The birth of beyond 5G (B5G) and emerge of 6G has made personal and industrial operations more reliable, efficient, and profitable, accelerating the development of the next-generation Internet of Things (IoT). The Industrial equipment in 6G contains a large number of wireless sensors, which collect a large amount of data by sensing the surrounding environment, but the data is not always useful. The emergence of data mining has undoubtedly found a breakthrough point for extracting effective information from massive data. In the pursuit of lower latency, edge computing has also begun to develop. Eventually, 6G can make intelligent decisions in real-time and realize automated equipment operations. However, with the application of various technologies, the energy consumption of the system has increased, but the energy carried by the sensor is still limited. This paper addresses the energy consumption problem with a system model of industrial wireless sensor networks based on a multi-agent system (MAS) for industrial 6G applications. The method uses distributed artificial intelligence (DAI) to cluster the sensor nodes in the system to find the main node and predict its location. The work initially uses the backpropagation neural network (BPNN) and convolutional neural network (CNN), which are respectively introduced for optimization. Furthermore, we analyze the correlation of mutual clusters to allocate resources to individual nodes in each cluster efficiently. The simulation results show that the proposed method reduces the waste of resources caused by redundant data, improves the energy efficiency of the whole network, along with information preservation.

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On the whole, the main basis for strengthening the quality of network service is the construction of network system, reasonable arrangement of network and adjustment of information resources. In order to enhance the quality of network services, we should minimize the problems. In order to improve the quality of network, we should study more new network technologies and make up for network loopholes in time. In this study, in order to solve the efficiency problem brought by mass candidate services to quality-aware service selection, a service selection strategy based on feature matching algorithm is proposed. Based on statistical method, a probability matrix describing the mapping relationship between quality demand pattern and quality-oriented service pattern is established, and the service selection algorithm is improved by using the probability matrix. A series of experiments on artificial simulated data sets generated from real service data sets prove the feasibility and effectiveness of the proposed method.

Search logs in a timely and efficient manner are an important part of SRE (Site Reliability Engineer). Logs help us solve the problems during our development work. In this paper, we will introduce you a way how to build an efficient logs analysis system based on kafka and Elastic Search. We hope you can learn something through the iteration of the Version and get some inspiration with your own log analysis system.

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