IoT Service Layer Interest-Based Recommendation Function for Dynamic Rate Adjustment

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Solution Overview

Problem

Current IoT systems face inefficiencies due to non-optimal interactions between IoT devices and applications, leading to resource depletion such as battery life, processing power, and messaging overhead.

Innovation Solution

The implementation of an Interest-based Recommendation Function (IRF) within the IoT Service Layer, which monitors interaction patterns and trends to assess interest between IoT devices and applications, and makes on-the-fly recommendations to optimize their interaction, such as adjusting publishing and sampling rates, filtering irrelevant data, and coordinating device usage.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If IoT devices publish data at high rates to ensure availability, then data availability is improved, but energy consumption and messaging overhead increase

Engineering Contradiction:

Improvedata availabilityVSAvoiddevice energy consumption

Core Design Contradiction:

ReliabilityVSUse of energy by moving object

Solution Approach 1:

The system dynamically adjusts the publishing rate of IoT devices based on real-time interest assessments. The Interest-based Recommendation Function continuously monitors which devices are currently interested in each other's data and modifies publishing schedules accordingly, transitioning from static high-rate publishing to dynamic rate adjustment that matches actual demand.

Inventive Principle:

Principle #15Dynamics

Solution Approach 2:

The patent changes the parameter of publishing rate from a fixed high value to a variable value determined by interest assessments. The system evaluates interest levels between devices and applications, then adjusts publishing rates as a parameter to optimize the balance between data availability and energy consumption based on actual interaction needs.

Inventive Principle:

Principle #35Parameter changes

2Measurement precision

If applications sample data frequently to ensure up-to-date information, then information freshness is improved, but processing resources and messaging overhead increase

Engineering Contradiction:

Improvedata freshnessVSAvoidprocessing power consumption

Core Design Contradiction:

Measurement precisionVSPower

Solution Approach 1:

The system implements a feedback mechanism where the Interest-based Recommendation Function continuously monitors sampling patterns and interest levels. This feedback loop allows the system to adjust publishing and sampling rates based on actual interaction history, ensuring that data freshness is maintained only when and where needed, rather than through continuous high-frequency sampling everywhere.

Inventive Principle:

Principle #23Feedback

Solution Approach 2:

Instead of requiring applications to sample data at maximum frequency continuously, the system applies partial sampling - only sampling at reduced frequencies when interest levels indicate lower immediate needs. This partial action approach maintains sufficient freshness for current needs while avoiding excessive processing power consumption during periods of low interest.

Inventive Principle:

Principle #16Partial or excessive action

3Adaptability or versatility

If multiple applications interact with the same device simultaneously, then service versatility is improved, but device processing load and scheduling complexity increase

Engineering Contradiction:

Improvedevice service capabilityVSAvoidinteraction scheduling complexity

Core Design Contradiction:

Adaptability or versatilityVSDevice complexity

Solution Approach 1:

The system segments the interaction management function by introducing a dedicated Interest-based Recommendation Function that operates independently from device processing. This segmentation handles the scheduling complexity at the service layer rather than within the device itself, allowing multiple applications to interact with the device through coordinated interest-based schedules without increasing device processing load or internal complexity.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The Interest-based Recommendation Function acts as an intermediary between multiple applications and the IoT device. It mediates the interactions by assessing interest levels from multiple applications and coordinating their access to the device, thereby enabling service versatility while centralizing scheduling complexity in a dedicated intermediary function rather than distributing it across device processing systems.

Inventive Principle:

Principle #24Intermediary (Mediator)

Data Source

PatentEP3906654B1Optimizing interaction between applications and devices in a communications network

Publication Date: 2025.02.05 CONVIDA WIRELESS LLC

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AI summary

An apparatus can monitor interactions between IoT devices and IoT applications. Based on the monitoring, the apparatus may identify an interaction between a given IoT device and a given IoT application that can be adjusted. In an example, the apparatus may generate a recommendation or instruction in response to identifying the interaction. A first instruction may indicate a change in behavior for one of the IoT device or the IoT application. The apparatus may send the instruction to the one of the IoT device or the IoT application, for example, so as to cause the one of the IoT device or the IoT application to change the respective behavior, thereby adjusting the interaction between the IoT device and the IoT application.