Abstract
The paper focuses on the phenomenon of phase transitions related to changes taking place directly in a network structure; i.e., in physical topology. Although these changes are related to the physical layer, they directly affect the functioning of higher layers, and thus the quality and efficiency of network transmission. The research presented herein was focused in particular on phase transformations in MESH type wireless topologies. The dependence between the values of radius (transmitter power) nodes and the coherence of the MESH network is presented. These considerations are important for understanding the dynamics of processes occurring in topological structures, and for optimizing the use of power sources for individual nodes. The paper also presents the use of phase transition analysis for early detection of network coherence loss at the physical layer level. A new mechanism for assessing the level of network communication and an active method of preventing its failure using the arbiter and a set of limited diagnostic information are described. The solution presented can be used both in Internet of Things (IoT) systems and Industry 4.0 to create a high level of reliability wireless connection infrastructure between mobile and fixed devices.
Highlights
Phase transitions occur in many areas of the world around us [1,2,3,4,5]
Phase transitions occur in various environments, both natural and artificial
This paper analyzed the relationship between the connection structure at the physical layer in wireless MESH networks and the power of the transmitter
Summary
Phase transitions occur in many areas of the world around us [1,2,3,4,5]. This applies to both phenomena occurring in natural systems, and in artificial (man-made) ones. Phenomena related to phase transitions occur in computer networks and systems [13,14,15] They are connected, e.g., with routing processes, data flow processes, and queuing, and changes in the structure of physical and logical topology. The solutions regarding MESH connection network failure described in the literature are associated with a reactive approach based on the early detection of a failure to a node or a communication channel. The prediction of network failure is usually based on the use of artificial intelligence methods or data mining techniques to analyze sets of a wide spectrum of network operational parameters, and the traffic that is transmitted through them. The results of these simulations give a completely new lookresults at the of these give a in completely look atand the issue of phase transitions in computer issue ofsimulations phase transitions computernew networks provide the basis for further research.networks and provide the basis for further research
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