To simulate the drone-based VANET (Vehicular Ad-hoc Networks) utilizing MATLAB, we can offer stepwise process which encompasses to design the communication amongst drones and vehicles in which drones work as communication relays or deliver prolonged network coverage. These drones, performing as flying nodes within a VANET that can support alleviate communication issues within active and complex vehicular environments by enhancing the metrics such as coverage, latency, and throughput. Send us a message to give you simulation assistance.
We can follow these steps for simulating drone-based VANET projects using MATLAB:
Steps to Simulate Drone Based VANET Projects in MATLAB
Step 1: Install Required Toolboxes
Make sure we have the following MATLAB toolboxes are installed on the machine:
- Communications Toolbox (for simulating communication protocols)
- Robotics System Toolbox (optional, for drone navigation and path planning)
- Optimization Toolbox (for optimizing drone placement and routing strategies)
- Simulink (optional, for large-scale system simulation)
Step 2: Define System Parameters for Drones and Vehicles
Initially, we describe the metrics for drones (UAVs) and vehicles within the VANET. It contains communication range, mobility parameters, network bandwidth, and data size.
Example: Define System Parameters
% Drone and Vehicle Parameters
numDrones = 3; % Number of drones in the network
numVehicles = 10; % Number of vehicles in the VANET
droneCommRange = 800; % Communication range of drones (in meters)
vehicleCommRange = 300; % Communication range of vehicles (in meters)
droneSpeed = 20; % Speed of drones (in meters/second)
vehicleSpeed = 15; % Speed of vehicles (in meters/second)
networkBandwidth = 10e6; % Network bandwidth (10 Mbps)
packetSize = 1e6; % Packet size (1 MB)
Step 3: Define Mobility Models for Drones and Vehicles
To replicate the drone-based VANETs, it is crucial describing the mobility models of both drones and vehicles. Drones need to move along predefined paths or arbitrarily over a region, even though vehicles are move along roads or arbitrarily within the simulation area.
Example: Define Mobility for Drones and Vehicles
% Initial Positions of Drones and Vehicles
dronePositions = rand(numDrones, 2) * 2000; % Random initial positions in 2000×2000 meters area
vehiclePositions = rand(numVehicles, 2) * 1000; % Random positions in 1000×1000 meters area
% Movement Directions (random for both drones and vehicles)
droneDirection = rand(numDrones, 1) * 2 * pi; % Random directions in radians
vehicleDirection = rand(numVehicles, 1) * 2 * pi; % Random directions in radians
% Velocity Vectors for Drones and Vehicles
droneVelocity = droneSpeed * [cos(droneDirection), sin(droneDirection)];
vehicleVelocity = vehicleSpeed * [cos(vehicleDirection), sin(vehicleDirection)];
Step 4: Simulate Mobility Over Time
Now we update the locations of the drones and vehicles over time as they move. Drones and vehicles will move based on the specified velocities and stay in the described simulation area.
Example: Update Positions Over Time
% Number of time steps for the simulation
timeSteps = 100;
for t = 1:timeSteps
% Update positions of drones
dronePositions = dronePositions + droneVelocity;
dronePositions = mod(dronePositions, 2000); % Ensure drones stay within the 2000×2000 area
% Update positions of vehicles
vehiclePositions = vehiclePositions + vehicleVelocity;
vehiclePositions = mod(vehiclePositions, 1000); % Ensure vehicles stay within the 1000×1000 area
end
Step 5: Communication Model Between Drones and Vehicles
In this stage, we replicate the communication among the drones and vehicles. We compute the distances among the drones and vehicles to find out that nodes can communicate rely on their communication range.
Example: Calculate Connectivity Between Drones and Vehicles
% Calculate the distances between drones and vehicles
distanceMatrix = pdist2(dronePositions, vehiclePositions); % Distance matrix between drones and vehicles
% Determine which vehicles are within the communication range of the drones
droneToVehicleConnections = distanceMatrix < droneCommRange; % Binary matrix for connectivity
% Display the connectivity matrix
disp(‘Drone to Vehicle Connectivity:’);
disp(droneToVehicleConnections);
Step 6: Implement Routing Protocol
In a drone-based VANET, routing protocols like AODV (Ad-hoc On-demand Distance Vector), DSR (Dynamic Source Routing), or flooding-based routing can be executed to manage the data packet transmission. For simplicity, we can replicate a simple flooding-based routing.
