Distributed Data Stream Analytics at the IoT Service Layer

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Solution Overview

Problem

Existing IoT/M2M systems lack a standardized, modular, and distributed architecture for real-time data stream analytics capabilities at the service layer, leading to inefficiencies in processing and analyzing high-throughput, dynamic data streams due to limited memory and disk I/O overheads, which is crucial for applications like Intelligent Intersection Traffic Systems.

Innovation Solution

Implement a Data Stream Analytics Service (DSAS) at the IoT/M2M service layer, comprising Stream Forwarder and Stream Analytics Engine modules, enabling distributed and modular data stream processing and analysis, with the Stream Forwarder handling preprocessing and routing, and the Stream Analytics Engine performing actual analytics.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Productivity

If data stream analytics is performed at the service layer with modular distributed architecture, then system efficiency and processing speed are improved, but device complexity and implementation difficulty increase

Engineering Contradiction:

Improvedata processing efficiencyVSAvoidsystem architecture complexity

Core Design Contradiction:

ProductivityVSDevice complexity

Solution Approach 1:

The system is divided into distinct modular components: Stream Forwarder for data ingestion and routing, Stream Analytics Engine for processing, and Data Store for persistence. Each module operates independently with well-defined interfaces, enabling distributed deployment while maintaining processing efficiency.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The Stream Forwarder acts as an intermediary component between data sources and the analytics engine, handling data routing, filtering, and preprocessing. This mediator pattern decouples the data ingestion layer from the processing layer, managing complexity while preserving system efficiency.

Inventive Principle:

Principle #24Intermediary (Mediator)

2Loss of time

If real-time analytics processing is implemented closer to data sources, then response time is reduced, but memory consumption and computational resources increase

Engineering Contradiction:

Improveanalytics response timeVSAvoidmemory resource consumption

Core Design Contradiction:

Loss of timeVSQuantity of substance

Solution Approach 1:

The architecture extracts and separates heavy computational tasks into the Stream Analytics Engine, while the Stream Forwarder maintains a lightweight operational state. Only essential data and metadata are retained in memory at the service layer, with detailed storage handled by external Data Store systems.

Inventive Principle:

Principle #2Taking out (Extraction)

Solution Approach 2:

The system transitions from a single-layer centralized architecture to a multi-dimensional distributed architecture, placing analytics capabilities at the service layer while maintaining external storage resources. This dimensional expansion allows real-time processing without proportionally increasing local memory requirements.

Inventive Principle:

Principle #17Another dimension (Dimensionality change)

3Adaptability or versatility

If standardized service layer architecture is implemented, then system interoperability and integration ease are improved, but implementation complexity and standardization overhead increase

Engineering Contradiction:

Improvesystem integration flexibilityVSAvoidstandardization implementation complexity

Core Design Contradiction:

Adaptability or versatilityVSDevice complexity

Solution Approach 1:

The Stream Forwarder and Stream Analytics Engine are designed as universal modules that can handle multiple data types and analytics operations through standardized interfaces. This multi-functionality enables diverse integrations without requiring custom implementations for each scenario.

Inventive Principle:

Principle #6Universality (Multi-functionality)

Solution Approach 2:

The system uses configurable parameters and metadata to adapt standardized modules to different scenarios. By changing operational parameters rather than structural components, the system achieves versatility while maintaining implementation simplicity through reuse of core standardized elements.

Inventive Principle:

Principle #35Parameter changes

Data Source

PatentUS12536178B2Data stream analytics at service layer

Publication Date: 2026.01.27 INTERDIGITAL PATENT HOLDINGS INC

AI summary

A modular and distributed architecture for data stream processing and analysis is described to incorporate data stream analytics capabilities, called Data Stream Analytics Service (DSAS) in the IoT/M2M service layer. Each service layer node hosting DSAS can be split into two independent modules, Stream Forwarder and Stream Analytics Engine. Stream Forwarder is a light weight processing modules that can be responsible for data preprocessing and routing. Stream Analytics Engine is responsible for performing actual analytics on the data stream. Separating the two functionalities enables the service layer nodes to efficiently distribute stream analytics tasks across multiple nodes.