# Energy Efficient Routing Protocol in Sensor Networks Using Genetic Algorithm
## 2 Related Work
Even if it is based on a random search, GA is able to provide a high-quality solution [18]. GA uses the principles of selection and evolution to produce multiple solutions to a given problem [19; 20]. Challenges in wireless networks such as nodes mobility, fading, congestion, collision, etc. have no negative impact on the GA and this is one of its main characteristics. In [21], the authors proposed a GA-based routing algorithm for flying ad-hoc networks (FANETs). The proposed FF considers several parameters including the maximum link bandwidth, the highest network link stability, and the most residual power of the nodes. These parameters are more crucial in FANETs than in other types of ad-hoc networks. However, having several components in the fitness function will negatively impact the convergence speed of the algorithm to select the efficient route. Similarly, in [22] several fitness function components are proposed which will slow down the route selection process. In [23], the authors introduced a FF that is calculated using the shortest route length in addition to the parameters measured in the route discovery phase including the delay for getting the shortest route and the number of available routes. This method consumes an excessive amount of processing time and energy. In [24], authors proposed at FF that suits those applications that have an excessive amount of data transmission and therefore data congestion is a key that should be considered. There are other methods such as in [25] where the proposed FF is complex and the route selection takes an extended time. In [26; 27; 28], it was introduced GA-based methods which require lengthy processing and memory in the source node as every possible route would be involved in the GA regardless of its fitness level. In [29], the authors proposed a routing protocol that considers the network as a directed graph optimizing routes based on the quality of links between every two nodes using a genetic algorithm. In this regard, the link status might be impacted by several factors such as noise. Therefore, with a small change in a link status, the routing protocol should go through the regeneration of routes based on the modified graph. Among the various maximum lifetime routing protocols, one type is called Efficient Power Aware Routing Protocol (EPAR) [30] which is the extension of the DSR protocol. EPAR selects the path that has the largest packet capacity at the smallest consumed packet transmission energy. Due to the mobility of the nodes in sensor networks, EPAR is proposed to find multi-path routes so the delay is taken to discover a new route will not consume time in case of the failure of the current route [17]. Alternatively, if a single intermediate node goes out of the coverage area, then the whole communication can be disturbed owing to topological change which would enlarge the data loss. This would result in an excessive amount of bandwidth usage and this, in turn, increases the number of packet retransmissions representing a low performance. Accordingly, EPAR using Breadth-First Search (BFS) was introduced to select the route with the minimum energy consumption. However, EPAR-BFS faces problems when the size of the network size increases. In this paper, we propose a solution for selecting the energy-efficient route which enhances the mobile node's lifetime.