Rumor Routing Algorithm For sensor Networks

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Rumor Routing Algorithm For sensor Networks

• David Braginsky,
• Computer Science Department, UCLA
• Presented By Yaohua Zhu
• CS691 Spring 2003

2
Outline

• Introduction
• Flooding Event
• Flooding Query
• Rumor Routing Algorithm
• Agents
• Query
• Simulation Results
• Related Work
• Future Work

3
Wireless Sensor Network

• Wireless communication capability
• Emerging low power
• Small form-factor processors
• Sensor
• Allow for larger-scale, extremely dense network

4
Design Consideration

• Each node does not posses significant
  computational power
• Sensing highly distributed
• Algorithms highly distributed, because local
  communication with stringent power requirements
• Self-configuring, Highly scalable, redundant
• Robust with shifting topologies
• Gather data from different parts of network
• Without taxing its limited bandwidth and power
• Reduce failure rates

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Basic Idea

• Routing queries to nodes that have observed a
  particular event
• Retrieve data on the event

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Event and Query

• Event
• An abstraction, identifying anything from sensor
  readings
• Assumed be localized phenomenon
• Occurring in a fixed region of space
• Query
• Be request for information
• Orders to collect more data
• Query arrives destination, begin to flow back to
  querys originator

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Flooding Event and Flooding Query

• Flood event
• For few events and many queries
• Set up gradients towards it
• Flood query
• For less queries per event
• Less data generated by each event

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Query Flooding

• Assume no collisions
• For N nodes we must perform N transmissions per
  queries
• For Q queries the transmissions total is
• N Q
• Energy used is independent of the number of
  events tracked by network
• Useful when the number of events is very high,
  compared to the number of queries

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Event Flooding

• When node witnesses an event, it can flood the
  network. All other nodes form gradients toward
  the event
• N is transmissions per event
• E is the number of events
• The total energy expended by event flooding is
• E N
• It is independent of the number of queries
• Efficient when the number of events is low,
  compared to the number of queries

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Query Flooding and Event Flooding
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Idea of Rumor Routing Algorithm

• Fill the region between query flooding and event
  flooding
• Only useful if the number of queries compared to
  the number of events is between the two
  intersection points
• An application of this ratio can use a hybrid of
  rumor routing and flooding to best utilize
  available power

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Algorithm Overview

• Assume network consists of densely distributed
  wireless sensor nodes with relatively short
  symmetric radio range
• Nodes records events and able to route queries
• Each node maintains a list of neighbors, events
  table, forwarding information to all the events
  it knows.
• Neighbors list created and maintained by actively
  broadcasting a request
• Since the simulation were static topology, each
  node broadcast its id at the beginning

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Contd(node)

• When node witnesses an event, it adds it to its
  event table, with a distance of zero to the event
• Node also has a random chance to generating an
  agent
• The probability of generating an agent is an
  algorithm parameter

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Contd(Agent)

• An agent is a long-lived packet
• Agent travels the network
• Propagating information about local events to
  distant nodes
• Contains events table
• Synchronizes with every node it visits
• Travels network some number of hops, then dies

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Contd(query)

• Any nodes may generate a query and routed to a
  particular event
• If node has a route to the event, it will
  transmit the query
• It it does not, it will forward the query in a
  random direction
• Continues until the query TTL expired, or query
  reaches a node that has observed the target event
• If the node originated the query did not reach a
  destination, it can always flood the query

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Agents

• Each agent informs nodes it encounters of any
  events along its route
• Carries a list of events
• Along with the number of hops to that event
• When it arrives node A from neighbor B, it
  synchronize its list with the nodes list

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Contd(Agents)

• As route to E1 is longer than the agents
• Agent does not know to route to E2

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Contd(Agents)

• After the table synchronization completes, the
  event table will contain the best routs to the
  event

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Contd(Agents)

• Straightening algorithm used to determine the
  agents next hop
• Agent maintains a list of recently seen nodes
• When arrives a node, it adds all nodes neighbors
  to the list
• When picking next hop, it first try nodes not in
  the list
• It allows agent to create fairly straight paths

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Contd(Agents)

