A Highly Accurate Deep Learning Based Approach for Developing Wireless Sensor Network Middleware

Abstract

Despite the popularity of wireless sensor networks (WSNs) in a wide range of applications, security problems associated with them have not been completely resolved. Middleware is generally introduced as an intermediate layer between WSNs and the end user to resolve some limitations, but most of the existing middleware is unable to protect data from malicious and unknown attacks during transmission. This paper introduces a secure wireless sensor network middleware (SWSNM) based on an unsupervised learning technique called generative adversarial network algorithm. SWSNM consists of two networks: a generator (G) network and a discriminator (D) network. The G creates fake data that are similar to the real sample and combines it with real data from the sensors to confuse the attacker. The D contains multi-layers that have the ability to differentiate between real and fake data. The output intended for this algorithm shows an actual interpretation of the data that is securely communicated through the WSN. The framework is implemented in Python with experiments performed using Keras. Results illustrate that SWSNM algorithm improves the accuracy of the data and enhances its security by protecting data from adversaries. In addition, the SWSNM algorithm consumes significantly less energy, has higher throughput, and lower end-to-end delay when compared with a similar conventional approach.