Performance analysis of wsn and iot using machine learning based efficient technique

Abstract

Wireless sensor network (WSN) systems are typically composed of thousands of sensors that are powered by limited energy resources. To extend the networks longevity, clustering techniques have been introduced to enhance energy efficiency. The Existing protocols are analyzed from a quality of service (QoS) perspective including three common objectives, those are energy efficiency, reliable communication and latency awareness. Understanding the user s requirements is critical in intelligent systems for the purpose of enabling the ability of supporting diverse scenarios. User awareness or user-oriented design is one remaining challenging problem in clustering. Therefore, the potential challenges of implementing clustering schemes to Internet of Things (IoT) systems in networks. As the current studies for WSNs are conducted either in homogeneous or low-level heterogeneous networks, they are not ideal or even not able to function in highly dynamic IoT systems with a large range of user scenarios. Moreover, when 5G is finally realized, the problem will become more complex than that in traditional simplified WSNs. But when WSN grows, the volume of data to be gathered processed and disseminated by the sensor nodes increases largely. Processing and transmitting such a large amount of data is impractical because of the limited energy of the sensors. Thus, there is a need for applying Machine Learning (ML) algorithms in WSNs. Several challenges related to applying clustering techniques to IoT need to be analyzed along with machine learning techniques to optimize the performance of WSN. This research study focused to design an energy efficient technique which can reduce the energy consumption and prolong the lifetime of network communication. newline