## 2 Literature Survey
### Problem Formation  
In remote IoT devices, a key problem is energy constraints. In this section, the different issues associated with energy management and the preceding energy-efficient clustering protocols are explained, along with their advantages and disadvantages. Some optimization algorithms have been implemented to overcome combinatorial and arithmetic optimization issues. These algorithms are categorized into various groups by considering whether they are stochastic, deterministic, iterative, based on population, and so on. This algorithm employs a probabilistic rule for enhancing the solution. After that, it is known as probabilistic or stochastic. It describes the meta-heuristic-based optimization algorithm methodology, and it can be used in WSNs for identifying the optimal cluster heads which effectively enhance a WSN's lifetime. As discussed in the literature survey, it is not a new research problem to resolve the network lifetime issue in WSNs, but when considering the security aspect, very few methods were proposed. [PERSON] et al. [27] discussed data aggregation with a WSN-based IoT model for secure and effective network transmission. [PERSON] et al. [28] explained a low-cost and secure SLEACH-PRO protocol. A multi-hop system neural network-based enhanced backpropagation method for improving node energy and transmission was discussed in [29]. Another LEACH-based routing protocol proposed for secured node communication was also presented in [30]. Additionally, the extended DE-LEACH protocol was proposed for improving consumption of energy in [31]. In summary, all these specific models consider the WSN nodes to be homogeneous sensor nodes. In the Internet of Things, many modes of communication can be joined to form complex heterogeneous systems, which can then be used to implement storage operations that offer data gathering, continuous monitoring, transmission, processing, and saving. Edge nodes, also known as RFID tags or sensor nodes, are responsible for data collection in the Internet of Things. These data are then transmitted to service providers or consumers via the network layer. In order to accomplish this, resources are put into an active mode and reserved for future use. The overall cost of the service can be reduced to its absolute minimum by improving both the quality of the service provided by network layer services and the performance of the end nodes. In the Internet of Things, it is still challenging to precisely define the quality of service and the network lifetime. Because the service in this context encompasses other features, such as information accuracy and service availability, conventional approaches to service quality are manifestly ineffective when applied to the Internet of Things (IoT). By considering all these issues, [PERSON] et al. [17] proposed a new methodology for heterogeneous WSNs to be energy-efficient and heterogeneously clustered for network lifetime enhancement. Additional research by [PERSON] et al. [32] proposed a new energy-efficient clustering technique (EECT) was applied to gathered data periodically in WSNs. These problems were also addressed through the single-hop energy-efficient clustering protocol for heterogeneous WSNs to improve network lifetimes, as discussed in [33]. All these models discussed the static nodes, and the authors did not consider the node mobility. Nodes in the network typically have a low-capacity power supply in IoT implementations. As a result, there is a pressing requirement for the development of novel approaches to decrease energy loss, which reduces the lifespan of networks. The increased density of network nodes, in combination with these limits, has led to a number of challenges in wireless network design and administration, as well as the need for energy optimization at every level of the protocol stack. For instance, at the network layer, we have an extreme requirement to locate data transmission and path discovery methods that minimize their impact on the environment while simultaneously maximizing their efficiency.