A Survey on the Network Models applied in the Industrial Network Optimization

Abstract

Network architecture design is very important for the optimization of industrial networks. The type of network architecture can be divided into small-scale network and large-scale network according to its scale. Graph theory is an efficient mathematical tool for network topology modeling. For small-scale networks, its structure often has regular topology. For large-scale networks, the existing research mainly focuses on the random characteristics of network nodes and edges. Recently, popular models include random networks, small-world networks and scale-free networks. Starting from the scale of network, this survey summarizes and analyzes the network modeling methods based on graph theory and the practical application in industrial scenarios. Furthermore, this survey proposes a novel network performance metric -system entropy. From the perspective of mathematical properties, the analysis of its non-negativity, monotonicity and concave-convexity is given. The advantage of system entropy is that it can cover the existing regular network, random network, small-world network and scale-free network, and has strong generality. The simulation results show that this metric can realize the comparison of various industrial networks under different models.

rSEM: System-Entropy-Measure-Guided Routing Algorithm for Industrial Wireless Sensor Networks

“…Assuming that N nodes and 1 sinknode are at known positions. Firstly, N ch (preset) cluster heads needs to be selected from N. Once the number of cluster heads is determined, N ch nodes that are most suitable to be cluster heads can be determined through traversal, and the selection method is as follows [11]:…”

“…The expression of system entropy is as follows [ 12 , 13 ]: where represents the parameter of system entropy and its value is 2, which is different from the description in the literature [ 11 ]. When using the capacity as a path metric, an of less than one leads to the system entropy in complex form.…”

“…Firstly, (preset) cluster heads needs to be selected from N . Once the number of cluster heads is determined, nodes that are most suitable to be cluster heads can be determined through traversal, and the selection method is as follows [ 11 ]: where represents all the cases that need to be considered in the problem, obviously, , where is the combinatorial function. represents the topology of the network composed of some nodes and the sinknode.…”

“…The reason why it will go to the two extremes is the lack of a systematic network measure. The concept of system entropy is proposed in [ 11 ], which measures the comprehensive performance of a network by its topology. There is only one variable, i.e., the Laplace matrix, in the expression of the system entropy measure, and the network topology matrix is used to evaluate the network performance in a more abstract way.…”