How to Simulate FSR Protocol Projects Using MATLAB

To simulate the Fisheye State Routing (FSR) protocol using MATLAB, we require executing the primary features of FSR, like updating the routing tables with changing levels of detail according to the distance to other nodes such as finer detail for closest nodes and coarser detail for distant nodes. FSR is specifically helpful for mobile ad-hoc networks (MANETs) in which sustaining entire routing information is ineffective by reason of frequent topology changes. Here, we provide the simulation process of Fisheye State Routing protocol in MATLAB.

Steps to Simulate FSR Protocol in MATLAB

1. Define Network Parameters:
   • Configure the number of nodes, network area, and transmission range.
   • Describe a “scope” metric for the fisheye view in which nodes contain additional frequent updates for nearby nodes and less frequent updates for distant nodes.

numNodes = 20;       % Number of nodes in the network

range = 50;          % Transmission range in meters

areaSize = [100, 100]; % Network area size

maxTime = 200;       % Total simulation time

dt = 1;              % Time step

% Initialize node positions randomly in the area

nodePositions = rand(numNodes, 2) * areaSize(1);

% Define Fisheye scope levels (scopes for nearby and distant nodes)

scopeLevels = [1, 2, 3]; % Closer levels have more detailed updates

1. Initialize Routing Tables:
   • Every single node sustains a routing table with entries to other nodes.
   • Consequently using the Euclidean distance and populate routing tables, place initial distances among the nodes.

% Initialize routing tables as a cell array (one table per node)

routingTables = cell(numNodes, 1);

for i = 1:numNodes

routingTables{i} = struct(‘destNode’, [], ‘nextHop’, [], ‘distance’, []);

for j = 1:numNodes

if i ~= j

dist = norm(nodePositions(i,:) – nodePositions(j,:));

routingTables{i}.destNode = [routingTables{i}.destNode, j];

routingTables{i}.nextHop = [routingTables{i}.nextHop, j];

routingTables{i}.distance = [routingTables{i}.distance, dist];

end

end

end

1. Implement FSR Update Mechanism:
   • Describe how often each node updates their routing table according to the scope levels.
   • Nodes nearer within the network update each other with finer details, even though farther nodes acquire updated less often.

% Function to update routing tables based on FSR scope levels

function updateRoutingTable(node, routingTables, nodePositions, range, scopeLevels)

for level = scopeLevels

for i = 1:length(routingTables{node}.destNode)

destNode = routingTables{node}.destNode(i);

distance = routingTables{node}.distance(i);

% Update only if within the scope range

if distance <= range * level

% Refresh next-hop information based on distance

routingTables{node}.nextHop(i) = destNode;

routingTables{node}.distance(i) = distance;

end

end

end

end

1. Simulate FSR Protocol Operation:
   • At each time step, modernize the routing tables for each node relies on their fisheye scope.
   • Envision node positions and modernize paths to monitor how the protocol works over time.

for t = 1:maxTime

for node = 1:numNodes

% Update routing table for each node based on FSR mechanism

updateRoutingTable(node, routingTables, nodePositions, range, scopeLevels);

end

% Visualization of network with FSR routing paths

clf;

hold on;

plot(nodePositions(:, 1), nodePositions(:, 2), ‘bo’); % Plot nodes

for i = 1:numNodes

for j = i+1:numNodes

% Plot line if nodes are within communication range

if norm(nodePositions(i,:) – nodePositions(j,:)) <= range

plot([nodePositions(i,1), nodePositions(j,1)], [nodePositions(i,2), nodePositions(j,2)], ‘k-‘);

end

end

end

title([‘FSR Protocol Simulation – Time Step: ‘, num2str(t)]);

axis([0 areaSize(1) 0 areaSize(2)]);

pause(0.1);

end

1. Analyze Performance Metrics:
   • Monitor how efficiently the protocol modernizes the routing data over time, which concentrating on parameters such as packet delivery ratio, route accuracy, and scalability with increased nodes.

% Performance Metrics

numUpdates = zeros(numNodes, 1); % Count of updates for each node

for i = 1:numNodes

for j = 1:length(routingTables{i}.distance)

if routingTables{i}.distance(j) <= range * scopeLevels(1)

numUpdates(i) = numUpdates(i) + 1;

end

end

end

avgUpdates = mean(numUpdates);

disp([‘Average number of updates: ‘, num2str(avgUpdates)]);

Explanation of Key FSR Components

• Fisheye Scope Levels: Scope levels find the frequency and detail of updates for nodes according to its distance from the updating node. Nearer nodes receive often updates, even though distant nodes are receive rare updates.
• Routing Table Maintenance: Every single node sustains a routing table updated along with next-hop data and distances to every other node including variable update frequency.
• Visualization and Analysis: The simulation offers a visual representation of the node connectivity and routing paths that allowing analysis of the routing accuracy and protocol effectiveness.

With MATLAB tool, we executed a detailed simulation approach for Fisheye State Routing (FSR) Protocol projects that were simulated and analysed and we are positioned to expand on the findings as more information becomes available. At phdprime.com, we offer the perfect solution for simulating FSR Protocol Projects using MATLAB, complete with comprehensive support. Just send us an email, and we’ll provide you with top-notch simulation guidance and thorough explanations.