Dijkstra's Algorithm: Real-World Application

Let's consider a network with nodes and links where AI is used to enhance Dijkstra's Algorithm.

Scenario:

Initial Network: Nodes A, B, C, D, and E with initial weights representing latency.

AI Prediction: AI predicts congestion in lnk C-> E and adjusts its weight dynamically.

Initial Weights:

A --(4) --> B

A --(2) --> C

B --(5) --> D

C --(8) --> D

C --(1) --> E

C --(3) --> E

AI-Adjusted Weights:

A --(4) --> B

A --(2) --> C

B --(5) --> D

C --(8) --> D

C --(5) --> E

C --(3) --> E

Initial Dijkstra's Calculation:

• Start at A: Distances: A=0, B=infinity, C=Infinity, D=infinity, E=infinity
• Update from A: A=0, B=4, C=2
• Move to C: A=0, B=4, C=2, D=10, E=3
• Move to E: A=0, B=4, C=2, D=6, E=3
• Move to B: A=0, B=4, C=2, D=9
• Move to D: No further updates

Shortest path A to E: A-->C-->E with total latency = 3 ms.

AI-Enhanced Dijkstra's Calculation:

• Start at A: Distances: A=0, B=infinity, C=infinity, D=infinity, E=infinity
• Update from A: A=0, B=4, C=2
• Move to C: A=0, B=4, C=2, D=10, E=7
• Move to B: A=0, B=4, C=2, D=9
• Move to D: A=0, B=4, C=2, D=9, E=6
• Move to E: No further updates

Updated shortest path A to E: A-->C-->E with total latency = 7 ms.

Conclusion

Dijkstra's Algorithm finds the shortest path in a network by considering static travel times. AI enhances this by dynamically adjusting these times based on real-time data, predictions, and ongoing analysis, ensuring the route chosen adapts to current conditions. This makes network routing smarter and more efficient.