1-1 Routing. 1-2 Data-Centric Routing r Paradigm shift from accessing data from individual nodes to...
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Transcript of 1-1 Routing. 1-2 Data-Centric Routing r Paradigm shift from accessing data from individual nodes to...
1-1
Routing
1-2
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
0
0.002
0.004
0.006
0.008
0.01
0.012
0.014
0.016
0.018
0 50 100 150 200 250 300
Ave
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e D
issi
pat
ed E
ner
gy
(Jo
ule
s/N
od
e/R
ecei
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Eve
nt)
Network Size
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.