Downlink Traffic Scheduling in Green Vehicular Roadside Infrastructure

Abdulla A. HammadDepartment of of Electrical and Computer Engineering

McMaster University

Hamilton, Ontario, CANADA

Outline

Introduction Energy Efficient Downlink Scheduling VBR scheduling CBR scheduling

VANET Applications

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VANET Architecture

V2V, V2I, hybrid and I2I communications Roadside Unit (RSU) is fixed infrastructure that

enables vehicle-to-infrastructure (V2I) communications.

V2V

V2I

Roadside Unit (RSU)

I2I

VANET overview Applications

– Safety (e-brakes, accidents)– Infotainment(VoD, file transfer, internet)– Traffic (Monitoring, management)

Architecture: OBU and RSU Difference from MANET

– Fast link disconnection– Temporal and spatial changing traffic density– Movement confined to road network– Energy Limits

RSU role– Increase network connectivity– Store delay tolerance contents– Internet connectivity– Safety: accidents and curvy road alerts

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Green Vehicular Infrastructure?● S. Peirce and R. Mauri, “Vehicle-Infrastructure Integration (VII)

Initiative: Benefit-Cost Analysis: Pre-Testing Estimates”, Intelligent Transportation Systems Joint Program Office, United States Department of Transportation, Washington, DC., March 30, 2007.

● Includes cost projections for an initial national vehicular infrastructure deployment (1B USD expenditure)

● 40% of all rural freeway roadside infrastructure would be solar powered. Unavailable power grid connectivity.

● over 63% of roadside unit costs consumed by solar provisioning costs, e.g., solar panel, battery and associated electronics. power savings ↑ energy provisioning cost ↓

● Objective is to decrease energy use at the RSUs as aggressively as possible.

● How much energy can we save using energy aware downlink scheduling?

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V2I Roadside Unit (RSU)

When a vehicle v arrives into RSU coverage, it communicates its R

v bit (delay tolerant) request. The RSU

schedules its downlink response for some future time.

Vehicles have unlimited energy reserves. RSU is energy constrained → focus on reducing downlink energy use.

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Air Interface AssumptionsConstant bit rate (CBR)

● Power control is used to adapt to channel conditions.

● Packet or slot based● Schedulers try to minimize the downlink

transmission energy needed to process vehicle requests.

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Air Interface Assumptions● Variable bit rate (VBR)

● fixed transmit power. The bit rate is adapted to channel conditions.

● Fixed size packet lengths at different bit rates

● Schedulers try to minimize the amount of downlink transmission time required to process vehicle requests.

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Delay-Tolerant CBR Example

P t 1P t 2

= (d 1

d 2

)α

(Distance dependent exponential path loss, e.g., α = 3)

Choice of Vehicle i communication time, e.g., t2 vs t

1.

Huge energy savings when communicating over shorter distances. Requires delay tolerance! Requires energy cost estimates.

For example:

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Another Example

Vehicle w is moving away from energy favourable locations. Vehicle v is moving towards energy favourable locations.

Therefore, we should serve Vehicle w first!

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Another Example● How to schedule when there are different vehicular

speeds and communication requirements?

● It seems that faster moving vehicles with higher communication requirements should be given scheduling priority.

Part 1: Variable Transmission Power - CBR Model Assumptions

– Vehicles can store (buffer) stream more than it needs currently

– No vehicle to vehicle communication – RSUs coverage do not overlap– BW is velocity independent

Offline Energy Bounds– Packet-Based Scheduling:

– Modeled as single machine scheduling with deadlines

– NP hard

– Timeslot-Based Scheduling

Mixed Integer Linear Program Formulation

Minimum Cost Flow Graph Formulation

Online Time slot-based Algorithms Greedy Minimum Cost Flow (GMCF)

Event driven Network Flow formed and Minimum Cost computed Seeks global minima for all vehicles in range High computation cost

Static Scheduler (SS) Seeks local minima for all vehicles in range Two phases: weight computation, scheduling Weight computation: happens once, based on preferred time slots Scheduling rule: static, based on highest weight first Average computation cost Weakness: bad performance under high variation in speed

Nearest Fastest Set (NFS) Scheduler Event driven, preferred slots selected upon arrival Scheduling:dynamic: only resolves contention when they actually

happen Strength: Low computation cost, handles high variation in speed Weakness: High energy cost, due to delayed contention resolution

