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

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

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

 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

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

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

 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

 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

 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

 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

 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

 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

 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

 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

 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

 A’s route to E1 is longer than the agent’s
 Agent does not know to route to E2

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

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

 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

 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

 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

 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

 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 node’s routing table to efficient path

 Query flooding: Et = Q * N
 Event flooding: Et = E * N
 The algorithm has addition energy for path setup and query routing
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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

 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

 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

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

 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

 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

 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

 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

 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

 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

 Future Work
 Conclusion
 References

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