A Machine Learning based Mission Critical Data Transmission Protocol in Wireless Sensor Networks

Wireless sensor networks (WSN) has been extensively used in many real time wireless sensor networks applications. Due to limitations of hardware resources and restricted communication capabilities of sensor nodes, it is very challenging to use wireless sensor networks in real time data transmission. Data collection and routing is the main issue in such applications. To enhance the performance under such real time transmission scenario, it is essential to make the protocol intelligent to choose the appropriate path with change in network scenario. Now a days, many machine learning and deep learning algorithms are used for improving the real time data transmissions in WSNs. From the survey on existing methods, it is clear that using machine learning makes the computational methods more reliable, powerful and economical. This paper proposes a machine learning based Medium Access Control (MAC) protocol to handle real time traffic in wireless sensor networks. To deal with the limitations of WSN in real time application, the proposed scheme can help to increase the performance of time-critical wireless sensor network applications. Simulation results authorize our work and confirm the accuracy of the proposed ML-MAC protocol strategy is higher than the existing work.

I. Introduction

A wireless sensor network (WSN) is a type of wireless network which consists of huge amount of small, self-programmed, short powered devices called sensor nodes or motes. Wireless sensor networks usually occupy a vast amount of specifically dispersed, tiny, battery activated devices which are all together used to supportively assemble the data, process it, and then handover the data to the base station. Sensor network has to organize the competencies of computing & processing all together with the help of sensor nodes which are minute processers that perform the task cooperatively to establish the network as shown in figure 1. Fig. 1:

Wireless sensor networks