Machine Learning at the Edge: GANs for Anomaly Detection in Wireless Sensor Networks

Machine Learning at the Edge: GANs for Anomaly Detection in Wireless Sensor Networks

Sundara Mohan, Alok Manke, Shanti Verma, K. Baskar
Copyright: © 2024 |Pages: 13
DOI: 10.4018/979-8-3693-3597-0.ch021
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Abstract

In this study, a novel system named “EdgeAnomaly,” is proposed, which leverages generative adversarial networks (GANs) for anomaly detection on wireless sensory networks (WSNs) which are operating at the edge. The proliferation of internet of things (IoT) for devices has led to an exponential increase in data generation by WSNs, necessitating efficient and effective anomaly detection mechanisms. In traditional anomaly detection methods often struggles to cope with the dynamically and diverse nature of WSN data, particularly in resource-constrained edge computing environment. To address these challenges, the employment of GANs, a type of deep learning model capable of generating synthetic data samples resembling the original data distribution. By training the GAN on normal WSN data, EdgeAnomaly will learn to generate representative samples of normal behavior, which enables it to identify deviations indicative of anomalies.
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Introduction

Wireless Sensory Networks (WSNs) will be a pivotal across numerous domains such as environmental monitoring, healthcare system, industrial automation process, and smart infrastructure Han et.al(2021). The networks will composed of group of sensor nodal points strategically will deployed for gathering and broadcasting of data-packets about their surrounding environment. The raw data will invaluable for informs decision-making and will facilitates real-time monitoring of physical phenomena. However, the volume and intricacy of data generated by WSNs will present formidable challenges, particularly in detection of anomalies or deviations in the data stream Kim et.(2020). Anomalies will be signify critical occurrence like environmental threats, equipment with malfunctions, or security breaches, underscores the urgency of timely detection to uphold the reliability, safety, and security of WSN-driven systems. In Conventional anomaly detection methodology often in handling the dynamic and diverse nature of WSN data, especially in settings with limited computational resources availability swaminathan et.al(2021). Hence, there will arises a pressing demand for sophisticated anomaly detection techniques with capable of analyzing WSN data in real-time, by facilitating swift identification and resolution of anomalies Lee et.al(2021) . By tackling the obstacles, WSN-based anomaly detection systems will plays a pivotal roles in fortifying the resilience and efficacy of various applications Sangeetha et.al(2023) thereby augmented for the overall performance and dependability of WSN deployments.

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