## 2 Existing Methods  
[PERSON] et al. [8] proposed a cluster-centric optimization using a cross-layered approach LEACH-CLO technique for reducing the power usage in Wireless Sensor Networks (WSNs). In this technique, the physical layer, link layer, and network layer were combined to provide the cross-layer optimization framework of WSN. First, the ring isomorphism forecasting region was created, and the optimum cluster nodes transfer method was developed based on the optimized hierarchical mechanism. However, its robustness and consistency were not effective for large-scale networks.  
[PERSON] et al. [9] proposed a geocentric multicast model for routing, which considered the trade-off between the delay and the lifetime of the network. A linear combination of metrics was used for decision-making while determining the next hop. Data were transmitted to the entire sink, which simultaneously identifies and removes duplications of data during transmissions. Moreover, the highest power use was considered as a sign of network lifespan. However, the mean delay and the computational difficulty were moderately high.  
[PERSON] et al. [10] proposed an EELAM path decision scheme with the help of an adaptive genetic algorithm. It depends on the tree topology that differentiates other trees in accordance with the multicast routing topologies by adapting a genetic algorithm. Based on this scheme, an optimized number of intermediary nodes with the maximum residual energy and the minimum energy depletion were decided. However, it was time-consuming.  
[PERSON] et al. [11] designed a Weight-based Energy-Efficient (WEEM) protocol in MANETs. In this WEEM, if multiple routes were predicted to stay active till the multicast session, then the weight was allocated to the routes by the target node. The route having the maximum weight was decided as the best. This weight was determined by considering the remaining power and multicast data transfer ability of nodes in the route, including the number of multicast targets situated in that route. If multiple routes have equal weights,then the route with the minimum latency was assigned a higher priority. But the control overhead was not efficiently reduced.  
[PERSON] et al. [12] developed Energy-aware cloud-based routing method for minimizing energy depletion in MANET. The proposed method has less energy consumption through efficient local route discovery among the nodes. Backup nodes were deployed in case of failure in connection. As the proposed method did not consider multicast routing, there were control overheads while transferring data.  
[PERSON] et al. [13] developed an Energy-Efficient Cross-layer Routing Protocol with Particle Swarm Optimization (EECRP-PSO) to reduce the energy consumption problem in MANETs. The proposed method estimated the metrics such as the success rate of data transfer, the mobility of a node, and the associated power of residue in advance. Then, the PSO algorithm was performed to create robust and power-effective routes. Further, the connection in the network was identified through the MAC layer and from the contention windows. Although efficient, the number of packets dropped, and the routing overhead was still very high.  
[PERSON] et al. [14] suggested a novel centralized method that evaluates the network's power use for optimizing the node duty cycles. In this method, a busy/idle mode to every node was assigned by the sink node regularly for the successive network cycle through pairing the remaining power, overall busy period, and the probable coverage region to lessen the power usage. However, it was not a completely distributed method and needed to analyze the effect on increasing the network life.  
Although the various literature has proved the possibility of addressing Energy depletion and lifespan issues in a MANET, there is no literature found to address these in a combined manner. Table 1 shows the state-of-the-art proposals for addressing the issue.