Enhanced Differential Crossover and Quantum Particle Swarm Optimization for IoT Applications

Abstract

An optimized design with real-time and multiple realistic constraints in complex engineering systems is a crucial challenge for designers. In the non-uniform Internet of Things (IoT) node deployments, the approximation accuracy is directly affected by the parameters like node density and coverage. We propose a novel enhanced differential crossover quantum particle swarm optimization algorithm for solving nonlinear numerical problems. The algorithm is based on hybrid optimization using quantum PSO. Differential evolution operator is used to circumvent group moves in small ranges and falling into the local optima and improves global searchability. The cross operator is employed to promote information interchange among individuals in a group, and exceptional genes can be continued moderately, accompanying the evolutionary process's continuance and adding proactive and reactive features. The proposed algorithm's performance is verified as well as compared with the other algorithms through 30 classic benchmark functions in IEEE CEC2017, with a basic PSO algorithm and improved versions. The results show the smaller values of fitness function and computational efficiency for the benchmark functions of IEEE CEC2019. The proposed algorithm outperforms the existing optimization algorithms and different PSO versions, and has a high precision and faster convergence speed. The average location error is substantially reduced for the smart parking IoT application.

Digital System Design for Quantum Error Correction Codes

“…Niwa et al in [ 5 ] constructed a simulation to investigate the effect of the quantum error-correcting code in five-qubit codes, seven-qubit codes, and nine-qubit codes and their fault tolerance operations when the error correction process produces an error. This study showed that the seven-qubit code scheme is the most effective in [ 4 – 10 ] or less of decoherent rate and when the standard operational error is 10–3 or less. In addition, the error correction process in the seven-qubit code is operated at every 50 to 200 main gates even though there is that both errors exist.…”

Exponential Particle Swarm Optimization for Global Optimization

“…In addition, a concept of iteration division, shown in Algorithm 2, is employed to reach a balanced diversification and intensification process. For the first number of iterations 𝑡𝑡 𝑣𝑣 , the range of velocity [𝑣𝑣 𝑚𝑚𝑖𝑖𝑛𝑛 , 𝑣𝑣 𝑚𝑚𝑎𝑎𝑚𝑚 ] is selected using the equation (39) with 𝑘𝑘 𝑏𝑏 = 1 (𝑡𝑡 < 𝑡𝑡 𝑣𝑣 ) and for the rest of the iterations with 𝑘𝑘 𝑏𝑏 ∈ (0,1) (𝑡𝑡 > 𝑡𝑡 𝑣𝑣 ). k is a constant called the decreasing velocity coefficient and belongs to the interval (0,1) [23].…”

“…A dynamic inertia weight strategy is integrated into the proposed algorithm, where the value of w is linearly decreased from 𝑤𝑤 𝑚𝑚𝑎𝑎𝑚𝑚 (initial value) to 𝑤𝑤 𝑚𝑚𝑖𝑖𝑛𝑛 (final value) according to equation (39), where 𝑟𝑟 is the damping coefficient selected within the interval (0,1]. With this strategy, the tendency of particles to global search is decreased continuously, and at the same time, their tendency to local search increases continuously.…”

Survey of Localization for Internet of Things Nodes: Approaches, Challenges and Open Issues

“…An enhanced ultrasound-based time of arrival technique [21] permits calculating the position in three dimensions and provides the data orientation. Cricket, a ToA-based localization approach [22], uses ultrasound transmitters with known locations. The anchor node evaluates the location of the unknown node with high accuracy.…”