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Applying Adaptive Security Techniques for Risk Analysis of Internet of Things (IoT)-Based Smart Agriculture

Author

Listed:
  • Abdur Rehman Riaz

    (UIIT, PMAS Arid Agriculture University, Rawalpindi 46000, Pakistan)

  • Syed Mushhad M. Gilani

    (Department of Computer Science, University of Agriculture, Faisalabad 38000, Pakistan)

  • Salman Naseer

    (Department of Information Technology, University of the Punjab Gujranwala Campus, Gujranwala 52250, Pakistan)

  • Sami Alshmrany

    (Faculty of Computer and Information Systems, Islamic University of Madinah, Madinah 42351, Saudi Arabia)

  • Muhammad Shafiq

    (Department of Information and Communication Engineering, Yeungnam University, Gyeongsan 38541, Korea)

  • Jin-Ghoo Choi

    (Department of Information and Communication Engineering, Yeungnam University, Gyeongsan 38541, Korea)

Abstract

In modern times, the Internet of Things (IoT) is having a major impact on agriculture. Risk and security parameters are always linked when researching, developing, implementing and deploying IoT-based devices. It is a myth that security does not play a major role in IoT applications in agriculture. Data accuracy and availability is a high-priority requirement for farmers who help achieve high yields. A secure IoT network requires a situational approach, often referred to as dynamic security. An advanced security approach to improving IoT security is adaptive security, a cybersecurity-based approach. The lack of security in a smart farming environment is a very important factor for agricultural growth. In this study, we introduce IoT together with adaptive security operations and integrate it into a smart farming environment. We propose an evaluation framework that can be applied to diverse smart farming environments. Several scenarios of an agricultural environment with smart devices and sensors are described for execution. Storylines with real-time environments are then derived from these scenarios to extend and incorporate adaptive security frameworks and scenarios in IoT-based agriculture.

Suggested Citation

  • Abdur Rehman Riaz & Syed Mushhad M. Gilani & Salman Naseer & Sami Alshmrany & Muhammad Shafiq & Jin-Ghoo Choi, 2022. "Applying Adaptive Security Techniques for Risk Analysis of Internet of Things (IoT)-Based Smart Agriculture," Sustainability, MDPI, vol. 14(17), pages 1-20, September.
  • Handle: RePEc:gam:jsusta:v:14:y:2022:i:17:p:10964-:d:905023
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    Cited by:

    1. Parthasarathy Velusamy & Jagadeesan Srinivasan & Nithyaselvakumari Subramanian & Rakesh Kumar Mahendran & Muhammad Qaiser Saleem & Maqbool Ahmad & Muhammad Shafiq & Jin-Ghoo Choi, 2023. "Optimization-Driven Machine Learning Approach for the Prediction of Hydrochar Properties from Municipal Solid Waste," Sustainability, MDPI, vol. 15(7), pages 1-14, March.

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