# Honesty-Based Social Technique to Enhance Cooperation in Social Internet of Things
## 2 Literature Review
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**S. No.** & **Paper** & **Contributions** & **Limitations** \\ \hline \multirow{8}{*}{3} & & & Considers the discriminatory behavior & \\  & & of the object to detect the behavior of & \\  & & the node in the network. & \\  & & Uses data mining techniques and the KNN & & The node’s selfish behavior is only \\  & & algorithm to & detected no action is taken to \\
0 & & extract the meaning of the social attrib & & stimulate the nodes. \\  & & of the node. & & No punishments for the selfish nodes. \\  & & Social similarity service, the remaining & & \\  & & energy and service importance are considered & \\  & & social attributes. & \\ \hline \multirow{8}{*}{4} & & & Multilevel trust model. & \\  & & It is a machine-based method. & \\  & & Three social attributes are used. & \\  & & User trust dimension & & Can prove costly in a large network. \\  & & Device trust dimension & & \\  & & Service trust dimension & & \\ \hline \multirow{8}{*}{5} & & & A multistep method is proposed to detect selfish & \\  & & nodes in the network having three Steps. & & \\  & & Formation of cluster & & No punishment for the selfish nodes. \\  & & Multi-person game theory to send data & & No incentives for the selfish nodes to \\  & & to its neighbors. & & stimulate them for active participation. \\  & & Detection of selfish nodes by the reputations & & \\  & & of the node. & & \\ \hline \multirow{8}{*}{6} & & Identification of network communities. & & \\  & & Local and global information is used to detect the & & \\  & & influential node in the community. & & Detects only network community. \\  & & Parameters-free scheme and no previous & & Do not detect the selfish nodes \\  & & information are needed for community detection. & & in the network. \\  & & Can be used in business applications to & & \\  & & recommend friends. & & \\ \hline \multirow{8}{*}{7} & & & Honesty-based mechanism to select friends. & \\  & & Uses an updated friendship directory. & & \\  & & Privacy protection paradigm. & & \\  & & Uses MIT and ARAS datasets to select friends in & & \\  & & the network. & & \\ \hline \multirow{8}{*}{8} & & & Honesty-based democratic scheme & No isolation of selfish nodes. \\  & & Election-based scheme. & & No credible community is created. \\  & & Nodes are punished after repeated & & Election process is overhead. \\  & & selfish behavior. & & \\ \hline \multirow{8}{*}{9} & & & Watchdog system to detect selfish nodes & \\  & & Cooperative exchange of node’s reputation for & Detects only selfish nodes. \\  & & selfish behavior & No incentives for the selfish nodes. \\  & & of node. & & \\ \hline \multirow{8}{*}{10} & & & Election based scheme & \\  & & Election of heads & & \\  & & Cluster head & & \\  & & Incentive head & & Election process is overhead. \\  & & Monitoring head & & \\  & & Incentives in the form of reputations and & & \\  & & punishment of selfish nodes & & \\ \hline \multirow{8}{*}{11} & & & Detection of untrusted behavior of nodes based on & \\  & & interest preferences. & & \\ \cline{1-1}  & & [PERSON] et al. [40] & Uses both direct and indirect trust to calculate the & & The selfish behavior of the nodes \\ \cline{1-1}  & & behavior of the node. & & is not identified. \\ \cline{1-1}  & & Grouped trustworthy and untrustworthy & & \\ \cline{1-1}  & & nodes separately. & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: _Cont._resources (battery, energy, bandwidth, etc.). Such nodes are selfish nodes and can degrade the network's performance. As the social network grows, the number of selfish nodes also increases, affecting the network's performance. Any node which forwards the data to its neighbor node is termed an honest node and gives honest recommendations. Researchers use many techniques and schemes to detect and omit the node's selfishness to improve the network's performance. Still, a novel technique is required to detect selfish behavior and dishonest node in the network. The key considerations in the proposed mechanism are given in Table 2.