Evaluating Sensor Data Quality in Internet ofThings Smart Agriculture Applications

TL;DR

This paper introduces a new model for evaluating sensor data quality in IoT-based Smart Agriculture, addressing the unique challenges of machine-to-machine communication and providing empirical validation with real-world data.

Contribution

It presents a novel sensor data quality model tailored for IoT applications, integrating it into Smart Agriculture and validating through empirical experiments.

Findings

Sensor data quality significantly impacts IoT application performance.

The proposed model effectively assesses sensor data quality in real-world scenarios.

Empirical results demonstrate the model's utility in Smart Agriculture applications.

Abstract

The unprecedented growth of Internet of Things (IoT) and its applications in areas such as Smart Agriculture compels the need to devise newer ways for evaluating the quality of such applications. While existing models for application quality focus on the quality experienced by the end-user (captured using likert scale), IoT applications have minimal human involvement and rely on machine to machine communication and analytics to drive decision via actuations. In this paper, we first present a conceptual framework for the evaluation of IoT application quality. Subsequently, we propose, develop and validate via empirical evaluations a novel model for evaluating sensor data quality that is a key component in assessing IoT application quality. We present an implementation of the sensor data quality model and demonstrate how the IoT sensor data quality can be integrated with a Smart Agriculture application. Results of experimental evaluations conducted using data from a real-world testbed concludes the paper.