Implementation of Artificial Intelligence in a Software-Defined Wireless Sensor Network

Abstract

Incorporating a Software-Defined Network (SDN) paradigm into a Wireless Sensors Network makes it possible to realise Software-Defined Wireless Sensor Networks (SDWSNs), also known as Software-Defined Wireless Sensor Networks (WSN). The reason for this is to investigate and discover answers to the issues that are brought about by WSN. Both artificial intelligence (AI) and machine learning play a key role in our culture and are assisting in the development of systems that are able to manage themselves on their own. Our culture is influenced significantly by both of these. WSNs have found use in a wide variety of industrial applications, including some in which the network's performance and dependability are of the utmost importance to the accomplishment of the overall project. Using a wide array of cutting-edge AI approaches can considerably improve both the usability and dependability of these apps. This can be accomplished by enhancing the functionality of the implementations. Looking further into application of Artificial Intelligence SDN techniques might result in advances in network management, security, or routing in SDWSN. If these issues are addressed, the network may become more reliable. In this paper, we examine the application of machine learning algorithms in software-defined networks (SDN) and analyse the prospect of utilising these AI in software-defined wireless sensor networks (SDWSN) to overcome the challenges presented by WSN and increase the performance as well as the dependability of the system.