ARCHITECTURAL APPROACH TO REAL-TIME PROCESSING IN STREAMING SYSTEMS

Abstract

Internet of Things systems are characterized by heterogeneous large-scale data streams that arrive
in real-time from distributed source and require their reliable and operational processing. This paper
considers an architectural approach focused on real-time data processing that treats scalability and
reliability as system-level properties of the data infrastructure. The proposed approach treats real-time
signal processing as an integral component of the system’s streaming architecture, functionality linked to
event flow control, time semantic, and resource constraints execution environment. Accordingly, data flow
management and signal modeling are integrated within a unified data platform that supports streaming,
quality control, scalable processing, and storage. The approach formulates system requirements for models
in streaming environments, considering the stochastic event nature, irregular time intervals, data quality,
and resource constraints of a multi-tiered architecture. The behavior of the model is interred as an
architecturally determined performance characteristic. The concept forms a methodological basis for the
design of analytical components of real time IoT systems.
Keywords: Internet of Things, real-time signal processing, streaming data architectures, signal
modeling in streaming systems, distributed analytics systems

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