# Survey on Power-Aware Optimization Solutions for MANETs
## 3 Analysis of Power-Aware Optimization Solutions in MANETs
### Routing Protocols Based on Adaptive Load Balancing
The main goal of MEA-DSR is to decrease the number of dead node-induced path disconnections in the network. Through performance evaluation, the authors demonstrated that the overall power consumption in the MEA-DSR scheme is significantly lesser than that of DSR, especially in higher mobility scenarios. Nevertheless, the routing overhead and PDR performance of MEA-DSR is not up to the mark in scenarios with lower mobility. This occurs since MEA-DSR dismisses the idea of DSR packet salvaging, hence, it certainly upsurges the packet loss probability compared to the original DSR. Meanwhile, MEA-DSR allows for the propagation of duplicate RREQs (by relaying nodes), which undoubtedly assist in increasing routing overhead. Afterwards, [PERSON] et al. [126] introduced Energy-efficiency and Load-balanced Geographic Routing (ELGR), which associates both load balancing and energy efficiency metrics to make forwarding conclusions. The ELGR scheme effectively assesses the link's quality for the packet reception level to increase the power efficiency of the network. Moreover, the ELGR protocol also suggests the method of identifying the network load (local) by adaptive learning policy to improve the load balancing in the network. Through experimental results, the authors exhibited that the ELGR scheme effectively improves the PDR performance than other geographical forwarding algorithms (i.e., DREAM [127], GPSR [128], GEAR [129]). However, high complexity in estimating the forwarding and reception rate parameters is the main drawback of this scheme. Moreover, the ELGR scheme blindly assumes that each node knows its location information, which is not fair to assume in such a constantly changing MANET environment. [PERSON] et al. [130] introduced the Multi-constrained and Multi-path QoS-Aware Routing Protocol (MMQARP) which estimates multiple paths by considering path link delay, reliability, and energy constraint as QoS parameters. Indeed, MMQARP, with the help of these QoS-based parameters, estimates multiple node disjointed paths. Subsequently, the scheme heavily relies on the overhead of maintaining proper management to calculate the geographical information and average delay to estimate path reliability. Consequently, MMQARP suffers from the problem of high routing overhead in the network. Additionally, MMQARP is not able to offer good QoS demands to the user in the case of lower mobility scenarios. In summary, the adaptive load-balancing schemes do not care whether the chosen path is smaller or larger. It depends on the availability of intermediate nodes that have a sufficient power level. Hence, such schemes will undoubtedly influence the overall end-to-end delay performance of the network. Moreover, the idea of dynamically utilizing multiple available paths for load-balancing does not always guarantee that the selected paths are optimized (paths) in terms of minimum energy consumption. That is why, instead of being completely dependent on such load-balancing policies, we also have to include other policies in the study so that we can design a better routing policy which is good in every way.