

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Source

Sink

Reinforcing the Best Path

Low rate event Reinforcement = Increased interest

The neighbor reinforces a path:1. At least one neighbor2. Choose the one from whom it first received the latest event (low delay)3. Choose all neighbors from which new events were recently received

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Local Behavior Choices

For propagating interests: In the example, flood.In the example, flood. More sophisticated behaviors possible: e.g.

based on GPS.

For setting up gradients: Data-rate gradients are set up towards Data-rate gradients are set up towards

neighbors who send an interestneighbors who send an interest.. Others possible: probabilistic

gradients, energy gradients, etc.

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Local Behavior Choices

For data transmission Multi-path delivery with selective quality along Multi-path delivery with selective quality along

different pathsdifferent paths Probabilistic forwarding Single-path delivery, etc.

For reinforcement Reinforce paths based on observed delaysReinforce paths based on observed delays Losses, variances etc.

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Initial simulation study of diffusion

Key metric Average Dissipated Energy per event delivered

• indicates energy efficiency and network lifetime

Compare diffusiondiffusion to FloodingFlooding Centrally computed tree (omniscient multicastomniscient multicast)

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Diffusion Simulation Details

Simulator: -2ns-2ns - Network Size: 50 250 Nodes Transmission Range: 40m Constant Density: 1.95x10-3 nnnnnnn2 nnnn(9 .8

s in radius) MAC: Modified Contention-based MAC Energy Model: Mimic a realistic sensor radio [Pottie

2000] nn nnnnnnnnnn nnn 660 , 3 9 5 ,

35mw in idle

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Diffusion Simulation

Surveillance application 5 sources are randomly selected within a 70m x

nnnnnn nn nnn nnnnn70 5 sinks are randomly selected across the field nnnn nn n nnnnnnnnnn2/ nnnnnnnnnn0.02/ Event size: 64 bytes 36Interestsize: byt es All sources send the same location estimate for b All sources send the same location estimate for b

ase experiments ase experiments

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Average Dissipated Energy

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DiffusionDiffusion

Omniscient MulticastOmniscient Multicast

FloodingFlooding

Diffusion can outperform flooding and even omniscient multicast.Diffusion can outperform flooding and even omniscient multicast.(suppress duplicate location estimates) (suppress duplicate location estimates)

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Directed Diffusion Variants

Original mechanism: 2-phase pull, i.e., interests and reinforcements.

1-phase pull variant: eliminates reinforcements as a separate phase. Sink floods interest. Data source selects best reverse path. Assumes links are bidirectional.

Push-diffusion: Initiative from sources, i.e., they advertise

their data along multiple paths; sink, if interested, reinforces one or some of the paths.

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Pull versus Push Diffusion

Overall performance is application dependent.

“Pull” is more energy-efficient in terms of route setup in the case of many active sources.

“Push” is more efficient when there are fewer sources and more sinks.

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Multipath Routing

Robustness/resilience to failures. Multipath versus alternate path routing. Totally- or partially disjoint paths.

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Directed Diffusion Resilience

Periodic flooding of interests and events to circumvent failures.

Problem?

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Braided Multipath Routing

Ganesan et al., MC2R 2002. Alternate path routing. Braided path: node/link disjointedness

between the multiple paths is not required.

Braided paths: For each nodein the main path, find path thatdoes not include that node.

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Observations

Primary path: “best” path. Data sent at lower rate on alternate

paths. Upon failure on primary path,

reinforcement on alternate path. If all alternate paths fail, flooding for

path re-establishment. Overhead: alternate path maintenance. Resilience measured as how often path

re-establishment is needed.

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Approach

Disjoint versus “braided” paths. How to build multiple paths with local

information only?

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Localized disjoint multipaths

Sink establishes primary path. Sink selects “next best” neighbor “A”. A propagates “alternate path”

reinforcement to its “best” neighbor “B”.

If B is already on a path between sink and source, B sends back a “negative reinforcement”.

Access to local information only may lead to longer paths.

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Braided multipath

Partially disjoint. For each node on primary path, find

best path from source to sink that does not contain that node.

Paths in the braid expend equivalent energy.

Reinforcement to “best” node and alternate reinforcement to “next best” node.

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Evaluation

Energy efficiency. Overhead.

Resilience to failures. Isolated versus patterned failures.

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Results

Braided multipaths are more energy efficient. Especially at lower densities.

Disjoint multipaths have better resilience to patterned losses.

Braided multipaths exhibit better resilience to isolated failures.

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Gradient Cost Routing (GRAd) Poor et al., ACM Queue 2003. All nodes keep estimated cost to

destinations (sinks); e.g., number of hops. When packet is sent, it includes cost so

far (i.e., number of hops traversed) and TTL.

Node receiving packet whose cost is smaller than packet TTL, forwards packet.

Increments packet cost by one; decrements TTL by one.

