## 5 System Design
### Strategic Game Model
A misbehaved node cannot mimic this mechanism, as the complete operation is carried out over private networks of the IoT-cloud system. The method uses the function of obtaining CIN information from the CSC module to extract information from a group of all of the nodes with higher values of \(QM\) and \(GT\) to be elected as a CIN. The system performs the computation of payoff \(P_{RIN-CIN}\) based on its respective attributes (i.e., \(QM_{RIN-CIN}\), \(GT_{RIN-CIN}\)), considering all the CIN present in the set obtained from this function of CSC. Using a direct propagation of control message among the mobile IoT nodes in an ad hoc network, the system shares computed payoff \(P_{CIN-RIN}\) among its adjacent mobile nodes. For better cost control, this operation is carried out in the private cloud; however, the final calculated value of the adjusted payoff is stored in a public cloud system where this information is appended to the CSC module using a discrete function of '_strategy_' shown in Figure 4.  
After executing the above-mentioned step, the proposed system aggregates the RIN interested in cooperation. At the same time, their recommended computed individual payoff values are used for maximising the gain of the selected CIN. In this part of the implementation, the core target is to confirm that the RIN is capable of increasing the payoff of the CIN in adherence to the allocated channel capacity value in the IoT-cloud system. Under these conditions, the presence of any misbehaved nodes demands the usage of more channel capacity, which the system instantly identifies due to contraction of the logical condition formed for selecting CIN. Apart from this operation, the scheme assists in directly propagating computed payoff to the accepted CIN by the RIN. Applying the concept of game theory, the proposed system mathematically defines the motivation of the CIN in the form of utility, based on their interest/motivation with respect to \(QM\), \(GT\), and \(P\). The expression for the utility of CIN is as written in Equation (6):  
\[\rho_{\text{{{CIN-RIN}}}}=\sum_{i=1}^{R}\Bigl{[}P_{(RIN-CIN)i}+QM_{(RIN-CIN)i}+GT _{(RIN-CIN)i}\Bigr{]} \tag{6}\]  
Using the list consisting of game strategy (as a dependent function of CSC), the system represents the node's interest with respect to their optimal payoff \(P\), \(GT\), and \(QM\). The initiation of the game is carried out by computing utility \(\rho_{\text{{{CIN-RIN}}}}\), \(QM_{\text{{{CIN-RIN}}}}\), and \(GT_{\text{{{CIN-RIN}}}}\). The system then computes the proportion of utility with respect to the demanded channel capacity followed up by evaluating the summation of all of the required channel capacity from the interested RIN. In the preliminary stage of this process, it accepts the whole list of selected RIN, which is reduced down by the filtering process. In this filtering process, the system checks if the total demanded channel capacity of the interested RIN is found to be more than the residual channel capacity at the CIN. Finally, the system filters out the RIN with a reduced value of utility \(\rho\). At the same time, the ultimate set of RIN is deployed by the selected CIN in the CSC module to confirm the final RIN. In the proposed SGM, the CIN announce its new role in the CSC module, which then computes the \(GT\) score in the GTC module. The identified role of the CIN is shared with the route-discovery control message that is exchanged with its adjacent mobile IoT nodes. All of the RIN validate the request message received from the CSC module using a function of obtaining CIN. The determination of the payoff is carried out by selecting CIN using the RIN after it gets a list of feasible CIN. The sharing of this payoff takes place in a private cloud among all of the RIN, which can further assist in fine-tuning their recommended payoffs and confirming the finally selected CIN. The announcement of the final CIN is carried out by the RIN, which updates all the nodes.