## 2 Related Works  
In the last several years, much research has been conducted and is currently being performed on distributed computation. Researchers have focused on various topics, including architectural design, bug report prediction, and overall system performance [37; 38]. Moreover, several optimization strategies are accessible for the mobile computing area [39; 40]. Scholars have focused on many elements of IoT optimization under various restrictions. Most researchers have focused on reducing power consumption and overall system delay and optimizing additional factors such as cost and bandwidth utilization. [PERSON] and [PERSON] [41] provided a general logistics benchmarking process with the Fuzzy Analytic Hierarchy Process (FAHP). Logistics' essential success elements are recognized and prioritized in the literature. Using these crucial success indicators, LOGINET, a 3 PL services provider situated in western India, is benchmarked against four other service providers. Their relative ranking has also been displayed using the standard. [PERSON] and [PERSON] [42] created an integrated technique for overcoming the inadequacies and gaps observed in previous benchmarking studies, as well as for benchmarking Intellectual Capital (IC) in the undeveloped logistics sector. The suggested technique integrated the analytic network process and the concept of thinking and non-thinking assets with the generic benchmarking procedure to address the lack of consideration of relationships among previous benchmarking concepts and the impacts of their managerial factors, as well as to examine the wide range of elements and indicators of IC influencing the sustainable development of organizations. [PERSON] and [PERSON] [43] developed a mathematical outline for minimizing latency and choosing nodes in edge-based networks. Furthermore, a solution based on branch-and-bound and outer approximation is thoroughly described. A comprehensive study of the suggested algorithm's convergence is provided, demonstrating that it converges linearly and offers the best answer. According to experimental data, the Outer Approximation Algorithm (OAA) successfully optimized the system as the total system throughput rose with node numbers. According to their findings, their technique can considerably minimize the time required for node selection and total latency while maximizing the output. To compare OAA outcomes, a many-to-one matching-based method was employed as a baseline. OAA beats the matching-based method, according to the results. Other features of the service model and different service parameters, such as workload balancing among MEC nodes, can be catered for in this work. Furthermore, [PERSON] and [PERSON] [44] proposed an optimum relay node-choosing technique to quickly forward data over time-varying IoT networks. Poisson point processes are used to describe the movement of dense IoT networks. Relay nodes are selected by balancing the network's data latency and risk of packet loss. The suggested approaches' performance is tested in a variety of network topologies. The effects of the risk of packet loss in a time-varying network, the duration of the connection history on data latency, data on delayed connectivity, node density, and transmission range are shown in the results. The suggested bi-objective optimization issue is equated to single-objective optimization in terms of performance. When the latter is optimized, the performance of such approaches is found to deteriorate. On the other hand, the technique works well while minimizing data delay and packet loss danger. In addition, for eliminating IoT nodes and selection in OppNets, [PERSON] and [PERSON] [45] provided two fuzzy-based schemes: Node removal and node selection. They employed three input measures for node removal, INDE, NFBS, and the IoT node's battery level, and four input measures for selecting nodes, namely 'Node Contact Duration', 'Node Inter Contact Time', 'Node's Unique Encounters', and Node's Number of Past Encounters.