A novel heuristic simulation-optimization method for critical infrastructure in smart transportation systems

A simulation-based optimization is a decision-making tool that helps in identifying an optimal solution or a design for a system. An optimal solution and design are more meaningful if they enhance a smart system with sensing, computing, and monitoring capabilities with improved efficiency. In situations where testing the physical prototype is difficult, a computer-based simulation and its optimization processes are helpful in providing low-cost, speedy and lesser time- and resource-consuming solutions. In this work, a comparative analysis of the proposed heuristic simulation-optimization method for improving quality-of-service (QoS) is performed with generalized integrated optimization (a simulation approach based on genetic algorithms with evolutionary simulated annealing strategies having simplex search). In the proposed approach, feature-based local (group) and global (network) formation processes are integrated with Internet of Things (IoT) based solutions for finding the optimum performance. Further, the simulated annealing method is applied for finding local and global optimum values supporting minimum traffic conditions. A small-scale network of 50 to 100 nodes shows that genetic simulation optimization with multicriteria and multidimensional features performs better as compared to other simulation-optimization approaches. Further, a minimum of 3.4% and a maximum of 16.2% improvement is observed in faster route identification for small-scale IoT networks with simulation-optimization constraints integrated model as compared to the traditional method. The proposed approach improves the critical infrastructure monitoring performance as compared to the generalized simulation-optimization process in complex transportation scenarios with heavy traffic conditions. The communicational and computational-cost complexities are least for the proposed approach.