# A Survey of Data Fusion in Smart City Applications

## TL;DR

This survey reviews data fusion techniques in smart city applications, categorizing approaches from multiple perspectives and evaluating their effectiveness across various urban domains, highlighting future challenges and directions.

## Contribution

It introduces a multi-perspectives classification framework for data fusion in smart cities and applies it to evaluate diverse applications.

## Key findings

- Multi-perspectives classification effectively evaluates data fusion approaches.
- Data fusion enhances quality and knowledge extraction in smart city data.
- Discussion of future challenges guides further research in the field.

## Abstract

The advancement of various research sectors such as Internet of Things (IoT), Machine Learning, Data Mining, Big Data, and Communication Technology has shed some light in transforming an urban city integrating the aforementioned techniques to a commonly known term - Smart City. With the emergence of smart city, plethora of data sources have been made available for wide variety of applications. The common technique for handling multiple data sources is data fusion, where it improves data output quality or extracts knowledge from the raw data. In order to cater evergrowing highly complicated applications, studies in smart city have to utilize data from various sources and evaluate their performance based on multiple aspects. To this end, we introduce a multi-perspectives classification of the data fusion to evaluate the smart city applications. Moreover, we applied the proposed multi-perspectives classification to evaluate selected applications in each domain of the smart city. We conclude the paper by discussing potential future direction and challenges of data fusion integration.