Structured Sensor Data Aggregation for Real-time Analysis in Cloud Computing

The rapid expansion of IoT deployments has intensified the need for efficient real-time sensor data aggregation.Conventional cloud-centric methods suffer from high latency and bandwidth limitations, making them unsuitable for latency-sensitive applications.To address these challenges, we developed a structured sensor data aggregation architecture comprising edge devices, fog nodes, and cloud infrastructure.The system was evaluated using synthetic and public datasets across sensor networks ranging from 50 to 1600 nodes.Edge device processing maintained latency below 200 ms (129.73-196.85ms), while fog node aggregation reduced bandwidth usage by up to 85%.Overall, the architecture achieved a 95% reduction in bandwidth consumption compared with cloud-only solutions.Accuracy declined from 94.23 to 80% as sensor density increased, and throughput dropped by approximately 90% (from 1805.01 to 169.24 events/s).Energy efficiency decreased from 91.91 to 20.57 arb. unit.The integrated preprocessing pipeline-combining wavelet denoising, spatiotemporal imputation, and multimethod outlier detection-improved accuracy by 22-32%.The architecture demonstrated adaptability across healthcare, smart cities, and industrial control systems, supporting subsecond response times and scalable deployment.These results validate the architecture's viability for real-time IoT applications, while highlighting the need for further optimization in dynamic environments and resource-constrained edge devices.Sensors and Materials, Vol.38, No. 1 (2026) autonomous sensors that monitor environmental and physical parameters, including temperature, pressure, humidity, and motion. (3)WSNs are capable of self-organization and establishing communication even without existing infrastructure.However, increased sensor density introduces challenges related to data volume, variety, veracity, and processing speed.The substantial data volume generated strains network transmission, processing, and storage, necessitating effective data aggregation methods to reduce redundancy and optimize resource utilization for data collection and processing. (4)espite significant advancements in IoT and cloud technologies, real-time sensor data aggregation remains fraught with challenges.Quality issues of collected data during aggregation exist, including interference noise, missing values due to hardware failures or network disruptions, and outliers caused by anomalous events, leading to erroneous information.Furthermore, different sampling rates, formats, and communication protocols of the data collected from heterogeneous sensor networks complicate data aggregation methods. (5)eal-time data aggregation is particularly difficult since the latency requirements of different applications, such as autonomous vehicles and healthcare monitoring, significantly vary.In addition to this, sensor networks include hundreds to millions of sensor nodes, and data transmission consumes considerable energy, raising concerns about energy efficiency, especially in battery-powered networks.Therefore, it is necessary to develop effective data aggregation methods that ensure ultralow latency and balance speed and measurement accuracy, (6) since conventional centralized data aggregation methods have been impractical owing to their communication bottlenecks, fault propagation, and potential failure of central nodes. (7)o solve the problems of the conventional methods, we developed an advanced sensor data aggregation architecture optimized for real-time analysis based on cloud computing.For the development of the data aggregation architecture, we analyzed existing methods used in cloud computing and determined optimization strategies for real-time sensor data aggregation.To evaluate the performance of the developed architecture, latency, throughput, accuracy, and energy efficiency were assessed.The developed architecture enhances data aggregation efficiency for instantaneous data analysis in virtual and general cloud computing environments tailored to IoT systems and reduces response times from seconds to sub-seconds.Data quality is enhanced through preprocessing in multilevel aggregation architectures adopting edge and fog computing and stream processing using Apache Kafka and Spark Streaming.The developed model also enables the analysis of synthetic sensor datasets and publicly available IoT data, and the employment of cryptographic mechanisms.It also ensures security and privacy in domainspecific applications.The results of this study can be used to address the trade-off between real-time performance and data quality in large-scale IoT networks for the development and evaluation of an integrated structured aggregation architecture.The architectural integration of a multistage data quality pipeline, which comprises wavelet denoising, spatial-temporal imputation, and multimethod outlier detection, is distributed across the proposed structure tiers for end-to-end data refinement.The implementation of a dynamic aggregation protocol enables the selection of compression and fusion based on data characteristics, yielding a high data reduction ratio while preserving analytical fidelity.Through performance evaluation, the architecture's scalability is