To simulate Hierarchical Routing within MATLAB, we can split the network into clusters or hierarchical levels. This technique minimizes the overhead of handling the large routing tables by dividing the network into smaller, controllable sections. Hierarchical routing is utilized within large-scale networks such as the Internet and particular kinds of ad-hoc networks in which it supports enhance the scalability and efficiency. In this guide, we will walk you through the simulation approach on how to simulate Hierarchical Routing in MATLAB.
Steps to Simulate Hierarchical Routing in MATLAB
- Define Network Topology with Hierarchical Levels:
- Split the network into several clusters or areas (subnetworks).
- Every single cluster has a cluster head or gateway router, which manages communication among clusters.
- Make an adjacency matrix or graph object to denote the connections within and among clusters.
- Set Up Routing Tables for Intra- and Inter-Cluster Routing:
- Each node within a cluster sustains a routing table for nodes in their cluster (intra-cluster routing).
- Cluster heads sustain routing tables for routes to other clusters like inter-cluster routing.
- Utilize the simpler routing approaches such as Dijkstra’s algorithm within clusters and route aggregation among clusters.
- Implement Cluster Head Election (Optional):
- For dynamic networks, execute a mechanism to select or allocate the cluster heads, according to the factors such as node ID or connectivity.
- It is specifically helpful for ad-hoc networks in which cluster heads need to modify over time.
- Simulate Packet Forwarding with Hierarchical Routing:
- Route packets in clusters utilizing the intra-cluster routing tables.
- For packets among clusters, route them to the cluster head that sends them to cluster head of the target cluster.
- Make certain the destination node within the target cluster acquires the packet via intra-cluster routing.
- Analyze and Visualize the Network:
- Plot the network topology that showing the clusters and cluster heads.
- Record packet paths, hop counts, and arrival times to compute the efficiency of hierarchical routing.
Example Code Outline
Below is a framework to replicate the hierarchical routing with a network splitted into clusters.
% Define network with hierarchical clusters
numClusters = 3;
clusterSize = 3;
numNodes = numClusters * clusterSize;
% Example adjacency matrix with weights (within and between clusters)
adjMatrix = [
0 1 2 5 0 0 0 0 0;
1 0 1 5 0 0 0 0 0;
2 1 0 5 0 0 0 0 0;
5 5 5 0 3 2 7 0 0;
0 0 0 3 0 1 2 5 0;
0 0 0 2 1 0 3 5 0;
0 0 0 7 2 3 0 5 0;
0 0 0 0 5 5 5 0 2;
0 0 0 0 0 0 0 2 0
];
% Define clusters and cluster heads
clusters = {[1, 2, 3], [5, 6, 7], [8, 9]};
clusterHeads = [4, 7, 8]; % Cluster heads for each cluster
% Function to find shortest path within a cluster using Dijkstra’s algorithm
function [dist, prev] = intraClusterDijkstra(adjMatrix, source, cluster)
numNodes = length(cluster);
dist = inf(1, numNodes);
dist(source) = 0;
visited = false(1, numNodes);
prev = NaN(1, numNodes);
for i = 1:numNodes
[~, u] = min(dist + visited*inf);
visited(u) = true;
for v = 1:numNodes
if adjMatrix(cluster(u), cluster(v)) > 0 && ~visited(v)
alt = dist(u) + adjMatrix(cluster(u), cluster(v));
if alt < dist(v)
dist(v) = alt;
prev(v) = u;
end
end
end
end
end
% Function to forward packet between clusters
function forwardPacket(src, dest, clusters, clusterHeads, adjMatrix)
% Identify source and destination clusters
srcCluster = find(cellfun(@(c) ismember(src, c), clusters));
destCluster = find(cellfun(@(c) ismember(dest, c), clusters));
if srcCluster == destCluster
% Intra-cluster routing
disp([‘Routing within Cluster ‘, num2str(srcCluster)]);
[dist, prev] = intraClusterDijkstra(adjMatrix, src, clusters{srcCluster});
else
% Inter-cluster routing through cluster heads
disp([‘Routing from Cluster ‘, num2str(srcCluster), ‘ to Cluster ‘, num2str(destCluster)]);
% Route from source to srcCluster head
[distSrc, prevSrc] = intraClusterDijkstra(adjMatrix, src, clusters{srcCluster});
% Route from srcCluster head to destCluster head
[distInter, prevInter] = intraClusterDijkstra(adjMatrix, clusterHeads(srcCluster), clusterHeads);
% Route from destCluster head to destination
[distDest, prevDest] = intraClusterDijkstra(adjMatrix, dest, clusters{destCluster});
end
% Display routing paths (in a real implementation, you would forward packets here)
disp([‘Path from ‘, num2str(src), ‘ to ‘, num2str(dest), ‘:’]);
disp([‘From Source to Cluster Head: ‘, num2str(distSrc)]);
disp([‘Between Cluster Heads: ‘, num2str(distInter)]);
disp([‘From Destination Cluster Head to Destination: ‘, num2str(distDest)]);
end
% Test the hierarchical routing
srcNode = 2;
destNode = 9;
forwardPacket(srcNode, destNode, clusters, clusterHeads, adjMatrix);
Explanation of the Code
- Network Topology: The adjacency matrix denotes a network in which nodes are classified into clusters. Each cluster has described cluster heads, which handle the inter-cluster communication.
- Clusters and Cluster Heads: Nodes are gathered into clusters including one cluster head in each cluster liable for inter-cluster communication.
- Intra-Cluster Routing: The intraClusterDijkstra function computes the shortest paths within a cluster with the help of Dijkstra’s algorithm.
- Inter-Cluster Routing: The forwardPacket function manages the routing among clusters, sending packets from the source to their cluster head, then from the source cluster head to the destination cluster head, and within the destination cluster to attain the destination node.
Visualize and Analyze Results
- Network Plot: Display clusters, cluster heads, and paths taken by packets using graph and plot functions.
- Performance Metrics: Analyse performance parameters like the number of hops, total delay, and packet delivery success to liken hierarchical routing efficiency.
Extending the Simulation
For a more advanced hierarchical routing simulation:
- Dynamic Cluster Head Selection: Execute the dynamic cluster head election according to the connectivity or node energy that is useful for wireless ad-hoc networks.
- Traffic Simulation: Replicate actual packet traffic among nodes to estimate the routing effectiveness and packet loss.
- Control Overhead Measurement: Assess the control message overhead needed to sustain the hierarchical routing data particularly for dynamic networks.
In this illustration, we aggregated the in-depth details of the Hierarchical Routing Projects and how to simulate and analyze them using MATLAB platform. For further references, we can provide the extra information to you.
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