# Honesty-Based Social Technique to Enhance Cooperation in Social Internet of Things
## 3 Proposed Model
### Decision Module
The results show that the PDR of the HBST is superior to those of all three other methods at pause time 100. Results for PDR in the prior experiment with 25% selfish nodes were marginally better than those in this experiment with 50% selfish nodes for all protocols. As fewer

Figure 8: Packet delivery ratio for the network with 50% selfish nodes. Figure 7: Routing overhead ratio for the network with 50% selfish nodes. communication channels were constructed, and packet drops were more common, an increase in selfish nodes lowered the PDR. The results for end-to-end delay are shown in Figure 9. The results show that once the network is set up, the HBST starts performing well. At stop time 10, the HBST's packet delays were 7.40% shorter than those of the CAIS, 13.80% shorter than those of the HBDS, and 19.35% shorter than those of SSAR. As the nodes start communicating and a credible community is formulated, delays in the HBST decrease, as seen in the results. From pause time 30 to 100, it improves further. This is due to forming a credible community to forward messages to its neighbor nodes. At pause time 60, delays were 21.81% shorter than those of the CAIS, 23.56% shorter than those of the HBDS and 28.34% shorter than those of the SSAR. Similarly, at pause time 100, HBST had shorter delays than CAIS, HBDS, and SSAR techniques by 22.5%, 41.87% and 48.34%, respectively. There were 50% selfish nodes in the network, which our technique separated from the main network, so no communication was performed with these selfish nodes in our scheme. This decreased the delays in the network, as no communication was possible with selfish nodes until they improved their honesty levels. ## 7 Conclusions and Future Work

In this paper, we have proposed a novel technique for SIoT networks to deal with the selfish behavior of nodes. If the selfish behavior of the nodes is not addressed, it can degrade the performance of the network and reduce the network's basic value. HBST is a social technique to enhance the cooperation level of the nodes in the network, using honesty as a performance parameter. Unlike many other techniques that permanently expel the selfish nodes from the network, the proposed technique creates a separate domain for the selfish nodes and prevents all other honest nodes from communicating with selfish nodes. The selfish nodes in the isolated domain are given a chance to improve their behavior and become part of the network again. The simulation results have shown that the proposed mechanism has greatly improved the network's performance with the presence of selfish nodes. The key performance metrics are throughput, overhead ratio, PDR, and packet delay. In the future, nodes will also be given incentives or isolation from the main network to improve their behavior. In addition, incentives for game-based models dealing with virtual currencies can also be incorporated. The existing scheme can also be updated to avoid the selfish behavior of nodes in the network. Figure 2:0 End-to-end delays for the network with 50% selfish nodes. **Author Contributions:** [PERSON] introduced the main idea and finalized the methodology; S.K. supervised the entire work; M.M.U. conducted investigations, simulations, and validations; S.K. conducted the formal analysis, implementation of methods, and administration; N.A.A. evaluated the prior works/literature review; S.M. carried out original draft preparation, review, editing, and visualization; N.A.A. funding acquisition; E.A.M. reviewed, revised, and updated the manuscript. All authors have read and agreed to the published version of the manuscript. **Funding:** This project is funded by Researchers Supporting Project number (RSPD2023R648), King Saud University, Riyadh, Saudi Arabia. **Institutional Review Board Statement:** Not applicable. **Informed Consent Statement:** Not applicable. **Acknowledgments:** The authors extend their appreciation to the Researchers Supporting Program for Project number (RSPD2023R648), at King Saud University for supporting this research project.