Multiobjective Topology Optimization Based on Mapping Matrix and NSGA-II for Switched Industrial Internet of Things

Abstract

With the deep integration of Information and Communication Technology (ICT), the network performance of existing switched Ethernet cannot meet the growing control and management demands of the industrial production. To solve the time-delay and sub-network transmission balance problem, a multi-objective Non-dominated Sorting Genetic Algorithm (NSGA-Ⅱ) based on mapping matrix is proposed, in which a new topology model is established to reflect the flexible Ethernet topology format. Meanwhile an adjacency mapping matrix is introduced as genetic code to embody the actual topology structure and several special properties of the mapping matrix caused by the industrial topology characteristics are proved. On the basis of these properties, an asexual crossover and double swap mutation operation is proposed to achieve the population evolution. Besides, in the genetic procedure of the proposed algorithm a self-adaptive measure and an elite reservation strategy are applied to accelerate the convergence speed. The validity of proposed algorithm is demonstrated by benchmark test and the case verification shows that the comprehensive performance of the switched Ethernet is improved. Index Terms-Internet of Things (IoT);

Data Management in Industry 4.0: State of the Art and Open Challenges

“…Typical IIoT applications include predictive maintenance [100], where a successful network configuration is able to determine the condition of the in-service equipment in order to estimate when maintenance should be performed, real-time RFID monitoring [96], for tracking products in the assembly line. Other research issues include IIoT topology optimization [97], packet scheduling [102], and IIoT network construction and operation under massive multiple-input multiple-output M2M communication [113].…”

Nondominated sorting genetic algorithm II with merged strategies for industrial network topology optimization

“…As Figure 10 shows, how to balance the communications between various network devices is challenging. To tackle this issue, Li et al [31] proposed a multiobjective industrial Ethernet topology optimization model.…”

“…For example, to accurately evaluate historical water use and local economic structure, Wang et al [5] designed three evaluation models, and incorporated them into a firefly algorithm. To optimize industry network topology structure, Li et al [31] proposed an asexual crossover operator, and then applied it to NSGA-II. After that, Zhang et al [32] further incorporated Levy distribution to NSGA-II to decrease the amount of repetitive individuals, and then incorporate it to the industry network topology optimization problem.…”

Differential Evolution-Based 3-D Directional Wireless Sensor Network Deployment Optimization

“…To achieve the data transmission stability in IoT, a stable and reliable network is essential [20]. Connectivity is basic for the reliable data transmission, and it's basic of the topology control and routing protocol.…”