Model for estimating probabilistic-temporal parameters of information interaction in the Internet of Things

DOI: 10.31673/2412-9070.2024.050465

Abstract

This paper presents a simulation model for analyzing the probabilistic and temporal parameters of information interaction in the Internet of Things (IoT) network. The IoT consists of a large number of devices, sensors, and machines that exchange data in real-time. The model implements various modes of data exchange between sensor devices and the server, including polling, interruptions, and multiple access. One of the critical challenges in such networks is ensuring efficient data transmission while minimizing delays, collisions, and the probability of packet loss. In this context, the proposed model focuses on the evaluation of probabilistic and temporal characteristics of data exchange, taking into account the stochastic nature of sensor devices' access to the server and the likelihood of collisions when multiple devices attempt to transmit data simultaneously.
The model also considers various access modes to the server, which is essential for understanding and mitigating potential sources of data collisions and communication delays in IoT networks. The stochastic nature of sensor devices and their asynchronous data transmission often result in collisions that degrade the network's performance. The model allows for the evaluation of these issues by simulating different scenarios and calculating the potential delays and packet loss probabilities under varying conditions of data access and transmission.
By incorporating real-time access and transmission scenarios, the model aims to provide a comprehensive tool for evaluating the reliability and stability of IoT networks. These evaluations are critical for the effective functioning of IoT systems, particularly in sectors where real-time data processing is crucial, such as industrial automation, smart cities, healthcare, and transportation systems.
The results obtained from this model can contribute to optimizing the performance of IoT networks by offering insights into the best practices for minimizing delays and collisions in various practical applications. For example, in industrial systems, where precision and timely data transmission are critical, or in healthcare systems that rely on real-time patient monitoring, optimizing these parameters can significantly improve efficiency and safety.

Keywords: Internet of things, probabilistic model, sensor device, information interaction, collision.