arXiv subjects

Hitesh Mohapatra

Publications and source records attributed to Hitesh Mohapatra.

3 recordsLinked to original sources

arXiv2025

A LoRa IoT Framework with Machine Learning for Remote Livestock Monitoring in Smart Agriculture

This work presents AgroTrack, a LoRa-based IoT framework for remote livestock monitoring in smart agriculture. The system is designed for low-power, long-range communication and supports real-time tracking and basic health assessment of free-range livestock through GPS, motion, and temperature sensors integrated into wearable collars. Data is collected and transmitted via LoRa to gateways and forwarded to a cloud platform for visualization, alerts, and analytics. To enhance its practical deployment, AgroTrack incorporates advanced analytics, including machine learning models for predictive health alerts and behavioral anomaly detection. This integration transforms the framework from a basic monitoring tool into an intelligent decision-support system, enabling farmers to improve livestock management, operational efficiency, and sustainability in rural environments.

cs.HC↗

arXiv2025

Golden Ratio Assisted Localization for Wireless Sensor Network

This paper presents a novel localization algorithm for wireless sensor networks (WSNs) called Golden Ratio Localization (GRL), which leverages the mathematical properties of the golden ratio (phi 1.618) to optimize both node placement and communication range. GRL introduces phi-based anchor node deployment and hop-sensitive weighting using phi-exponents to improve localization accuracy while minimizing energy consumption. Through extensive simulations conducted on a 100 m * 100 m sensor field with 100 nodes and 10 anchors, GRL achieved an average localization error of 2.35 meters, outperforming DV- Hop (3.87 meters) and Centroid (4.95 meters). In terms of energy efficiency, GRL reduced localization energy consumption to 1.12 microJ per node, compared to 1.78 microJ for DV-Hop and 1.45 microJ for Centroid. These results confirm that GRL provides a more balanced and efficient localization approach, making it especially suitable for energy-constrained and large-scale WSN deployments.

cs.NI↗

arXiv2023

Exploring AI Tool's Versatile Responses: An In-depth Analysis Across Different Industries and Its Performance Evaluation

AI Tool is a large language model (LLM) designed to generate human-like responses in natural language conversations. It is trained on a massive corpus of text from the internet, which allows it to leverage a broad understanding of language, general knowledge, and various domains. AI Tool can provide information, engage in conversations, assist with tasks, and even offer creative suggestions. The underlying technology behind AI Tool is a transformer neural network. Transformers excel at capturing long-range dependencies in text, making them well-suited for language-related tasks. AI Tool has 175 billion parameters, making it one of the largest and most powerful LLMs to date. This work presents an overview of AI Tool's responses on various sectors of industry. Further, the responses of AI Tool have been cross-verified with human experts in the corresponding fields. To validate the performance of AI Tool, a few explicit parameters have been considered and the evaluation has been done. This study will help the research community and other users to understand the uses of AI Tool and its interaction pattern. The results of this study show that AI Tool is able to generate human-like responses that are both informative and engaging. However, it is important to note that AI Tool can occasionally produce incorrect or nonsensical answers. It is therefore important to critically evaluate the information that AI Tool provides and to verify it from reliable sources when necessary. Overall, this study suggests that AI Tool is a promising new tool for natural language processing, and that it has the potential to be used in a wide variety of applications.

cs.HC↗