Power Optimizer for M-IoT Devices
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Solution Overview
Problem
Service providers face challenges in optimizing communication with Massive IoT (M-IoT) devices due to difficulties in identifying and configuring diverse categories of IoT devices, leading to erratic power consumption and inefficient communication, which results in higher maintenance costs and potential misconfigurations that are hard to detect at scale.
Innovation Solution
A power optimizer system that uses Machine Learning and Artificial Intelligence techniques to discover misconfigurations and anomalies in M-IoT devices, cluster them based on operational parameters, and reconfigure optimal parameters for efficient network usage and power consumption, deployed in the 5G communication system core, on-premise, or at the edge.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If M-IoT devices use simplified communication protocols to reduce processing power requirements, then device complexity is reduced, but power consumption becomes erratic and harder to optimize
Solution Approach 1:
The patent segments M-IoT devices into different clusters based on their operational characteristics, communication patterns, and power consumption profiles. This segmentation allows the system to apply tailored optimization strategies to each cluster, resolving the contradiction by managing device complexity at scale while optimizing power consumption for each segment individually through machine learning-based clustering and configuration.
2Use of energy by moving object
If service providers manually configure each M-IoT device to optimize power consumption, then power efficiency improves, but the time and resources required for configuration and maintenance increase significantly
Solution Approach 1:
The patent implements self-service through automated machine learning models that independently analyze device operational parameters, identify optimization opportunities, and reconfigure devices without human intervention. The system autonomously clusters devices, determines optimal configurations, applies settings, and monitors results, eliminating manual configuration time while maintaining power optimization effectiveness.
Solution Approach 2:
The patent establishes a feedback loop where the system continuously monitors device power consumption, communication patterns, and operational status. This feedback enables the machine learning models to iteratively improve configurations, adapt to changing conditions, and maintain optimal power efficiency automatically, resolving the contradiction between power optimization and configuration time.
3Reliability
If M-IoT devices communicate frequently to ensure reliable data exchange, then communication reliability improves, but power consumption increases
Solution Approach 1:
The patent applies dynamics by enabling M-IoT devices to dynamically adjust their communication behavior based on real-time conditions. The machine learning models optimize transmission intervals, data volumes, and communication modes adaptively, allowing devices to maintain reliable data exchange while consuming minimal power. Devices can switch between frequent communication when reliability is critical and infrequent communication when power conservation is prioritized.
Data Source
AI summary
The present disclosure provides techniques for optimizing power consumption of Massive-Internet of Things (IoT) devices comprising a plurality of IoT devices. According to some examples, a power optimizer system may obtain one or more operational parameters of a plurality of IoT devices. The power optimizer system may estimate a Power Cost Function (PCF) based on one or more operational parameters to determine power consumption of each IoT device. The power optimizer system may determine a variation in the PCF of one or more IoT devices out of the plurality of IoT devices due to variations in uplink and downlink operations of each IoT device. The power optimizer system may identify one or more optimal operational parameters and then, configure the one or more IoT devices of the plurality of IoT devices with the one or more optimal operational parameters.