An Energy Efficient Mathematically Modified Monarch Butterfly Optimization for Routing in WSN

Abstract

Wireless Sensor Networks (WSNs) are significant for monitoring physical and environmental variables, requiring efficient routing algorithms. This paper presents a novel approach called Energy Efficient Mathematically Modified Monarch Butterfly Optimization (EEMMBO) for routing in Wireless Sensor Networks (WSNs). The hybrid technique combines the accuracy of Mathematical Model Integer Linear Programming (ILP) in decision-making with the exploratory nature of Monarch Butterfly Optimization (MBO). ILP effectively handles specific choices and complex limitations, while MBO introduces variety into solutions, avoiding being stuck in suboptimal outcomes. The inspiration for this research is the fundamental importance of Wireless Sensor Networks (WSNs) in many applications, where efficient routing is vital for successful data gathering and monitoring. The sensor nodes come with limited energy and processing capacities, whereas conventional routing algorithms frequently face challenges in balancing decision-making accuracy and adaptability to changing settings. This research seeks to tackle these obstacles by introducing a prominent technique, the Energy Efficient Mathematically Modified Monarch Butterfly Optimization (EEMMBO), for routing in Wireless Sensor Networks (WSNs). The primary research goal is two-fold: firstly, to create a strong Mathematical Model using Integer Linear Programming (ILP) that guarantees accurate decision-making, and secondly, to incorporate the exploratory characteristics of Monarch Butterfly Optimization (MBO) to improve solution diversity and adaptability. This research aims to improve the efficiency, resilience, and adaptability of WSN routing by using a hybrid approach. The proposed EEMMBO combines different methods effectively and demonstrates its ability to enhance energy efficiency, resilience, and adaptability in WSN routing scenarios.