The rapid growth of the internet of things (IoT) has led to an overwhelming volume of data generated by interconnected devices. Effectively extracting valuable insights from this data in real-time is crucial for informed decision-making and optimizing IoT applications. This research explores the integration of traditional complex event processing (CEP) with semantic web technologies to detect complex events in real-time streaming data analysis within the IoT domain. The research develops a semantic complex event processing framework tailored specifically for IoT applications. By leveraging the strengths of traditional CEP in detecting complex event patterns and semantic web technologies in providing standardised data representation and reasoning capabilities, the integrated approach proves to be a powerful solution for event detection. The framework demonstrates enhanced accuracy, real-time analysis capabilities, and the ability to handle heterogeneous data sources. The proposed traditional CEP with semantic web technologies framework is thoroughly evaluated and experimented with to assess its performance and effectiveness in real-time event detection. Performance metrics, including event detection efficiency, scalability, and accuracy of generated insights, are used to compare the framework against traditional CEP. The research findings emphasize the significance of integrating traditional CEP with semantic web technologies in real-time IoT analytics. The proposed framework improves event detection efficiency, scalability, and accuracy, empowering IoT applications with intelligent event processing capabilities. These results provide valuable insights into IoT data analytics and have the potential to revolutionise the way we analyse and leverage IoT data for informed decision-making and optimised system performance. / Petroleum Technology Development Fund (PTDF) OSS, Nigeria

Wireless sensor networks are application-specific networks that necessitate the development of specific network and information processing architectures that can meet the requirements of the applications involved. The most important challenge related to wireless sensor networks is the limited energy and computational resources of the battery powered sensor nodes. Although the central processing of information produces the most accurate results, it is not an energy-efficient method because it requires a continuous flow of raw sensor readings over the network. As communication operations are the most expensive in terms of energy usage, the distributed processing of information is indispensable for viable deployments of applications in wireless sensor networks. This method not only helps in reducing the total amount of packets transmitted and the total energy consumed by sensor nodes, but also produces scalable and fault-tolerant networks. Another important challenge associated with wireless sensor networks is that the possibility of sensory data being imperfect and imprecise is high. The requirement of precision necessitates employing expensive mechanisms such as redundancy or use of sophisticated equipments. Therefore, approximate computing may need to be used instead of precise computing to conserve energy. This thesis presents two schemes that distribute information processing for event-driven reactive applications, which are interested in higher-level information not in the raw sensory data of individual nodes, to appropriate nodes in sensor networks. Furthermore, based on these schemes, a fuzzy rule-based system is proposed that handles imprecision, inherently present in sensory data.