Improved Security-Based Model for Heterogeneous Wireless Sensor Networks Using the Dispense Incline Drop Algorithm

Abstract

Numerous real-time applications rely on heterogeneous wireless sensor networks (HWSNs), including the Internet of Vehicles (IoV), Internet of Sensors (IoS), medical applications, and so on. Due to the restricted computational capacity, memory, and connectivity of wireless nodes, it is essential to establish authentication techniques that consume less energy, yet guarantee strong security. It takes a lot of time and energy to construct trust models between sensor devices that can recognize and differentiate between benign and malicious attackers, which, in turn, reduces the detection rate and increases overhead. This article suggests a hybrid forewarn approach based on improved security to attain this degree of safety in HWSN communication called the Improved Security-based hybrid forewarn model (ISBHF) and the Dispense Incline Drop Algorithm. It is recommended to offer a trust score and evaluations for messages and knowledge transfer to improve detection capabilities against attackers targeting essential data transmission. To further minimize packet loss, a reliability secure measure is created, and a weighted averaging strategy is implemented to ensure the robustness and veracity of the secure scores. Furthermore, empirical evidence from measurements and simulations will demonstrate that unsecured paths of communication are key to identifying fraudulent nodes that are causing an outsized fraction of network traffic or data breaches. The suggested protocol is superior to the current approach in terms of efficiency and security, making it ideal for use in HWSN networks by decreasing communication overhead, increasing detection performance, and other parameters such as packet delivery ratio and connectivity ratio.