Alpha coverage bounding the interconnection gap for vehicular internet access
Download
1 / 15

Alpha Coverage: Bounding the Interconnection Gap for Vehicular Internet Access - PowerPoint PPT Presentation


  • 45 Views
  • Uploaded on

Alpha Coverage: Bounding the Interconnection Gap for Vehicular Internet Access. Presented by: Prasun Sinha Authors: Zizhan Zheng † , Prasun Sinha † and Santosh Kumar * † The Ohio State University, * University of Memphis. Internet Access for Mobile Vehicles. Applications

loader
I am the owner, or an agent authorized to act on behalf of the owner, of the copyrighted work described.
capcha
Download Presentation

PowerPoint Slideshow about ' Alpha Coverage: Bounding the Interconnection Gap for Vehicular Internet Access' - joel-pitts


An Image/Link below is provided (as is) to download presentation

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.


- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -
Presentation Transcript
Alpha coverage bounding the interconnection gap for vehicular internet access

Alpha Coverage: Bounding the Interconnection Gap for Vehicular Internet Access

Presented by: PrasunSinha

Authors: ZizhanZheng†, PrasunSinha† and Santosh Kumar*

†The Ohio State University, * University of Memphis


Internet access for mobile vehicles
Internet Access for Mobile Vehicles

  • Applications

    • Infotainment

    • Cargo tracking

    • Burglar tracking

    • Road surface monitoring

  • Current Approaches

    • Full Coverage

      • Wireless Wide-Area Networking (WWAN)

      • Fully Covered WiFi Mesh

    • Opportunistic Service

      • Roadside WiFi


Current approach i of ii full coverage
Current Approach I (of II): Full Coverage

  • Wireless Wide-Area Networking

    • 3G Cellular Network

    • 3GPP LTE (Long Term Evolution)

    • WiMAX

      • Either long range coverage (30 miles) or high data rates (75 Mbps per 20 MHz channel)

      • 3 Mbps downlink bandwidth reported in one of the first deployments in US

  • Google WiFi for Mountain View

    • 12 square miles, 400+ APs

    • 1 Mbps upload and download rate

    • Not very practical for large scale deployment due to the prohibitive cost of deployment and management

Google Wifi Coverage Map

http://wifi.google.com/city/mv/apmap.html


Current approach ii of ii opportunistic service via in situ aps
Current Approach II (of II): Opportunistic Service via In-Situ APs

  • Prototype

    • Drive-Thru Internet (Infocom’04,05)

  • In-Situ Evaluation

    • DieselNet (Sigcomm’08, Mobicom’08)

      • Interactive WiFi connectivity (Sigcomm’08)

      • Cost-performance trade-offs of three infrastructure enhancement alternatives (Mobicom’08)

    • MobiSteer (Mobisys’07)

      • Handoff optimization for a single mobile user in the context of directional antenna and beam steering

    • Cabernet (Mobicom’08)

      • Fast connection setup (QuickWiFi) and end-to-end throughput improvement (CTP)

  • Problems

    • Opportunistic service, no guarantee

    • Unpredictable interconnection gap

Internet

AP

AP

AP

Our solution: an intermittent coverage model that provides predictable data service to mobile users at low cost


Roadmap
Roadmap

  • Alpha Coverage – An Intermittent Coverage Model

    • A general definition – intuitive but intractable

    • Two simplifications

      • Alpha Network Coverage (N-Coverage)

        • Applies when route information is unknown

          • Ex: Burglar tracking

        • Allows a factor log (n) approximation

      • Alpha Path Coverage (P-Coverage)

        • Applies when route information is given

          • Ex: bus trace in DieselNet, cached model in Mobisteer

        • Allows a more efficient factor log (n) approximation

  • Evaluation

  • Future Work


Road network model and problem statement
Road Network Model and Problem Statement

v1

v2

v3

  • Model

    • Model a road network R as an undirected graph GR with edge length at most  (by inserting artificial intersections if needed).

    • Model a movement as a path on GR (not necessarily ending at intersections).

    • Model access points as points on GR (modeling the worst case of communication range).

  • Given GR and A0µV [GR] that models a set of APs previously deployed

    • Determine if the deployment provides the desired coverage (to be defined), and if not

    • Find a minimum set of pointsA in GR so that when new APs are deployed at these locations, A0[A provides the desired coverage.

v4

v5

v9

v8

v6

v7

v1

v2

v3

s

v4

v5

v9

t

v7

v8

v6


Alpha coverage an intermittent coverage model
Alpha Coverage: an Intermittent Coverage Model

  • A deployment provides -Coverage to a road network R if any path of length  on GR touches at least one point representing an access-point.

