Optimize Energy Consumption of Wireless Sensor Networks by using modified Ant Colony Optimization ACO

Routing represents a pivotal concern in the context of Wireless Sensor Networks (WSN) owing to its divergence from traditional network routing paradigms. The inherent dynamism of the WSN environment, coupled with the scarcity of available resources, engenders considerable challenges for industry and academia alike in devising efficient routing strategies. Addressing these challenges, a viable recourse lies in applying heuristic search methodologies to ascertain the most optimal path in WSNs. Ant Colony Optimization (ACO) is a well-established heuristic algorithm that has demonstrated notable advancements in routing contexts. This paper introduces a modify routing protocols based on Ant colony optimization. In these protocols, we incorporate the inverse of the distance between nodes and their neighbours in the probability equations of ACO along with considering pheromone levels and residual energy. These formulation modifications facilitate the selection of the most suitable candidate for the subsequent hop, effectively minimizing the average energy consumption across all nodes in each iteration. Furthermore, in this protocol, we iteratively fine-tune ACO's parameter values based on the outcomes of several experimental trials. The experimental analysis is conducted through a diverse set of network topologies, and the results are subjected to comparison against well-established ACO algorithm and routing protocols. The efficacy of the proposed protocol is assessed based on various performance metrics, encompassing throughput, energy consumption, network lifetime, energy consumption, the extent of data transferred over the network, and the length of paths traversed by packets. These metrics collectively provide a comprehensive evaluation of the performance attainments of the routing protocols

Optimal Raw Material Inventory Analysis Using Markov Decision Process with Policy Iteration Method

Inventory of raw materials is a big deal in every production process, both in company production and home business production. In order to meet consumer demand, a business must be able to determine the amount of inventory that should be provided. The purpose of this research is to choose an alternative selection of ordering raw materials that produce the maximum amount of raw materials with minimum costs. The raw material referred to in this study is pandan leaves used to make pandan mats. Analysis of raw material inventory used in this research was the Markov decision process with the policy iteration method by considering the discount factor. From the analysis conducted, it is obtained alternative policies that must be taken by producers to meet raw materials with minimum costs. The results of this study can be a consideration for business actors in the study location in deciding the optimal ordering policy that should be taken to obtain the minimum operational cost