# Survey on Power-Aware Optimization Solutions for MANETs
## 5 Challenges and Future Research Directions
### Challenges
To accomplish a multi-objective target-oriented CMAC protocol, the authors formulated the optimization problem to extend the network lifetime. They considered symmetric and asymmetric transmit power rules. In particular, they suggested a distributed relay selection process to choose the finest retransmitting node among the qualified relays. This selection process takes into account the transmit power of the node, the sufficient residual energy after cooperation, and a high cooperative gain. The LEA-CMAC protocol can obtain a multi-objective target orientation by exploiting an asymmetric transmit power rule to improve the network performance. Recently, [PERSON] et al. [228] have suggested a reinforcement learning-based (e.g., Q-Learning) relay selection scheme without considering any prior information and network models. The suggested scheme has been extensively evaluated over parameters, such as system capacity, power consumption, and outage probability. Their widespread evaluated results show that the performance of the suggested scheme is better than other approaches in terms of the abovementioned parameters. As was mentioned previously, unplanned energy conservation methods decrease the node's lifetime and deface the consistency of packet flows. This results in a tradeoff between network throughput and node energy, resulting in post-network failure. The post-network failure results in limited Time to Live (TTL) values of the nodes and retarded network throughput with higher control overhead. To bridge the gap between network throughput and energy conservation under limited control overhead, [PERSON] et al. [229] proposed a Transition State supporting cooperative MAC broadcast (TSMP) protocol for both conserving node energy and to utilize available nodes efficiently before their energy drain. The TSMP protocol reduces the total energy consumption to a maximum extent of 14-21% higher than the Dynamic Power Consumption MAC protocol (DPCMP) and 24-33% higher than the Static Power Consumption MAC protocol (SPCMP). Comparatively, the routing overhead falls almost 45-52% higher than SPCMP and 27-31% higher than DPCMP. (2) Multipath routing based on hybrid modeling: Multipath routing schemes can be based on predicted nodes positions. New hybrid methods are required for estimating the node location. Indeed, to set up a routing path with much longevity, it is supportive to have a routing protocol that is based on predicted node positions. Most conventional prediction methods are based only on the past locations of the node. New hybrid methods are required that will be based on the temporal and spatial characteristics concerning the node's neighborhood. Heuristic methods can use soft computing approaches to predict the locations of nodes. Precisely, machine learning (ML) algorithms can be trained by using features extracted from mobility patterns. The future locations of the node can be obtained by using this ML predictor. For instance, [PERSON] et al. [230] proposed a mobility prediction based on an extreme learning machine. Every node knows its position, direction of movement, and velocity. Future node positions, velocity, and movement can be predicted. Based on the predicted future distances, the routing protocol is adjusted to choose the next hop. The effectiveness of the method depends on the training volume. At each node, the mobility is predicted using past information and it involves a lot of cost in exchanging this information to neighbor nodes. Recently, [PERSON] and [PERSON] [231] suggested a multipath routing protocol that uses estimated probability locations with path diversion at required places along the path for improving routing performance without bigger packet overhead. (3) Fuzzy-logic support in multicast routing protocols: Multicast on-demand routing protocols use tables for selecting optimal multiple routes to a set of destination nodes. A fuzzy set of rules can be used for this purpose.