SDN-Driven Multi-Objective Task Offloading in IoT-Enabled UAVs in Edge-Cloud Computing Using Double DQN

Effective task offloading is crucial for overcoming constraints like resource limitations, latency, and energy consumption in Internet of Things (IoT)-enabled Unmanned Aerial Vehicles (UAVs) running in edge-cloud computing environments. Due to ineffective handling of task offloading many issue can arise, such as higher latency, increased computational power, and utilization of extra resources. It is essential to efficiently optimize task offloading. This can be resolved by optimizing task allocation and resource utilization, which is achieved by the dynamic and intelligent integration of Double Deep Q Networks (DDQN) and Software-Defined Networking (SDN). In this paper, we present a hybrid SDQNEC, a new architecture that uses DDQN to optimize task offloading in actual time and SDN for centralized network control and administration. The Markov Decision Process (MDP) formulation of the task offloading issue allows the DDQN to evaluate the environment and identify the best way to distribute tasks across edge and cloud resources. Minimizing total costs and delays, improving resource utilization, and minimizing task rejection rates are the objectives of the suggested approach. Results from simulations show that SDQNEC provides a 40% improvement in resource utilization over baseline DDQNEC models and a 50% lower task rejection rate than classic DQNEC approaches. Furthermore, SDQNEC guarantees effective path selection for task offloading and significantly decreases system costs, attaining up to 55% optimum path utilization under high task loads. These outcomes demonstrate how well the framework works in dynamic and resource-constrained environments to increase task acceptance rates, optimize resource efficiency, and reduce delays. SDQNEC guarantees an effective trade-off between latency and resource cost by strategically assigning tasks to edge or cloud servers according to their availability and resource requirements. This research offers a strong basis for developing edge-cloud computing in Internet of Things networks, with possible uses in vital fields including disaster recovery, industrial automation, and healthcare.