# Energy Efficient Routing Protocol in Sensor Networks Using Genetic Algorithm
## 2 Related Work  
There are various routing methods were proposed to enhance the network performance. The author in [9] developed a dynamic energy ad-hoc on-demand distance vector routing protocol (DE-AODV) to minimize the packet delay, maximize the network lifetime, and reduce energy consumption. The DE-AODV protocol is used to select the shortest route from the source to the destination with nodes that are energy-efficient. The early protocols proposed for proactive routing in ad hoc networks were destination sequenced distance vector (DSDV) [10] based on the Distributed Bellman-Ford (DBF) algorithm and the Optimized Link State Routing protocol (OLSR) [11]. In Link State schemes and when the network activity is low while the topology changes frequently, the amount of traffic can still be significant which would consume more energy. In [11], the authors introduced a routing mechanism where the residual energy together with the QoS metrics were considered. In this protocol, information about the network topology should be shared with the whole network. This would overwhelm the network with information that is not always necessary for all nodes and this, in turn, would degrade the performance of the network and lead to traffic problems such as data congestion especially in the case of dense networks. Another approach is the proactive path-finding algorithms. This approach combines features of the Distance Vector and Link State approaches and every node in the network constructs a Minimum Spanning Tree (MST). The Path Finding algorithm reduces both the amount of control data and also the possibility of temporary routing loops and this, in turn, would minimize the traffic problems. An example of this type of routing mechanism is the Wireless Routing Protocol (WRP) [12]. The main concern when applying the proactive approach to the WSN environment is the fact that as the network constantly changes, the cost of updating the topological information would be unduly high. On the other hand, there are reactive routing protocols based on some type of request/reply scheme. A reactive mechanism seeks a process to find a route to the sink. Examples of reactive or on-demand protocols include the Ad hoc On-Demand Distance Vector (AODV) [13], the Dynamic Source Routing (DSR) [14] and the Temporally Ordered Routing Algorithm (TORA) [15]. In these reactive methods, the route discovery procedure is commonly used to find the best route. Mostly, shortest distance, the minimum number of hops, shortest latency, and least bandwidth usage may be used as the optimization conditions. These criteria are utilized to overcome main challenges in the network such as the low signal and attenuation, unreliable channels, higher delay, nodes mobility, and random packet loss. In the case of nodes with high mobility speed, the network entirely might get interrupted and the remaining energy will be quickly depleted. In this case, AODV has to find another path as single-path reactive category protocols do. In TORA, route replies use controlled flooding to distribute the routing information through a form of a Directed Acyclic Graph (DAG) [16]. AODV and DSR protocols use unicast communication to direct the reply to the sender of the routing request. In AODV, the route information is stored at the next hop within the nodes on the path. The reversed path is engraved into the request packet as an accumulated route in the DSR and is used for source routing. AOMDV [17] is another reactive protocol based on AODV. AOMDV finds several alternative routes (multi-path) during the route discovery phase. As a result, AOMDV is an enhanced version of the AOMD where the network can adapt to one of the alternative paths during the failure of the current route and the transmission time is saved as no need to go often through the route discovery procedure for the established data communication session. A genetic algorithm (GA) is a heuristic search algorithm based on natural selection and genetics. GA is an excellent approach to solving a problem for which little is known.