IoT Service Layer Analytics Management Service Segmentation
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Solution Overview
Problem
Current IoT Service Layers lack native support for data analytics, leading to repetitive configuration and inefficient data processing, especially when handling large-scale IoT deployments with multiple data sources and diverse analytics requirements.
Innovation Solution
The introduction of an Analytics Management Service (AMS) within the IoT Service Layer, which enables configuration of analytics functions across multiple IoT sources, organizes results in a customizable manner, and provides granular authorization, allowing for efficient data analysis and access to insights.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If native analytics support is added to the IoT Service Layer, then data analysis efficiency improves, but device complexity increases
Solution Approach 1:
The patent segments the analytics functionality into a separate Analytics Management Service (AMS) that operates independently from the core Service Layer. The AMS receives analytics requests from applications, coordinates with multiple Analytics Functions (AFs), and manages results separately, thus adding analytics capability without complicating the fundamental Service Layer architecture.
Solution Approach 2:
The Analytics Management Service acts as an intermediary layer between applications and Analytics Functions. It mediates analytics requests, coordinating multiple AFs and managing their results, thereby enabling efficient analytics processing without requiring direct integration complexity in the Service Layer itself.
2Adaptability or versatility
If multiple analytics functions are coordinated across multiple IoT sources, then analytics comprehensiveness improves, but configuration complexity increases
Solution Approach 1:
The Analytics Management Service provides universal coordination capabilities that work with multiple different Analytics Functions and IoT data sources through standardized interfaces. It implements a unified configuration model that can handle diverse analytics requirements (data streams, events, queries) through a single flexible framework, reducing configuration complexity while maintaining broad analytics coverage.
Solution Approach 2:
The AMS implements dynamic configuration where analytics functions can be added, removed, or modified at runtime without reconfiguring the entire system. The service dynamically coordinates multiple AFs based on incoming analytics requests and data availability, allowing the system to adapt to changing analytics requirements while maintaining manageable configuration.
3Measurement precision
If granular authorization is implemented for analytics results, then access control precision improves, but system complexity increases
Solution Approach 1:
The Analytics Management Service implements preliminary authorization by establishing access control policies for analytics results before they are generated or distributed. Applications must declare their authorization requirements in advance when submitting analytics requests, and the AMS pre-configures result distribution permissions, thereby achieving precise access control without adding complex runtime authorization logic.
Data Source
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AI summary
Methods, systems, and apparatuses that enable a Service Layer (SL) to support analysis of internet of things (IoT) data and enable shared access to information generated by the analysis. An analytics management service may allow SL entities to configure analytics functions for many different IoT sources of data and organize the results in a customizable manner. The SL may support coordinating the analysis of IoT data from multiple independent sources and organizing the results of the analysis.