Algebraic Recognition Approach in IoT Ecosystem

Anvar Kabulov, Islambek Saymanov (), Akbarjon Babadjanov and Alimdzhan Babadzhanov
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Anvar Kabulov: School of Mathematics and Natural Sciences, New Uzbekistan University, Mustaqillik Ave. 54, Tashkent 100007, Uzbekistan
Islambek Saymanov: School of Mathematics and Natural Sciences, New Uzbekistan University, Mustaqillik Ave. 54, Tashkent 100007, Uzbekistan
Akbarjon Babadjanov: Department of Information Systems, University of Maryland, Baltimore County, 1000 Hilltop Circle, Baltimore, MD 21250, USA
Alimdzhan Babadzhanov: Department of Algorithmization, Engineering Federation of Uzbekistan, Tashkent 100003, Uzbekistan

Mathematics, 2024, vol. 12, issue 7, 1-26

Abstract: The solution to the problem of identifying objects in the IoT ecosystem of the Aral region is analyzed. The problem of constructing a correct algorithm with linear closure operators of a model for calculating estimates for identifying objects in the IoT ecosystem of the Aral region is considered. An algorithm operator is developed, which is considered correct for the problem Z , is the sum of q operators of the assessment calculation model, and is described by a set of numerical parameters 3 · n · m · q , where n is the number of specified features, m is the number of reference objects, and q is the set of recognized objects. Within the framework of the algebraic approach, several variants of linear combinations of recognition operators are constructed, the use of which gives the correct answer on the control material, and this is proven in the form of theorems. The constructed correct recognition algorithms, which are the easiest to use, where there is no optimization procedure, make it possible to quickly solve the issue of identifying incoming information flows in the IoT ecosystem of the Aral region.

Keywords: algebraic recognition; correct algorithm; ecosystem; IoT; analyzing; operation; associative; commutative; distributive (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2024
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