## 2 Literature Survey  
To enhance the performance of MANET and IoT environments, the authors suggest a new routing protocol. The existing techniques are compared in this section. The Dynamic Critical Node Identification (DCNI) method was suggested by the authors [PERSON] et al. [7] and [PERSON] et al. [10], who also identified the MANET-IoT network critical nodes. In this section, the currently used methods are contrasted. The DCNI approach controls the dynamically changing topology to identify fusion nodes in one topology. The outcome of DCNI supports the identification of the crucial nodes and results with enhanced quality and time complexity. However, only simple DOS attacks were used to evaluate the DCNI technique, [PERSON] et al. [11]. The author [PERSON] et al. [12] and [PERSON] et al. [13] discusses the operation of MANET systems in IoT. This is a basic MANET with an IoT technique that already exists. This technology uses programmers to run the MANET, where some systems have more end devices. The Dynamic Range Clustering (DRC) method with a learning-based routing scheme implements the load balancing strategies that are optimising the performance of MANETs, [PERSON] et al. [14] (LR). The management of network data and energy is improved by this strategy. The cluster has increased network stability with distinctive neighbours for learning-based routing and enabled non-congested data transformation at various network traffic rates. Because of the dynamic topology, the LR method's delay time is getting longer. A new multi-objective scheme in a heterogeneous network is introduced as a multi-characteristics model by [PERSON] et al. [15]. This scheme achieves better throughput, energy usage, packet delivery ratio, fewer dead nodes throughout more rounds, and increased network lifetime. Using various network metrics, the cluster creates the network path and transfers the data from the source node to the neighbour node. However, large routing networks have little fault tolerance and significant delay times in this case. To avoid the pitfall of becoming stuck in local optima, the author devised the Energy Efficient Cluster Head Selection Using Improved Sparrow Search Algorithm (EECHS-ISSA-DE), [PERSON] et al. [16] method. Based on residual energy, this algorithm chooses CH. Although the CH is not distributed evenly, stability is good. This algorithm's results for handling vast amounts of data take less time, and the crossover and CH selection procedures are enhanced. Nonetheless, the breadth and connectivity of this approach are crucial. In WSN, the Butterfly and Ant approach is suggested by [PERSON] et al. [17]. In WSN, route generation and the best CH selection are challenging tasks. The path is chosen based on the nodes' energy, their distance from one another and the base station and the node's degree. This methodology's simulation results are taken into account for the local base station and global network. To choose the best energy-saving nodes, the author [PERSON] et al. [18] proposed a modified Bellman-Ford algorithm. This algorithm was tested in both static and dynamic networks. This scheme found that the static network provided accuracy between 34 and 48 percent and the dynamic network provided energy savings between 35 and 42 percent. However, we found that the amount of energy saved depends on the network's size. In addition, routing protocols play a major role in energy-saving plans. In comparison, Adhoc On-Demand Routing Protocol (AODV) and Energy Quantized (EQ-AODV) routing protocols, the Energy Efficient AODV (EE-AODV) in Mobile Ad-hoc network has increased the energy level of the nodes. Since the ties in this protocol are less stable, the scientists intend to use a genetic algorithm to strengthen the links in their subsequent work. [PERSON] et al. [19] proposed a novel EED algorithmic approach. The authors used two parameters such as hop count and residual node energy to estimate an optimal load distribution-based routing mechanism.