To simulate Temporally Ordered Routing Algorithm (TORA) in MATLAB, we want to design a reactive, adaptive routing protocol which introduces routes only when required and adjust to network topology variations. TORA is intended for dynamic, multi-hop wireless networks and manage link failures and topology varies by using a directed acyclic graph (DAG) rooted at the destination.
Here’s a brief techniques to implementing a TORA simulation in MATLAB:
Steps to Simulate TORA in MATLAB
- Define Network Topology:
- Utilize an adjacency matrix or a graph object to signify nodes and links in the network.
- Allocate weights to each link to signify distances or costs among nodes.
- Initialize Height Values for Nodes:
- Each node sustains a height to represent its kin position in the network with detail to the destination.
- Start, set the height of all nodes to infinity (Inf), excluding for the destination, that has a height of zero.
- Route Discovery with Directed Acyclic Graph (DAG):
- Utilize the height values to generate a DAG rooted at the destination.
- Route configures messages are broadcast from the destination to introduce paths, with nodes updating their heights according to neighbors’ heights.
- Handle Link Failures and Adaptation:
- When a link miscarries or a topology variation happens, nodes modify their heights to reestablish the DAG.
- Broadcast link failure notifications to cause height updates in impacted area of the network.
- Simulate Packet Forwarding:
- Packets are transmitting along paths in the DAG, subsequent reducing height values until attainment the destination.
- Visualize and Analyze Results:
- Plot the network topology and DAG, emphasizing paths taken by packets.
- Monitor the parameters such as packet delay, amount of control messages, and path length to measure TORA’s performance in dynamic environment.
Example Code Outline
Here’s an outline of MATLAB code to replicate TORA with DAG construction and adaptive routing.
% Define network topology as an adjacency matrix with link weights
adjMatrix = [
0 2 0 0 7;
2 0 3 8 0;
0 3 0 1 6;
0 8 1 0 4;
7 0 6 4 0
];
numNodes = size(adjMatrix, 1);
% Define the destination node
destination = 5;
% Initialize height values for all nodes (destination height is 0)
heights = inf(1, numNodes);
heights(destination) = 0;
% TORA Route Setup to build the DAG rooted at the destination
function heights = routeSetup(adjMatrix, heights, destination)
numNodes = size(adjMatrix, 1);
queue = [destination];
while ~isempty(queue)
node = queue(1);
queue(1) = []; % Dequeue the current node
% Update neighbors’ heights based on current node’s height
neighbors = find(adjMatrix(node, 🙂 > 0);
for i = 1:length(neighbors)
neighbor = neighbors(i);
if heights(neighbor) > heights(node) + 1
heights(neighbor) = heights(node) + 1; % Increment height
queue = [queue, neighbor]; % Enqueue the neighbor
end
end
end
end
% Build the initial DAG
heights = routeSetup(adjMatrix, heights, destination);
% Visualize the initial network and DAG heights
G = graph(adjMatrix);
figure;
plot(G, ‘EdgeLabel’, G.Edges.Weight);
title(‘Network Topology’);
% Display height values
disp(‘Initial Heights for each node:’);
disp(heights);
% Function to simulate link failure and reestablish the DAG
function heights = handleLinkFailure(adjMatrix, heights, failedLink)
adjMatrix(failedLink(1), failedLink(2)) = 0;
adjMatrix(failedLink(2), failedLink(1)) = 0; % Make the link bidirectional
disp([‘Link failure between nodes ‘, num2str(failedLink(1)), ‘ and ‘, num2str(failedLink(2))]);
% Re-run route setup to update heights based on the new topology
heights = routeSetup(adjMatrix, heights, failedLink(1));
end
% Simulate a link failure and update the DAG
failedLink = [1, 2]; % Example of a failing link between node 1 and node 2
heights = handleLinkFailure(adjMatrix, heights, failedLink);
% Display updated heights after handling the link failure
disp(‘Updated Heights after link failure:’);
disp(heights);
% Function to forward a packet based on heights in the DAG
function forwardPacket(source, destination, heights)
current = source;
path = [current];
while current ~= destination
% Find the neighbor with the smallest height
neighbors = find(adjMatrix(current, 🙂 > 0);
[~, idx] = min(heights(neighbors));
nextHop = neighbors(idx);
path = [path, nextHop];
current = nextHop;
end
disp([‘Packet path from ‘, num2str(source), ‘ to ‘, num2str(destination), ‘: ‘, num2str(path)]);
end
% Test packet forwarding from a source to the destination
sourceNode = 1;
disp(‘— Packet Transmission —‘);
forwardPacket(sourceNode, destination, heights);
Explanation of the Code
- Network Topology: The adjacency matrix adjMatrix describes the network, with nodes and weighted links.
- Height Initialization: Each node’s height is set to infinity, excluding for the destination node that has a height of zero.
- DAG Construction: The routeSetup function introduces the initial DAG by broadcasting height values from the destination. Nodes modernize their heights according to their neighbors’ heights to create a directed acyclic graph rooted at the destination.
- Handling Link Failures: The handleLinkFailure function eliminates a link from the adjacency matrix, re-runs routeSetup, and modernizes the heights to reestablish the DAG erection.
- Packet Forwarding: The forwardPacket function sends a packet from the origin to the destination, subsequent the DAG’s reducing height values.
Visualization and Analysis
To evaluate the TORA simulation:
- DAG Visualization: Utilize MATLAB’s graph and plot functions to envision the network and enlighten the paths and height values in the DAG.
- Performance Metrics: Observe the parameters like amount of hops, delay, number of control messages transmit after a failure, and routing path length.
Extending the Simulation
For a more cutting-edge TORA simulation:
- Multiple Failures: Mimic multiple link failures and discern how the DAG adjusts.
- Partition Detection: If link failures trigger a network partition, identify it and demonstration a message representing the unreachable destination.
- Priority-based Paths: Enable priority-based routing by adapting link weights, and then learn on how TORA adjust when diverse paths are favoured in terms of conditions.
- Jitter and Congestion Simulation: incorporate changing latency to replicate congestion and track on how TORA might adjust to varying link conditions.
This project idea delivers extensive range of implementations using the Temporally Ordered Routing Algorithm in MATLAB, helping you explore numerous contexts of the Temporally Ordered Routing Algorithm performance in scenarios. We plan to deliver the detailed instructions to these projects to in further manual.
If you have any questions regarding your Temporally Ordered Routing Projects using MATLAB, feel free to email us at phdprime.com! We’re ready to assist you with simulation support and can recommend project topics that fit your requirements. Our team will support you through each phase of your MATLAB simulation, so don’t hesitate to contact us for expert guidance! Our developers also specialize in dynamic, multi-hop wireless networks and are equipped to manage link failures and topology.
