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

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

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

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

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!!

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

Geographic Forwarding A B C D F C’s radio range E G 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!

Desirable Properties of Location 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

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 region using 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)

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

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

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

Location update Overhead

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

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

ScaLAble Location Management (SLALoM) Define a hierarchy of regions : Order(3), Order(2), Order(1) Each Order(2) region consists of K 2 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 far HRs find way to near ones for exact location of destination

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

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

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

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 Location database to store ? A (A_loc) B (B_loc) …

Location Query V W 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 Query

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

... 1 GLS Updates 9 23, 2 11, location table content location update 2 Invariant (for all levels): For node n in a square, n’s successor in each sibling square “knows” about n.

, 2 11, location table content query from 23 for 1 GLS Query

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

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 Level 0Level ILevel IILevel III

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 Update Broadcast

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 U V QueryResponseData

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

Scalability with Mobility (High load) HGRID performs best, with throughput more than 90% Surprisingly, SLALoM K2 performs better than others Explained by lower location discovery delay and packet buffer SLURP performs worst ThroughputDiscovery Delay

Scalability with Mobility 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 Data DelayControl Overhead

Scalability with Network Size Tradeoff between signaling overhead and throughput/delay HGRID performs best overall Packets deliveredData Delay

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

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