# Honesty-Based Social Technique to Enhance Cooperation in Social Internet of Things
## 1 Introduction
These three metrics are combined through a machine learning-based method to detect the behavior of nodes in the SIOT. Various trust-based techniques have been used to detect and omit the selfish behavior of nodes in networks. Unlike current incentive and reputation-based techniques, our proposed social technique has different approaches to (i) credible community creation, (ii) selfish nodes after detection, and (iii) selection of heads. Other techniques lack the creation of a credible community. Nodes can be assessed by their honesty levels, and a credible community can be formed. In other techniques, the nodes with selfish behavior are warned and then expelled from the community after showing repeated selfish behavior. In our social technique, the nodes with selfish behavior are isolated in the selfish domain. Each selfish node is given a chance to rejoin the community by increasing its honesty level. The selection criteria of heads involve the node's seniority level, which is not considered in other techniques. A senior node has better knowledge of the network, having spent a long time in the network to make decisions about nodes' performances. In the proposed technique, trust parameters and social behavior are used to detect the selfish nodes in the network and stimulate them for better performance. A credible community is formulated based on the honesty levels of the nodes. A social technique is then used to select the heads of the community for the incentivization and punishment of nodes in the community. Detection of the selfish behavior of nodes is the major problem in SIoT networks. This issue has been addressed by many researchers, and our motivations for this study were as follows:  
1. Detection of selfish nodes in the network. 2. Increasing the number of honest nodes in the network to enhance the performance of the network. 3. Devising a social technique for the heads' selection to overcome the complexity in some contemporary schemes for the selection of heads. 4. Separating the selfish nodes from honest nodes for the better utilization of network resources. The following are the key benefits of the proposed scheme:  
1. Selfish nodes are stimulated for cooperation easily. 2. Network performance is optimized. 3. Our technique realizes the social aspects of the nodes in the SIoT. 4. Our technique effectively handles the detection of the selfish behavior of the nodes. 5. The isolation of selfish nodes helps with improving their social behavior. The primary contributions of our proposed scheme are:  
1. An honesty-based, credible community is established. 2. The seniority of the node is considered for head selection. 3. Isolation of selfish nodes from the community to improve the network's performance. The following are the objectives of the proposed technique:  
1. Establishing a credible community based on honesty attributes. 2. Enabling reliable communication among the nodes in the network to increase the number of honest nodes in the network for better utilization of the resources. 3. Proposing a mechanism to detect dishonest and selfish nodes in the network. 4. Separating the selfish nodes in a selfish domain. 5. Providing a chance to the selfish and dishonest nodes to participate in the network and hence increase the network's performance. The rest of the paper is organized as: The literature review is given in Section 2. The proposed model is defined and explained in Section 3. The procedures for the isolation of nodes and punishment are explained in Section 4. Section 5 discusses the monitoring system. The simulation results, comparisons, and performance analysis are given in Section 6. Finally, Section 7 concludes the proposed work and discusses the future directions.