Deep learning-enabled energy optimization and intrusion detection for wireless sensor networks

Ad hoc wireless networks can limit creativity, and wireless sensor networks can enhance innovation. The functionalities of components inside a wireless ad hoc network depend on characteristics such as random access memory capacity, battery life, and storage capacity. Several factors such as lack of protection, insufficient infrastructure, ease of construction, proximity to war zones, and absence of security measures make the buildings susceptible to various risks. The rising frequency of network attacks has greatly affected various characteristics like energy consumption, packet loss, latency, throughput, and uptime. Intrusion detection systems and other conventional security measures may not offer an adequate guarantee of the consistency of the system that they are protective of. This article proposes a two-pronged technique for understanding the addressing Restricted Boltzmann Machines (RBMs), a specific form of computer system. The main emphasis is on this specific method. The Restricted Boltzmann Machine (RBM) methodology often surpasses state-of-the-art methods. The objective is achieved through the use of an approach known as chaotic ant optimization (CAO). We have developed a method using Restricted Boltzmann Machines (RBMs) to determine the optimal confidence level for each sensor node. Our research provides increased credibility to a multi-modal approach utilizing deep learning to address challenges in intrusion detection and energy optimization through wireless sensor networks, often known as WSNs. RBM’s data processing capabilities with CAO’s routing and security advantages, our comprehensive solution enhances the performance and reliability of Wireless Sensor Networks (WSNs) across different scenarios.