A multi-objective task offloading and resource allocation in blockchain-enabled IoT networks

The integration of Mobile Edge Computing (MEC) and Blockchain has emerged as an efficient approach to support the development of secure and delay-sensitive Internet of Things (IoT) applications. Despite these advantages, blockchain-enabled IoT systems face a fundamental challenge because the security and trust benefits of blockchain are accompanied by additional computation and communication overheads that can degrade latency and energy efficiency in resource-constrained environments. This paper develops a unified multi-objective optimization framework for task offloading and resource allocation in blockchain-enabled IoT networks. The considered system architecture incorporates IoT devices, MEC servers, and a cloud data center while explicitly modeling blockchain operations as part of the offloading process. A multi-objective optimization problem is formulated to jointly minimize total system latency and energy consumption while maximizing the aggregate reputation of the selected blockchain management nodes. To solve this problem, two evolutionary solution approaches are employed. The Non-dominated Sorting Genetic Algorithm II (NSGA-II) is used to identify Pareto-optimal trade-off solutions, and a Genetic Algorithm (GA) is adopted to solve a weighted-sum scalarized formulation. Comprehensive simulation results indicate a demonstrable trade-off between latency and energy consumption. Specifically, the analysis reveals that prioritizing high-reputation nodes for blockchain consensus roles significantly reduces transaction verification latency, thereby enhancing the overall system performance despite the operational overheads. The framework allows stakeholders to select solutions that best meet specific IoT application requirements.