A Node Density Control Learning Method for the Internet of Things.

Shumei Lou,Shuai Liu,Gautam Srivastava

doi:10.3390/s19153428

Abstract

When examining density control learning methods for wireless sensor nodes, control time is often long and power consumption is usually very high. This paper proposes a node density control learning method for wireless sensor nodes and applies it to an environment based on Internet of Things architectures. Firstly, the characteristics of wireless sensors networks and the structure of mobile nodes are analyzed. Combined with the flexibility of wireless sensor networks and the degree of freedom of real-time processing and configuration of field programmable gate array (FPGA) data, a one-step transition probability matrix is introduced. In addition, the probability of arrival of signals between any pair of mobile nodes is also studied and calculated. Finally, the probability of signal connection between mobile nodes is close to 1, approximating the minimum node density at T. We simulate using a fully connected network identifying a worst-case test environment. Detailed experimental results show that our novel proposed method has shorter completion time and lower power consumption than previous attempts. We achieve high node density control as well at close to 90%.

Highlights

With the rapid development of modern technology, Internet of Things (IoT) computing integrates multiple discipline technologies such as modern sensors, microelectronics, communication, embedded computing, and distributed information processing
In IoT, wireless sensor networks (WSNs) are inexpensive to install and easy to use, leading to their popularity and use. It is applied in practical applications of IoT where it is inconvenient or necessary to eliminate wired connections and is widely used in many fields
Faced with many practical applications, how can implementers ensure the connectivity of an IoT network and maximize the lifetime of the long network? Sensor nodes were generally randomly distributed in places with harsh natural environments

Summary

Introduction

With the rapid development of modern technology, Internet of Things (IoT) computing integrates multiple discipline technologies such as modern sensors, microelectronics, communication, embedded computing, and distributed information processing. It has attracted worldwide attention and gradually has been integrated into people’s daily lives [1]. In IoT, wireless sensor networks (WSNs) are inexpensive to install and easy to use, leading to their popularity and use It is applied in practical applications of IoT where it is inconvenient or necessary to eliminate wired connections and is widely used in many fields. When the density of the nodes is too small, the connectivity of the system and the coverage of the monitored area cannot be guaranteed

Methods

Results

Conclusion