# Survey on Power-Aware Optimization Solutions for MANETs
## 3 Analysis of Power-Aware Optimization Solutions in MANETs
### Proactive (Link-State-Based) Routing Protocols
The ECAO model attains better performance in terms of the number of transmission control messages, PDR, average time, end-to-end delay, and link delay and energy consumption. [PERSON] and [PERSON] [158] introduced a new mechanism for selecting MPR among the nodes' neighbor sets to make OLSR more energy-efficient by taking into account the willingness of the node. The proposed energy-aware MPR selection mechanism was incorporated in Modified Dynamic OLSR (MD-OLSR) and compared with the conventional OLSR. Simulation results showed improved performance, such as higher throughput, larger Packet Delivery Ratio (PDR), and lesser end-to-end delay. In [159], a novel location-based routing protocol has been proposed. It improves routing in MANETs in terms of both link stability and energy efficiency. The protocol is called GBR-DTR-CNR and uses the stable routing protocol Greedy-based Backup Routing (GBR) with a Dynamic Transmission Range (DTR) with Conservative Neighborhood Range (CNR) for neighbor selection. The GBR-DTR-CNR algorithm selects the links of the route based on two criteria: (1) an estimation of link expiration time; (2) a conservative neighborhood range. The GBR-DTR-CNR protocol enables high connection throughput but it enhances energy efficiency by exploiting an adjustable dynamic transmission range that considers node mobility. Consequently, GBR-DTR-CNR chooses the next node from those stable neighboring nodes that will not move out of the transmission range. It was proven that the connections formed in GBR-DTR-CNR are substantially more stable than other routing algorithms. Compared to similar routing schemes, GBR-DTR-CNR achieves higher throughput and PDR. It also improved the energy efficiency in terms of maximum energy consumed per node and average energy consumed per packet delivered, while requiring fewer routing control message exchanges. Nevertheless, over various node densities, GBR-DTR-CNR did not outperform the other protocols in terms of the average energy consumed per node. This is because in GBR-DTR-CNR, the nodes in the idle state do not forward packets, and they consume slightly more energy than idle nodes in other routing algorithms. [PERSON] and [PERSON] [25] proposed the Memetic Optimized Adjacent Exponentially Distributed Routing (MO-AEDR) which considers route distance and power during route selection. In MO-AEDR, the route discovery selects a route that optimizes a weighted function of route distance and energy. In particular, MO-AEDR includes route energy consumption in its calculations and employs the Adjacent Exponentially Distributed Route Maintenance mechanism to include an energy awareness feature with mean data packet arrival rate and link breakage rate to the identified route discovery mechanism. The simulation results showed that MO-AEDR increases the PDR while reducing the end-to-end delay and routing overhead. Some link-state-based routing protocols for MANETs are based on a Q-Routing algorithm that embeds a learning policy at every node to adapt itself to the changing network conditions, which leads to a synchronized routing. For example, the Mobile Q-Routing (MQ-Routing) protocol [160] has a modified Q-Routing algorithm that is quickly adaptable to the changes in the network topology. During the routing process, MQ-Routing considers resource usage and link stability by examining three metrics:  
* The residual energy of the nodes. This metric is used for increasing the (minimum) lifetime of nodes and for balancing the traffic among them. * The prediction of GPS-based link availability. This metric is used in order of Q-Routing to avoid the loss of a constant traffic part if it is required to manage a link failure due to node mobility. * Node mobility. The MQ-Routing algorithm chooses more stable nodes with a high mobility feature. These nodes are not expected to rapidly change their neighbor sets. By using these metrics, changes in the network topology and the levels of node energy are taken into account. These metrics enable MQ-Routing to be fully adaptable in such changes.