# Robust Evolutionary-Game-Based Routing for Wireless Multimedia Sensor Networks
## 1 Introduction
The use of multiple sinks can be advantageous for WMSNs. It is necessary to deploy multiple sinks in the network to make it robust to sink failure. That is, a node has length-bound paths to at least two sinks. If any multimedia sensor node fails to send data because of sink failure, it can send data to another sink node alternatively. Furthermore, multiple sinks are helpful in implementing advanced applications and programming abstractions for routing algorithms and avoiding network congestions. So far, two routing protocols have been proposed for WMSNs that implement multiple sinks. In [14], multi-sink aware operations were integrated into an opportunistic routing framework to reduce energy consumption. [PERSON] et al. [15] proposed CodeMesh, a coding-aware cross-path anycast routing protocol, to maximize the lifetime of time-driven multi-sink networks. CodeMesh combines proactive and reactive protocol features while benefiting from multiple sinks. Its route establishment does not rely on clock synchronization. However, it lacks a scheduling mechanism to improve real-time performance. Recently, game theory has become a popular and useful tool in WSNs. This is because game theory can be widely used for modeling the interactions among entities with conflicting interests, which are competent to each other in a resource-constrained scenario. Four types of games have been used for routing in WSNs, which are cooperative games, non-cooperative games, evolutionary games (EGs), and reputation-based games [16]. In EG, the behavior of numerous agents that repeatedly engage in strategic interactions is studied. In EG, there are many behaviors involving the interaction of multiple organisms in a population. The success of any organism is highly dependent on how it interacts with others [16]. Therefore, the fitness of an organism should be measured in the paradigm of the full population and not individually. Two of the biggest advantages of using EG to design routing protocols for WMSNs are that cluster heads (CHs) can be elected optimally and it can exploit energy imbalance among the nodes in EG formulation. Many routing protocols have been designed for traditional WSNs using evolutionary game theory. Two of the prominent protocols are game-theory-based energy efficient clustering routing protocol (GEEC) [17] and energy-efficient routing protocol based on evolutionary game (EEREG) [18]. However, for WMSNs, there is only one routing protocol that is partially based on game theory. A combination of game theory and ant colony algorithm is 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. In this routing algorithm, game theory is based on 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 evaluation of the proposed routing scheme. To the best of our knowledge, no routing protocol using EG for WMSNs has been reported till now in the literature. In this study, an EG-based routing (EGR) protocol for WMSNs with multiple sinks is proposed, which is a cluster-based routing protocol with the intelligent mechanism of CH election. Clusters are formed based on the overlapping FoVs of the nearby sensor nodes. For every cluster, a CH is elected using EG. Furthermore, a data redundancy avoidance mechanism is also proposed. The overlapping area of the FoV of an object shared by two nearby sensor nodes is calculated, and it is decided if the captured data is redundant. This approach decreases the number of redundant transmissions, thereby increasing energy efficiency and network performance. The proposed EGR is compared with the five protocols. They areGEEC, EEREG, QoS-aware multi-sink opportunistic routing (QMOR) [14], a routing protocol designed by [PERSON] and [PERSON] [20] that we will refer to as AB routing in this study, and low-energy adaptive clustering hierarchy-centralized (LEACH-C) [21].