## 2 Literature Survey
This work expected the outcomes like optimal energy usage, maximum packet delivery ratio and maximum network lifetime. However, this method implemented a load-balancing routing mechanism to the MANET without considering IoT. In the description of [PERSON] et al. [20], MANET and IoT networks with energy optimization rules are suggested to increase network efficiency. Additionally, the improved cluster method for a heterogeneous network uses the sensor node energy level to determine the route. Due to insufficient load balancing, the tiny size of a node in the network consumes more battery power than the huge size of the network. Similarly, [PERSON] et al. [21] found energy efficiency and safe weight of the cluster approach when MANET and WSN are combined. In this work, all routes' costs are represented by the cluster-weighted values from the sources to the receiver. In MANET, the dynamic genetic algorithm is utilised to determine the dynamic topology based on the many node characteristics of power, position, availability, and latency, [PERSON] et al. [22] [PERSON] et al. [23]. This approach is one of the path-finding optimization techniques. However, in a large-scale network, the network's accuracy is lower. The aforementioned existing algorithm has improved network performance using a variety of techniques such as residual energy calculation and clustering procedures. It has also optimized packet loss, hop count, distance, increased throughput, and packet delivery ratio. It has also formed a heterogeneous network with low cost and high performance. However, in this approach, every author focuses on a distinct characteristic and omitting any one of the crucial ones has decreased the service's quality. The existing works are formed by heterogeneous networks with different algorithms, like cluster creation and genetic algorithms, to improve the performance of the network. However, the existing work has not fulfilled the expectations. Some clustering algorithms do not distribute clusters evenly, consequently, CH energy is drained soon. The genetic algorithm saves the network energy but has less fault tolerance and a high time delay. In this proposed work, the energy utilisation of network nodes is improved using clustering, [PERSON] et al. [24] and a trust route selection method. The nodes in the network are divided into small segments using cluster techniques. The cluster nodes are formed by mobile ad-hoc nodes and sensors. The sensors are linked to neighbouring mobile ad-hoc nodes. The node with more sensors is elected as CH. Otherwise, the mobile ad-hoc node is acting as an adjacent node between the sensor (source) and destination. The CH is re-elected by the residual energy of the node. The route discovery process [PERSON] et al. [25] is selecting the different routes from source to destination. However, the best and trusted route is evaluated by the total energy utilisation of the nodes, and the distance between the source and destination, [PERSON] et al. [26]. This type of route selection provides a better result in terms of saving the total energy of the route, [PERSON] et al. [27] under both malicious (DoS) and non-malicious conditions.