Abstract Environmental monitoring, modeling, and managing allow a better understanding of major processing and techniques for managing environmental changes. The pollution level has risen over time due to many factors such as a rise in population and the use of the vehicle, industrialization, and urbanization that have a direct impact on people ‘s health. Hence, in this paper, Artificial intelligence assisted Semantic Internet of Things (AI-SIoT) has been proposed using a wireless sensor network (WSN) for the environmental monitoring system and the real economy. The Artificial Intelligence technique can very effectively analyze data and make precise decisions on the provision of services in different types. This study provides a mathematical framework for the analysis of interdependent aspects of the WSN protocol for communication and design of signal processing. The Internet of Things (IoT) based framework comprises the complete information system from the sensor level to data management about the environment. The experimental results show that the proposed method provides an effective way to analyze the long-term monitoring of environmental data. The proposed AI-SIoT method using the WSN method enhances accuracy(95.6%), performance(98.7%) increase efficiency (93.7%) with reliability (97.4%) when compared to other existing methods.

中文翻译：

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人工智能在环境监测系统与实体经济中的应用深度融合研究

摘要 环境监测、建模和管理可以更好地理解管理环境变化的主要处理和技术。由于人口和车辆使用量的增加、工业化和城市化等诸多直接影响人们健康的因素，污染水平随着时间的推移而上升。因此，在本文中，人工智能辅助语义物联网（AI-SIoT）被提出使用无线传感器网络（WSN）用于环境监测系统和实体经济。人工智能技术可以非常有效地分析数据，并对提供不同类型的服务做出精确决策。本研究为分析用于通信和信号处理设计的 WSN 协议的相互依赖方面提供了一个数学框架。基于物联网 (IoT) 的框架包括从传感器级别到有关环境的数据管理的完整信息系统。实验结果表明，所提出的方法为分析环境数据的长期监测提供了一种有效的方法。与其他现有方法相比，所提出的使用 WSN 方法的 AI-SIoT 方法提高了准确性 (95.6%)、性能 (98.7%)、效率 (93.7%) 和可靠性 (97.4%)。实验结果表明，所提出的方法为分析环境数据的长期监测提供了一种有效的方法。与其他现有方法相比，所提出的使用 WSN 方法的 AI-SIoT 方法提高了准确性 (95.6%)、性能 (98.7%)、效率 (93.7%) 和可靠性 (97.4%)。实验结果表明，所提出的方法为分析环境数据的长期监测提供了一种有效的方法。与其他现有方法相比，所提出的使用 WSN 方法的 AI-SIoT 方法提高了准确性 (95.6%)、性能 (98.7%)、效率 (93.7%) 和可靠性 (97.4%)。