Quantum Computing-Based Cryptography Model with Task Offloading and Data Management Using Machine Learning Algorithms

Internet of Things (IoT) is growing, leading to enhanced data exchange in several areas. Achieving maximum security in the IoT environment is still under the open research. Protecting the confidential data from the external environment in complication for the applications of IoT network. For that purpose, in this article presents a Quantum Computing-Based Cryptography Model with Task Offloading and Data Management (QCCTOD) designed to enhance security and efficiency in IoT networks. It consists of a three-tier architecture, which includes a perceptual tier for data gathering, a network tier that incorporates lightweight cryptography to ensure secure data transmission via sophisticated signature methods to avert data modification, and an application tier for smart decision-making. An intelligent caching system that stores commonly used services from E-APs, improving service provision. The communication model uses an enhanced method for SINR to guarantee swift and effective data transfer while maintaining Quality of Service (QoS). The task offloading process shifts demanding computing tasks from devices with constrained resources to edge servers. This method evaluates energy and latency expenses to find equilibrium between computation and transmission lags, reducing power usage and improving efficiency for IoT applications. A lightweight bio-latticed cryptographic addresses IoT security for the post-quantum era, utilizing methods like the Shortest Vector Problem (SVP) and Galois field computations. The implementation of the proposed QCCTOD is carried out in python and the parameters which are measured to validate the network performance are include signature size analysis, throughput, packet success rate, energy consumption, and network usage. From the final comparison it is proven that the proposed QCCTOD attain better overall performance in terms of network security and data management.