# Survey on Power-Aware Optimization Solutions for MANETs
## 3 Analysis of Power-Aware Optimization Solutions in MANETs
### Multicast-Based Routing Approaches
The size of the multicast group determines how fast the PEMA algorithm is running, and thus PEMA is scalable for dense MANETs. The following three parameters are required to select the optimum routing path based on the predicted path energy values:  
* The location of group members;
* The average node density in the network;
* The individual unicast routes from the source node to the group members. PEMA can exactly predict the communication energy consumption by defining two bounds (upper and lower) on energy consumption. After that, the predicted energy values become weighted averages of these two bounds. Using these predicted energy values, PEMA decides how to efficiently send packets to the destination (group) members. PEMA improves energy conservation compared to other related algorithms. However, the energy model used in PEMA does not take into account the energy consumedby retransmissions, due to the MANET dynamic conditions (e.g., interference and packet collision). This information was neglected in the performance analysis of PEMA. In MANETs, the quality of group communication depends on various QoS parameters, such as path loss, link life, mobility, channel fading, signal quality, the transmission and reception energy of nodes, and battery backup. In general, the problem of QoS multicast routing with multiple QoS constraints is NP-complete [144]. To resolve this problem, [PERSON] et al. [145] proposed an energy-efficient genetic algorithm mechanism. Particularly, they deployed a source-tree-based routing algorithm and constructed the shortest-path multicast tree to minimize the delay time. For this reason, a small population size was used in the genetic algorithm. In the route computation, only a few nodes were involved. The genetic sequence and topology encoding, which calculate the residual battery energy of all nodes in the multicast tree, were improved significantly. As a result, the lifetime of mobile nodes was prolonged. Extensive simulation results showed that their method is a competent and robust algorithm used for multicast route selection. An energy-efficient and delay-constrained genetic algorithm for MANETs was proposed in [146]. For the period of route selection, the genetic algorithm examines two criteria: (1) the bounded end-to-end delay; (2) the minimum energy cost of the multicast tree. The second criterion is examined to reduce the total energy consumption of the multicast tree. This source-based genetic algorithm performs crossover and mutation processes on probable trees. This facilitates the coding operation and rejects the coding/decoding process. A heuristic mutation method can reduce the entire energy consumption of a multicast tree and, as a result, extend the battery lifetime of nodes. The performance of the genetic algorithm was proven through simulation experiments. The authors [146] demonstrated its efficiency in terms of success ratio, convergence performance, and running time, compared to the least delay multicast tree algorithm. A drawback of the suggested algorithm is that it does not examine shared multicasting trees and it focuses completely on source-based routing trees. [PERSON] [147] suggested an energy-aware multicast algorithm that enhances the network lifetime using a tradeoff. This tradeoff is based on minimizing energy consumption and load. It is also achieved by discovering a multicast that is inclined to minimize the variation of residual battery energy across all nodes. Particularly, if a source node wants to send multicast packets, it selects a node with greater residual battery energy. If all middle nodes have the same residual battery energy, it selects a node with the larger relay capacity. The algorithm takes into account two metrics: (1) the capacity of the residual battery; (2) the relay capacity of the node to multicast packets from the source to the destination nodes.