# Towards Semantic Integration of Heterogeneous Sensor Data with Indigenous Knowledge for Drought Forecasting

arXiv: https://arxiv.org/abs/1601.01920 · HTML: https://mlsanity.com/paper/1601.01920/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Jan 2016.

## Built on by (most cited first, 2 of 2)

- [ESTemd: A Distributed Processing Framework for Environmental Monitoring based on Apache Kafka Streaming Engine](https://mlsanity.com/paper/2104.01082/) (2021)
- [Towards a Microservice-based Middleware for a Multi-hazard Early Warning System](https://mlsanity.com/paper/2312.15323/) (2023)

## Abstract (arXiv)

In the Internet of Things (IoT) domain, various heterogeneous ubiquitous devices would be able to connect and communicate with each other seamlessly, irrespective of the domain. Semantic representation of data through detailed standardized annotation has shown to improve the integration of the interconnected heterogeneous devices. However, the semantic representation of these heterogeneous data sources for environmental monitoring systems is not yet well supported. To achieve the maximum benefits of IoT for drought forecasting, a dedicated semantic middleware solution is required. This research proposes a middleware that semantically represents and integrates heterogeneous data sources with indigenous knowledge based on a unified ontology for an accurate IoT-based drought early warning system (DEWS).