Overview:

• Research project focused on enhancing energy-efficient routing in Wireless Sensor Networks (WSNs) using Swarm Intelligence Algorithms.

Role:

• Varun Niraj Agarwal, Avaneesh Kanshi, and Navneet Vinod Melarkode from VIT Computer Science and Engineering Dept.
• Responsibilities: algorithm design, coding, simulation, and analysis.

Technologies Used:

• Python and MATLAB for implementing traditional, improved, and DB-IABR protocols.
• Utilized Matplotlib for real-time visualization.
• DB-IABR built upon Ant Colony Optimization (ACO).

Key Achievements:

• Developed Direction Based-Improved Ant Based Routing Algorithm (DB-IABR).
• Successfully implemented and tested protocols in Python and MATLAB.
• Demonstrated superior performance of DB-IABR in terms of energy efficiency and network optimization.
• Simulated existing routing protocols for comparison.

Challenges Overcome:

• Addressed challenges related to efficient packet forwarding, path optimization, and energy conservation.
• Mitigated challenges through careful algorithmic design.

Collaboration:

• Collaborative effort among team members from VIT's School of Computer Science Engineering.
• Regular meetings, brainstorming, and shared responsibilities for effective problem-solving.

Learnings:

• Insights into Swarm Intelligence algorithms, particularly Ant Colony Optimization.
• Understanding trade-offs between energy efficiency, path optimization, and security in WSNs.

Key Skills Demonstrated:

• Algorithm design and optimization.
• Python and MATLAB programming.
• Simulation and analysis.
• Effective collaboration and teamwork.

Results:

• Simulated traditional, improved, and DB-IABR protocols in a similar network with Python and MATLAB.
• Simulated existing routing protocols for benchmarking and comparison.
• Compared results in various parameters, including network lifetime, average path hops, path length, and energy levels.

Featured Content:

• Published in the IEEE Explore Journal

Conclusion:

• Contributed significantly to advancing energy-efficient routing in WSNs.
• DB-IABR demonstrated superior performance, offering a balance between energy conservation and network optimization.
• Pride in being part of a project with tangible results.
• Future work may focus on optimizing security aspects and ensuring scalability of the DB-IABR algorithm in real-world applications.

How to Run

Simulation Visual Representation

The visual representation and simulation of changing networks using different protocols can be executed through the following Python files located in the code/Simulations directory:

• ABR.py: Simulates the network using the ABR protocol.
• IABR.py: Simulates the network using the IABR protocol.
• DBIABR.py: Simulates the network using the DBIABR protocol.

To run a simulation, navigate to the code/Simulations directory and execute the desired Python file:

python ABR.py

Protocol Comparison

The main protocol comparison is performed using the main.py file located in the /code/ directory. This file compares the protocols over the same network and produces results in various parameters, including network lifetime, average path hops, path length, and energy levels.

To run the protocol comparison, navigate to the /code/ directory and execute the main.py file:

python main.py

This will generate comprehensive results and metrics for each protocol, allowing for a detailed analysis and comparison.

Make sure you have Python installed on your machine and all required dependencies are satisfied before running the simulations. Additionally, feel free to explore and customize the simulation parameters within the respective Python files to suit your specific needs.