Intrusion Detection System in Wireless Sensor Networks and Fair Resource Allocation Using Geometric Deep Learning Techniques

Prasad, S. Narasimha; Selvan, K. Senthamil; Dhevi, B. Lakshmi

doi:10.1007/s11277-021-09294-2

Cited by 5 publications

(1 citation statement)

References 22 publications

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“…Ref. [101] proposed an approach to analyze the traffic flow processing time in the existence of an attacker, but it has no influence on the unfairness of the traversing flow. The use of Graph Neural Networks to conserve network flow from source to sink is being implemented.…”

Section: Ai Based Solutions To Security Challenges In Wsnsmentioning

confidence: 99%

Recent Advances and Future Prospects of Using AI Solutions for Security, Fault Tolerance, and QoS Challenges in WSNs

et al. 2022

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The increasing relevance and significant acceptance of Wireless Sensor Network (WSN) solutions have aided the creation of smart environments in a multitude of sectors, including the Internet of Things, and offer ubiquitous practical applications. We examine current research trends in WSN using Artificial Intelligence (AI) technologies and the potential application of these methods for WSN improvement in this study. We emphasize the security, fault detection and tolerance, and quality of service (QoS) concerns in WSN, and provide a detailed review of current research that used different AI technologies to satisfy particular WSN objectives from 2010 to 2022. Specifically, this study’s purpose is to give a current review that compares various AI methodologies in order to provide insights for tackling existing WSN difficulties. Furthermore, there has been minimal existing related work concentrating employing AI approaches to solve security, fault detection and tolerance, and quality of service (QoS) concerns associated to WSN, and our goal is to fill the gap in existing studies. The application of AI solutions for WSN is the goal of this work, and we explore all parts of it in order to meet different WSN challenges such as security, fault detection and tolerance, and QoS. This will lead to an increased understanding of current AI applications in the areas of security, fault detection and tolerance, and QoS. Secondly, we present a comprehensive study and analysis of various AI schemes utilized in WSNs, which will aid the researchers in recognizing the most widely used techniques and the merits of employing various AI solutions to tackle WSN-related challenges. Finally, a list of open research issues has been provided, together with considerable bibliographic information, which provides useful recent research trends on the topics and encourages new research directions and possibilities.

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Section: Ai Based Solutions To Security Challenges In Wsnsmentioning

confidence: 99%