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Scalable Location Management for Large Mobile Ad hoc Networks

Scalable Location Management for Large Mobile Ad hoc Networks

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Scalable Location Management for Large Mobile Ad hoc Networks

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  1. Scalable Location Management for Large Mobile Ad hoc Networks Sumesh J. Philip

  2. Contents • Wireless Ad hoc networks • Issue of Scalability • Geographic Routing • Scalable Location Update based Routing • SLALoM - Scalable Location Management • Grid Location Service • Hierarchical Grid Location Management • Numerical study • Conclusion

  3. Wireless Ad hoc networks • Infrastructure-less networks that can be easily deployed • Each wireless host acts as an independent router for relaying packets • Network topology changes frequently and unpredictably • Key challenge lies in routing packets • Quite a lot of protocols proposed in literature (table driven/reactive/hybrid) • Dynamic source Routing (DSR) works well for small networks

  4. Issue of Scalability • Increasing density increases average node degree, decreases average path length • Routing cost less • Any reasonable scheme might work! • To test scalability, area (playground size) must increase with nodes • Average node degree constant • Will present a mobility model that consolidates the above relationship

  5. Traditional Protocols • Table driven • incur large overheads due to routing table maintenance • Delayed topology updates can cause loops • On-demand • flood the entire network with discovery packets • long latency for discovery • Path maintenance means additional state • No separation between data and control • Ultimately, data suffers!!

  6. Any contenders ? • Not many invariants to play with (IP address, local connectivity) • Nodes physically located closer likely to be connected by a small number of radio hops • Geolocation techniques can be used to identify a node’s physical position • Geographic forwarding • Packet header contains the destination’s location • Intermediate nodes switch packets based on location

  7. C’s radio range Geographic Forwarding A D F C G B E • A addresses a packet to G’s latitude, longitude • C only needs to know its immediate neighbors to forward packets towards G. • Geographic forwarding needs location management!

  8. Desirable Properties ofLocation Management • Spread load evenly over all nodes • Degrade gracefully as nodes fail • Queries for nearby nodes stay local • Per-node storage and communication costs grow slowly as the network size grows

  9. Scalable Location based Routing Protocol (SLURP) • Hybrid Protocol that has a deterministic manner of discovering the destination • Topography divided into square grids • Each node (ID) selects a home regionusing f(ID),and periodically registers with the HR • Nodes that wish to communicate with a node query its HR using f--1(ID) • Use geographic forwarding to send data, once location is known (e.g. MFR)

  10. Example ID = 22; RT= 12; HR=22%12 = 10; - Home region [12] - Update/Query [10] - Data - Location Database f(ID) - ID Mod(RT) DST = 22; RT= 12; HR=22%12 = 10;

  11. Cost of Location Management • Location Registration • Periodic • Triggered • Location Maintenance • Operations for database consistency • Location Discovery • Query/response • Data Transfer

  12. Mobility Model • Each node moves independently and randomly • Direction , Velocity [v-c, v+c] at t • New direction and velocity at destination • Node degree = • To keep degree constant, A must grow linearly with N

  13. Location update Overhead

  14. Location update Overhead

  15. Home Region Maintenance • On region crossing • Inform previous region of departure • Inform new region of arrival • Update from any node in new region

  16. Total Overhead • Cost of Locating • Send a Location query to Home region • Total Overhead = Sum of all overheads for all nodes

  17. ScaLAble Location Management (SLALoM) • Define a hierarchy of regions : Order(3), Order(2), Order(1) • Each Order(2) region consists of K2 Order(1) regions • Each node assigned a HR in an Order(2) region • To reduce location update overhead, define far and near HRs; near regions updated frequently • Nodes that wish to communicate with another node query its HR in current Order(2) grid • Queries from farHRs find way to near ones for exact location of destination

  18. Order-1 Home region Order-2, K = 4 Grid Ordering in SLALoM • Terrain divided into Order-1 regions • K2 Order-1 regions combined to form Order-2 regions • Function f maps ID to home region in Order-2 region