• Policy to generate agent
• Node that witnessed an event generate an agent
• The number of agents depends on the number of
  event, event size, and the node density
• Event table have expiration timestamp

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Queries

• A query can be generated at any time by any nodes
  and target to an event
• If a node has a route toward target event, it
  forwards the query along the route
• If it does not, forward to random neighbor and
  assume the query has not exceeded its TTL
• Query employs same mechanism as the agent, keep
  list of recently seen nodes
• Some queries may not reach their destination,
  application must detect the failure, flooding
  query again or increase the queries TTL

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Rumor Routing

• Create paths leading to each event
• Event flooding creates a network-wide gradient
  field
• Query sent random walk until find the event path
• No flooding event across the network
• Query discovers event path, then route directly
  to the event
• If path cannot be found, application
  re-submitting the query, flooding it

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Set up Path
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Contd

• Agent A1 create path state leading to E1
• Agent A2 create path state leading to E2
• When A2 crosses the path created by A1, it create
  aggregate path state leading to E1 and E2

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Contd

• Agent optimize the path if they find shorter ones
• When agent find a node route to event is more
  costly than its own, it will update the nodes
  routing table to efficient path

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Comparison Event Routing and Query Routing

• Query flooding Et Q N
• Event flooding Et E N
• The algorithm has addition energy for path setup
  and query routing
• Et Es Q (Eq N (1000 Qf) / 1000 )
• N (1000 Qf) additional send
• Qf is the number of delivered queries
• Eq is the energy spent routing queries

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Simulation Results
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Simulation Results
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Algorithm Stability

• Because this algorithm relies on random
  decision(agent and queries)
• Performance not vary significantly over several
  runs is important
• To test same set of parameters run 50
  simulations, Average Te 118, 99 of the values
  for Te will be found between 104 and 131
• Algorithm is stable for particular configuration

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Fault Tolerance

• After the routes were established some of the
  nodes were disabled
• Probability of delivery degraded slowly for 0-20
  node failure
• Over 20 node failure, the performance degraded
  more severely

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Related Work

• GRAdient Broadcast
• Gossip Routing
• Ant Algorithm
• Directed Diffusion and Geo-Routing
• Data-Centric Storage in Sensornets

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GRAdient Broadcast(GRAB)

• Build a cost field toward a particular node, then
  reliably routing queries across a limited size
  mesh toward that node
• With overhead of network flood
• Queries route along short paths
• Delivered cheaply and reliably
• Not designed specifically to support the network
  process, influenced the work of event-centric
  routing state in network

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Gossip Routing

• Nodes flood by sending message to some of
  neighbors
• By the redundancy in the link, most nodes receive
  the flooded packed
• Used to deliver query or flood events
• Less overhead than conventional flooding
• Not be designed specifically for energy
  constrained contexts

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Ant Algorithm

• Agent traverse the network encoding the quality
  of the path they have traveled, and leave it the
  encoded path as state in the nodes
• At every node, an agent picks its next hop
  probabilistically, biased toward already know
  good paths
• Faster and more through exploration good regions
• Very effective in dealing with failure, because
  always some amount if exploration
• But due to large number of nodes, the ant agents
  required to achieve good results

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Directed Diffusion and Geo-Routing

• Provide a mechanism for doing a limited flood of
  a query toward the event
• Set reverse gradients to send data back along the
  best route
• Results in high quality paths
• But requires an initial flood of the query for
  exploration

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Data-Centric Storage in Sensornets

• Allow access to named data by hashing the name to
  a geographic region in the network
• Used to efficiently deliver queries to named
  events by storing the location of the events
• Relies on a global coordinate system

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Rumor Routing Algorithm

• Presents good alternative to event and query
  flooding
• Performance depend on event distribution,
  guarantee better results than event flooding
• Successful under all simulated node and event
  densities
• Difficult to predict the max query to event ratio
• No reliable trend has been found in the max query
  to event ratio with increasing node and event
  densities

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Summary Slide

• Future Work
• Conclusion
• References

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Future Work

• Wider range of simulation scenarios
• Network dynamics
• Consider collisions
• Asynchronous event
• Non localized event
• Non random query pattern
• Algorithm design alternatives
• Non random next hop selection
• Use of constrained flooding
• Parameter setting exploration