Implementing adaptive dragonfly optimization for privacy preservation in IoT

IoT (Internet of Things) is a sophisticated analytics and automation system that utilizes networks, big data, artificial intelligence, and sensing technology, and is controlled by an embedded module. It allows one to use affordable wireless technology and transmits the data into the cloud at a component level. It also provides a place to save the data - however, the significant challenges in IoT relay on security restrictions related with device cost. Moreover, the increasing amount of devices further generate opportunities for attacks. Hence, to overcome this issue, this paper intends to develop a promising methodology associated with data privacy preservation for handling the IoT network. It is obvious that the IoT devices often generate time series data, where the range of respective time series data can be vast. Under such circumstances, proper information extraction through effective analysis and relevant privacy preservation of sensitive data from IoT is challenging. In this paper, the problem that occurred in the data preservation is formulated as a non-linear objective model. To solve this objective model, an improved, optimized Dragonfly Algorithm (DA) is adopted, which is termed the Improved DA (IDA) algorithm. Here, the proposed model focused on preserving the physical activity of human monitoring data in the IoT sector. Moreover, the proposed IDA algorithm is compared with conventional schemes such as Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Ant Bee Colony (ABC), Firefly (FF) and DA and the outcomes prove that the suggested scheme is highly used for preserving the sensitive information uploaded in IoT.