  19. Near Home region Far Home region Near and Far Home Regions • 9 home regions around U’s current O-2 are near • Rest are far home regions

  20. Location Update • If movement within O-2, update near home regions • Otherwise update all home regions via multicast • Near home regions know exact location of U • Far home regions know approximate location (O-2) Movement Update

  21. A (A_loc) B (B_loc) … Location database to store ? Location Maintenance • On entry into a grid, a node broadcasts its presence • A server node replies with location information that the newly arrived node has to store • Use of timers to avoid a broadcast storm Mobile Node Movement

  22. Location Query • If U and V in same O-1, V knows U’s location • Otherwise, send a query to U’s closest home region • If far home region, route to nearest “near” home region V Query W

  23. sibling level-0 squares s n s s s s sibling level-1 squares s s sibling level-2 squares s s Grid Location Service (GLS) • s is n’s successorin that square. • (Successor is the node with “least ID greater than” n )

  24. location table content location update GLS Updates ... Invariant (for all levels): For node nin a square, n’s successor in each sibling square “knows” about n. 9 ... 1 11 1 1 2 ... 3 11, 2 9 6 ... 23 29 2 16 ... 23, 2 7 6 ... ... ... 17 5 ... 26 25 ... ... ... 8 21 4 ... 19

  25. GLS Query ... 9 ... 1 11 1 1 2 ... 3 11, 2 9 6 ... 23 29 2 16 ... 23, 2 7 6 ... ... ... 17 5 ... 26 25 location table content ... ... ... 8 21 4 query from 23 for 1 ... 19

  26. Using Multilevel Hierarchies • Random node movements and communication assumptions • Not realistic for all applications for large networks • Localized node movement; network traversals rare • Update cost proportional to mobility • Frequent data connections may occur in a locality • Multiple server regions redundant • Local queries stay local • Ideal for a hierarchical set up of node locations • Unfortunately, formation and maintenance of hierarchy is cumbersome

  27. Level III Level II Level I Level 0 Hierarchical Grid Ordering(HGRID) • Grid hierarchy built from unit grids recursively • At each level, one of the four lower level leaders selected as the leader for the next level • Grid ordering arbitrary; alternate orderings possible

  28. Update Location Update • Nodes update servers as they cross grid boundaries • Number of updates, and distance traversed by the updates depends upon boundary hierarchy • Localized movement results in low overhead Broadcast

  29. Query Response Data Location Discovery & Data Transfer • Source sends query to its leader • Query visits leaders until approximate location of destination is found; sends response • Data forwarded to more accurate locations until it reaches the destination V U

  30. Performance Study • Glomosim: packet level simulator • Simulator setup Application CBR Transport UDP Network IP Location Management Random Waypoint LL/MAC IEEE802.11 No Noise Radio Geographic Routing Free Space Mobility PHY

  31. Scalability with Mobility (High load) Throughput Discovery Delay • HGRID performs best, with throughput more than 90% • Surprisingly, SLALoMK2 performs better than others • Explained by lower location discovery delay and packet buffer • SLURP performs worst

  32. Scalability with Mobility Data Delay Control Overhead • HGRID performs best overall due to low signaling overhead • SLALoM performs worst due to congestion caused by network wide updates • Interestingly, overhead (bytes) more for HGRID than SLURP

  33. Scalability with Network Size Packets delivered Data Delay • Tradeoff between signaling overhead and throughput/delay • HGRID performs best overall

  34. Scalability with Network Size Database Size Control Overhead • Overhead (bytes) highest for SLALoM; maintenance of large databases increases overall overhead of HGRID • Storage cost grows slightly with network size for HGRID

  35. Summary • Issue of scalability in mobile ad hoc routing • Topology updates congest the network • Discovery, maintenance cause unnecessary flood • Geographic routing is a potential candidate • Localized and guaranteed • Need scalable location management schemes • Grid based protocols (Flat vs. Hierarchical) • SLURP, SLALoM, GLS, HGRID • Relative scalability of LM protocols dependant on location update, maintenance and discovery • Performance studies show HGRID scales well with network size, mobility