## 2 Related Work  
[PERSON] et al. [21] presented the Topological change Adaptive Ad hoc On-demand Multipath Distance Vector (TA-AOMDV) for the dynamic topology of the MANET. Here, the stable path selection approach was designed by considering the queue length, available bandwidth, and residual energy, along with the probability of link stability. Then, the proposed TA-AOMDV estimation was combined with the routing for adapting to the rapid changes in the network. However, the EED of the TA-AOMDV was not less while broadcasting the data packets. [PERSON] and [PERSON] [22] developed the multipath routing in the MANET to minimize energy consumption. Initially, the K-medoid clustering was used to group the nodes into clusters. Next, the Opposition Genetic-based Fish Swarm Optimization (OGFSO) was used to discover restricted-energy utilization-based multipath routing over the MANET. However, the designed OGFSO used the minimization of energy consumption as the main objective rather than the other parameters e.g., mobility and distance. [PERSON] and [PERSON] [23] implemented the simulated annealing-based disjoint multipath routing for the MANET. The cost metrics used in the simulated annealing were link availability, network load, and rate of energy drain. According to the derived path cost, the simulated annealing discovers the multiple-node disjoint paths over the network. However, the PDR of the simulated annealing-based routing was decreased when there was an increment in nodes. [PERSON] [24] presented the Cuckoo Energy Efficient Load Balancing on-demand Multipath Routing Protocol (CEELBRP) for the MANET. An adequate routing path was discovered using the CEELBRP according to the residual energy of the nodes. Next, the failure route was swapped for the unused path which has higher residual energy during the path maintenance phase. The designed energy-based routing was used to minimize the routing overhead. However, the developed CEELBRP was mainly concentrated only on the residual energy during path generation. [PERSON] and [PERSON] [25] developed the Particle Swarm Optimization (PSO)-based Energy-Efficient Cross-layer Routing Protocol (EECRP) to improve communication. The fitness metrics considered in the PSO are residual energy, mobility, and DSR. Accordingly, the PSO was used to generate an optimal path over the network. Moreover, the contention calculated from the MAC layer and residual energy was used to perform the dynamic adjustment of CW, which was used to minimize the energy utilization. However, the designed EECRP-PSO failed to consider the distance to discover the optimal path. If the transmission distance was high, then the energy consumption of the nodes was high in the network. Swarm Intelligence has been used to detect Cross-Layer Packet Drop Attacks in the MANET (CLPDM-SI) by [PERSON] [26]. This protocol used a cluster-based collective swarm intelligence detection approach to locate a rogue node in a real-time data collecting system that was being attacked with packet drops. This protocol (CLPDM-SI) was compared with the Adapting Cross-Layer Approach for Detecting and Segregating Malicious Nodes (ACLDSM) methods, which do not use swarm intelligence. False positives based on memory and CPU use were used to identify malicious nodes using heartbeat signals. Adaptive cluster creation was used to overcome deficiencies such as frequent topological changes, which were examined in various ways. However, it was noted that, as the number of nodes grows, the delay grows as well. [PERSON], [PERSON], and [PERSON] [27] developed a new adaptation approach to improve QoS in mobile ad hoc networks based on cross-layer and TCP over protocols. This research offered a new optimization method that was influenced by various modifications to the cross-layer technique for improving decision making. The goal is to reduce the impact of retransmissions of lost packets and energy usage by analyzing and selecting a suitable TCP routing protocol that can improve QoS in the MANET.