GPSR Greedy Perimeter Stateless Routing for Wireless Networks

GPSR: Greedy Perimeter Stateless Routing for Wireless Networks B. Karp, H. T. Kung Borrowed some Richard Yang‘s slides 1

Motivation r A sensor net consists of hundreds or thousands of nodes m m Scalability is the issue Existing ad hoc net protocols, e. g. , DSR, AODV, ZRP, require nodes to cache e 2 e route information Dynamic topology changes Mobility r Reduce caching overhead m Hierarchical routing is usually based on well defined, rarely changing administrative boundaries m Geographic routing • Use location for routing 2

Scalability metrics r Routing protocol msg cost m How many control packets sent? r Per node state m How much storage per node is required? r E 2 E packet delivery success rate 3

Assumptions r Every node knows its location m Positioning devices like GPS m Localization r A source can get the location of the destination r 802. 11 MAC r Link bidirectionality 4

Geographic Routing: Greedy Routing Closest to D S A D - Find neighbors who are closer to the destination - Forward the packet to the neighbor closest to the destination 5

Benefits of GF r A node only needs to remember the location info of one-hop neighbors r Routing decisions can be dynamically made 6

Greedy Forwarding does NOT always work GF fails r If the network is dense enough that each interior node has a neighbor in every 2 /3 angular sector, GF will always succeed 7

Dealing with Void: Right-Hand Rule r Apply the right-hand rule to traverse the edges of a void m Pick the next anticlockwise edge m Traditionally used to get out of a maze 8

Right-Hand Rule Does Not Work with Cross Edges z u D l w x originates a packet to u Right-hand rule results in the tour x-u-z-w-u-x l x 9

Remove Crossing Edge z u D l. Make w the graph planar l. Remove x (w, z) from the graph Right-hand rule results in the tour x-u-z-v-x l 10

Make a Graph Planar q Convert a connectivity graph to planar non- crossing graph by removing “bad” edges m m Ensure the original graph will not be disconnected Two types of planar graphs: • • Relative Neighborhood Graph (RNG) Gabriel Graph (GG) 11

Relative Neighborhood Graph r Connection uv can exist if w u, v, d(u, v) < max[d(u, w), d(v, w)] not empty remove uv 12

Gabriel Graph r An edge (u, v) exists between vertices u and v if no other vertex w is present within the circle whose diameter is uv. w u, v, d 2(u, v) < [d 2(u, w) + d 2(v, w)] Not empty remove uv 13

Properties of GG and RNG r RNG is a sub-graph of RNG GG m Because edges RNG removes more GG r If the original graph is connected, RNG is also connected 14

Examples Full graph GG subset RNG subset • 200 nodes • randomly placed on a 2000 x 2000 meter region • radio range of 250 m • Bonus: remove redundant, competing path less collision 15

GPSR greedy fails Greedy Forwarding greedy works Perimeter Forwarding have left local maxima greedy fails 16

Implementation Issues r Graph planarization m RNG & GG planarization depend on having the current location info of a node’s neighbors m Mobility may cause problems m Re-planarize when a node enters or leaves the radio range • What if a node only moves in the radio range? • To avoid this problem, the graph should be re-planarized for every beacon msg m Also, assumes a circular radio transmission model m In general, it could be harder & more expensive than it sounds 17

Performance evaluation r Simulation in ns-2 r Baseline: DSR (Dynamic Source Routing) r Random waypoint model m A node chooses a destination uniformly at random m Choose velocity uniformly at random in the configurable range – simulated max velocity 20 m/s m A node pauses after arriving at a waypoint – 300, 600 & 900 pause times 18

r 50, 112 & 200 nodes m 22 sending nodes & 30 flows m About 20 neighbors for each node – very dense m CBR (2 Kbps) r Nominal radio range: 250 m (802. 11 Wave. Lan radio) r Each simulation takes 900 seconds r Take an average of the six different randomly generated motion patterns 19

Packet Delivery Success Rate 20

Routing Protocol Overhead 21

Related Work r Geographic and Energy Aware Routing (GEAR), UCLA Tech Report, 2000 m Consider remaining energy in addition to geographic location to avoid quickly draining energy of the node closest to the destination r Geographic probabilistic routing, International workshop on wireless ad-hoc networks, 2005 m Determine the packet forwarding probability to each neighbor based on its location, residual energy, and link reliability 22

r Beacon vector routing, NSDI 2005 m Beacons know their locations m Forward a packet towards the beacon r A Scalable Location Service for Geographic Ad Hoc Routing, Mobi. Com ’ 00 m Distributed location service r Landmark routing m Paul F. Tsuchiya. Landmark routing: Architecture, algorithms and issues. Technical Report MTR-87 W 00174, MITRE Corporation, September 1987. m Classic work with many follow-ups 23

Questions? 24