Deep Learning-Based Indoor Localization Using Wireless Sensor Network: An Efficient Approach for Livestock Monitoring

Abstract

Indoor localization for livestock is important as it facilitates effective monitoring and management of animals within confined spaces, such as barns or stables. By accurately tracking the position of individual animals, farmers and livestock managers can gain valuable insights into their behavior, health, and welfare. This information enables the early detection of potential issues, such as diseases or injuries, allowing for prompt intervention and treatment. While GPS sensors offer global position estimation, they are limited to outdoor environments and inherently exhibit inaccuracies of several meters. In indoor spaces, alternative sensors like lasers and cameras can estimate positions, but they necessitate maps and substantial computational resources to process complex algorithms. Presently, Wireless Networks (WN) are extensively accessible in indoor environments, providing efficient global localization with relatively low cost and computing demands. This paper presents a novel approach to estimate the location of cows in a given area using Deep Neural Networks (DNNs) applied to LQI data. This method aims to improve the efficiency of livestock management, particularly in large-scale farming operations, by enabling precise tracking and monitoring of individual animals. Our proposed model leverages data from wireless sensor networks (WSNs) and demonstrates promising results in terms of accuracy and computational efficiency. This study contributes to the ongoing research in smart agriculture and the application of advanced technologies in the livestock industry.