Optimization of Data Distributed Network System under Uncertainty

Abstract

The major network design or data distributed problems may be described as constrained optimization problems. Constrained optimization problems include restrictions imposed by the system designers. These limitations are basically due to the system design’s physical limitations or functional requirements of the network system. Constrained optimization is a computationally challenging job whenever the constraints/limitations are nonlinear and nonconvex. Furthermore, nonlinear programming methods can easily deal same optimization problem if somehow the constraints are nonlinear and convex. In this paper, we have addressed a distributed network design problem involving uncertainty that transmits data across a parallel router. This distributed network design problem is a Jackson open-type network design problem that has been formulated based on the M/M/1 queueing system. Because our network design problem is a nonlinear, convex optimization problem, we have employed a well-known Kuhn–Tucker (K-T) optimality algorithm to solve the same. Here, we have used triangular fuzzy numbers to express uncertain traffic rates and data processing rates. Then, by applying α -level interval of fuzzy numbers and their corresponding parametric representation of α -level intervals, the associated network design problem has been transformed to its parametric form and later has been solved. To obtain the optimal data stream rate in terms of interval and to illustrate the applicability of the entire approach, a hypothetical numerical example has been exhibited. Finally, the most important results have been reported.

A Novel SIR Approach to Closeness Coefficient‐Based MAGDM Problems Using Pythagorean Fuzzy Aczel–Alsina Aggregation Operators for Investment Policy

“…Furthermore, we can spread them to other aggregation operators, such as power mean AOs, Bonferroni mean AOs, Hamacher AOs, Hamy mean AOs, and Dombi AOs with SIR techniques. In the future, there is a lot of potential in machine learning, information retrieval, data mining, artifcial intelligence, social network analysis, and many other areas in uncertain scenarios [38][39][40][41][42][43][44][45][46][47][48]. Tese are all fascinating topics for future studies.…”

Improvement of Wireless Sensor Network Lifetime via Intelligent Clustering Under Uncertainty

“…Due to the inherent limitations and challenges in recharging node power sources, research has increased to enhance energy balance and efficiency in WSNs [9]. Sensor devices have a finite lifespan, prompting efforts to extend their operational longevity by developing energy-efficient routing protocols [10][11]. Routing is the distinguishing hallmark of WSNs compared to other ad hoc wireless networks.…”

Shortest Path of a Random Graph and its Application

“…Additionally, several methods have been developed that use graph theory to find a complex network's shortest path. The works of Triana and Syahputri [13], Sahoo et al [14], Singh and Mishra [15], Brodnik et al [16], Ramadiani et al [17] and others might be consulted for additional information. Random graph (RG) is another term for a different kind of specialized graph that is used in the literature [18,19].…”