To simulate the Vehicular Sensor Networks (VSNs) using MATLAB, it includes designing the vehicles as mobile nodes are furnished with sensors, which interact with each other (V2V), infrastructure (V2I), or other networks (V2X). These simulations normally concentrate on the wireless communication protocols, mobility models, and performance parameters like latency, throughput, and packet delivery ratio.
Use the given simulation guide to replicate VSNs in the MATLAB:
Steps to Simulate VSN Projects Using MATLAB
- Define the Vehicular Network Parameters
Initially, we describe the simple network metrics like the amount of vehicles, mobility model, communication range, and the simulation area.
% Network parameters
numVehicles = 10; % Number of vehicles
areaSize = [0 1000 0 1000]; % Simulation area (in meters)
commRange = 250; % Communication range in meters
- Generate Vehicle Mobility Model
A crucial feature of vehicular networks is mobility. We can utilize a mobility model, like Random Waypoint Mobility, to replicate vehicle movement within a specified area.
Instance of a simple random mobility model:
% Generate random positions and directions for vehicles
vehiclePositions = [areaSize(2)*rand(numVehicles, 1), areaSize(4)*rand(numVehicles, 1)]; % Random (x, y)
vehicleSpeeds = 10 + 20 * rand(numVehicles, 1); % Random speed between 10 and 30 m/s
vehicleDirections = 2 * pi * rand(numVehicles, 1); % Random direction (in radians)
% Time parameters
simulationTime = 100; % Total simulation time (in seconds)
timeStep = 0.1; % Time step for vehicle movement (in seconds)
numSteps = simulationTime / timeStep;
% Simulate vehicle movement over time
for t = 1:numSteps
vehiclePositions(:, 1) = vehiclePositions(:, 1) + vehicleSpeeds .* cos(vehicleDirections) * timeStep;
vehiclePositions(:, 2) = vehiclePositions(:, 2) + vehicleSpeeds .* sin(vehicleDirections) * timeStep;
% Ensure vehicles remain within the simulation area
vehiclePositions = mod(vehiclePositions, [areaSize(2), areaSize(4)]);
% Plot vehicle positions at the current time step
clf; hold on;
plot(vehiclePositions(:, 1), vehiclePositions(:, 2), ‘bo’, ‘MarkerSize’, 10, ‘MarkerFaceColor’, ‘b’);
axis([areaSize(1) areaSize(2) areaSize(3) areaSize(4)]);
xlabel(‘X Position (m)’); ylabel(‘Y Position (m)’);
title([‘Vehicle Positions at t = ‘, num2str(t*timeStep), ‘ seconds’]);
grid on;
pause(0.05); % Pause for animation
end
- Implement V2V Communication Model
Vehicles interact with the support of Vehicle-to-Vehicle (V2V) communication in a described range. Replicate the V2V communication by broadcasting packets to adjacent vehicles in the communication range.
Sample of broadcasting packets in V2V:
% Broadcast data packets from one vehicle to its neighbors
sourceVehicle = 1;
for vehicle = 1:numVehicles
if vehicle ~= sourceVehicle
dist = sqrt(sum((vehiclePositions(sourceVehicle, 🙂 – vehiclePositions(vehicle, :)).^2));
if dist <= commRange
fprintf(‘Vehicle %d receives packet from Vehicle %d (distance: %.2f meters)\n’, vehicle, sourceVehicle, dist);
end
end
end
- Implement V2I Communication (Vehicle-to-Infrastructure)
V2I communication permits vehicles to interchange the data including roadside infrastructure like base stations or access points. Describe fixed infrastructure nodes also mimic the data exchange.
% Define infrastructure positions (e.g., traffic lights, base stations)
numInfrastructureNodes = 2;
infrastructurePositions = [500, 500; 800, 800]; % Fixed positions
% Check if vehicles are within communication range of infrastructure
for vehicle = 1:numVehicles
for inf = 1:numInfrastructureNodes
dist = sqrt(sum((vehiclePositions(vehicle, 🙂 – infrastructurePositions(inf, :)).^2));
if dist <= commRange
fprintf(‘Vehicle %d communicates with Infrastructure %d (distance: %.2f meters)\n’, vehicle, inf, dist);
end
end
end
- Simulate Data Transmission and Reception
Replicate the reception of data packets and transmission among the vehicles. We can utilize the Additive White Gaussian Noise (AWGN) to design the impact of noise within the wireless channel.
