Dynamic Task Allocation and Energy-Aware Scheduling in Heterogeneous Networks

Effective and accurate management of dynamic workloads in wireless sensor networks (WSNs) is critical to ensure energy efficiency, network longevity, and reliable information delivery in heterogeneous environments. Traditional heterogeneous WSN solutions often suffer from unbalanced energy usage, poor node placement and load distribution optimization, leading to poor performance and high operating costs. This proposes a new energy-aware load balancing architecture for heterogeneous WSNs that incorporates a multi-level optimization approach. First, Energy Weighted Probabilistic Deployment (EWPD) takes energy efficiency into account, guaranteeing optimal node placement to maximize network coverage and extend lifetime. Second, network initialization and node classification based on Sliding Time Windows (STW) predict task execution patterns and allow adaptive allocation of nodes according to task performance. Third, Expected Time to Task Completion (ETTC) scheduling with cluster formation and path discovery can predict workload times to maximize routing selection. Fourth, workload analysis is a tool based on Spider Swarm Intelligence (SSI) that measures the computational needs of nodes and finds the most efficient allocation of workload resources. Finally, the Minimum-Maximum Priority Energy-Aware Self-Scheduling Mechanism (DMMPS-EASSM) is a dynamic operation and migration mechanism designed to balance energy consumption and provide efficient data transmission. Experimental results demonstrate that the proposed framework can significantly improve network performance, reduce energy wastage, and improve resource utilization compared with traditional approaches based on heterogeneous WSNs.