## Related Works
It provides better QoS and performance for the static and highly dynamic network. An energy-aware routing model named enhanced intellects masses optimizer energy-efficient and secure optimized link state routing (EIMO-ESOLSR) is proposed in [45] for energy efficient and secure MPR selection in OLSR routing scheme. The MPR selection in the route discovery process is based on the willingness and Composite Eligibility Index (CEI) for each node in the network. The willingness value is estimated based on the available bandwidth, lifetime, and queue occupancy metric of the network nodes, whereas the CEI value is calculated based on the power factor, misbehaving probability, and forwarding behavior of the network nodes. The results show that the proposed EIMO-ESOLSR routing scheme reduces the energy consumption, provides a secured selection of MPR nodes, and minimizes flooding of packets in the network. One of the limitations in MANETs is the traffic congestion, that occurs when the excessive flow of packets injected on a single mobile node and carry most of the network traffic that affect the quick depletion of the node's battery. Due to this, routing approaches suffer from balancing the load among the intermediate nodes of the network, which can further significantly degrade the network performance. In addition to that, the authors in [46] addressed a network utility maximization (NUM)-based congestion control paradigm for cross-layer designs for wireless networks. It considered that the problem of cross-layer congestion control arises from the several dimensions, specifically, elastic or inelastic traffic, same or multitimescale, and single or multipath transmissions. Moreover, it gives comprehensive discussions on various mathematical techniques and solutions used for cross-layer congestion control problems. The Successive Convex Approximation (SCA) methods is also discussed, which strongly depend on initial conditions and network scenarios. Overall, it concluded that the cross-layer congestion control in wireless networks can be solved by utilizing network generation network such as SDN and Cloud-RAN with efficient central controller and network statistics. In [47], the authors introduced Proactive Source Routing protocol (PSR) to facilitate opportunistic data forwarding in MANETs. Each node has full information about the network topology and periodically exchanges messages with neighbors' nodes. The exchanged messages contain link cost information based on the number of nodes along the path from source to destination. The PSR routing maintains more network topology information than distance vector routing, making it ideal for source routing since it incurs less routing overhead with improve data transmission capability. In [48], a routing protocol called Least Common Multiple based Routing (LCMR) is proposed for MANETs which load balances among the different possible paths. The LCMR routing distributes packets and properly uses them for computation of route along multiple paths to minimize the overall routing time. For link instabilities, energy resource constraints and node mobility of frequently topology changing network, [49] introduced Multipath-ChaMeLeon (M-CML) routing protocols to stabilize and enhance the QoS and performance of the network. The introduced routing protocol helps to minimize routing overhead and energy consumption of mobile nodes by cutting down the amount of generated of duplicate packets in the network. In a dynamically changing network topology, link quality is badly impacted, resulting in frequent link failure in the network. To solve this, a Smooth Mobility and Link Reliability-based OLSR (SMLR-OLSR) routing scheme is proposed in [50]. Moreover, Semi-Markov Smooth and Complexity Restricted mobility model (SMS-CR) is used to provide enough smoothness and low complexity for reliability enhanced MPR selection that facilities longer MPR lifetime and less routing overhead in the network.