Performance Evaluation Two sets of results

– Best case scenario: accurate prediction of speed and position based on deterministic path loss scenario

– Average case scenario: including shadowing effect (minimal in HW environment because of LoS)

Traffic Model– Difference between urban and HW environment– Tendency to maintain speed– Lane speed limits (different speed classes)– Arrival model: Poisson Distribution– Traffic direction

Performance Evaluation

•Effect of speed •Two Classes:

•18m/s•18,23,28,33 m/s

•No shadowing•Light and medium load•arrival rate:1/22 v/s•Light Load: GMCF closeto Bound•SS

•More energy•Less comp. intensive

Effect of demand No shadow 3 Classes Fig.7: same speed Fig.8:18,24,33 m/s

Shadowing: 4,12 dB vs increasing demand

Platooning Fig.12: same speed Fig.13: Three different speeds

Publications

AA Hammad, GH Badawy, TD Todd, AA Sayegh, D Zhao, 2010 IEEE Global Telecommunications Conference (GLOBECOM 2010)

Hammad, A.A.; Todd, T.D.; Karakostas, G.; Dongmei Zhao, "Downlink Traffic Scheduling in Green Vehicular Roadside Infrastructure," IEEE Transactions on Vehicular Technology

Part2: Variable Bit-rate Model Assumptions

– RSU with Constant Transmission power

– Limited, non-overlapping coverage area

– RSU battery powered with renewable power source

– Highway environment traffic (constant velocity and platooning)

– Vehicle announce requirement, velocity and location upon arrival

– Vehicle demand can be satisfied by different combinations of time slots between which the received bit rate can be differ.

– The objective is to minimize the number of time slots during which the AP is transmitting, while satisfying all vehicle demands.

Integer Programming Model

Analysis of VBR Scheduling Problem

Problem Relaxation

Relaxation can be done by relaxing the binary condition.

Problem: unbounded Integrality gap– Solver can assign 1 time slot to m vehicles

each assigned 1/m of the slot

Offline Energy Bounds Generalized Flow Graph Dynamic Network Topology Graph

Generalized Flow Graph Fast computations based on special combinatorial algorithms Ease of approximation to integral solution due to high

percentage of flows are integers

Dynamic Network Topology Graph GF drawbacks:

– Time slots inversely proportional to velocity– Depending on slot size: large period of

times may encounter no bit rate change. DNTG: includes time in model

DNTG: Time Expanded Graph

Online Scheduling Algorithms

Bounds provide theoretical optimum energy usage (but assumes future arrivals knowledge)

Online Scheduling Algorithms– FCFS– Fastest First – Greedy GF Algorithm– Greedy DNTG Algorithm

FCFS

First Come First Serve Vehicles are assigned best possible

time slots if they were not reserved for earlier arriving vehicles

Static Assignment: no decision revoking Simple to implement Fairness questionable

Fastest First Algorithm

Faster moving vehicle spend less time inside the RSU range than slower ones.

To increase fairness, faster vehicles are assigned slots prior to slower ones.

Not static: decisions can be revoked.

Greedy GF Algorithm

Use GF design used to calculate the Bound

No future knowledge of arrival Event based: activates upon vehicle

arrival Integer approximation upon execution

Greedy GF Algorithm

Greedy DNTG Algorithm

Greedy Implementation of the Dynamic Network Topology Graph

Event-based: triggered upon arrival of new vehicles

Outline similar to Greedy GF Algorithm outlined before

Performance Comparison Highway environment Vehicles maintain velocity/platoon Poisson Arrival Vehicles announce velocity,location and

requirements Dropping is allowed Deterministic exponential path-loss and

Log-normal shadowing component

Throughput (no shadowing random component)

Two classes of vehicles (18, 30 m/s) Arrival rate 1/28 v/s Platoon 10% No Dropping

Demand Drop

Online algorithms under high demand Two classes:

18, 30 m/s Platooning

10% Arr.rate: 1/30

v/s Throughput

saturates

Dropping under high demand

Energy vs Speed

Shadowing: throughput

Shadowing: Dropping

Jain’s Fairness Index As dropping is allowed, fairness across

algorithms needs to be measured

Jain’s Fairness Index

Thank you