IoT Performance Management System for Battery Optimization
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Solution Overview
Problem
Existing IoT systems face challenges in efficiently managing battery life due to various factors such as network coverage, environmental conditions, and communication patterns, leading to performance degradation that is often reactive and unaware of broader trends, lacking proactive management and feedback loops.
Innovation Solution
An IoT performance management system that uses a performance management client on each device and a service on a platform or edge computing node, employing an Intelligent Optimizer Algorithm and virtual twin modeling to adapt configuration settings based on device-specific and environmental data, optimizing battery life through iterative performance vector datasets.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If reactive human-triggered policy changes are used to manage device performance, then device configuration can be adjusted, but the system lacks proactive management capability and awareness of broader performance trends
Solution Approach 1:
The patent implements a feedback mechanism where performance data from multiple IoT devices is collected, analyzed, and used to generate optimized configuration policies. The system continuously monitors device performance metrics, compares them against target thresholds, and automatically adjusts configurations based on aggregated insights from the device population, enabling proactive management and trend awareness.
Solution Approach 2:
The system performs preliminary actions by pre-configuring optimized performance vectors based on historical data and simulated environments (virtual twins) before deploying them to actual devices. This allows the system to proactively adjust configurations before performance degradation occurs, rather than reacting to problems after they manifest.
2Reliability
If device configurations are customized for specific use cases, then initial performance can be optimized, but the configurations cannot adapt to real-world environmental variations and conditions
Solution Approach 1:
The patent makes device configurations dynamic by implementing continuous monitoring and automatic adjustment mechanisms. Performance vectors are not static but are continuously updated based on real-time environmental conditions, device state, and aggregated performance data from the device population, allowing configurations to adapt to changing conditions while maintaining reliability.
Solution Approach 2:
The system changes configuration parameters dynamically by adjusting performance vectors based on environmental factors such as temperature, humidity, network conditions, and device usage patterns. These parameter changes are driven by machine learning models that analyze the relationship between environmental conditions and optimal device performance.
3Adaptability or versatility
If extensive hardware alternatives are used for IoT devices, then system design flexibility increases, but the design process complexity and investment requirements increase substantially
Solution Approach 1:
The patent implements a universal performance management system that can manage diverse IoT devices with different hardware configurations through a common platform. The system uses standardized performance vectors and configuration parameters that can be applied across multiple device types, reducing design complexity while maintaining hardware flexibility through software-based optimization.
Solution Approach 2:
Instead of designing custom hardware for each use case, the system achieves adaptability through parameter changes in software configurations. Performance vectors contain adjustable parameters that can be optimized for different hardware platforms and environmental conditions, allowing a single hardware design to serve multiple purposes through software configuration rather than requiring complex custom hardware designs.
4Duration of action of moving object
If device behavior is fixed at deployment, then initial battery life can be estimated, but the battery lifetime cannot be optimized in response to actual operating conditions and usage patterns
Solution Approach 1:
The patent implements feedback loops that continuously monitor device performance metrics including battery consumption, operational status, and environmental conditions. This feedback is used to dynamically adjust device behavior and configuration parameters to optimize battery lifetime while maintaining required functionality, allowing the system to adapt to actual usage patterns rather than relying on fixed deployment-time estimates.
Solution Approach 2:
The system enables devices to self-optimize their behavior by automatically adjusting configurations based on monitored performance data and aggregated insights from the device population. Devices can autonomously modify their operational parameters, communication patterns, and power management settings to extend battery lifetime without requiring manual intervention or fixed pre-configured behavior.
Data Source
Figure 1~2
AI summary
The present invention relates to a method for operating an internet-of-things system using a internet-of-things performance management system, wherein the internet-of-things system comprises a plurality of internet-of-things communication devices in a communication network that provides radio coverage in a predetermined geographical area, and - by means of transmission of internet-of-things payload data from the internet-of-things communication devices, respectively, to the communication network and/or from the communication network to the internet-of-things communication devices, respectively - the internet-of-things system provides an internet-of-things service, wherein each of the internet-of-things communication devices comprises a performance management client functionality and operates according to configuration information of a respective performance vector dataset, wherein the performance vector dataset is adapted to the internet-of-things communication device, respectively, and its respective environment or situation and/or is iteratively adapted or able to be iteratively adapted, wherein, in order to operate a specific internet-of-things communication device, the method comprises the following steps: -- in a first step, a first downlink performance vector dataset is transmitted, triggered by the internet-of-things performance management system, towards the specific internet-of-things communication device, -- in a second step, a first uplink performance vector dataset is transmitted, by the specific internet-of-things communication device, towards the internet-of-things performance management system.