Offloading dependent tasks in MEC-enabled IoT systems: A preference-based hybrid optimization method

The rapid development of IoT-based services has resulted in an exponential increase the number connected smart mobile devices (SMDs). Processing massive data generated by large SMDs is becoming a big problem for devices, servers, and wireless communication channels. A Multi-access Edge Computing (MEC) paradigm partially mitigates this deploying edge server nodes at networks nearby SMDs, but challenge still remains due to limited computation capacity MEC servers bandwidth In addition, dependency tasks applications on increases complexity problem. paper, we propose constrained multiobjective offloading optimization solution resolve task under resources. This improves Quality Service (QoS) through minimizing latency, energy consumption, rate failure caused We two-staged hybrid method solve first stage, decisions are made based preferences tasks. Then, second nearly optimal solutions found using modified Non-Dominated Sorting Genetic Algorithm (NSGA-III). overall efficiency proposed increased owing preference-based algorithm reinforcing NSGA-III generating better initial population. results extensive experiments show that significantly than existing methods.