# Survey on Power-Aware Optimization Solutions for MANETs
## 3 Analysis of Power-Aware Optimization Solutions in MANETs
### Multicast-Based Routing Approaches
Further, the proposed algorithm forwards data packets through three tables: (1) the neighboring-node table; (2) the routing table; (3) the group table that includes information for all the destination nodes. The author claims that the algorithm can select more reliable paths and achieve the best results in terms of node lifetime, network lifetime, and throughput, compared to previous multicast algorithms. However, the main disadvantages of this algorithm are the following:  
* The algorithm is inclined to produce extra control traffic (although the same happens in any power-aware multicast protocol). * It ignores an important practical issue--it does not examine packet loss in the network. The Residual-Energy-based Reliable Multicast Routing (RERMR) protocol [148] achieves a longer network lifetime, improved path reliability, and forwarding rate. In REMR, an energy model observes the residual energy of nodes used for selecting paths with nodes with a high energy level to maximize the network lifetime. REMR selects the most reliable path for forwarding data, and thus it improves data delivery. In REMR, a network model is also used to estimate the reliability of a path. It can be said that REMR achieves a balance between data forwarding and energy reserves. REMR selects a higher-quality path with minimal packet loss and minimal energy consumption, while it reduces the number of route request packets and retransmissions. In a high-mobility environment, REMR outperforms similar schemes in terms of network stability rate, packet reliability rate, end-to-end delay, and communication overhead. However, REMRR is based on assumptions that are inappropriate in practice. Such assumptions are prior knowledge of the nodes' directions of motion, the location of the center of a trustable loop, and the constant rate of data delivery, irrespective of the transmission power. The demand for an optimal path amongst MANET nodes has attracted the use of Swarm Intelligence (SI)-based techniques, such as Particle Swarm Optimization (PSO) and Ant-Colony Optimization (ACO). SI-based techniques can solve many routing problems because routing in MANETs must be implemented by considering node mobility. [PERSON] et al. [149] introduced an interesting SI-based scheme for multipath routing. It is called particle swarm optimization-based bandwidth and link availability prediction algorithm for multipath routing. Their scheme is based on local rerouting and ensures forwarding continuity with compound link failures. The authors used particle swarm optimization based on available bandwidth and link quality. To provide the multipath routing in MANET, their scheme is based on the mobility prediction algorithm. In the prediction phase, the available bandwidth, link quality, and mobility parameters are used to select the node based on their fuzzy logic. The selected node broadcasts information among all the nodes and the details are verified before transmission. In the case of a link failure, the nodes are stored into a blacklisted link. Furthermore, the routes are diverted and sent back again to find a good link as a forwarder or intermediate node. The proposed scheme can achieve a significant improvement in PDR, path optimality, and end-to-end delay. The Predictive Energy-Efficient and Reliable Multicast Routing (PEERMR) protocol [150] uses a PSO algorithm to construct a reliable, energy-efficient multicast tree. The fitness function of the PSO algorithm takes into account various parameters to extend the node lifetime and ensure the stability of the path among the source and the destination. These parameters are path delay, expected path energy, and path stability. In PSO, each node moves to random destinations in the search space and has a randomized speed. The best global and individual positions are set by the fitness value. The PSO algorithm entails three basic steps: (1) calculation of the fitness value of each node; (2) renewing of positions and individual and global fitness values; (3) renewing of the velocity and position of every particle.