MOQACA: A Novel Multiobjective Quantum Adaptive Cloning Algorithm for Range-Free Localization in Internet of Things

Range-free localization methods are pivotal in Internet of Things (IoT) applications. These techniques obviate the necessity of retrofitting nodes with additional localization modules, and they adeptly determine the coordinates of unknown nodes in the IoT using a smaller set of known anchor nodes. However, susceptibility to obstacles and network topology in the IoT can detrimentally impact localization performance. This article introduces a novel range-free localization algorithm, a multiobjective quantum adaptive cloning algorithm (MOQACA), tailored explicitly for IoT node localization. The algorithm harnesses the computational power of quantum computing coupled with a multiobjective strategy to estimate distances between nodes during the localization process precisely. Employing an adaptive quantum tuning operator, MOQACA efficiently searches for optimal node coordinates in multiple dimensions. Introducing a second objective function diminishes hopping errors by incorporating a multiperceptual radius technique and global weighting to correct the number of hops between the anchor and unknown nodes. MOQACA designs a constraint operator to reduce the impact of network irregularities and obstacles on the algorithm by decreasing the weight of the remote nodes in the localization process, thus transforming the global localization problem into a localized localization problem. Simulation experiments were conducted by varying the anisotropy parameters in different network topologies. Experimental results show that MOQACA achieves lower localization errors, better robustness, and improved localization performance in anisotropic networks compared to existing algorithms.