Maximizing the Contact Opportunity for Vehicular Internet Access

Authors: Zizhan Zheng†, Zhixue Lu†, Prasun Sinha†,

and Santosh Kumar§

† The Ohio State University, § University of Memphis

INFOCOM 2010, San Diego, CA

1

04/21/23

Outline

Motivation Three Metrics

Contact Opportunity in Distance Contact Opportunity in Time Average Throughput

Evaluations Summary and Future Work

2

Motivation: Internet Access for Mobile Vehicles

3

Applications Infotainment Cargo tracking Burglar tracking Road surface monitoring

Current Approaches Full Coverage Opportunistic Service Sparse Coverage

Current Approach I (of III): Full Coverage

4

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 (Baltimore, MD)

Google WiFi for Mountain View 12 square miles, 500+ APs, 95% coverage 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 Maphttp://wifi.google.com/city/mv/apmap.html

Current Approach II (of III): Opportunistic Service via In-Situ APs

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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

Current Approach III (of III): Sparse Coverage with Performance Guarantees6

Basic Idea Planned deployment Sparse coverage with performance

guarantees Alpha Coverage (Infocom ’09 mini)

Placing an upper bound on the maximum diameter of coverage holes in a road network

Pure geometric Does not correspond to the quality of data

service directly

Contact Opportunity: A More Expressive Sparse Coverage Mode

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Contact Opportunity – fractional distance/time within range of APs Closer to user experience Can be translated to average throughput if all

uncertainties resolved Our Approach

Worst Case perspective Start with distance measure that involves least

uncertainties Extend to time measure by modeling road traffic Further extend to average throughput by also

modeling data rates, user density, and association

Contributions8

Propose Contact Opportunity, an expressive sparse coverage mode.

Propose efficient solutions with provable performance bounds to maximize the worst-case Contact Opportunity with various uncertainties considered.

Develop the foundations towards providing scalable data service to disconnection-tolerant mobile users with guaranteed performance.

Outline9

Motivation Three Metrics

Contact Opportunity in Distance Contact Opportunity in Time Average Throughput

Evaluations Summary and Future Work

Models and Assumptions10

Road Network An undirected graph G Assumption 1: A set of candidate deployment

locations is given, denoted as A. Mobile Trace

A set of paths on G Assumption 2: A set of frequently traveled

paths is known, denoted as P. AP Coverage

Geometric model is used Assumption 3: The covered region for each

candidate location is known (but not necessary a disk).

Contact Opportunity in Distance

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For a subset S µ A, a path p 2 P, the Contact Opportunity in Distance of p:

- the cost of S

200m 1000

m

The Properties of Set Function ´d

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The set function ´d(, p): 2A ! [0,1] is Normalized: ´d(;, p) = 0 Nondecreasing: ´d(S, p) · ´d(T, p) if S µ T Submodular: adding a new AP to a small

set helps more than adding it to a large set

Submodular Set Function13

A set function F : 2A ! R is submodular if for all S µ T µ A and a 2 AnT, F(S [ {a}) – F(S) ¸ F(T [ {a}) – F(T) Discrete counterpart of convexity Example: F(S) = ´d(S, p)S

T

a

a

Approximation Algorithm (for a relaxed version)

Hard to approximate directly An instance of budgeted submodular set covering problem No polynomial time approximation unless P = NP

Relaxing the budget B - a binary search based algorithm For a given ¸ 2 [0,1], solve the subproblem - find a

deployment S of minimum cost that provides worst-case Contact Opportunity of ; An instance of submodular set covering problem A greedy algorithm has a logarithmic factor (L.A. Wolsey

1982) If w(S) > B, a lower ¸ is used; otherwise, a higher ¸ is used; Repeat until no higher ¸ can be achieved; output ¸

OPT(B) achieved if ²B is allowed (Andreas Krause 2008) OPT(B) - max-min Contact Opportunity of an optimal

solution ² - a logarithmic function of problem parameters

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Contact Opportunity in Time15

For a subset S µ A, a path p 2 P, the Contact Opportunity in Time of p:

Challenge - uncertain contact time and travel time Traffic jams, accidents, stop signs, etc.

Solution Worst-Case perspective Interval based modeling - for each road segment, an

interval of possible travel times is known.

200m

1000m

20s

10s

10s

10s

20s

Contact Opportunity in Time (Cont.)

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A traffic scenario k - an assignment of travel time (any value from the interval) to each road segment

kS - the worst traffic scenario

Unfortunately, ´t(S, p, kS) 8S µ A is not submodular Approximation by the “mean” scenario

“mean” scenario assigns the average travel time to each road segment

- an upper bound on the ratios of maximum and minimum travel times for all road segments

Factor achieved by using “mean” scenario

From Contact Opportunity to Average Throughput

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More Assumptions Each candidate location a 2 A is associated

with a worst case data rate ra

The maximum number of users moving on each road segment is known The maximum number of users in the range of

an AP at a 2 A can be computed, denoted as va

A user always selects the AP with the highest normalized rate (ra/va) in range to associate

Handoff time is small enough to be ignored

From Contact Opportunity to Average Throughput (Cont.)

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For a subset S µ A, a path p 2 P, the Average Throughput when moving through p can be estimated as:

Solution similar to “Contact Opportunity in Time” Limitations

Simplified association protocol Fairness has been ignored

ra = 1 Mbps

200m 1000

m

20s

10s

10s

10s

20s

2 2 3

Outline19

Motivation Three Metrics

Contact Opportunity in Distance Contact Opportunity in Time Average Throughput

Evaluations Summary and Future Work

Simulations20

Baseline Algorithms Uniform random sampling Max-min distance sampling

Road network A 6x6km2 region, 1802 intersections, Obtained from 2008 Tiger/Line Shapefiles Each edge is associated with an interval of travel

speed [-5, ] (m/s), 2 [10,20] Movements: all pair shortest paths ¸

2km Each AP has unit cost and a sector

based coverage model with radius in [100,200](m)

To evaluate average throughput Ns-2 based simulation Restricted random waypoint 1Mbps for each AP CBR traffic

Simulation Results21

A small controlled experiment in a parking lot at OSU (result in paper)

Min Contact Opp in Time Avg Contact Opp in Time Avg Throughput (2x2km2, 20 APs, 5 users)

Outline22

Motivation Three Metrics

Contact Opportunity in Distance Contact Opportunity in Time Average Throughput

Evaluations Summary and Future Work

Summary and Future Work23

We have proposed Contact Opportunity, an expressive sparse coverage mode for providing data service to mobile users, and efficient solutions that maximize the worst-case Contact Opportunity with various uncertainties considered.

Future Work - Expected Contact Opportunity or Throughput Offline - stochastic modeling of

uncertainties on mobility and data flows Online scheduling to improve fairness

Contact Opportunity in Time (Cont.)

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A traffic scenario k - an assignment of travel time (any value from the interval) to each road segment

KS - the worst traffic scenario that minimizes ´t (S, p) for each p, which assigns the minimum travel time to every segment covered by S and maximum travel time to every segment not covered

Contact Opportunity in Time (Cont.)

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Unfortunately, ´t(S, p, kS) 8S µ A is normalized, nondecreasing, but not submodular

Approximation by a single scenario independent of S “mean” scenario assigns the average travel time to

each road segment, denoted as k0

S0 - optimal deployment with respect to k0

S* - optimal deployment with respect to kS If the ratio between the maximum and the

minimum travel time is bounded by for all road segments, then ´t(S*, p, kS*) · ´t(S0, p, kS0).