Machine-Learning Access Point Power Management for Reliable Connectivity

Solution Overview

Problem

Existing power management techniques for wireless networking devices, such as access points (APs), are static and result in inefficient energy consumption and connectivity interruptions, failing to adapt to dynamic network usage patterns.

Innovation Solution

A network management system utilizing machine learning (ML) to analyze telemetry data for client associations and network activity to intelligently transition APs to power-saving modes, deactivating or throttling power-consuming components based on learned utilization patterns and roaming characteristics.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Loss of energy

If static power management techniques are used to reduce energy consumption, then energy savings are achieved, but connectivity interruptions occur and the system cannot adapt to dynamic network usage patterns

Engineering Contradiction:

Improveenergy consumptionVSAvoidconnectivity reliability

Core Design Contradiction:

Loss of energyVSReliability

Solution Approach 1:

The patent implements dynamic power management by transitioning from static time-based scheduling to demand-responsive power states. The AP continuously monitors network activity and client associations, dynamically adjusting its operational state (active, doze, or sleep mode) based on real-time conditions. This dynamic approach ensures connectivity is maintained when needed while maximizing energy savings during low-activity periods, directly resolving the contradiction between energy efficiency and connectivity reliability.

Inventive Principle:

Principle #15Dynamics

Solution Approach 2:

The system employs feedback mechanisms where the AP monitors network activity, client associations, and traffic patterns, then uses this information to intelligently determine appropriate power states. The feedback loop continuously adjusts power management decisions based on actual network conditions rather than predetermined schedules, ensuring both energy efficiency and reliable connectivity are maintained according to actual demand.

Inventive Principle:

Principle #23Feedback

2Reliability

If APs are kept in active state to ensure seamless connectivity, then connectivity reliability is maintained, but energy consumption increases

Engineering Contradiction:

Improveconnectivity reliabilityVSAvoidenergy consumption

Core Design Contradiction:

ReliabilityVSUse of energy by moving object

Solution Approach 1:

The patent implements dynamic power management by transitioning from static time-based scheduling to demand-responsive power states. The AP continuously monitors network activity and client associations, dynamically adjusting its operational state (active, doze, or sleep mode) based on real-time conditions. This dynamic approach ensures connectivity is maintained when needed while maximizing energy savings during low-activity periods, directly resolving the contradiction between energy efficiency and connectivity reliability.

Inventive Principle:

Principle #15Dynamics

Solution Approach 2:

The system changes operational parameters (power states) based on network conditions. The AP can transition between active, doze, and sleep states, adjusting its operational characteristics according to monitored network activity and client associations. This parameter adjustment allows the system to optimize the balance between connectivity reliability and energy consumption by selecting appropriate operational modes based on actual demand.

Inventive Principle:

Principle #35Parameter changes

3Loss of energy

If time-based scheduling is used to manage power consumption, then energy savings are achieved during low utilization periods, but the system lacks adaptability to actual network usage patterns

Engineering Contradiction:

Improveenergy savingsVSAvoidadaptability to network usage patterns

Core Design Contradiction:

Loss of energyVSAdaptability or versatility

Solution Approach 1:

The patent implements dynamic power management by transitioning from static time-based scheduling to demand-responsive power states. The AP continuously monitors network activity and client associations, dynamically adjusting its operational state (active, doze, or sleep mode) based on real-time conditions. This dynamic approach ensures connectivity is maintained when needed while maximizing energy savings during low-activity periods, directly resolving the contradiction between energy efficiency and connectivity reliability.

Inventive Principle:

Principle #15Dynamics

Solution Approach 2:

The AP autonomously monitors its own network activity, client associations, and traffic patterns, then self-determines the appropriate power state without external control. This self-service capability allows the system to adapt to actual usage patterns in real-time, maximizing energy savings while ensuring connectivity is maintained when clients actually need access, rather than following rigid predetermined schedules.

Inventive Principle:

Principle #25Self-service

Data Source

PatentUS20250267572A1Power management of access points in a network

Publication Date: 2025.08.21 HEWLETT PACKARD ENTERPRISE DEV LP

AI summary

An example method and a network management system for reducing power consumption by access points (APs) deployed in a network are presented. The network management system identifies a candidate AP for power saving from the plurality of APs based on telemetry data and using a machine learning model. The telemetry data includes information about client associations and network activity of the plurality of APs. Further, the network management system infers a power-saving transition for the candidate AP based on the telemetry data using the machine learning model. Then, as per the power-saving transition, the network management system operates the candidate AP in a power-saving mode.