Pervasive Wireless Sensor Networks (PWSNs) have emerged as a prominent technology for diverse applications such as environmental monitoring, industrial automation, healthcare, smart cities, etc. However, the efficient integration of data from numerous sensor nodes in a WSN remains a challenging task due to the inherent limitations of resource-constrained sensor devices and dynamic environmental conditions. This paper presents a comprehensive study on enhancing data integration in PWSNs by leveraging context-aware techniques. The proposed approach aims to improve data integration processes’ accuracy, reliability, and overall performance those are essential for informed decision-making in various applications. This work explores the concept of context awareness, that is, contextual information where contextual information related to sensor nodes, network topology, environmental conditions, and user requirements during data aggregation, fusion, and transmission under a universal Context-aware Virtual Machine (CWVM) in heterogeneous pervasive mode. The proposed method has been deployed with a scalable and flexible solution for deploying context-aware applications in heterogeneous IoT (Internet of Things) environments with improved data accuracy where leaf nodes may differ significantly in their hardware capabilities and configurations. Through comprehensive real-world experiments, the effectiveness of the context-aware approach is demonstrated with improving data accuracy, reducing energy consumption, and enhancing overall network performance.