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Economic data analytic AI technique on IoT edge devices for health monitoring of agriculture machines

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Abstract

In the era of Internet of things (IoT), network Connection of an enormous number of agriculture machines and service centers is an expectation. However, it will be with a generation of massive volume of data, thus overwhelming the network traffic and storage system especially when manufacturers give maintenance service typically by various data analytic applications on the cloud. The situation is more complex in the context of low latency applications such as health monitoring of agriculture machines, although require emergency responses. Performing the computational intelligence on edge devices is one of the best approaches in developing green communications and managing the blast of network traffic. Due to the increasing usage of smartphone applications, the edge computation on the smartphone can highly assist the network traffic management. In connection with the mentioned point, in the context of exploiting the limited computation power of smartphones, the design of an AI-based data analytic technique is a challenging task. On the other hand, the users’ need for economic technology makes it not to be easily pierced. This research work aims both targets by presenting a bi-level genetic algorithm approach of an optimized data analytic AI technique for monitoring the health of the agriculture vehicles which can be economically utilized on smartphone end-devices using the built-in microphones instead of expensive IoT sensors.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Oakland University, Rochester, MI, USA

    Neeraj Gupta & Nilesh Patel

  2. Media Integrated Communication Laboratory, Graduate School of Engineering, Osaka University, Osaka, Japan

    Mahdi Khosravy

  3. Techno International New Town, Kolkata, India

    Nilanjan Dey

  4. Department of Computer Science, Banasthali Vidyapith, Rajasthan, India

    Saurabh Gupta

  5. John Deere India Pvt Ltd., Pune, India

    Saurabh Gupta

  6. Centre for Development of Advanced Computing, Pune, Maharashtra, India

    Hemant Darbari

  7. Faculty of Engineering,Department of Computer Science, International University of La Rioja, La Rioja, Spain

    Rubén González Crespo

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  1. Neeraj Gupta
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Correspondence to Mahdi Khosravy.

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CRediT authorship contribution statement

Neeraj Gupta: Conceptualization, Methodology, Software, Investigation, Formal analysis, Writing-original draft, Visualization, Validation. Mahdi Khosravy: Conceptualization, Methodology, Investigation, Formal analysis, Visualization, Validation. Nilesh Patel: Formal Analysis, Resources, Project administration, Writing - reviews & editing, Nilanjan Dey: Formal Analysis, Investigation, Data curation, Reviews & editing, Saurabh: Conceptualization, Methodology, Software, Investigation, Formal analysis, Writing, Visualization, Validation. Hemant Darbari: Supervision, Formal Analysis, Resources, Project administration, Rubén González Crespo: Supervision, Formal Analysis, Investigation, Data curation, Reviews & editing.

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Gupta, N., Khosravy, M., Patel, N. et al. Economic data analytic AI technique on IoT edge devices for health monitoring of agriculture machines. Appl Intell 50, 3990–4016 (2020). https://doi.org/10.1007/s10489-020-01744-x

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