Decision Intelligence Framework for Network Energy Optimization
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Solution Overview
Problem
Energy issues such as power outages, voltage fluctuations, and insufficient power capacity in network devices can lead to network instability, hardware failures, and environmental impacts like carbon emissions.
Innovation Solution
A decision intelligence (DI)-based computerized framework that manages, controls, and configures energy and network availability, consumption, and usage by devices at a location, using real-time data analysis and AI/ML techniques to identify patterns and optimize energy usage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If more devices are connected to the network at a location, then network functionality and device availability increase, but power consumption and energy demands increase
Solution Approach 1:
The system dynamically adjusts power allocation and device operational states based on real-time network conditions, usage patterns, and power availability. The DI framework continuously learns and adapts to changing demands, transitioning devices between active, standby, and sleep states to optimize the balance between device availability and power consumption.
Solution Approach 2:
The system changes operational parameters such as power consumption levels, network traffic routing, and device activation thresholds based on learned patterns and current conditions. By adjusting these parameters dynamically, the system maintains network functionality while adapting to varying power constraints and usage requirements.
2Reliability
If power capacity is increased to support more devices, then network stability improves, but energy consumption and environmental impact worsen
Solution Approach 1:
The system performs preliminary learning and pattern recognition during low-demand periods to predict future power requirements and network usage. By anticipating peak demands and preparing optimal power allocation strategies in advance, the system maintains network stability without requiring excessive standby power capacity, thereby reducing overall energy consumption.
Solution Approach 2:
The DI framework implements continuous feedback loops that monitor power consumption, network performance, and device states. This feedback enables real-time optimization of power allocation, allowing the system to maintain stability by adjusting power distribution based on actual needs rather than providing excessive continuous power, thus reducing energy waste.
3Loss of energy
If energy-efficient operations are implemented, then power consumption decreases, but network performance and device responsiveness may worsen
Solution Approach 1:
The system implements periodic deep-learning analysis cycles combined with faster rule-based decision-making for routine operations. During periodic learning phases, the system optimizes energy allocation patterns, while during operational phases, it executes pre-determined efficient routing and device management rules. This periodic approach maintains high network performance while achieving energy efficiency through optimized patterns.
Solution Approach 2:
The DI framework enables devices and network components to self-optimize their operational parameters based on learned patterns. Devices automatically adjust their power consumption and network usage behaviors to achieve energy efficiency without sacrificing critical performance requirements, as the system learns and adapts to maintain necessary service levels while reducing overall energy demand.
Data Source
AI summary
Disclosed are systems and methods that provide a computerized location management framework for deterministically managing, controlling and/or configuring. energy and/or network availability, consumption and/or usage by devices at the location. The disclosed framework operates to find and leverage “green hours” at a location based on the network usage of the devices operating at and/or providing the network for the location (e.g., smart phones connected to a Wi-Fi network at the location and/or an access point (AP) device providing the Wi-Fi network, for example). Accordingly, such identified “green hours” can be identified and leveraged to reduce the location's carbon footprint as well as optimize the network for improved network availability and usage via the connected devices.