% Simulate data transmission with AWGN
data = randi([0 1], 1000, 1); % Random data (binary)
modulatedData = pskmod(data, 4); % Modulate using QPSK
% Transmit and add noise
snr = 20; % Signal-to-noise ratio (in dB)
receivedSignal = awgn(modulatedData, snr, ‘measured’);
% Demodulate at the receiver
receivedData = pskdemod(receivedSignal, 4);
% Calculate bit error rate (BER)
[numErrors, ber] = biterr(data, receivedData);
fprintf(‘Bit Error Rate (BER): %.5f\n’, ber);
- Routing in Vehicular Networks
Execute the routing protocols such as AODV (Ad-hoc On-Demand Distance Vector) or GPSR (Greedy Perimeter Stateless Routing) to send data among the vehicles or to infrastructure.
Example: AODV Routing
In AODV, nodes (vehicles) are launching the routes on demand. Execute a simple AODV-like protocol in which vehicles establish routes actively according to their positions.
% Example: Route establishment in AODV-like protocol
sourceVehicle = 1;
destinationVehicle = numVehicles;
% Search for a route to the destination vehicle
routeFound = false;
for vehicle = 1:numVehicles
if vehicle ~= sourceVehicle
dist = sqrt(sum((vehiclePositions(vehicle, 🙂 – vehiclePositions(destinationVehicle, :)).^2));
if dist <= commRange
fprintf(‘Vehicle %d establishes a route to Vehicle %d (distance: %.2f meters)\n’, sourceVehicle, destinationVehicle, dist);
routeFound = true;
break;
end
end
end
if ~routeFound
fprintf(‘No direct route found between Vehicle %d and Vehicle %d\n’, sourceVehicle, destinationVehicle);
end
- Performance Analysis
To estimate the efficiency of the vehicular sensor network, we can examine the crucial performance parameters like Packet Delivery Ratio (PDR), latency, throughput, and energy consumption.
Packet Delivery Ratio (PDR)
% Calculate Packet Delivery Ratio
totalPackets = 100;
receivedPackets = 95; % Example: 95 packets received successfully
pdr = (receivedPackets / totalPackets) * 100; % Packet Delivery Ratio (PDR) in percentage
fprintf(‘Packet Delivery Ratio (PDR): %.2f%%\n’, pdr);
End-to-End Latency
% Calculate end-to-end delay (propagation delay + transmission delay)
txRate = 10e3; % Transmission rate in bps
packetSize = 1024 * 8; % Packet size in bits (e.g., 1024 bytes)
transmissionDelay = packetSize / txRate; % Transmission delay in seconds
propagationSpeed = 3e8; % Speed of signal propagation (in m/s)
dist = sqrt(sum((vehiclePositions(1, 🙂 – vehiclePositions(2, :)).^2)); % Distance between two vehicles
propagationDelay = dist / propagationSpeed;
totalDelay = transmissionDelay + propagationDelay;
fprintf(‘End-to-End Delay: %.4f seconds\n’, totalDelay);
- Visualization
We can utilize the MATLAB’s plotting capabilities to envision the vehicle movements, data routes, and performance parameters over time.
Example: Visualizing Vehicle Communication Range
% Plot communication range for vehicles
figure;
hold on;
scatter(vehiclePositions(:, 1), vehiclePositions(:, 2), ‘filled’);
viscircles(vehiclePositions, repmat(commRange, numVehicles, 1), ‘LineStyle’, ‘–‘);
xlabel(‘X Position (m)’); ylabel(‘Y Position (m)’);
title(‘Vehicle Communication Range’);
grid on;
Example Vehicular Sensor Network Project Ideas:
- Energy-Efficient Routing in VSNs: Estimate and replicate the energy-efficient routing protocols within vehicular sensor networks.
- V2X (Vehicle-to-Everything) Communication Simulation: Mimic V2X communications in which vehicles are interact with other vehicles, infrastructure, pedestrians, and networks.
From this approach, we can exhaustively know how to simulate the Vehicular Sensor Network projects using MATLAB environment and we understand the key concepts of wireless communication protocols, mobility models. If you have any doubts about this simulation, we will help you out of it.
Contact us to explore the simulation of vehicular sensor network projects utilizing MATLAB. Our team at phdprime.com is ready to assist you with your work. We specialize in vehicle-to-vehicle (V2V), vehicle-to-infrastructure (V2I), and other vehicle-to-everything (V2X) networks. Reach out to us to receive a well-aligned topic for your projects.
