Intelligent Task Offloading for Blockchain-Integrated Edge-Cloud Systems using MOO and NFIS-based Decision Making

Resource constraints, mobility issues, and network fluctuations are the major barriers to task offloading in blockchain-integrated edge- cloud computing systems. This paper proposes a practical method that uses multi-objective optimization and non-fuzzy inference systems for dynamic task distribution. The proposed method improves performance by reducing response times, energy consumption, and resource usage under varying environments. Development of mobility-aware offloading technique in blockchain-edge systems for task distribution with real-time decision-making. The approach is concerned with reducing delay and energy consumption with optimal resource usage while network dynamics along with device mobility are ever-changing. The paper introduces a multi-objective optimization and non-fuzzy inference system to tackle the abovementioned problems. It is designed to permit decentralized decision-making and adaptive resource allocation for mobile blockchain-enabled edge computing. The approach is shown, through simulations, to be able to reduce response time by 14-21% and increase accuracy, precision, recall, and F1 score. It decreases the root mean error against the traditional offloading technique. The proposed solution for mobility-aware task offloading in edge-cloud systems using multi-objective optimization and non-fuzzy inference outperforms traditional approaches. It enhances effectiveness, minimizes response times, and maximizes resource usage for blockchain-integrated edge computing in dynamic environments.