ESTemd: A Distributed Processing Framework for Environmental Monitoring based on Apache Kafka Streaming Engine

TL;DR

ESTemd is a distributed framework leveraging Apache Kafka to enable real-time processing and analysis of heterogeneous environmental data streams, supporting environmental monitoring, decision-making, and early warning systems.

Contribution

It introduces a novel distributed stream processing framework specifically designed for heterogeneous environmental data using Kafka, addressing data heterogeneity and real-time processing challenges.

Findings

Effective handling of heterogeneous environmental data streams.

Supports real-time analytics and early warning systems.

Demonstrates the usefulness of big data techniques in environmental monitoring.

Abstract

Distributed networks and real-time systems are becoming the most important components for the new computer age, the Internet of Things (IoT), with huge data streams or data sets generated from sensors and data generated from existing legacy systems. The data generated offers the ability to measure, infer and understand environmental indicators, from delicate ecologies and natural resources to urban environments. This can be achieved through the analysis of the heterogeneous data sources (structured and unstructured). In this paper, we propose a distributed framework Event STream Processing Engine for Environmental Monitoring Domain (ESTemd) for the application of stream processing on heterogeneous environmental data. Our work in this area demonstrates the useful role big data techniques can play in an environmental decision support system, early warning and forecasting systems. The proposed framework addresses the challenges of data heterogeneity from heterogeneous systems and real time processing of huge environmental datasets through a publish/subscribe method via a unified data pipeline with the application of Apache Kafka for real time analytics.