Portrait of Dr. Zhen LIU.

Our team congratulates Xinlu Li [李新路], who, from today (2019-02-13) on, is Dr. Xinlu Li [李新路博士]! Earlier this year, Xinlu successfully defended his PhD thesis  "Energy-Efficient Swarm Intelligence based Routing Protocol for Wireless Sensor Networks" at the Technological University Dublin (TU Dublin) in Dublin, Ireland in front of Professor Liam Murphy from the University College Dublin (UCD) and Dr. Paul Doyle (TU Dublin). His work was co-supervised by Dr. Fred Mtenzi and Dr. Brian Keegan. Today his academic award has been approved by the Academic Council of TU Dublin. Congratulations, Dr. Li!

In the framework of his thesis, Dr. Li conducted research on Swarm Intelligence (SI) based routing for Wireless Sensor Networks (WSNs). SI 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 similarity between bio-inspired systems and routing in networks, especially in WSNs. Dr. Li designed a SI-based routing protocol capable of being deployed on large-scale WSNs to ensure efficient energy usage and prolonging the network lifetime.

Xinlu Li after his PhD defense.
Dr. Xinlu Li after his PhD defense. Left to right: Dr. Brian Keegan (supervisor), Dr. Xinlu Li, and the external examiners Prof. Liam Murphy (UDC) and Dr. Paul Doyle (TU Dublin).


Wireless Sensor Networks (WSNs) consist of a large number of distributed sensor nodes and have broad application prospects in many fields, including agriculture and industry. Due to the limitations of WSN sensor nodes power supplies, processing capacity and communication channel, the most important challenge of a routing protocol for WSNs is the energy consumption and the extension of the network lifetime. Dr. Li's thesis presents a 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. After an initial study of the characteristics of the Ant Colony Optimization (ACO) framework and hierarchical networks, an ACO-based clustering routing algorithm, ACCR, was proposed. ACCR presented a novel approach by providing a clustering algorithm to divide the network into several clusters along with an ACO based routing algorithm used in inter-cluster communication. The clustering algorithm is developed with an estimating average energy scheme. ACCR considers both the energy levels and path length as key metrics when updating the local heuristic value and pheromone trail. To enhance the performance of ACCR, an energy efficient load balancing ACO-based routing algorithm for WSNs (EBAR) is then presented. EBAR utilizes a pseudo-random proportional scheme in the routing discovery algorithm and an opportunistic broadcast algorithm instead of flooding for control packets. Furthermore, EBAR presents an improved metaheuristic update algorithm. Next, a novel hybrid SI-based routing algorithm, FSACO, is proposed. FSACO combines the Artificial Fish Swarm Algorithm (AFSA) with ACO with the result of reducing the convergence time and avoiding stagnation. FSACO was developed with an AFSA-based initial route discovery algorithm and a dynamic crowd factor combined with an ACO routing algorithm. FSACO demonstrated a 68.6% improvement in energy efficiency performance when compared current best practice WSN routing algorithm. Lastly, the performance evaluation of the aforementioned SI-based routing algorithms is verified by simulations and a physical testbed. The simulation and testbed results validate the improvements of the proposed methods in terms of better energy efficiency and longer network lifetime.


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