The exponential proliferation of the Internet of Things (IoT) and optical IoT (O-IoT) has introduced substantial challenges concerning computational capacity and energy efficiency. IoT devices generate vast volumes of aggregated data and require intensive processing, often resulting in elevated latency and excessive energy consumption. Task offloading has emerged as a viable solution; however, many existing strategies fail to adequately optimize both latency and energy usage. This paper proposes a novel task-offloading approach based on deep Q-network (DQN) learning, designed to intelligently and dynamically balance these critical metrics. The proposed framework continuously refines real-time task offloading decisions by leveraging the adaptive learning capabilities of DQN, thereby substantially reducing latency and energy consumption. To further enhance system performance, the framework incorporates optical networks into the IoT–fog–cloud architecture, capitalizing on their high-bandwidth and low-latency characteristics. This integration facilitates more efficient distribution and processing of tasks, particularly in data-intensive IoT applications. Additionally, we present a comparative analysis between the proposed DQN algorithm and the optimal strategy. Through extensive simulations, we demonstrate the superior effectiveness of the proposed DQN framework across various IoT and O-IoT scenarios compared to the BAT and DJA approaches, achieving improvements in energy consumption and latency of 35%, 50%, 30%, and 40%, respectively. These findings underscore the significance of selecting an appropriate offloading strategy tailored to the specific requirements of IoT and O-IoT applications, particularly with regard to environmental stability and performance demands.

The exponential growth of the Internet of Things (IoT) has generated significant challenges related to computing capacity and energy consumption. IoT devices produce large amounts of combined data and demand processing tasks, often leading to high latency and redundant energy consumption. Task offloading has emerged as an applicable solution; however, existing strategies often neglect optimization of latency and energy consumption. This paper introduces a novel task-offloading approach based on deep Q-network (DQN) learning, designed to intelligently and dynamically balance these important metrics. The proposed framework continuously optimizes real-time task offloading decisions by deploying the adaptive learning capabilities of DQN, thus significantly improving latency and energy consumption. To further enhance performance, the framework integrates optical networks into IoT-fog-cloud architecture, leveraging their high-bandwidth and low-latency capabilities. This integration enables tasks to be distributed and processed more efficiently, especially in data-intensive IoT applications. In addition, we present a comparative analysis of the proposed DQN algorithm and the optimal strategy. Through extensive simulations, we demonstrate the effectiveness of the proposed DQN framework in different IoT scenarios over the BAT and DJA approaches, enhancing energy consumption and latency by 35%, 50%, 30%, and 40%, respectively. Our results further demonstrate the importance of selecting an appropriate strategy according to the specific requirements of the IoT application, particularly in terms of environmental stability and performance requirements.