Maximizing Wireless Sensor Network Lifetime With Energy Efficiency And Load Balancing

Abstract

In the world of wireless sensor networks, two essential limits stand out as the most significant: transmission range and energy source constraints. These elements are critical to maximizing the longevity of sensor networks. A full evaluation of their performance is based on a number of parameters, including data transfer capacity, transmission strategy, and network longevity. Analytically, these factors are extremely important, especially in defining favourable design solutions. Exploring the limitations of these parameters allows us to successfully handle the network maximization challenge. Other network factors, such as beginning energy, number of sensor nodes, and operating area, as well as network management features such as routing, energy optimization, and topology, are also important in evaluating network optimization.

These concerns are fundamental in both hypothetical and real-world wireless sensor network studies. They provide useful information for network scalability, feasibility, and performance evaluation. We suggest using the most favorable distance (MFD) strategy to maximize energy efficiency and limit energy depletion while encouraging network lifetime maximization. To do this, we present a heuristic ACO (Ant Colony Optimization) technique. To discover the shortest path probabilistically, this algorithm integrates control parameters (alpha, beta), evaporation rate, heuristic information, and pheromone updates. We can efficiently optimize energy balance and increase the network's lifetime by adopting this strategy. We conducted experiments in the MATLAB environment to validate the proposed methodology, taking into account both theoretical and mathematical aspects. The collected findings demonstrate the efficacy and efficiency of our strategy for maximizing network lifetime while optimizing energy use.