# Game theory-based Routing for Wireless Sensor Networks: A Comparative Survey
## 3 Game Theory-Based Routing Protocols in WSNs
### Routing Protocols Based on Evolutionary Games  
#### 3.3.1 Game Theoretic Energy Balanced (GTEB) Routing Protocol  
Sensor nodes in the network are energy constrained and the lifetime of the network is always limited in a WSN. It is better for the network if the nodes die at the same time, as the network functionality is ensured if all the nodes are alive at the same time. Addressing this problem, [PERSON] et al. presented a novel game theoretic energy balanced (GTEB) geographical routing protocol that could successfully increase network lifetime, ensuring the fact that the forwarding nodes will die at approximately the same time [40]. Based on the network density, the forwarding regions of a sender node were divided into a set. To deduce the amount of forwarding traffic to be sent to those regions, the EGT was used. This operation also depended on the available energy in a region. The classical game theory (CGT) was used to select one forwarding node within the forwarding region to forward a data packet. The main design concern of GTEB is to ensure energy balance to the randomly deployed homogeneous sensor nodes, that are assumed to have a transmission range of \(r\) and an initial energy of \(E\). The nodes are assumed to have location awareness, with each possessing the knowledge of their destination node. Every single node could report any nearby event periodically to the destination node. The transmission range in GTEB is divided into \(K\) subregions, depending on the network density \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Protocol** & \multicolumn{2}{c}{**Technical Strengths**} & \multicolumn{2}{c}{**Technical Weaknesses**} \\ \hline Resource allocation strategy for clustering-based WSNs [36] & \(\bullet\) & Energy consumption model with resource allocation scheme. & \\ \multirow{3}{*}{\begin{tabular}{c} Intercluster routing \\ algorithm for WSNs \\ [39] \\ \end{tabular} } & \(\bullet\) & \begin{tabular}{c} Leads to higher residual energy of \\ the nodes. \\ \end{tabular} & \(\bullet\) & \begin{tabular}{c} Suffers from an energy \\ hole problem. \\ Communication between \\ CHs and CMs should have \\ been defined or assumed. \\ \end{tabular} \\ \hline Routing framework based on energy and delay conservation [37] & \(\bullet\) & \begin{tabular}{c} The protocol can successfully decrease \\ network delay to a significant amount. \\ Energy consumption was successfully \\ decreased to a fair amount. \\ \end{tabular} & \(\bullet\) & \begin{tabular}{c} Do not consider sufficient \\ routing metrics in the \\ performance comparison. \\ \end{tabular} \\ \hline COMO [1][38] & \(\bullet\) & \begin{tabular}{c} Considerable amount of throughput is \\ gained in presence of the selfish nodes. \\ \end{tabular} & \(\bullet\) & \begin{tabular}{c} No comparison with the \\ existing state-of-art \\ protocols \\ was demonstrated. \\ \end{tabular} \\ \hline Energy-aware trust deviation scheme [32] & \(\bullet\) & Security of a WSN is considered with highest priority. & \(\bullet\) & \
\begin{tabular}{c} No performance \\ comparison with the \\ existing \\ state-of-the-art algorithms. \\ \end{tabular} \\ \hline GERA [33] & \(\bullet\) & Energy is well conserved by preventing the nodes that were CHs in the previous round from being CHs in the ongoing round. & \(\bullet\) &
\begin{tabular}{c} The protocol cannot adapt \\ to any kind of topology \\ change and node failure \\ \end{tabular} \\ \hline \end{tabular}  
* Cooperation Optimal Protocol for Multirate Opportunistic Routing; [2] Game-Theory-Based Energy-Efficient Routing Algorithm.  
\end{table} 
Table 5: Comparison of routing protocols based on noncooperative games in terms of technical strengths and weaknesses. \(\rho\). A sending node forwards a data packet to a certain subregion based on EGT. The neighboring nodes of the sender in the subregions must decide for which region the data packet has been sent.