1. Dissemination protocols for large sensor networks Fan Ye, Haiyun Luo, Songwu Lu and Lixia Zhang Department of Computer Science UCLA Chien Kang Wu

2. Outline Introduction Current Dissemination strategy  Reverse path forwarding  Cost field based forwarding  Routing with virtual hierarchy  Additional approaches Future work

3. Introduction(1/2) Goal : Communicate organized data using data-centric paradigm  Scalable and distributed solution  Energy efficiency  Robustness Definition  Data source : sensor node which generate data  Sink : user collect data from sensor networks

4. Introduction(2/2) Assumption  Application semantics  Location awareness  Stationary nodes  Dense deployment Solution roadmap  Query-Reply process  Install dissemination states in intermediate nodes

5. Outline Introduction Current Dissemination strategy  Reverse path forwarding  Cost field based forwarding  Routing with virtual hierarchy  Additional approaches Future work

6. Reverse path forwarding Including:  Declarative routing protocol (DRP)  Directed Diffusion (DD) Sink sends out a query using flooding strategy through the network Data flow in the reverse direction of query Set up forwarding state in the form of vector Sensor broadcast data to neighbors, and neighbor recursively forward it.

7. Declarative routing protocol Establish a routing tree for every sink Use factors to select which node of the next Neighborhood the query should be sent Using cashed data to improve efficiency Reachability,remaining energy directionality, link quality

8. Declarative routing protocol Base on :  Location awareness  Node with small buffer Faced challenges:  Buffer management  Route inconsistency  Data aggregation strategy

9. Directed diffusion Similar to DRP, but focus more on scaling to multiple sinks  Node do not keep per sink state Each node has a cache to detect loop and drop redundant packets sink can use reinforcement mechanism to help neighbors select the best quality path To handle network dynamics, source need to maintain alternative paths

10. Directed diffusion Base on:  Location awareness  Node with small buffer Faced challenges:  Maintain alternative paths to handle nodes failure  Quick Path repairing methods  In-network processing

11. Problems in reversed path Overhead has to be paid to maintain vector states Sink mobility problems

12. Outline Introduction Current Dissemination strategy  Reverse path forwarding  Cost field based forwarding  Routing with virtual hierarchy  Additional approaches Future work

13. Cost-field based forwarding Each node store forwarding state named scalar denoting the node’s distance to sink Scalar of all nodes forms a cost-field Cost-field is per-sink based, a node keep cost for each sink Data report flow from higher cost to smaller cost Hop count, energy consumption physical distance

14. Cost-field based forwarding If query-packet(ADV) flow from node i to node j, C(i new) = C(j) + C(i, j) (set 0 at sink) set C(i) to C(i new), if C(i) > C(i new) There is no loop in cost-field Two-ways to make forwarding decision  Receiver-decided  Sender-appointed

15. Receiver-decided Data sender includes its cost in a report and broadcast to every neighbor Let receiver decides forwarding or not  Only receiver with cost less than sender may forward the report Robust data forwarding:  Typically, several neighbors with smaller cost than the sender  Need strategy to prevent data redundancy problem

16. Sender-appointed Single path forwarding, sender choose only one neighbor for each report Can not ensure robustness, need to maintain states regarding which neighbors are still alive Statistically distribute strategy,save more energy than receiver-based

17. Problems in Cost-field based Mobile sinks problems Does not scale well with numerous sinks, each sink needs a separate cost at every node

18. Outline Introduction Current Dissemination strategy  Reverse path forwarding  Cost field based forwarding  Routing with virtual hierarchy  Additional approaches Future work

19. Virtual hierarchy Sensor nodes have different functionality, network hierarchy is formed during data dissemination Sink mobility support Including:  Two-tier data dissemination  Low-energy adaptive clustering

20. Two-tier data dissemination Data source construct virtual grid infrastructure for query and data forwarding, each grid is an α*α square Data Source propagate data announcement to reach all other crossing of grid, called dissemination point Sink forward query to its upstream dissemination node  dissemination node further forward the query toward data source

21. Grid infrastructure

22. Two-tier query-reply process

23. Low-energy adaptive clustering Designed towards energy-optimal network organization Assumption :  Sensor can adjust power consumption, adapt network topology  Sensor’s max power is able to communicate with the sink Data message from sensor first transmitted to local cluster head and then forward to base station System using distributed algorithm to elect a number of cluster heads

24. Virtual hierarchy summary Advantage:  Structure suitable for Data aggregation  Mobile sink is feasible Faced challenges:  Structure maintain problem  Cluster head election problem

25. Outline Introduction Current Dissemination strategy  Reverse path forwarding  Cost field based forwarding  Routing with virtual hierarchy  Additional approaches Future work

26. Additional approaches Real-time delivery  Define: distance  d,packet lifetime  t desired velocity v = d / t  Required velocity is updated at each hop  A node can use multiple FIFO queue with different priority to handle packets  Choose neighbor with high velocity to forward the report

27. Outline Introduction Current Dissemination strategy  Reverse path forwarding  Cost field based forwarding  Routing with virtual hierarchy  Additional approaches Future work

28. Network topology:  Location awareness  Hierarchy structure maintain  Path maintain issue  Alternative paths reinforcement General issues:  Buffer management and data aggregation  Data redundancy problem