Crop Area Management Based on Fuzzy Analysis of Historical Sensor Readings Combined Within a Unified IoT Platform-Reference-Cited by-同舟云学术

Crop Area Management Based on Fuzzy Analysis of Historical Sensor Readings Combined Within a Unified IoT Platform

Published:2024-08-08 Issue: Volume:21 Page:374-384
ISSN:2224-3402
Container-title:WSEAS TRANSACTIONS ON INFORMATION SCIENCE AND APPLICATIONS
language:en
Short-container-title:

Author:

Aliyev Elchin¹,Rzayev Ramin²,Almasov Asgar ,¹,Rahmanov Abulfat¹

Affiliation:

1. Laboratory of Intelligent Decision-Making Methods and Computer Systems, Institute of Control Systems, Baku, AZERBAIJAN

2. Laboratory of Information Decision Support Systems, Institute of Control Systems, Baku, AZERBAIJAN

Abstract

The network-centric approach to building control systems using wireless technologies has shown its effectiveness in practice. Currently, active research is underway in the field of implementing a network-centric approach to improve the management of business entities. A network-centric control system is a distributed control system in which its main components are integrated into a single information space. The purpose of this research is to develop proposals for building a network-centric model for managing geographically distributed crop areas based on a digital IoT-platform, which represents a universal info-communication environment. The features of network-centric control using fuzzy modeling systems that provide analytical support for decision-making under uncertainty are considered. As an example, it is considered a method of fuzzy modeling and forecasting of segments of averaged sensor readings from web-devices, which can provide information support for predictive and prescriptive analytical solutions. Proposed solutions can be used not only in the field of precision agriculture, but also to build a digital network-centric management platform for any business entity.

Publisher

World Scientific and Engineering Academy and Society (WSEAS)

Reference18 articles.

1. A. M. Abbasov, R. R. Rzayev, “Artificial intelligence and digital economy: development prospects”, Lecture Notes in Networks and Systems, vol. 610, pp. 147-153, 2023.

2. S. M. Chen, and N. Y. Chung, “Forecasting enrollments of students by using fuzzy time series and genetic algorithms”, International Journal of Information and Management Sciences, vol. 17, no. 3, pp. 1-17, 2006.

3. S. M. Chen, and N. Y. Chung, “Forecasting enrollments using high order fuzzy time series and genetic algorithms”, International Journal of Information and Management Sciences, vol. 21, pp. 485-501, 2006.

4. S. M. Chen, and J. R. Hwang, “Temperature prediction using fuzzy time series”, IEEE Trans. Systems, Man, and Cybernetics, Part B: Cybernetics, vol. 30, no. 2, pp. 263-275, 2000.

5. S. M. Chen, and P. Y. Kao, “TAIEX forecasting based on fuzzy time series, particle swarm optimization techniques and support vector machines”, Information Sciences, vol. 247, pp. 62-71 2013.