Offloading is one of the critical enablers of the Internet of Things (IoT) as it helps overcome the resource limitations of individual objects. Offering enough computational power for IoT applications at the edge has become a severe problem. An intelligent edge is a potential approach for pushing intelligence to network edges, which has played the role of intelligent decision-making in many elements of edge, notably task offloading. By leveraging the edge, IoT devices with limited battery capacity can offload a portion of the tasks that can dramatically reduce latency and improve battery life. Because IoT devices have limited battery capacity, employing deep learning approaches in such devices results in higher energy consumption. As a result, several studies used energy harvester modules, which are not available to IoT devices in real-world scenarios because many IoT devices lack such modules. This article proposes the offloading problem by leveraging the Markov decision process. Furthermore, we built a lightweight version of the reinforcement learning technique to decrease complexity and deployed it in IoT devices. Then, we used a convolutional neural network to accelerate learning and put it on the edge platform. Throughout the entire working duration of the system, these two methods collaborate to provide the optimal offloading strategy. Also, transfer learning was used to initialize Q-table values to improve the system’s efficiency. The results showed that the proposed method outperforms five benchmarks in terms of delay by 3.3%, IoT device efficiency by 3.2%, energy use by 4.2%, and task failure rate by 2.9% on average.

The number of Internet of Things (IoT)-related innovations has recently increased exponentially, with numerous IoT objects being invented one after the other. Where and how many resources can be transferred to carry out tasks or applications is known as computation offloading. Transferring resource-intensive computational tasks to a different external device in the network, such as a cloud, fog, or edge platform, is the strategy used in the IoT environment. Besides, offloading is one of the key technological enablers of the IoT, as it helps overcome the resource limitations of individual objects. One of the major shortcomings of previous research is the lack of an integrated offloading framework that can operate in an offline/online environment while preserving security. This paper offers a new deep Q-learning approach to address the IoT-edge offloading enabled blockchain problem using the Markov Decision Process (MDP). There is a substantial gap in the secure online/offline offloading systems in terms of security, and no work has been published in this arena thus far. This system can be used online and offline while maintaining privacy and security. The proposed method employs the Post Decision State (PDS) mechanism in online mode. Additionally, we integrate edge/cloud platforms into IoT blockchain-enabled networks to encourage the computational potential of IoT devices. This system can enable safe and secure cloud/edge/IoT offloading by employing blockchain. In this system, the master controller, offloading decision, block size, and processing nodes may be dynamically chosen and changed to reduce device energy consumption and cost. TensorFlow and Cooja’s simulation results demonstrated that the method could dramatically boost system efficiency relative to existing schemes. The findings showed that the method beats four benchmarks in terms of cost by 6.6%, computational overhead by 7.1%, energy use by 7.9%, task failure rate by 6.2%, and latency by 5.5% on average.