ppt

1Penn State, 4-29-08

Wireless Mesh with Mobility3Q Update

Thomas F. La Porta ([email protected]) & Guohong Cao ([email protected])

The Pennsylvania State University

Students: Hosam Rowaihy, Mike Lin, Tim Bolbrock, Qinghua Li

Wireless Mesh with Mobility

1. Executive Summary

2. Schedule

3. Centralized

4. Distributed

5. Status


Wireless Mesh with Mobility: Executive Summary

Network example 1: Large retail back-room

– central server acts as database

– mobile readers (automated and with personnel) keep data fresh and respond real-time

– generalize to large warehouses with wifi

Network example 2: Make-shift large warehouse

– no central server; use distributed cache

– multi-hop communication optimized for inventory system

Problems

– scheduling robot movement to meet delay constraints

– locating inventory with no central controller

Benefits to Vendors

– faster customer response: inventory aggressively updated

– less expensive infrastructure: mobile readers cover large areas


Schedule

Milestones:Q1: Querying algs for multiple robots defined, centralized cache implementedQ2: Mobile mesh implemented, CacheData implementedQ3: Querying algs implemented, sim results, CachePath implemented, caching policiesQ4: Measurements

Cost Share:• CISCO: consulting• Vocollect: equipment and consulting• Accipiter: engineering and consulting

Platform• Custom (small) robot• Gumstick Linux processors• RFID readers from Vocollect


Centralized Architecture

Query algorithms

– Naïve

– Return to center

– Area of Responsibility

– Flexible Grid

reader

Crate

Crate Crate

Crate

Crate

Crate

Crate

CrateCrate

Crate

Crate

Crate

Crate

Crate

Crate

Crate

reader

reader

reader

CentralServer/Cache

1. Receives queries2. Implements cache3. Broadcasts queries

Readers1. Receive queries2. Determine who serves3. Move to read data4. Upload results to cache


Area of Responsibility

Areas of responsibility

– Change dynamically according to queries served (weighted moving average)

– If no readers covers a crate, closest serves it

Resting circle

– Mobile reader can reach any location within area of responsibility in < tseconds

– other basic scheme; return to center

Heavy load,Small area

Resting circle


Area of Responsibility

Scenario: query arrives for tag located outside all areas of responsibility

a) Mobile RFID reader 1 calculates that it should move

b) Mobile RFID reader 1 moves

c) New AR is calculated

1

1 1


Rest point

Readers must reside on or within circumference of rest circle

– Center will reposition based on movement


Flexible Grid

Area of responsibility center remains constant

– Circumference changes based on movement

– Leads to stable data distribution


Centralized Architecture Evaluation

• Consider both skewed and uniform queries

• Skewed queries are distributed using a burstiness algorithm to model temporal locality of queries and the Zipf distribution to model popular items

• 1,000,000 sq. ft. warehouse with 10,000 uniformly distributed RFID tags

• 1000 queries to 4 and 16 mobile readers

• Skewed and uniform results are similar


Centralized Algorithm: Delay Results

Naïve solution is the best

16 robots

4 robots


Centralized Algorithm: Distance Results

Naïve results are the best


Distributed Architecture

Multi-hop network: may become disconnected due to mobility

– Algorithm updates required

–“Connected readers” run algorithm; search for others while moving

– Results returned to query point (similar process)

Implications

– Pre-positioning may help maintain connectivity

– Limiting movement may help maintain connectivity

reader

Crate

Crate Crate

Crate

Crate

Crate

Crate

CrateCrate

Crate

Crate

Crate

Crate

Crate

Crate

Crate

reader

reader

readerReaders1. Receive queries2. Locate “server”3. Return answer4. Local cache

Crate

Crate Crate

Crate

Crate

Crate

Crate

Cratereader

reader


Distributed Architecture Example and Analysis

Comm

Q

R1

R2

R3

Gets Query

Moves

Comm

Moves

d1

d2

lg

,

A

typenetdDefinitions:

Alg – RP (rest point) or Naïve (N)

Net – multi-hop (MH) or centralized (C)

Type – non-reader (nr), or reader (r)

Total delay, T:

For centralized:

For fully connected network:

lg,

lg,

lg Arnet

nr

Anrnet

Anet ddT

0lg, Anrcd

0lg, Anrnetd

lg,ArcdT

lg,

ArnetdT


Distributed Architecture vs. Centralized

More realistic case: network has some partitions

Naïve algorithm

AR algorithms

Nrmh

nr

Nnrmh

Nmh ddT ,,

Nrc

Nrmh dd ,, (we may or may not pick the optimal reader)

ARrmh

nr

ARnrmh

ARmh ddT ,,

ARrc

ARrmh dd ,, (we may or may not pick the optimal reader)

Nrmh

ARrmh dd ,, (based on empirical data)

nr

Nnrmh

nr

ARnrmh dd ,,

(based on empirical data)

ARmh

Nmh TT


Multihop Evaluation

• 1,000,000 sq. ft. warehouse with 10,000 RFID tags

• Skewed and uniform queries (results are similar)

• Queries now originate from query sources on the edge of the warehouse

• Wireless transmission range of 300 ft.


