## 2 Literature Survey
### MCDM for Data Gathering  
The second part of the literature survey presents the usage of MCDM models in various WSN-IoT applications. In [36], entropy-based technique for order preference by similarity to an ideal solution is a multiobjective decision-making method (TOPSIS). the field observation instruments networks (FOINs) clustering routing algorithm (ETC) was proposed in this method. Through several variables, the ETC algorithm selects the perfect cluster head (optimal CH). It primarily addresses the issue that certain multiobjective optimization algorithms in use today cannot assign weights in a dynamic and objective manner. Each decision-making criterion for the issue reflects a characteristic for o multi-objective decision-making problem of CHs selection in the clustering routing method. As a result, the decision-making criteria are treated as characteristics and any node in the FOIN having values for the various criteria is chosen as an alternative.  
In [37], the use trusted, energy- and spatial-aware dynamic distance source routing is suggested. The method determines the assessment metrics that are stated in the QoS. In addition, this model introduces a novel hierarchical trust method that uses several features of numerous wireless sensor nodes in accordance with data transfer speed, data size, energy consumption, and suggestions.  
In [38], an MCDM framework was used to resolve challenging routing issues under the assumption of metric threshold limitations. Such needs are captured by the mathematical framework, which also determines the routing.  
In [39], a hybrid network architecture for disaster area wireless networks (DAWNs) is proposed. To find the ideal next-hop node in DAWNs, researchers applied a multi-criteria decision-making method for emergency communication protocol (MCDM-ECP) that makes use of the analytic hierarchy process (AHP) method and the technique for order preference by similarity to an ideal solution (TOPSIS) method.  
Authors [40] put forward a framework for sensor selection and correlation that allows a reported irregularity in the monitored environment to be activated and verified. It frames and solves the sensor selection problem as a multiobjective optimization (MOO) problem, taking into account the edge gateway device's free CPU and RAM, network quality, remaining battery life, sensor correlation value, and power requirement of the sensors. In order to validate the anomaly, the sensor nodes are chosen using a multicriteria decision-making process (MCDM)  
Authors [41] created a route design technique that is optimized for energy efficiency, extending network lifespan and improving connectivity. The method consists of four steps. The first divides the sensing field into equal sections based on the number of mobile sinks that have been deployed to solve the energy-hole issue. A stable election algorithm (SEA), a heuristic clustering technique, was developed to reduce message transmission between sensor nodes and avoid frequent cluster head rotation. In order to determine the ideal site for the sinks to halt and gather data from cluster heads, a sojourn location determination technique was suggested based on the minimal weighted vertex cover problem (MWVCP). Finally, using multiobjective evolutionary algorithms, three optimization strategies are used to assess the trajectory of the modified mobile sinks (MOEAs). They provided that the multiobjective evolutionary algorithms (MOEAs) may be deemed as an optimum solution for multiple-criteria decision-making (MCDM) problems because MOEAs' evaluation encompasses many metrics. However, no explicit MCDM algorithm was provided for selecting a solution.  
Overall, it was found in Table 1 that none of the existing approaches in WSNs applies MCDM for enabling decision makers to select one solution out of the set of nondominated solutions in the data gathering based on the factors of trust, effort, cost, and time. Hence, we aimed filling this gap by using the simple additive weighting (SAW) from MCDM for this purpose.