To simulate Flat Topology in MATLAB, it is a network structure in which every node work at the similar level, without any hierarchical organization. This topology is general in distributed sensor networks or peer-to-peer (P2P) networks in which all nodes contains identical responsibility and can directly interact with any other node.
To replicate a flat topology using MATLAB, we can set nodes arbitrarily within a 2D area and enable every node to interact with others, which fall in a described communication range that constructing an ad-hoc network with no central authority. Let’s see how to simulate the Flat Topology through following steps in MATLAB.
Steps to Simulate a Flat Topology in MATLAB
- Define the Network Structure
- Arbitrarily locate nodes in a defined area.
- Describe a communication range to find which nodes can interact with each other.
- Establish Connections Based on Communication Range
- Associate nodes which fall in communication range of each other, making a flat network in which every node are equally able of starting communication.
- Simulate Data Transmission
- Execute the data transmission amongst connected nodes.
- Optionally, replicate multi-hop communication in which information can relay via intermediate nodes.
- Visualize the Flat Topology
- In the communication range, plot nodes and its connections.
- Evaluate Performance Metrics
- Compute performance parameters such as total data transmitted, latency, or packet success rate.
Example MATLAB Code for Simulating a Flat Topology
Below is a MATLAB script to replicate a flat topology with nodes arbitrarily located within a 2D area in which each node associates to others in a specified communication range.
% Parameters
numNodes = 15; % Number of nodes in the network
areaSize = 50; % Size of the area (side length of square)
communicationRange = 15; % Maximum distance for a connection
transmissionDelay = 0.5; % Delay per transmission (in seconds)
dataPackets = randi([10, 30], 1, numNodes); % Data packets each node sends to neighbors
% Generate random positions for nodes within the area
nodePositions = areaSize * rand(numNodes, 2);
% Establish connections based on communication range
connections = zeros(numNodes); % Adjacency matrix for connections
for i = 1:numNodes
for j = i+1:numNodes
% Calculate the Euclidean distance between nodes
distance = sqrt((nodePositions(i,1) – nodePositions(j,1))^2 + (nodePositions(i,2) – nodePositions(j,2))^2);
if distance <= communicationRange
connections(i, j) = 1; % Mark as connected
connections(j, i) = 1; % Ensure symmetry
end
end
end
% Plot the flat topology
figure;
hold on;
for i = 1:numNodes
for j = i+1:numNodes
if connections(i, j) == 1
% Plot line between connected nodes
plot([nodePositions(i,1), nodePositions(j,1)], [nodePositions(i,2), nodePositions(j,2)], ‘k–‘);
end
end
end
% Plot nodes
for i = 1:numNodes
plot(nodePositions(i,1), nodePositions(i,2), ‘bo’, ‘MarkerSize’, 8, ‘DisplayName’, [‘Node ‘, num2str(i)]);
end
title(‘Flat Topology Network’);
xlabel(‘X Position’);
ylabel(‘Y Position’);
grid on;
axis([0 areaSize 0 areaSize]);
axis equal;
hold off;
% Step 1: Simulate Data Transmission between Connected Nodes
disp(‘Data Transmission Simulation:’);
for i = 1:numNodes
% Transmit data to each connected neighbor
neighbors = find(connections(i, 🙂 == 1);
for j = neighbors
fprintf(‘Node %d sends %d packets to Node %d…\n’, i, dataPackets(i), j);
pause(transmissionDelay); % Simulate transmission delay
fprintf(‘Node %d received data from Node %d\n’, j, i);
end
end
% Step 2: Display Network Metrics (Example: Total Data Transmitted)
totalDataTransmitted = sum(dataPackets) * length(find(connections))/2; % Each packet sent to neighbors
disp([‘Total data transmitted in the network: ‘, num2str(totalDataTransmitted), ‘ packets’]);
Explanation of Code
- Node Positioning and Communication Range:
- Nodes are arbitrarily located in a square area of size areaSize.
- Nodes within the specified communicationRange are associated that making a flat structure in which every node can be connected to other close nodes.
- Topology Visualization:
- Nodes are signified as blue circles, and dashed lines denote the connections among nodes in the communication range.
- Data Transmission Simulation:
- Every single node sends data packets to their connected neighbors. A delay is launched to replicate the real-time transmission.
- The total data sent over the network is computed and indicated.
Extensions and Variations
- Multi-Hop Communication:
- Execute a multi-hop routing algorithm, like Dijkstra’s shortest path, to mimic data relaying via intermediate nodes.
- Dynamic Node Mobility:
- Enable nodes to travel arbitrarily in the area and update connections according to the new positions, which replicating mobile nodes in a flat topology.
- Packet Loss and Delay Variability:
- Launch random packet loss probabilities and variable delays to design real wireless network conditions.
- Measure Additional Performance Metrics:
- Monitor packet success rates, latency, and energy consumption for additional in-depth performance analysis.
Visualization of Data Transmission Metrics
To envision the number of packets for each node sends, we can utilize a bar plot.
% Plot the number of packets each node transmits
figure;
bar(1:numNodes, dataPackets);
title(‘Data Packets Transmitted by Each Node’);
xlabel(‘Node Index’);
ylabel(‘Packets Transmitted’);
grid on;
Advanced Simulation Scenarios
- Traffic Load and Congestion Analysis:
- Launch changing traffic loads over diverse nodes to examine the congestion’s effect on packet delivery and latency.
- Resilience and Fault Tolerance:
- Replicate node or connection failures and then envision how successfully the network can resume communication between remaining nodes.
- Implementing Routing Protocols:
- Execute the routing protocols such as AODV (Ad hoc On-Demand Distance Vector) to discover the best multi-hop routes within the flat topology.
We furnished the in-depth simulation steps, Matlab coding with explanation, extension for Flat Topology projects that were simulated and implemented using MATLAB environment. Based on your needs, we will be shared additional specific insights on this topology.
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