1 / 25

INFOCOM 2010, San Diego, CA

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. TexPoint fonts used in EMF.

kaia
Download Presentation

INFOCOM 2010, San Diego, CA

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. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Maximizing the Contact Opportunity for Vehicular Internet AccessAuthors: Zizhan Zheng†, Zhixue Lu†, Prasun Sinha†, and Santosh Kumar§† The Ohio State University, § University of Memphis INFOCOM 2010, San Diego, CA TexPoint fonts used in EMF. Read the TexPoint manual before you delete this box.: AAAAA

  2. Outline • Motivation • Three Metrics • Contact Opportunity in Distance • Contact Opportunity in Time • Average Throughput • Evaluations • Summary and Future Work

  3. Motivation: Internet Access for Mobile Vehicles • Applications • Infotainment • Cargo tracking • Burglar tracking • Road surface monitoring • Current Approaches • Full Coverage • Opportunistic Service • Sparse Coverage

  4. Current Approach I (of III): 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 (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 Map http://wifi.google.com/city/mv/apmap.html

  5. Current Approach II (of III): Opportunistic Service via In-Situ APs Internet • 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 AP AP AP

  6. Current Approach III (of III): Sparse Coverage with Performance Guarantees • 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

  7. Contact Opportunity: A More Expressive Sparse Coverage Mode • 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

  8. Contributions • 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.

  9. Outline • Motivation • Three Metrics • Contact Opportunity in Distance • Contact Opportunity in Time • Average Throughput • Evaluations • Summary and Future Work

  10. Models and Assumptions • 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).

  11. Contact Opportunity in Distance • For a subset SµA, a path p2P, the Contact Opportunity in Distance of p: • - the cost of S 200m 1000m

  12. The Properties of Set Function ´d • The set function ´d(, p): 2A! [0,1] is • Normalized: ´d(;, p) = 0 • Nondecreasing: ´d(S, p) ·´d(T, p) if SµT • Submodular: adding a new AP to a small set helps more than adding it to a large set

  13. Submodular Set Function • A set function F : 2A!R is submodular if for all SµTµA and a2AnT, F(S[ {a}) – F(S) ¸F(T[ {a}) – F(T) • Discrete counterpart of convexity • Example: F(S) = ´d(S, p) S a a T

  14. 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

  15. Contact Opportunity in Time • For a subset SµA, a path p2P, 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. 20s 10s 10s 10s 20s 200m 1000m

  16. Contact Opportunity in Time (Cont.) • 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) 8SµA 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

  17. From Contact Opportunity to Average Throughput • More Assumptions • Each candidate location a2A 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 a2A 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

  18. From Contact Opportunity to Average Throughput (Cont.) • For a subset SµA, a path p2P, 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 20s 10s 10s 10s 20s ra= 1 Mbps 2 2 3 200m 1000m

  19. Outline • Motivation • Three Metrics • Contact Opportunity in Distance • Contact Opportunity in Time • Average Throughput • Evaluations • Summary and Future Work

  20. Simulations • 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

  21. Simulation Results • A small controlled experiment in a parking lot at OSU (result in paper) Avg Throughput (2x2km2, 20 APs, 5 users) Min Contact Opp in Time Avg Contact Opp in Time

  22. Outline • Motivation • Three Metrics • Contact Opportunity in Distance • Contact Opportunity in Time • Average Throughput • Evaluations • Summary and Future Work

  23. Summary and Future Work • 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

  24. Contact Opportunity in Time (Cont.) 24 • 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

  25. Contact Opportunity in Time (Cont.) 25 • Unfortunately, ´t(S, p, kS) 8SµA 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).

More Related