## 2 Related Works
IoT Node Selection Possibility (INSP) is the output parameter. The findings demonstrate that the suggested techniques correctly excluded and selected IoT nodes. They found from both systems that in the Node Elimination System (NES), the nodes that did not meet the fundamental resource readiness criterion were removed from the selection process. They discovered in NES that IoT nodes with adequate principal resources are chosen for further job selection. [PERSON] and [PERSON] [46] presented an integrated model for producing intellectual capital performance indicators, which can be used in a financial shared-services organization to enhance the standard IC process model. Their research aims to employ IC management in conjunction with the MCDM approach to build an IC measurement system and use the best-worst method to determine the weights of IC performance indicators to prioritize KPIs. These prioritized IC performance indicators assist managers in focusing on the important components of their IC management and effectively allocating the organization's limited resources. Their hybrid solution was applied in four commercial courier businesses. The suggested technique prioritized and determined the magnitude of the aspects under consideration, including the IC elements and their performance metrics. The findings show that management prioritizes the IC of the best performer and other enterprises. Their benchmark results revealed gaps, the potential for improvement, and prospects for sustainable development for inferior logistics businesses. [PERSON] and [PERSON] [27] developed a three-phase implementation technique. The network is initially installed in a hierarchical cluster architecture, expanding to any level. The effective relay node selection technique is used in the second stage to elect the sensor node as a relay node. The quantity of relay nodes available in each cluster is determined by the node and relay node communication range density, and it varies for every cluster. The Energy-Efficient Communication (EEC) protocol transmits network data to the base station in the last phase. They compared the suggested approach to several protocols and found that it is superior in the context of depletion of energy and lifespan of the network. On a green WSN-Assisted IoT deployment, the energy-efficient consumption may be simply deployed. Furthermore, the method continues to outperform the comparable protocol as the size of the network and the number of nodes grow. [PERSON] and [PERSON] [47] offered an effective relay node selection technique based on IoT network contact patterns. Digital traces of computer equipment give a priori connect pattern information that aided in improving the data forwarding scheme's performance. This information was used in the approach, which combined data latency and connection dependability for reliable data forwarding over time-varying IoT systems. The technique's performance is assessed in a variety of network topologies. The results showed how using a priori network contact patterns may reduce data latency and improve dependability. The impact of the reliability of a link and connection history duration on data latency, node density, mobility variance, and transmission range is also investigated. The technique works well while minimizing data latency and increasing connection dependability. Finally, [PERSON] and [PERSON] [24] suggested a two-stage node selection technique that employed genetic and greedy algorithms. The first step used a cluster-based methodology associated with a genetic algorithm to obtain primary data about the source. Then, the active node readings are dynamically employed in the second phase to pick the following best group of active nodes using an individual-based greedy selection process. Both steps are combined in the Data-driven Active Node Selection (DANS) framework, a dynamic framework for tackling localization issues in IoT sensing applications. In addition, a methodology for determining coverage has been devised for sensors that may not have a detecting range.