Adaptive Load Balancing Strategies for Reducing System Failures in Network Infrastructures

WSN are now essential components of contemporary network infrastructures, providing instantaneous data collection and environmental surveillance in diverse domains. Nevertheless, these networks encounter substantial obstacles such as congestion, system failures, and inefficient energy usage. Adaptive load balancing strategies are essential for enhancing network performance and resilience in order to tackle these issues. This study introduces a new method that utilizes density-based cluster head selection algorithms to enhance load balancing, effective data aggregation, routing, and energy management in wireless sensor networks (WSN). The proposed adaptive load balancing strategy employs an advanced clustering algorithm that chooses cluster heads based on both node density and residual energy, guaranteeing effective network organization and communication. This method employs adaptive mechanisms to accommodate fluctuations in network conditions, ensuring equitable distribution of traffic throughout the network and mitigating the potential for system failures caused by excessive load. The proposed strategy shows substantial enhancements in energy efficiency and network lifespan when compared to conventional density-based cluster head selection methods, as evidenced by experimental evaluations. The findings demonstrate that our methodology results in decreased energy usage and enhanced energy retention within the network. The results indicate that the proposed method exhibits lower energy consumption (170 units) compared to density-based cluster head selection (299 units), while also maintaining higher remaining energy levels (13,930 units) compared to the traditional approach (13,801 units). The findings emphasize the effectiveness of the adaptive load balancing strategy in prolonging the lifespan of WSN and decreasing the likelihood of system failures.