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智能优化算法 神经网络预测 雷达通信 无线传感器 电力系统
信号处理 图像处理 路径规划 元胞自动机 无人机
⛄ 内容介绍
It is a challenging task to develop an efficient routing solution for a reliable data delivery in urban vehicular environments. Indeed, it is difficult to find a shortest end-to-end connected path especially in urban city given the mobility attern of the vehicles and the various obstructions to a clear transmission such as buildings. To overcome these difficulties, we investigate how unmanned aerial vehicles (UAVs) can assist vehicles on the ground in relaying in urban areas. In this paper, we propose UVAR (UAV-Assisted VANET Routing Protocol), a new routing technique for Vehicular Ad hoc Networks (VANets). This protocol is based on the use of the traffic density and the knowledge of vehicular connectivity in the streets. With this approach UAVs collect information about the traffic density on the ground and the state of vehicles connectivity, and exchange them with vehicles through Hello messages. These information allow UAV to place themselves so as to allow relaying data when connectivity between sole vehicles on the ground is not possible. Through vehicleto-UAV (V2U) communication, the overall connectivity between vehicles is improved and therefore the routing process is efficiently improved. The performance of the proposed protocol is evaluated and the results to different scenarios are discussed.
⛄ 部分代码
function out = PSOSearch(param, position)
% Import trace file(SUMO)
filename = param.filename;
filename_obj = param.filename_obj;
filename_connec = param.filename_connec;
% Number of vehicles available in the dataset(SUMO)
nVehicle = param.nVehicle; % Have to change according to the trace file
niter =param.niter;
% Infrastructure Position
pos = position; % Have to change according to the trace file
infRadius = 500;
% Reading table
T = readtable(filename);
T_obj = readtable(filename_obj);
T_connec = readtable(filename_connec);
% Number of rows in the table
n_rows_obj = height(T_obj);
% Finding minimum and maximum of Position X and Y
minPosX = min(T{:,4});
minPosY = min(T{:,5});
maxPosX = max(T{:,4});
maxPosY = max(T{:,5});
% Vehicle template
empty_vehicle.id = [];
empty_vehicle.Position = [];
empty_vehicle.angle = [];
% Creating templates for storing Previous connectivity history of vehicles
empty_pre_conection.id = [];
empty_pre_conection.id1 = [];
empty_pre_conection.t1 = 0;
empty_pre_conection.id2 = [];
empty_pre_conection.t2 = 0;
empty_pre_conection.id3 = [];
empty_pre_conection.t3 = 0;
empty_pre_conection.id4 = [];
empty_pre_conection.t4 = 0;
empty_pre_conection.id5 = [];
empty_pre_conection.t5 = 0;
% Create vehicles connections history array
pre_conection = repmat(empty_pre_conection, nVehicle, 1);
% Create vehicles array
object_vehicle = repmat(empty_vehicle, nVehicle, 1);
for i=1:n_rows_obj
for j=1:nVehicle
if strcmp(T_obj{i,1},['veh' num2str((j-1),'%d')])
object_vehicle(j).id = T_obj{i,1};
object_vehicle(j).angle = T_obj{i,2};
object_vehicle(j).Position = [T_obj{i,3}, T_obj{i,4}];
end
end
end
% Creating vehicles id for 'connection history' data structure
for i=1:nVehicle
pre_conection(i).id = ['veh' num2str((i-1), '%d')];
pre_conection(i).id1 = T_connec{i,1};
pre_conection(i).t1 = T_connec{i,2};
pre_conection(i).id2 = T_connec{i,3};
pre_conection(i).t2 = T_connec{i,4};
pre_conection(i).id3 = T_connec{i,5};
pre_conection(i).t3 = T_connec{i,6};
pre_conection(i).id4 = T_connec{i,7};
pre_conection(i).t4 = T_connec{i,8};
pre_conection(i).id5 = T_connec{i,9};
pre_conection(i).t5 = T_connec{i,10};
end
% Storing available vehicle's position in this time-slot (For scatter
% ploting only)
counter = 1;
for i=1:nVehicle
if ~isempty(object_vehicle(i).id)
x(counter) = object_vehicle(i).Position(1);
y(counter) = object_vehicle(i).Position(2);
all_vehicle(counter).id = object_vehicle(i).id;
all_vehicle(counter).angle = object_vehicle(i).angle;
all_vehicle(counter).Position = object_vehicle(i).Position;
all_vehicle(counter).x = object_vehicle(i).Position(1);
all_vehicle(counter).y = object_vehicle(i).Position(2);
all_vehicle(counter).connec_sum = pre_conection(i).t1 + pre_conection(i).t2 + pre_conection(i).t3 ...
