Research On Data Aggregation Scheduling Manufacturing Of Internet Of Things

The complex network characteristics of the Manufacturing Internet of Things, including coexistence of wired and wireless networks, various of sensors, drivers, sensor nodes, and actuator in manufacturing, sensor nodes in the manufacturing sector with different manufacturing environments and dynamic change and channel availability dynamic characteristics, which make the Manufacturing Internet of Things with a large scale, highly heterogeneous and dynamic topologies features. Leading to end-to-end delay is difficult to predict, heterogeneous networks to collaborative, real-time status to cognitive issues. An aggregation problem of massive data in real time in large scale dynamic networks of heterogeneous environment has been put forward.In this Dissertation, the problem of real-time and reliable data aggregation in complex and dynamic environment of the Manufacturing Internet of Things will be researched. In particular, in heterogeneous nodes loads flow conditions, as equal time slot assignment leads to a node frequent switching, increased aggregation delay and energy consumption. In addition, for new nodes or failure nodes, which change the network topology in dynamic network topology in heterogeneous environments, real-time and reliable aggregation of data cannot be guaranteed. This needs research related data aggregation strategy to protect data reliable convergence aggregation in real time.In view of the above problems, the main research contents and innovation points are as follows:(1) According to analysis the characteristics of heterogeneous load flow of nodes in Manufacturing Internet of Things, which produced the problem of data aggregation. Research on centered aggregation scheduling strategy of based on conflict diagram of network of load flow aware. An aware algorithm of based on load flow continuous slot allocation was proposed. The algorithm is based on the conflict between network conflict and interference. The continuous slot allocation to load flow based on the conflicts of network, which reduced the energy consumption of nodes and aggregation delay.(2) According to analysis the characteristics of data aggregation in real-time and dynamic change of network topology, which produced the problem of minimum delay data aggregation (MDAS). By studying the existing scheduling algorithm for data aggregation, proposed a distributed data aggregation model and optimization algorithm for MDAS, reduced the minimum delay. Meanwhile, for new node added or node failure due to network topology changes, proposed an adaptive MDAS schedule extended algorithm, which has a lower time delay and communication overhead for network topology changes.Finally, the theoretical simulation and performance evaluation of the proposed algorithm in this paper showed that has a better performance and energy consumption data aggregation compared with existing algorithms. Meanwhile, proposed scheduling algorithm for adaptive extensions has lower latency and overhead in dynamic change of network topology.