Search of the Shortest Path in a Communication Network with Fuzzy Cost Functions

Abstract

A communication network management system takes the measurements of its state variables at specific instants of time, considering them constant in the interval between two consecutive measurements. Nevertheless, this assumption is not true, since these variables evolve in real time. Therefore, uncertainty is inherent in the processing of the measurements during the intervals so that they cannot be efficiently managed using crisp variables. In this paper, we face this problem by modeling the communications network as a type-V fuzzy graph, where both the nodes and the links are described with precision, but the cost of each link is modeled as a triangular fuzzy number. Different fuzzy cost allocation functions and fuzzy optimization strategies are described and applied to the search for the shortest path between two nodes. An experimental study has been conducted using two representative networks: the backbone network of Nippon Telegraph and Telephone Corporation (NTT) and the National Science Foundation’s Network (NFSNET). In these networks, our fuzzy cost functions and strategies have been compared with the well-known crisp equivalents. The optimal search strategies are based on the proposed Fuzzy Dijkstra Algorithm (FDA), which is described deeply. The simulation results demonstrate that in all cases the fuzzy alternatives surpass or equal the crisp equivalents with statistically significant values. Specifically, the so-called Strategy 8 presents the best throughput, as it significantly exceeds the performance of all those evaluated, achieving a Global Mean Delivery Rate (GMDR) close to 1.

Communication Times Reconstruction in a Telecontrolled Client–Server Scheme: An Approach by Kalman Filter Applied to a Proprietary Real-Time Operating System and TCP/IP Protocol

“…For this reason, it was necessary to analyze the covariance of input noise Q T C,k and the covariance of output noise R T C,k+1 in execution times τ c,k , reception times τ Rx,k , and transmission times τ Tx,k . Then it is possible to use Equation (7) to calculate the Kalman's gain K T C,k , which will allow the reconstruction of execution times τ c,k , reception times τ Rx,k , and transmission times τ Tx,k .…”

“…Then, it is possible to use Equation (7) to calculate the Kalman's gain K T C,k (see Figure 12) that will allow the reconstruction of execution times τ c,k , reception times τ Rx,k , and transmission times τ Tx,k . In Figure 13, the measured reception times τ Rx,k and the reconstructed reception times τRx,k can be observed.…”

“…Consequently, it "is used" to imitate the behavior of the times involved in the extraction of basic properties, expressed in a set of mathematical attributes. This is in order to improve the design of network access, maximize the ratio between sent and received information for any pair of source-destination nodes [7], and create efficient traffic control schemes which indirectly reduce transport time on the Internet [8]. In the case of telecontrolled systems, it is possible to improve the system's behavioral dynamics with the design and development of strategies based onmeasuring the telecontrol time T Tc,k .…”

Kairos: Enabling Prompt Monitoring of Information Diffusion Over Temporal Networks

“…If a source vertex is suspected of a fraudulent transaction, following the movement of the transactions can help identify other suspect vertices. • Communication Network [15]: A communication network consists of id1,id2,(s,f ) e(id 1 |calls|id 2 ) (s,f ) to represent the communication between a router v(id 1 ) and a router v(id 2 ) at a time period (s, f ). Analyzing this type of network helps identify the optimal path from a source router to a destination router to reduce information transmission delays [15].…”

“…In the case of the communication network, TSP can identify the cost of the path(e.g., used bandwidth, packet delay, etc. ), which would help in establishing routing protocols [15].…”