Geo Location Service

CS218 Fall 2008

Yinzhe YuYinzhe Yu, et al : , et al : Enhancing Location Service Enhancing Location Service Scalability With HIGH-GRADEScalability With HIGH-GRADE , MASS 2004, Oct 2004

What is a Location Service?

A pre-requisite of Position-Based Routing is a Location Service Allows a source node to obtain the location of a destination before data

traffic follows

Location Service is a cooperative service Each node in the MANET stores the current locations of some other

nodes in the network, serving as their location server A node updates its location servers as it moves around A node trying to communicate with another node queries that node’s

location servers to get its current location

Basic Problem

For a node B wishing to communicate with another node A, how to discover current location of A?

How does A choose a set of nodes as its location servers, and how to update these servers as A moves around? (Location Server Organization)

What exact information about A’s location are stored on its location servers? (Location Information Granularity)

How does B find appropriate server(s) of A to obtain its location?

Proposed : HIGH-GRADE

HIerarchical Geographical Hash with multi-GRained Address DElegation A better scheme that incorporates good design

choices, and provides better scalability

Possible application: geo-routing in the urban vehicular grid

HIGH-GRADE Location Update

A

HIGH-GRADE divides a network area recursively into levels of “squares”.

Each node chooses location servers around some hash points, one in each level of square.

Each location server stores the information of “which next level square does A resides in ?”.

Location Info at Hash Points

E

F D

C

G

H

When there is no node at the exact location of the hash point, the update packet travels around the “perimeter” of the hash point and the location information is stored on all nodes on the perimeter.

HIGH-GRADE Location Query

A

B

• A querying node B uses the same hash functions to try potential location servers

• Once a location server is found, it follows a series of servers at smaller and smaller area to pin-point A’s location

• The total distance traveled by a location query message is proportional to the side length of A and B’s least common square

Analysis: Model assumption

A common set of assumptions to analyze costs of maintaining and using a location service A network with N nodes in an area of A

constant node density . Average progress towards the destination point in

each packet forwarding step is z. Simplified random way-point mobility model with no

pause time. Average node speed is v.

AN=γ

Metrics Location Update Cost

Number of forwarding operations each node needs to perform in a second to handle the location update packets.

Location Query Cost Number of forwarding operations each node

needs to perform in a second to handle the location queries.

Storage Cost Number of location records a node needs to

store as a location server.

Summary of Results

HIGH-GRADE GLS DLM SLURP SLALoM

Location Update Cost

O ( v log N )

Location Query Cost (uniform traffic)

O ( log N )(localized traffic)

(uniform traffic)

O ( log N )(localized traffic)

(both) (both) (both)

Storage Cost O ( log N ) O ( log N ) O ( 1 )

( )NO ( )NO ( )3 NO ( )3 NO( )NO

( )3 2NO( )3 NO

( )3 NvO ⋅( )NvO ⋅( )3 NvO ⋅( )NvO ⋅

Observations:

1. Design of a location service involves tradeoffs among all three metrics.

2. Not all schemes exploit the benefits of a localized traffic equally well.

3. For localized traffic HIGH-GRADE achieves impressive asymptotic scalability.

Main Innovation in High Grade

In an urban environment, the frequency of queries is orders of magnitude smaller than the frequency of updates - thus, updates dominate the cost

Update cost in High Grade is O ( v log N ) because only the lowest level location server in the hierarchy is updated

In GLS the update cost is because the servers at all levels (from bottom to top) are updated.

Thus , High Grade scales to N, while GLS does not This is the main innovation of the High Grade paper

( )NvO ⋅

Simulation

Compare GLS and HIGH-GRADE Confirm analytical results

ns2 with CMU Monarch extensions N = 100 ~ 600 Node density fixed at 100/km2

Transmission range is 250 m Mobility model: random waypoint (w/o pause)

Maximum speed 10~30 m/s Load

Each node generates 15 location queries for random destination nodes during a 300 sec simulation time

Location Update Cost vs. N and v

HIGH-GRADE GLS

Location Update Cost O ( v log N ) ( )NvO ⋅