The rapid development of the Internet of Things (IoT) and its widespread applications in fog computing environments have underscored the urgent need for secure, scalable, and energy-efficient data exchange mechanisms. This study introduces a hybrid consensus architecture designed to address these challenges by combining Delegated Proof of Stake (DPoS) and Whale Optimization Techniques (WOT). The primary objective of this model is to optimize resource allocation, enhance security, and minimize energy consumption while ensuring scalable and efficient data sharing within fog-based IoT networks. The proposed methodology utilizes DPoS to limit node validation to a select group of trusted delegates, reducing computational overhead and improving scalability by streamlining the consensus process. Meanwhile, WOT enhances decision-making by mimicking the bubble-net feeding behavior of humpback whales, allowing for dynamic and efficient optimization of resource allocation. The integration of these two techniques significantly boosts system performance. Empirical results demonstrate that the hybrid model achieves a 95% increase in security and a 94% improvement in energy efficiency compared to conventional IoT consensus methods. Additionally, the model optimizes processing times, increases data throughput, and minimizes latency, facilitating real-time, low-latency communication that is essential for IoT applications. This combination of DPoS and WOT balances resource utilization and effectively addresses the trade-offs between security, energy efficiency, and scalability. Consequently, the hybrid DPoS-WOT consensus model emerges as a robust and practical solution for secure, efficient, and scalable IoT data sharing in fog computing environments.

Hybrid Consensus Delegated Proof of Stake (DPoS) Whale Optimization Technique (WOT) Fog Computing IoT Data Sharing Energy Efficiency