TY - JOUR
T1 - Computing dominant metric dimensions of certain connected networks
AU - Ali, Imtiaz
AU - Javaid, Muhammad
AU - Shang, Yilun
N1 - Funding information: The work of Muhammad Javaid was supported by the Higher Education Commission of Pakistan through the National Research Program for Universities under Grant 20-16188/NRPU/R&D/HEC/2021.
PY - 2024/2/29
Y1 - 2024/2/29
N2 - In the studies of the connected networks, metric dimension being a distance-based parameter got much more attention of the researches due to its wide range of applications in different areas of chemistry and computer science. At present its various types such as local metric dimension, mixed metric dimension, solid metric dimension, and dominant metric dimension have been used to solve the problems related to drug discoveries, embedding of biological sequence data, classification of chemical compounds, linear optimization, robot navigation, differentiating the interconnected networks, detecting network motifs, image processing, source localization and sensor networking. Dominant resolving sets are better than resolving sets because they carry the property of domination. In this paper, we obtain the dominant metric dimension of wheel, gear and anti-web wheel network in the form of integral numbers. We observe that the aforesaid networks have bounded dominant metric dimension as the order of the network increases. In particular, we also discuss the importance of the obtained results for the robot navigation networking.
AB - In the studies of the connected networks, metric dimension being a distance-based parameter got much more attention of the researches due to its wide range of applications in different areas of chemistry and computer science. At present its various types such as local metric dimension, mixed metric dimension, solid metric dimension, and dominant metric dimension have been used to solve the problems related to drug discoveries, embedding of biological sequence data, classification of chemical compounds, linear optimization, robot navigation, differentiating the interconnected networks, detecting network motifs, image processing, source localization and sensor networking. Dominant resolving sets are better than resolving sets because they carry the property of domination. In this paper, we obtain the dominant metric dimension of wheel, gear and anti-web wheel network in the form of integral numbers. We observe that the aforesaid networks have bounded dominant metric dimension as the order of the network increases. In particular, we also discuss the importance of the obtained results for the robot navigation networking.
KW - Connected networks
KW - Dominant metric dimension
KW - Metric dimension
UR - http://www.scopus.com/inward/record.url?scp=85184580167&partnerID=8YFLogxK
U2 - 10.1016/j.heliyon.2024.e25654
DO - 10.1016/j.heliyon.2024.e25654
M3 - Article
AN - SCOPUS:85184580167
SN - 2405-8440
VL - 10
JO - Heliyon
JF - Heliyon
IS - 4
M1 - e25654
ER -