## 6 Conclusions and Future Work  
This article provides DO2 QIEO, a new learning-based technique for offloading in IoT-edge platforms. We used DO2 QIEO to solve the formulated problem we modeled as an MDP. This method is applicable in both online and offline environments. Obtaining sufficient samples to explore each state can be difficult, resulting in method crashes. Therefore, rather than calculating Q-values for each state-action pair, we estimate pair state-action utilizing deep Q-learning, which is the key premise of DO2 QIEO. The TL system is also employed in the process of initiating the system. In addition, an online deep Q-learning method is presented for the online mode to generate the optimum policy on the fly. In online mode, our solution applies the PDS mechanism, which makes use of partially known knowledge about dynamic systems and allows the edge node to incorporate such knowledge into its learning experience to accelerate learning. Moreover, edge computing is employed in this system to reduce reaction time and conserve energy. Our proposed technique implies that RF energy harvesters, wind turbines, and solar modules are examples of sources linked to IoT devices that can convert renewable resources to power to extend battery life. In addition, there are various drawbacks in present blockchain-enabled IoT systems, such as inadequate efficiency of the blockchain's consensus mechanism, significant computing overhead of network systems, and unacceptably high energy consumption for computation jobs. To address the concerns and challenges, we have integrated an edge system into blockchain-enabled IoT networks to boost IoT objects' computing capability and the consensus process's competence. Compared to traditional online DL techniques, the suggested technique substantially enhances high processing and is managed efficiently. The circumstances for both local processing and fully offloading are supplied, and the performance of the proposed system after convergence is tested under various standard situations. A comparison of DO2 QIEO and benchmarks revealed that the DO2 QIEO out  
Figure 13: System latency in comparison to different blockchain nodes.  
performed in reduced energy consumption, system cost, lower computational overhead, shorter delay, and lower task failure rate.  
For future work, the Internet-of-Behavior (IoB) is thought to be the next generation of the IoT. The IoB is based on IoT and results in dynamic adaption and behavior creation. Due to its networked nature, data analytics could be used to adapt and manipulate behavior rapidly. Our next project is online dynamic offloading in the IoB environment. Furthermore, task reliability is a critical scenario due to the numerous network components. It is usually true that the service provider must ensure the dependability of the edge servers. As we all know, determining user-side reliability is difficult. Future researchers can also use 5G/6G technologies to tackle this problem. Additionally, using other methods such as a recurrent neural network [59; 60], fuzzy sets [61], recursive neural net (RvNN) [62], Elman neural networks (ENN) [63; 64], and temporal convolution network (TCN) [65] to solve this problem can be investigated in the future. Additionally, the efficient mutation operator and Gaussian process regression [66; 67; 68] can increase the efficiency of the proposed method. Finally, considering other parameters such as multistability [69] must be investigated in future work.  
Conceptualization, A.H., methodology, A.H., and M.A.J.J. and N.J.N.; software, A.H.; validation, M.A.J.J., N.J.N. and S.A.; investigation, A.H., M.A.J.J., N.J.N. and S.A.; writing--original draft preparation A.H.; writing--review and editing, N.J.N.; supervision, M.A.J.J. and N.J.N. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
All data are reported in the paper.  
The authors declare no competing interests.