Quantum Computing-Inspired Network Optimization for IoT Applications

Internet of Things (IoT) is defined as the interconnection of millions of wireless devices to acquire data in a ubiquitous manner. With multiple devices targeting to perceive data over a common platform, it becomes indispensable to analyze accuracy for realizing an optimal IoT environment. Inspired from these aspects, this article presents a novel quantum computing-inspired (IoT-QCiO) optimization technique to maximize data accuracy (DA) in a real-time environment of IoT application. Specifically, the presented model incorporates quantum formalization of sensor-specific parameters to quantify IoT devices in terms of sensors in vicinity (SIV) and optimal sensor space (OSS). The optimality of the presented algorithm is estimated in terms of three key performance indicators of data cost (DC), DA, and data temporal efficiency (DTE). For validation purposes, the proposed algorithm is implemented for monitoring geographical traffic to address vehicular routing problems using 90 WiSense nodes, Raspberry Pi v3, and quantum simulators. Results obtained were compared with several state-of-the-art optimization algorithms. Based on the results, significant improvement was registered for the proposed model in terms of statistical parameters of precision, sensitivity, specificity, and F-measure. Moreover, enhanced values of reliability depict the optimal performance of the proposed approach.