Wireless Sensor Networks (WSNs) are fundamental to distributed sensing and monitoring applications, yet their resource-constrained and dynamically changing environments pose significant challenges in achieving energy-efficient, secure, and reliable communication. Conventional routing mechanisms generally optimize individual network objectives and exhibit limited adaptability to energy imbalance, malicious behavior, and node or link failures. To address these limitations, this paper proposes an Intelligent Hybrid Optimization Framework (IHOF) for adaptive and resilient WSN communication. The framework introduces a coordinated four-stage strategy comprising energy-aware clustering, trust-guided secure routing, learning-based fault recovery, and adaptive data aggregation. Initially, a hybrid Harris Hawk Optimization–Salp Swarm Algorithm (HHO–SSA) jointly considers residual energy, communication distance, and node density to determine balanced cluster-head configurations. Subsequently, blockchain-assisted trust management is coupled with Whale Optimization Algorithm (WOA) to construct secure and energy-aware routing paths while isolating unreliable nodes. A Deep Q-Network (DQN) then learns network-state-dependent recovery policies for dynamic rerouting under node and link failures. Finally, Artificial Bee Colony (ABC) optimization performs multi-objective data aggregation by jointly minimizing redundant transmissions, energy consumption, and communication delay. The framework is evaluated through network-level simulations using network lifetime, energy consumption, packet delivery ratio, end-to-end delay, throughput, attack detection rate, and fault recovery time. The proposed coordinated optimization strategy is designed to improve the overall energy efficiency, communication security, and fault resilience of WSNs under dynamic operating conditions.
Wireless Sensor Networks (WSNs) are fundamental to distributed sensing and monitoring applications, yet their resource-constrained and dynamically changing environments pose significant challenges in achieving energy-efficient, secure, and reliable communication. Conventional routing mechanisms generally optimize individual network objectives and exhibit limited adaptability to energy imbalance, malicious behavior, and node or link failures. To address these limitations, this paper proposes an Intelligent Hybrid Optimization Framework (IHOF) for adaptive and resilient WSN communication. The framework introduces a coordinated four-stage strategy comprising energy-aware clustering, trust-guided secure routing, learning-based fault recovery, and adaptive data aggregation. Initially, a hybrid Harris Hawk Optimization–Salp Swarm Algorithm (HHO–SSA) jointly considers residual energy, communication distance, and node density to determine balanced cluster-head configurations. Subsequently, blockchain-assisted trust management is coupled with Whale Optimization Algorithm (WOA) to construct secure and energy-aware routing paths while isolating unreliable nodes. A Deep Q-Network (DQN) then learns network-state-dependent recovery policies for dynamic rerouting under node and link failures. Finally, Artificial Bee Colony (ABC) optimization performs multi-objective data aggregation by jointly minimizing redundant transmissions, energy consumption, and communication delay. The framework is evaluated through network-level simulations using network lifetime, energy consumption, packet delivery ratio, end-to-end delay, throughput, attack detection rate, and fault recovery time. The proposed coordinated optimization strategy is designed to improve the overall energy efficiency, communication security, and fault resilience of WSNs under dynamic operating conditions.
 

Wireless Sensor Networks; Hybrid Metaheuristic Optimization; Energy-Aware Clustering; Secure Routing; Blockchain-Assisted Trust Management; Deep Reinforcement Learning; Fault-Tolerant Communication; Adaptive Data Aggregation; Network Lifetime.