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Edge networking plays a major part in issues with computer networks and issues with the path. In this article, in linear Diophantine fuzzy (LDF) graphs, we present special forms of linear Diophantine fuzzy bridges, cut-vertices, cycles, trees, forests, and introduce some of their characteristics. Also, one of the most researched issues in linear Diophantine fuzzy sets (LDFS) and systems is the minimum spanning tree (MST) problem, where the arc costs have linear Diophantine fuzzy (LDF) values. In this work, we focus on an MST issue on a linear Diophantine fuzzy graph (LDFG), where each arc length is allocated a linear Diophantine fuzzy number (LDFG) rather than a real number. The LDFN can reflect the uncertainty in the LDFG’s arc costs. Two critical issues must be addressed in the MST problem with LDFG. One issue is determining how to compare the LDFNs, i.e., the cost of the edges. The other question is how to calculate the edge addition to determine the cost of the LDF-MST. To overcome these difficulties, the score function representation of LDFNs is utilized and Prim’s method is a well-known approach for solving the minimal spanning tree issue in which uncertainty is ignored, i.e., precise values of arc lengths are supplied. This technique works by providing more energy to nodes dependent on their position in the spanning tree. In addition, an illustrated example is provided to explain the suggested approach. By considering a mobile charger vehicle that travels across the sensor network on a regular basis, charging the batteries of each sensor node.

Lifetime prolongation of a wireless charging sensor network using a mobile robot via linear Diophantine fuzzy graph environment