GRAd = limited flood for robustness at expense of overhead.

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Gradient Broadcast (GRAB)

Ye et al., IPSN 2003. Enhances GRAd with “credits”

decremented at each hop. Earlier hops receive greater credit and thus

higher spreading initially. Ensures diverse paths converge to sink.

SD

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Energy-Efficient Routing

Maximize network lifetime. Techniques range from:

Use of suitable shortest-path metric. Derive energy-efficient routes using global

optimization. Traffic spreading for load balancing.

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Power-Aware Routing for MANETs

Singh et al., ACM Mobicom 98. Pick nodes with longer remaining

battery lifetime as intermediate relays. If Ri is remaining energy of node i, then

link metric is C=1/Ri. Shortest-path algorithm finds route that

minimizes i 1/Ri.

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Traffic Spreading

Load balance across multiple paths.

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Traffic spreading approaches

Stochastic: node picks next-hop randomly (chosen from neighbors with equal gradient).

Energy-based: node increases its “height” when its energy falls below a certain threshold. All nodes need to adjust their height accordingly.

Stream-based: divert streams from nodes that are part of paths used b other streams.

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Geographic Routing

Useful for location-specific interests/queries.

Deliver packets to nodes or regions based on their geographic location.

Typically, nodes know their position and immediate neighbors.

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Geographic Forwarding

Simplest form of geographic-based forwarding. Finn, ISI Tech Report, 1987. Greedy approach. Forwards packet to neighbor closest to

destination.

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Basic Geographic ForwardingBasic Geographic Forwarding

B. Karp and H.T. Kung. GPSR: Greedy Perimeter stateless Routing for Wireless Networks. MobiCom2000.

Greedy: send packet to neighbor that is closest to destination

Can get stuck in voids. GPSR proposes a perimeter routing mode to avoid this.

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Trajectory Based ForwardingTrajectory Based Forwarding

D. Niculescu and B. Nath, Trajectory Based Forwarding and Its Applications. MOBICOM 2003.

Pre-encode arbitrary geographic trajectory; packet goes through nodes closest to this trajectory.

Particularly well suited for large networks with high density.

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Geographic routing without location information (Rao et al.) Apply geographic routing when (most)

nodes do not have position information. Approach: “virtual coordinates”.

Use local connectivity information.

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Assumptions

Nodes know their own coordinates. Nodes know coordinates of nodes in the

2-hop neighborhood.

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Data Forwarding

Greedy: forward to neighbor closest to destination.

When packet arrived to destination, stop.

If stuck, do expanding ring search until closer node found.

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Coordinate construction

A node’s coordinates is the average of its neighbors’ coordinates.

Finding perimeter nodes’ coordinates. Beacon nodes flood “Hello” message. Perimeter nodes discover distance in hops

to other perimeter nodes. Perimeter nodes broadcast their perimeter

vector. Perimeter nodes use triangulation to find

coordinates of all perimeter nodes.

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Coordinate construction (cont’d) Deciding whether a node is on

perimeter: Use distance to beacon nodes. If node is the farthest away from beacon

node compared to all its 2-hop neighbors, then it’s on the perimeter.

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Evaluation

Comparison between greedy routing using real- versus virtual coordinates.

Metrics: Success rate: number packets reaching

destination using purely greedy routing. Average path length. Routing load. Overhead.

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Results

Scalability. Network size. Density.

Mobility. Losses. Obstacles. Trade-offs.

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Routing with Mobile Nodes Significant previous work on routing for

MANETs where potentially all nodes can move.

Sensor networks are assumed to be predominantly static. However, a few nodes (e.g., the sinks) can be mobile. E.g., robots, humans roaming in the area,

etc. Advantages of mobility:

Enable collecting information in a timely manner.

Provide network connectivity.

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Data MULEs

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Target deployments.

Sparse networks. Multi-tiered deployments.

Sensors. Wired access points. Mules.

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Approach

Mobile agents. MULEs: mobile ubiquitous LAN

extensions. Mobility. Communication (short range).

• UWB radios? [low power and ability to handle bursts].

Buffering.

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Pros and cons

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Pros and cons

Pros: Energy efficiency ?

• Listen for the mule. Intermittent connectivity.

Cons: Increased latency.

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3-tier architecture

Wired APs. Mules. Sensors.

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Considerations

APs have no limitations. Mules:

Storage, mobility, ability to communicate with sensors and APs.

Unpredictable movement patterns. Can talk to other mules.

• Benefits?

Robustness. Reliability.

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More considerations…

No routing overhead. Mules can transport data for multiple

applications. High latency.

Delay bounds? Mobility limitations.

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Main results

Buffer requirements at sensors inversely proportional to ratio of number of mules to grid size.

Buffer requirement at mule inversely proportional to ratio of number of mules to grid size and ratio of APs to grid size.

Relationship between buffer capacity, number of mules, and reliability.