Multi-hop Results

Flexible grid performs the best, naive is one of the worst


Multi-hop Results

Flexible grid outperforms by a significant margin


Analysis

Flexible grid outperforms the other algorithms by a wide margin

Performance can be characterized by looking at the secondary distance travelled

– Secondary distance is the total distance travelled to respond to a query by readers that were not the first reader to receive the query:

Comm

Q

R1

R2

R3

Gets Query

Moves

Comm

Moves

d1

d2


Secondary Distance

Flexible grid has a very small secondary distance compared to other algorithms


Analysis

The forced structure of the flexible grid algorithm reduces the secondary distance

df - distance saved by forwarding query

dfFlex Grid is much higher relative to the overall distance travelled

Naive Flex Grid

dt 138745.5 16099.7

ds 118432.8 10072.8

ds/dt 85% 62%

df 391874.6 113249.8


Analysis

Although all algorithms begin on a grid, only the flexible grid algorithm retains the structure, which increases the efficiency of forwarding queries and reduces the average distance the reader must travel


Discussion

Centralized scheme will always be the best

– Always choose optimal reader

– No extra movement

– BUT: not always feasible

Flexible Grid scheme is best in a disconnected network

– Network is more “connected”


Caching

Cache Path: keep record of how to reach data

– This is done in all mobile robots

– Used to determine nearest robot

– Results included in mobile reader results presented in previous slides

Cache Data: keep copies of data that have been gathered or forwarded

– Will greatly reduce query time

– Improvement depends on:• Cache hit/miss ratio• Cache time-out

– Important factors• How much information is learned• Shortest path is not always the best for learning• Moving more robots may be better


Cache Data Policy

Basic: Time-to-live

– Data is considered useful if it has been refreshed within time T

Advanced: Item-specific time-to-live

– Hot items have a lower T• Inventory changes more frequently

– Current: set by manager


Centralized: Basic Simulation Results (Naïve Mobility)

Query latency reduced from non-caching case by up to 25% with 3600 second TTL

Random queries, 4 robots Random queries, 16 robots


Centralized: Advanced Policy Simulation Results (Naïve mobility)

Query latency reduced from non-caching case by up to 35% when hotspots present

“Hot items” have lower TTL (POP_TTL on x-axis), but are queried more, resulting in updated data and cache hits

“Cold items” have long TTL, so also experience cache hits

Skewed queries, “Cold” item TTL = 3600 second


Multihop Caching Policies

Path planning policies

– Symmetric – robots retrieve and return data on the same path

– Asymmetric – robots use different paths to read and return data to learn more information

Caching policies

– No exchange – robots forwarding responses only learn a single data item

– Exchange – robots exchange full caches when communicating to learn more information


Multihop Cache Simulation Results

• Flexible Grid Algorithm Used

• No concurrent queries (worst case)

– Only a single robots moves at any instance

• Reduce query latency by up to 25%

4 robots, random queries 16 robots, random queries



• Flexible Grid Algorithm Used

• Skewed queries

• No concurrent movement (worst case)

• Reduce query latency by up to 35%

16 robots, skewed queries



• Concurrent queries allowed

• Multiple robots move at once

– More information being learned per unit time

– Most realistic case

• Reduce query latency by up to 70% over case with only a single query at a time

16 robots, skewed queries


Comparison with Goals

Scale to networks with 10’s of robots and 1,000’s of nodes

– Simulations cover up to 16 robots and 10,000 tags

–Show response times in 15 second range

Extend RFID network lifetimes over active tag hierarchy by factor of 2

– No active tags used, so RFID components have no lifetime constraints

Reduce search times by factor of 2 over pure RFID solution

– Pure RFID equivalent to single robot case (person = reader)

– We show greater than factor of 2 reduction when we go from 4 to 16 robots without caching

– We show an addition factor of 3 reduction with cache data and concurrent queries


Status

Centralized Architecture

– Architecture defined

– Querying algorithms in place and simulated

– Integration with robots and centralized cache complete

Distributed Architecture

– Architecture defined

– Mesh formation algorithms designed

– Simulation complete

Caching

– Cache path in system

– Cache data analyzed and implemented in simulator

– Simulation for centralized and distributed caching complete

– Porting to robots underway

Robots

– Design and implementation complete

– RFID equipment from Vocollect integrated

– Integration with Querying and Caching ongoing

– Preliminary testing underway – see next chart!


Testbed

RFID tags

Robots

Business

ppt