## 3 System Model and Problem Statements
### System Model  
Offloading is useful for overcoming potential IoT constraints such as low battery power and bandwidth [36]. Offloading is defined as determining which computing tasks should be offloaded and how limited resources should be planned to ensure QoS metrics. Furthermore, the offloading decision should take into account the user's QoS needs, such as processing delay, energy usage limits, and so on. Figure 1 shows a three-layer structure for IoT task offloading. The edge computing structure is viewed as an interlayer between the two. This structure is critical for increasing data offloading privacy while processing tasks quickly and returning them to the IoT system. In this system, IoT delay-sensitive tasks can be split into equal pieces and offloaded to other layers. Machine Learning (ML) components are integrated into the objects and the edge nodes [37,38]. Nevertheless, thanks to evolving edge device technology, the combination of blockchain and edge allow for secure network and computation access and control at the edges. Since both blockchain and edge have a distributed structure, they are better suited to each other. The incorporation of edge and blockchain into IoT is advantageous, given the high-security requirements and intensive computation in IoT networks. The proposed system model is divided into three layers, as shown in Figure 2: IoT layer, edge layer, and blockchain. These three layers' models are depicted and portrayed in the following manner. The list of symbols used in the paper is provided in Table 2.  
Figure 1: Using multiple layers and networks to offload different aspects of tasks.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Symbol** & **Description** \\ \hline \(O\) & The number of IoT devices \\ \hline \(E\) & The number of edge devices \\ \hline \(\mathbb{T}\) & Time slots \\ \hline \(n\) & The job of IoT devices \\ \hline \(A\) & Set of network tasks \\ \hline \(p\) & IoT object’s attributes \\ \hline \(w\) & Task attributes \\ \hline \(g\) & Network attribute \\ \hline \(\beta\) & Harvested energy \\ \hline \(b\) & Battery level \\ \hline \(f\) & Frequency of CPU clocks \\ \hline \(Z\) & The set of cells \\ \hline \(Z^{n}\) & Group of controllers \\ \hline \(d_{i}\) & The size of the computation task \\ \hline \(d_{j}\) & The delay provisions of the job \\ \hline \(CE\) & Contains the energy of all controllers \\ \hline \(cc\) & Computation’s overhead \\ \hline \(CC(t)\) & The total computing cycles \\ \hline \(\hat{\eta}\) & Computation resources \\ \hline \(E_{local}\) & Local energy consumption \\ \hline \(E_{offload}\) & The energy consumption of data transmission \\ \hline \(E_{local}(t)\) & Energy consumption of Total execution \\ \hline \(E_{blockchain}(t)\) & Energy consumption of blockchain system \\ \hline \(q_{t}\) & The computational complexity of the tasks \\ \hline \(\mathbb{g}_{z}\) & Energy Consumption Coefficient \\ \hline \(pt\) & The MC’s transmission capacity \\ \hline \(bn\) & The number of blockchain nodes \\ \hline \hline \end{tabular}
\end{table}
Table 2: Notations List.  
Figure 2: The DO\({}^{2}\)QIEO network scenario’s architecture, which includes a cloud, edge, and blockchain system.