# Game theory-based Routing for Wireless Sensor Networks: A Comparative Survey
## 4 Comparison of the Routing Protocols
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Protocol** & **Routing Metric** & \multicolumn{1}{c}{**Major Features**} & **Advantages** & **Limitations** \\ \hline Resource allocation & & \(\bullet\) & Use of Nash bargaining solution to & & \(\bullet\) & Not feasible for multihop WSN\({}_{\text{c}}\) \\ strategy for & & & analyze clustering: & & \(\bullet\) & Camot function as a \\ clustering-based WSN [36] & & \(\bullet\) & Uniform routing scheme for energy & \(\bullet\) & Failure of the first node does not & \(\bullet\) \\ clustering-based WSN [36] & & & consumption on CHs, cluster members, & & \(\bullet\) & distributed scheme; \\  & & & and sink node. & & \(\bullet\) & No comparative study shown \\ \hline Intercluster routing & & \(\bullet\) & Heagonal cluster topology like & \(\bullet\) & The game model shows a & \(\bullet\) \\ algorithm for WSNs [37] & Energy & \(\bullet\) & Multihop routing. & & unique Nash equilibrium & metric considered. \\  & & & Interoperability between WSNs and & & solution and reduces energy as & No comparative study shown \\  & & & mobile networks. & & well as prolongs lifetime. & with other existing protocols. \\ \hline Routing framework & & \(\bullet\) & Sensor nodes are fully static; & \(\bullet\) & Considers SINR to determine & \(\bullet\) \\ based on energy and & & \(\bullet\) & Game modeled as dynamic, & most optimal paths; & \(\bullet\) & Infinite amount of interference; \\ delay conservation [37] & Energy & & \(\bullet\) & nonoperative, and & Nash equilibrium is & \(\bullet\) \\  & & & incomplete information; & self-imposed when the nodes & \(\bullet\) \\  & & & Cost function comprises of SINR. & use their best response strategy. & \(\bullet\) \\ \hline  & & \(\bullet\) & Guarantee of achieving strong & & \\  & & & Pareto-efficient Nash equilibrium with & & \\  & & & faithfulness as as a given property: & & \\ COMO 1 [58] & End-to-end throughput & \(\bullet\) & Take link links be probability as input and & \(\bullet\) & When all the nodes follow the routing and incentive protocol, & \(\bullet\) \\  & & & outputs the number of times of & & system optimization occurs. & \\  & & & forwarding and dissemination bit rate; & & system optimization occurs. & \\  & & & Random selection of & & source-destination pairs. & \\ \hline Energy-aware trust & & \(\bullet\) & Proposal of a risk strategy model to & \(\bullet\) & Handles bad mouthing and & \\ deviation scheme [2] & Energy, average packet & \(\bullet\) & simulate cooperation among nodes & distributed denial of service & \(\bullet\) & Overhead of the trust requests \\  & delivery ratio, packet & \(\bullet\) & Use of mixed strategy Nash equilibrium & (DOoS) attacks very well: & \(\bullet\) & not considered; \\  & loss ratio & \(\bullet\) & to obtain the optimal state of gain to cost & & Manages routing overhead & \(\bullet\) \\  & & & and the probability of selected strategy. & while maintaining adequate & & with other protocols. \\  & & & Multi-sink and multihop network. & & security of WSNs. & \\ \hline  & & & Setting of the utility function based on the & \(\bullet\) & The protocol is compared with & \\  & & & benefit and energy consumption; & an already established & \\ GERA 2 [33] & Energy packet delivery & \(\bullet\) & Iterative selection of CHs; & protocol, LEACI: & \(\bullet\) & Under maximum transmission radius for \\  & & & Coverage of both intercluster and & Introduces a balancing factor to & \\  & & & intracluster routing. & & limit the cluster size. & \\ \hline \hline \end{tabular}  
* \({}^{1}\) Cooperation Optimal Protocol for Multirate Opportunistic Routing: \({}^{2}\) Game-Theory-Based Energy-Efficient Routing Algorithm.  
\end{table} 
Table 8: Comparison of routing protocols based on noncooperative games.