Sarcouncil Journal of Multidisciplinary

Abstract: Edge analytics is a game-changing data processing paradigm for Internet of Things ecosystems that moves computational resources from remote cloud servers to near or edge locations of data-source devices. This paradigm shift makes decision-making possible in real time and transcends the latency constraints of conventional cloud-based architectures. This article covers the core concepts of edge analytics, such as its technical realization through multi-level architectures integrating optimized hardware accelerators and minimalist communication protocols. It discusses varied use cases across manufacturing, energy management, and healthcare domains where edge processing brings significant gains in system performance, resource efficiency, and operational effectiveness. The article reviews both the compelling benefits of edge analytics, like lower latency, optimized bandwidth, and better reliability, and ongoing technical challenges like security exposure and resource constraints. Lastly, it covers existing research trends based on lightweight machine learning frameworks, edge-cloud integration mechanisms, and autonomous orchestration platforms that are expected to further enhance the capabilities of edge analytics in IoT-enabled products.