| Internet of manufacturing things is the combination of networks, embedded, RFID, sensors technology. It’s the one of new manufacturing modes, and used in the manufacturing process to achieve the automatic perception and acquisition of information, intelligent processing and control.Compared to the traditional wireless network, the network architecture of the Internet of Manufacturing things is more complex. For complex manufacturing production environment and changeable production process, it makes sensing and transmission problems for nodes in IOMT. The nodes deployment requires flexible in dynamic environment. The acquisition and transmission of data need to fully consider the energy consumption and reliability problems. In this dissertation, we mainly focus on the nodes deployment and network topology control strategy in IOMT, focusing on the characteristics of the complex network:(1) We synthetically analyzed the characteristics and structure of the IOMT, summarized the current nodes deployment and topology control strategy, and analyzed their advantages and disadvantages.(2) According to the complex environment factors and changing manufacturing process applying in the internet of manufacturing things scenes, node deployment should meet the needs of flexible. The paper proposed a kind of mixed fish swarm algorithm and the virtual force algorithm optimization of sensor nodes deployment strategy, combined with the advantages of the two algorithms, and it can quickly find the optimal solution and ensure the high network coverage and the overall performance of the network.(3) After the completion of the deployment of the nodes, fully considering the characters of network, the paper proposed a multi hop clustering topology control algorithm based on particle swarm optimization, aiming at balancing the load and energy consumption in the network nodes. It can prolong the life cycle of the whole network, and at the same time ensure to reduce node energy consumption.Finally, the two algorithms proposed in this paper are simulated and compared with some classical algorithms. Hybrid optimization of node deployment strategy based on fish swarm and virtual force algorithm improves the coverage rate, and verifies that the optimization effect is better in the case of increasing number of mobile nodes. The multi hop clustering topology control algorithm based on particle swarm optimization reduces the energy consumption of nodes, and achieves the effect of load balancing. |
