A Multiobjective Evolutionary Algorithm for Infrastructure Location in Vehicular Networks

A Multiobjective Evolutionary Algorithm for Infrastructure Location in Vehicular Networks */20

A Multiobjective Evolutionary Algorithm forInfrastructure Location in Vehicular Networks

Universidad de la República, Montevideo, Uruguay Universidad de Málaga, Málaga, Spain

A Multiobjective Evolutionary Algorithm for Infrastructure Location in Vehicular Networks

Index

1. Introduction2. The RSU Deployment Problem3. A Multiobjective Evolutionary Algorithm for RSU-DP4. Experimental Analysis 5. Conclusions and Future Work


Introduction

• Vehicular Ad-Hoc Network (VANET): vehicles + roadside units (RSU)

• RSU deployment problem: find number, type, and location of RSUs to maximize QoS, while satisfying deployment cost requirements

This is a hard-to-solve optimization problem on city-scaled areas. Heuristics and metaheuristics allow computing good infrastructure

designs in reduced execution times.


The RSU Deployment Problem (RSU-DP)Definition• A set of road segments S = {s1 , . . . , sn}. Each segment si is defined

by a pair of points pj , pk P = {p∈ 1 , . . . , pm} identified by (lat, long).RSUs can be placed at any location within each segment si

• An estimation of the number of vehicles per time period across each segment NV(si)

• The average vehicle speed for each segment sp(si).• A set of RSU types T = {t1 , . . . , tk}, each one with a given cost and

coverage areaFind a set of locations and the type of each RSU to deploy, to maximize the number of vehicles served and

simultaneously minimize the total cost of deployment


The RSU Deployment Problem (RSU-DP)Related work• Lochert et al. (2008): GA and VANET simulator to evaluate the QoS

– applied to a given area of Brunswick, Germany, considering 100 possible predefined locations for RSUs

– good cost/utility trade-off using between 10 and 30 RSUs• Cavalcante et al. (2012): compared GA against a greedy approach

for the maximum coverage with time threshold problem– used data from four Swiss regions– the GA outperformed the greedy approach

No related works using explicit multi-objective methods. We propose using the NSGA-II algorithm to solve the RSU-DP.


MOEA for the RSU-DPSolution encoding• Solutions are represented as real arrays of length n = #S.• RSU type: integer part of the real number (0 stands for no RSU,

1...k represent types t1… tk )• Position within the segment: fractional part of the real number,

maps the interval [0, 1) to points in segment [pj , pi).


MOEA for the RSU-DPEvolutionary operators• Population is randomly initialized using reals from [0, k+r],

with k = #T and r [0, 1)∈• Intermediate Recombination; offspring of parents x and y satisfy

αixi + (1 − αi )yi and βiyi + (1 − βi)xi, with αi , βi randomly chosen in [−p, 1 + p] for a given p.

• Ad-hoc mutation operator:– with probability πA removes the RSU (if any) from the segment– with probability πB changes the type of the RSU (if any) to a

random type picked uniformly in T– with probability 1 − πA − πB applies a Gaussian Mutation with

standard deviation σ.


MOEA for the RSU-DPComputing the objective functions• Total cost: adds the cost (according to the type) of each RSU placed• QoS:

– Find the portions of segments covered by the RSUs– QoS considers the amount of vehicles and speed in each

portion covered.


Experimental analysisInstances, configuration, and environment• Real map of Málaga (Spain) covering 42.557 km2: 121 segments with

lengths between 55 and 1556 meters• Real traffic data: normal, low and high traffic scenarios• Three types of IEEE 802.11p omni-directional antennas:

• Parameter configuration: population size=72, crossover prob.=0.95, mutation prob.=0.01, πA=0.5, πB=0.25, σ=0.25

• Parallel evaluation on AMD Opteron 6172 24 cores 2.10 GHz, 24GB RAM [Cluster FING, HPC facility at Universidad de la República]


Experimental analysisComparison against two greedy strategies• Greedy QoS

– Places RSUs sequentially over non-covered segments at a random position

– Starts with those with higher ratio between number of vehicles and average speed

– A segment is considered covered if it has a portion of λ inside the coverage area of any RSU

• Greedy cost – analogous, but stops when the QoS is equal to α·Q – Q: best QoS value achieved by the greedy QoS with λ = 0.75– α [0, 1]∈– Experimental analysis: λ {0.9, 0.95, 1.0}, α {0.7, 0.75, 0.8}∈ ∈


• Up to 73.9% improvement in QoS with same cost than greedy cost • Up to 365.1% improvement in cost with same QoS than greedy QoS

• Small generational distance values and RHV suggest good distributions and convergence to an ideal Pareto front

Experimental analysisNumerical results


Experimental analysisPareto front (PF), normal traffic• Global PF vs. best greedy solutions, normal traffic scenario


Conclusions

• Main contribution: multiobjective evolutionary approach to solve the problem of locating roadside infrastructure for vehicular networks

• The experimental analysis was performed using real traffic information from the city of Málaga.

• The proposed MOEA has shown good problem solving capabilities:– computing accurate Pareto fronts (according to standard

multiobjective metrics)– significantly improving two greedy heuristics (up to 365.1% in

cost and 73.9% in QoS)


Current and future work

• Overlapping antennas need to be considered in the model.


Current and future work

• Considering different QoS metrics including:– additional information, such as accidents– different media transmitted by the RSUs (text, audio, video).

• Comparing against other techniques:– Different greedy methods

• PageRank algorithm• Knapsack

• Extending the experimental analysis to other cities (difficulty in getting access to data).

A Multiobjective Evolutionary Algorithm for Infrastructure Location in Vehicular Networks

Engineering

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