+ pre_conection(i).t4 + pre_conection(i).t5;
counter = counter + 1;
end
end
% Number of vehicles both covered and uncovered
uncovered_vehicle = all_vehicle;
nall_vehicle = size(uncovered_vehicle, 2);
counter1 = 1;
% Finding all the uncovered vehicles
for j = 1:niter
for i = 1:nall_vehicle
dist(j).inf = sqrt(sum((pos(j).inf - uncovered_vehicle(i).Position) .^2));
if dist(j).inf > infRadius
uncovered_vehicle(counter1).id = uncovered_vehicle(i).id;
uncovered_vehicle(counter1).angle = uncovered_vehicle(i).angle;
uncovered_vehicle(counter1).Position = uncovered_vehicle(i).Position;
uncovered_vehicle(counter1).connec_sum = uncovered_vehicle(i).connec_sum;
counter1 = counter1 + 1;
end
end
uncovered_vehicle = uncovered_vehicle(1:counter1-1);
nall_vehicle = size(uncovered_vehicle, 2);
counter1 = 1;
end
% Number of uncovered vehicles
nuncovered_vehicle = size(uncovered_vehicle, 2);
% Problem Definition
nVar = 2; % Decision Variable
VarSize = [1 nVar]; % Matrix Size of Decision Variables
VarMin = min(minPosX, minPosY); % Lower Bound of Decision Variables
VarMax = max(maxPosX, maxPosY); % Upper Bound of Decision Variables
% Parameters of PSO
MaxIt = param.MaxIt; % Maximum Number of Iterations
nPop = param.nPop; % Population Size
w = 1; % Intertia Coefficient
wdamp = 0.99; % Damping Ratio of Intertia Coefficient
c1 = 2; % Personal Acceleration Coefficient
c2 = 2; % Social Acceleration Coefficient
MaxVelocity = 0.15*(VarMax-VarMin);
MinVelocity = -MaxVelocity;
% Initialization
% The particle template
empty_particle.Position = [];
empty_particle.Velocity = [];
empty_particle.Cost = [];
empty_particle.Best.Position = [];
empty_particle.Best.Cost = [];
% create population array
particle = repmat(empty_particle, nPop, 1);
% Initialize Global Best
GlobalBest.Cost = -inf;
GlobalBest.Position = [0,0];
% Initialize population members
for i=1:nPop
% Generate Random Solution
particle(i).Position = unifrnd(VarMin, VarMax, VarSize);
% Initialize Velociy
particle(i).Velocity = zeros(VarSize);
% Evaluation
evaluation = objfuntest(particle(i).Position, nuncovered_vehicle, uncovered_vehicle, pos, niter);
particle(i).Cost = evaluation.Fitness;
nMi = evaluation.M;
nNi = evaluation.N;
% Update the Personal Best
particle(i).Best.Position = particle(i).Position;
particle(i).Best.Cost = particle(i).Cost;
% Update Global Best
if particle(i).Best.Cost > GlobalBest.Cost
GlobalBest = particle(i).Best;
end
end
% Array to Hold Best Cost Value on Each Iteration
BestCosts = zeros(MaxIt, 1);
nM = 0;
nN = 0;
% Main Loop of PSO
for it=1:MaxIt
for i=1:nPop
% Update Velocity
particle(i).Velocity = w*particle(i).Velocity ...
+ c1*rand(VarSize).*(particle(i).Best.Position - particle(i).Position) ...
+ c2*rand(VarSize).*(GlobalBest.Position -particle(i).Position);
% Apply Velocity Limits
particle(i).Velocity = max(particle(i).Velocity, MinVelocity);
particle(i).Velocity = min(particle(i).Velocity, MaxVelocity);
% Update Position
particle(i).Position = particle(i).Position + particle(i).Velocity;
% Apply Lower and Upper Bound Limits
particle(i).Position = max(particle(i).Position, VarMin);
particle(i).Position = min(particle(i).Position, VarMax);
% Evaluation
evaluation = objfuntest(particle(i).Position, nuncovered_vehicle, uncovered_vehicle, pos, niter);
particle(i).Cost = evaluation.Fitness;
% Update Personal Best
if particle(i).Cost > particle(i).Best.Cost
particle(i).Best.Position = particle(i).Position;
particle(i).Best.Cost = particle(i).Cost;
% Update Global Best
if particle(i).Best.Cost > GlobalBest.Cost
GlobalBest = particle(i).Best;
nM = evaluation.M;
nN = evaluation.N;
end
end
end
% Store the Best Cost Value
BestCosts(it) = GlobalBest.Cost;
% Damping Intertia Coefficient
w = w * wdamp;
end
out.Fitness = GlobalBest.Cost;
out.Position = GlobalBest.Position;
out.Uncov_veh = evaluation.V;
out.nM = nM;
out.nN = nN;
out.x = x;
out.y = y;
out.VarMin = VarMin;
out.VarMax = VarMax;
out.Total_vehicle = size(all_vehicle, 2);
out.Best_cost = BestCosts;
end
⛄ 运行结果
⛄ 参考文献
[1] Oubbati O S , Lakas A , Lagraa N , et al. UVAR: An intersection UAV-assisted VANET routing protocol[C]// 2016 IEEE Wireless Communications and Networking Conference (WCNC). IEEE, 2016.
[2] Wang X , Fu L , Zhang Y , et al. VDNet: an infrastructure‐less UAV‐assisted sparse VANET system with vehicle location prediction[J]. Wireless Communications & Mobile Computing, 2016.
[3]Gan, Xiaoying, Wang,等. VDNet: an infrastructure-less UAV-assisted sparse VANET system with vehicle location prediction[J]. Wireless Communications & Mobile Computing, 2016.
