Open Access

ARTICLE

Wireless Network Security Using Load Balanced Mobile Sink Technique

Reem Alkanhel¹, Mohamed Abouhawwash^2,3, S. N. Sangeethaa⁴, K. Venkatachalam⁵, Doaa Sami Khafaga^6,*

1 Department of Information Technology, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia
2 Department of Mathematics, Faculty of Science, Mansoura University, Mansoura, 35516, Egypt
3 Department of Computational Mathematics, Science, and Engineering (CMSE), Michigan State University, East Lansing, 48824, MI, USA
4 Department of Computer Science and Engineering, Bannari Amman Institute of Technology, Sathyamangalam, 638401, India
5 Department of Computer Science and Engineering, CHRIST (Deemed to be University), Bengaluru, 560074, India
6 Department of Computer Sciences, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia

* Corresponding Author: Doaa Sami Khafaga. Email:

Intelligent Automation & Soft Computing 2023, 35(2), 2135-2149. https://doi.org/10.32604/iasc.2023.028852

Received 19 February 2022; Accepted 07 April 2022; Issue published 19 July 2022

Abstract

Real-time applications based on Wireless Sensor Network (WSN) technologies are quickly increasing due to intelligent surroundings. Among the most significant resources in the WSN are battery power and security. Clustering strategies improve the power factor and secure the WSN environment. It takes more electricity to forward data in a WSN. Though numerous clustering methods have been developed to provide energy consumption, there is indeed a risk of unequal load balancing, resulting in a decrease in the network’s lifetime due to network inequalities and less security. These possibilities arise due to the cluster head’s limited life span. These cluster heads (CH) are in charge of all activities and control intra-cluster and inter-cluster interactions. The proposed method uses Lifetime centric load balancing mechanisms (LCLBM) and Cluster-based energy optimization using a mobile sink algorithm (CEOMS). LCLBM emphasizes the selection of CH, system architectures, and optimal distribution of CH. In addition, the LCLBM was added with an assistant cluster head (ACH) for load balancing. Power consumption, communications latency, the frequency of failing nodes, high security, and one-way delay are essential variables to consider while evaluating LCLBM. CEOMS will choose a cluster leader based on the influence of the following parameters on the energy balance of WSNs. According to simulated findings, the suggested LCLBM-CEOMS method increases cluster head selection self-adaptability, improves the network’s lifetime, decreases data latency, and balances network capacity.

---

Keywords

---