# Energy Efficient Multipath Routing Algorithm for Wireless Multimedia Sensor Network
## 2 Related Work
Suppose a particular sensor node is member of ring level \(n\) and it sends \(s\) data to \(n\)-\(1\) ring level. This technique improved LEACH routing by 27.58% and direct routing by 113.06%. Since energy utilization and the lifetime of the network were not considered in the proposed work, these can be considered as future work. A multipath multispeed protocol (MMSPEED) [29] is proposed with the aim of guaranteeingcorrect delivery of packets in a WSN. The packets are queued in sequence based on their speed level. MMSPEED uses a priority service policy to service the queued messages. Those packets that have higher priority will be serviced first and packets with lower priority will be serviced last. Unfortunately, energy-related parameters and network lifetime maximization were not considered in this work. An ant colony optimization (ACO)-based multipath routing algorithm is presented in [30]. Residual energy, transmission energy consumption, and length of the path were some of the metrics considered in the design. After CH is chosen in the cluster, ACO is applied to find multiple paths between CH and the base station. CH is dynamically selected to deliver data based on probability, which is a function of various parameters such as energy consumption. The proposed technique minimized the average energy consumption and maximized the network lifetime. Genetic algorithm (GA) is a meta-heuristic that is based on the theory of natural selection where the fittest chromosome will survive for reproducing new offspring in the next generation. Many researchers have used a genetic algorithm for node deployment, routing, and even clustering in wireless sensor networks [31; 32; 33; 34; 35]. In [36], a genetic algorithm for energy efficient routing is proposed, where GA is used for scheduling data gathering and aggregation of relay sensor nodes. The performance analysis shows that the approach finds an optimal solution for both small and large networks. In [37], a genetic algorithm is used to solve coverage and connectivity issues in WSNs. This method is used to find a few appropriate positions to deploy sensor nodes, satisfying both coverage and connectivity in the target area. The algorithm provides k-coverage to all targets and m-connectivity to each sensor node. The GA's chromosome was represented efficientlyand the objective function was also derived properly with all operators. A GA-based clustering and routing approach for wireless sensor networks [38]. Distances from the nodes to their CH, along with the residual energy of gateways were taken as a decisive parameter for cluster formation. The routing protocol depends on the tradeoff between multiple re-transmissions of data, and transmission distance and the remaining energy of the gateways. Similarly, in this study, we employed GA for finding multiple paths in a wireless multimedia sensor network. Our scheme uses the advantage of GA's computational efficiency to quickly find a solution to the problem. As a result, our GA-based approach can quickly compute an alternative new routing path from a source node to the sink node.