Enhancing Sensor Data Integration and Analytics Using Edge Computing and Cloud-Based AI: A Hybrid Approach

Abstract

In the modern era of data-driven decision-making, sensor networks have proliferated across various domains, generating vast and ever-expanding volumes of data. This explosion of data presents significant challenges to traditional data processing paradigms, which often need help to keep pace with the speed and scale of sensor-generated information. As a result, there is a critical need for more efficient and adaptive data integration and analytics methods. This paper presents a detailed review of the hybrid approach that combines edge computing and cloud-based artificial intelligence (AI) to address these challenges. We investigate the distinct advantages and limitations of edge computing, which excels in reducing latency and enhancing real-time processing capabilities, and cloud-based AI, which offers advanced analytics and scalability. By exploring the synergies between these two technologies, we demonstrate how their integration can provide a comprehensive solution to contemporary data processing challenges. The paper thoroughly examines recent advancements in edge computing and cloud-based AI, supported by case studies and practical applications across various sectors such as smart cities, healthcare, and industrial automation. We aim to offer a nuanced understanding of how this hybrid approach can optimize sensor data integration and analytics, leading to more efficient, responsive, and insightful data-driven decision-making.