Swarm Intelligence is a meta-heuristic methodology used to solve numerical optimization problems by simulating swarm behaviors found in nature. These techniques demonstrate the desirable properties of interpretability, scalability, effectiveness, and robustness. There is a certain similarity between bio-inspired systems and routing in networks, especially in Wireless Sensor Networks (WSNs). We are working on the design for a Swarm Intelligence (SI) based routing protocol capable of being deployed on large-scale WSNs to ensure efficient energy usage and prolonging the network lifetime.
Xinlu Li, Brian Keegan, Fredrick Mtenzi, Thomas Weise, and Ming Tan. Energy-Efficient Load Balancing Ant Based Routing Algorithm for Wireless Sensor Networks. IEEE Access 7:113182–113196. August 2019.
doi:10.1109/ACCESS.2019.2934889 / pdf@IAO / pdf@IEEE
Indexing: EI, SCIE, 2区
Xinlu Li, Brian Keegan, and Fredrick Mtenzi. Energy Efficient Hybrid Routing Protocol Based on the Artificial Fish Swarm Algorithm and Ant Colony Optimization for WSNs. Sensors, 18(10):3351, October 2018.
doi:10.3390/s18103351 / pdf
Indexing: SCI, EI, 3区.
Xinlu Li, Brian Keegan, and Fredrick Mtenzi. Clustering Opportunistic Ant-Based Routing Protocol for Wireless Sensor Networks. In Proceedings of the 7th International Conference on Computer Engineering and Networks (CENet2017), July 22-23, 2017, Shanghai, China, volume 299 of Proceedings of Science (PoS), Trieste, Italy: Sissa Medialab.
doi:10.22323/1.299.0038 / pdf
Xinlu Li, Brian Keegan, and Fredrick Mtenzi. Ant Colony Clustering Routing Protocol for Optimization of Large-Scale Wireless Sensor Networks. In Cristina Muntean and Pramod Pathak, editors, Proceedings of the 14th Information Technology and Telecommunications Conference (IT&T'2015), October 29-30, 2015, National College of Ireland, Dublin, Ireland, pages 2–9. ISSN 1649-1246