# Robust Evolutionary-Game-Based Routing for Wireless Multimedia Sensor Networks
## 2 Related Works  
In this section, we review the routing protocols designed for WMSNs. Several routing protocols have been proposed that exploit different types of methods to achieve data reliability and energy efficiency to facilitate WMSNs. In [22], an ant-based service-aware routing algorithm (ASAR) for WMSNs was proposed. The routing algorithm opts for the appropriate paths for variant QoS requirements. The routing scheme is mainly based on the data transfer between CHs and sink node. The CHs transfer different classes of data. To quicken the convergence of the algorithm and optimized network resources, the proposed algorithm (also discussed in [22]) quantifies the phenomenon value on the sink node to reduce transmission frequency and control messages. A combination of game theory and ant colony algorithm was presented in [19] to overcome the problem of QoS routing in WMSNs. This combination is adopted to overcome the drawback of the current ant-based routing protocols for WMSNs, which is the long time required by the forwarding ants to determine the destination and overhead occurred because of the use of many backward ants that update the routing probability distribution. In this routing algorithm, game theory is used under the assumption that the sensor nodes are rational and opt for selfish actions (maximizing pay-offs at minimum costs). However, the study does not provide any performance analysis. An image transmission framework for the optimization of the perceptual quality and energy expenditure in WMSNs was proposed in [23]. The goal of the proposed scheme was to ensure the perceptual quality at the end user using an analytical distortion model. The model could predict the image distortion resulting from any error pattern. The outstanding part in [23] is the use of a content-aware packet prioritization along with an energy and delay aware routing protocol. \begin{table}
\begin{tabular}{c c} \hline \(R_{i}\) & Reward for any node \(i\) \\ \(R_{i}\) & Penalty for any node \(i\) \\ \(\alpha\) & The reward to be given to a node in set \(NH\) if they become CHs \\ \(\beta\) & The reward to be given to a node in set \(NL\) if they become CHs \\ \(\delta\) & The step of encouragement or discouragement for being a CH \\ \(\rho\) & The rate of nodes in \(NH\) set to select \(T_{CH}\) strategy \\ \(\eta\) & Rate of nodes in \(NL\) set to select \(T_{CH}\) strategy \\ \(N_{RE}\) & Residual energy level of a node \\ \(I_{n}\) & Number of rounds \\ \(Q\) & Highest number of nodes in a cluster \\ \(I_{f}\) & Final round \\ \(m\) & Any random integer \\ \(RE_{i}\) & Residual energy of any node \(i\) \\ \(Tx_{avg}\) & Average number of packets transmitted \\ \(DT_{CH_{i}}\) & Data packets transmitted by any CH in the network \\ \(PDR_{avg}\) & Average packet delivery ratio \\ \(Rx_{sink}\) & Data received by the sink node \\ \(p\) & Number of nodes who have higher residual energy \\ \(q\) & Number of nodes who have lesser residual energy \\ \(Y\) & Number of sink nodes \\ \hline \end{tabular}
\end{table} 
Table 1: _Cont._A QoS-aware routing protocol was presented in [24] that supports high data rate for WMSNs and ensures the bandwidth, end-to-end delay requirements of real-time data. The routing scheme proposed in [24] uses multiple paths, channels, and QoS packet scheduling techniques based on dynamic bandwidth adjustment and path-length-based proportional delay differentiation techniques to fulfill the bandwidth and delay requirements, respectively. The bandwidth and delay requirements are adjusted locally at each node. The nodes discussed in [24] are homogeneous and are responsible for performing application-specific tasks, such as video, audio or scalar data acquisition. A protocol proposed in [24] improved the average delay per real-time packet, average lifetime of a node, and throughput of non-real-time data. A dynamic node collaboration scheme based on FoV for a target tracking application was presented in [25]. Unlike traditional sensing models, it is a nonlinear localization-oriented sensing model for WMSNs.