  • Features

    • Provides a guarantee on the worst case inter-contact gap

    • Provides an estimation of the cumulative data service

  • Challenges

    • Even verifying -Coverage is NP-complete since there is a reduction from HAMILTONIAN PATH to it

    • Simplified models are needed


Alpha coverage w o route information
Alpha Coverage w/o Route Information

  • A deployment provides Network Coverage of distance  ( N-Coverage for short)if any path f(a,b) with dist(a,b) (graph distance) at least  is covered by at least one AP

    • –Coverage implies  N–Coverage, but not vice versa

s

s

v1

v1

v2

v2

v3

v3

t

t

v5

v9

v9

v4

v4

v5

-Coverage

N -Coverage

 = 5

v7

v8

v6

v7

v8

v6


Alpha coverage w o route information cont
Alpha Coverage w/o Route Information (Cont.)

 = 2

v1

v2

v3

  • Polynomial time verifiable

  • The optimization problem ( N-Cover) is NP-hard

    • Reduction from VERTEX COVER restricted to triangle-free, 3-connected, cubic planar graphs

  • O(log |V|) approximation

    • Assumption: New APs are deployed only at the vertices of GR (real or artificial road intersections)

      • Introducing a factor of 2

    • Reduce  N-Cover to node version low diameter graph decomposition

      • GVY algorithm

    • High computation time complexity for large networks

v4

v5

v9

v6

v7

v8

v1

v3

v4

v6

v8


Alpha coverage with route information
Alpha Coverage with Route Information

  • Motivation: use route information to design a more efficient algorithm

  • Assumption: a set of paths F is given where |F| = O(p(|V|))

    • Ex 1) a set of shortest paths obtained from a road network database

    • Ex 2) a set of most frequently traveled paths learned from historical traffic data

    • Decompose each given path into -paths

  • A deployment provides Path Coverage of distance  ( P-Coverage for short) if any -path in F is covered by at least one AP.

  • Polynomial time verifiable, the optimization problem is still NP-hard

  • O(log |V|) approximation: reduce P-Cover to Minimum Set Cover


Simulation setting
Simulation Setting

  • Road network

    • A 4km x 4km region around the center of Franklin County, OH

    • About 1000 intersections, 1300 road segments

    • Obtained from 2007 Tiger/Line Shapefiles + Mercator projection

  • Moving scenarios

    • Restricted random way point: each movement follows a shortest path and has length at least 

    • 5 mobile nodes, moving 1 hour each, 10 scenarios

    • Various speed limits

    • Ns-2 simulation

      • The transmission range of each AP is 100m


Simulation setting cont
Simulation Setting (Cont.)

  • Deployment methods

    • P–Coverage

    • Rand-1: a set of randomly selected vertices of GR

    • Rand-2: a set of points on randomly selected edges of GR

    • Rand-3: the region is divided into 50m x 50m cells; APs are deployed at the centers of a set of randomly selected cells.

An instance of P -Cover,  = 3000 m


Simulation results
Simulation Results

Standard deviation (sec)

CDF

Inter-contact gap (sec)

= 3000m

 (m)

  • 21 APs are used

  • The maximum gap for P-Coverage is about 214 sec, bounded by the time spent on two adjacent moves

  • The maximum gap for a random deployment can be larger than 2000 sec


Future work
Future Work

  • Improve the efficiency of  N-Coverage

    • Combinatorial algorithms for fractional vertex multicut

  • Connected -Coverage

    • Connect each AP to at least one of the gateways with Internet backhaul

    • Joint Coverage and connectivity optimization

    • A bound on the number of hops to gateways

  • (,)-Coverage: Enabling Assured Data Service

    • Guarantees that each user moving through a path of length  has access to at least  units of data.

    • Challenges: variable data rates, traffic density, and contact durations; unknown association schedules


Alpha coverage w o route information cont1
Alpha Coverage w/o Route Information (Cont.)

  • Polynomial time verifiable

  • The optimization problem, called N-Cover, is NP-hard

    • There is a reduction from VERTEX COVER restricted to triangle-free, 3-connected, cubic planar graphs

  • O(log |V|) approximation: reduce N-Cover to Minimum Vertex Multicut

    • Assumption: New APs are deployed only at the vertices of GR (real or artificial road intersections) => introducing a factor 2

    • Step1: Find the set of -pairs, treat their midpoints as terminals

    • Step2: Solving the fractional vertex multicut problem -- the dual of node version maximum multicommodity flow problem

    • Step 3: Rounding the solution by low diameter graph decomposition (GVY).