Example: Simulate Basic Flooding-Based Routing
numMessages = 5; % Number of messages to be transmitted in the network
for msg = 1:numMessages
% Randomly select a source vehicle or drone
sourceNode = randi([1, numDrones + numVehicles]);
if sourceNode <= numDrones
% Source is a drone
disp([‘Drone ‘, num2str(sourceNode), ‘ is sending a message.’]);
receivers = droneToVehicleConnections(sourceNode, :);
else
% Source is a vehicle
disp([‘Vehicle ‘, num2str(sourceNode – numDrones), ‘ is sending a message.’]);
receivers = distanceMatrix(:, sourceNode – numDrones) < vehicleCommRange;
end
% Display the nodes receiving the message
disp([‘Nodes receiving the message: ‘, num2str(find(receivers))]);
end
Step 7: Simulate QoS Metrics (Latency, Throughput, Packet Loss)
Compute the Quality of Service (QoS) metrics such as latency, throughput, and packet loss, the performance of drone-based VANETs can estimate.
Example: Calculate Latency and Throughput
% Calculate the latency based on the distance between nodes
latencyMatrix = distanceMatrix / (networkBandwidth / packetSize); % Latency in seconds
% Simulate the throughput of the network
throughput = networkBandwidth * timeSteps / numMessages; % Throughput in bits per second
% Display the results
disp([‘Average latency: ‘, num2str(mean(latencyMatrix(:))), ‘ seconds’]);
disp([‘Network throughput: ‘, num2str(throughput / 1e6), ‘ Mbps’]);
Step 8: Simulate Drone Relay for Distant Vehicles
In a drone-based VANET, drones perform as relays to associate the vehicles, which are very far at a distance. Here, we replicate the drones relaying data among the vehicles.
Example: Simulate Drone as a Relay
% Select two vehicles that are out of direct communication range
vehicle1 = 2;
vehicle2 = 8;
% Check if direct communication is possible
if distanceMatrix(vehicle1, vehicle2) > vehicleCommRange
% Use drones as relays
for drone = 1:numDrones
if distanceMatrix(drone, vehicle1) < droneCommRange && distanceMatrix(drone, vehicle2) < droneCommRange
disp([‘Drone ‘, num2str(drone), ‘ is relaying data between Vehicle ‘, num2str(vehicle1), ‘ and Vehicle ‘, num2str(vehicle2)]);
break;
end
end
else
disp([‘Direct communication possible between Vehicle ‘, num2str(vehicle1), ‘ and Vehicle ‘, num2str(vehicle2)]);
end
Step 9: Visualization of Network Topology
Envisioning the network topology is necessary to monitor the connections among the drones and vehicles on top of their mobility patterns.
Example: Plot the Network Topology
% Plot the positions of drones and vehicles
figure;
plot(dronePositions(:, 1), dronePositions(:, 2), ‘ro’, ‘MarkerSize’, 10, ‘DisplayName’, ‘Drones’); hold on;
plot(vehiclePositions(:, 1), vehiclePositions(:, 2), ‘bx’, ‘MarkerSize’, 8, ‘DisplayName’, ‘Vehicles’);
legend(‘Drones’, ‘Vehicles’);
% Plot communication range circles for drones
for i = 1:numDrones
viscircles(dronePositions(i, :), droneCommRange, ‘LineStyle’, ‘–‘, ‘EdgeColor’, ‘r’);
end
title(‘Drone-Based VANET Network Topology’);
xlabel(‘X Position (m)’);
ylabel(‘Y Position (m)’);
grid on;
Step 10: Full System Simulation Using Simulink (Optional)
If we require a more complex and large-scale simulation then we can utilize the Simulink to design the whole network with drones and vehicles that containing their communication channels, mobility models, and data flow. Simulink permits to make visual block diagrams for system-level simulations.
We had clearly illustrated the entire simulation process with sample coding to simulate the Drone based VANET projects utilizing MATLAB environment and their toolboxes. If you want further data, we will also be made available.
