An Advanced Approach of Optimal Routing Protocol for WSN Using Grey Wolf Optimizer


  • Omar Hisham Rasheed alsadoon College of Islamic sciences, Al-Iraqia University, Baghdad, Iraq



computational cost, cluster heads, sink node mobility, energy consumption, routing protocol


This study investigates the performance of an optimal routing protocol in Wireless Sensor Networks (WSNs) based on the Grey Wolf Optimizer (GWO), which is considered an outstanding Metaheuristic optimization technique. Unlike numerous previous studies, this research develops an advanced approach by optimizing the cluster head nodes and sink node mobility, which are essential elements for achieving an optimal routing protocol in WSNs. Several crucial factors, including the rate of energy consumption, and others  are considered to ensure trustworthy routing. To demonstrate the feasibility of the proposed approach, three other methods are utilized in a comparative study. Various evaluation measures are exploited, including the number of alive nodes, mean throughput, probability of avoiding unsuitable cluster heads, energy consumption, and computation cost. The simulation results clearly reveal the superiority of the GWO compared to the aforementioned schemes over 5000 implementation rounds. Throughout the simulations, the total number of nodes (700) in the proposed WSN remained operational up to 4500 rounds, with the attained throughput staying above 1000 (bit/sec). Notably, the number of dead nodes remains at 0 after 1500 rounds. Moreover, the probability of avoiding unsuitable cluster heads is the highest, and the packet delivery ratio consistently remains at or above 68%. Additionally, the energy consumption of nodes achieved by the GWO is the lowest and does not exceed 200 Joules, while the computational cost significantly declines to reach 30.5%.


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How to Cite

Hisham Rasheed alsadoon, O. (2024). An Advanced Approach of Optimal Routing Protocol for WSN Using Grey Wolf Optimizer. Al-Iraqia Journal for Scientific Engineering Research, 3(1), 17–33.