AI Mobile Energy and Data Planning for Context-Aware Network Control
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing solutions for managing energy and data in mobile platforms are inadequate due to their complex and dynamic nature, often relying on manual interventions or basic automation, failing to consider context-specific factors, and lacking comprehensive integration of energy, data, and external conditions, leading to suboptimal decision-making and inefficiencies.
Innovation Solution
A system leveraging AI, data analytics, and context-aware computing to optimize energy and data use in mobile platforms by collecting, processing, and analyzing data from various sources, generating optimized recommendations for storage, transmission, and device settings, with modular architecture for scalability and integration with existing systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If manual interventions or basic automation techniques are used for managing energy and data in mobile platforms, then device complexity is reduced, but energy efficiency and operational optimization deteriorate
Solution Approach 1:
The system employs machine learning models that automatically learn from historical data and make autonomous decisions about energy allocation, data transmission timing, and computational task scheduling without requiring manual intervention. The system self-optimizes by continuously monitoring performance metrics and adjusting parameters to maximize energy efficiency while maintaining operational requirements.
Solution Approach 2:
The system implements closed-loop feedback mechanisms where performance data, energy consumption metrics, and operational outcomes are continuously collected and fed back to the machine learning models. This feedback enables the system to learn from past decisions, identify optimization opportunities, and dynamically adjust energy and data management strategies to improve efficiency over time.
2Reliability
If comprehensive data collection and analysis from multiple sources is implemented, then decision-making quality improves, but data management complexity and processing requirements increase
Solution Approach 1:
The system segments data management into distinct functional modules: data collection from multiple sources, data preprocessing and validation, feature extraction, model training, and decision execution. Each module handles specific aspects of the data pipeline, reducing overall complexity while enabling comprehensive data analysis. The segmentation allows parallel processing and independent optimization of each component.
Solution Approach 2:
The system introduces intermediary processing layers including edge computing nodes and cloud-based analytics platforms that act as mediators between data sources and decision-making systems. These intermediaries perform data aggregation, filtering, and preliminary analysis, reducing the burden on mobile devices and enabling more sophisticated decision-making without proportionally increasing device complexity.
3Productivity
If real-time optimization and context-aware decision-making are implemented, then operational efficiency improves, but computational resource requirements and energy consumption increase
Solution Approach 1:
The system dynamically adjusts the level of optimization based on contextual factors such as available energy, network conditions, and operational priorities. During low-energy states or when operating in resource-constrained environments, the system reduces computational intensity and defers non-critical optimizations. During high-energy states with favorable conditions, the system intensifies optimization efforts to maximize operational efficiency, creating a dynamic balance between computational effort and energy expenditure.
Solution Approach 2:
The system performs preliminary data processing, feature extraction, and model inference preparations during periods of high energy availability or when connected to external power sources. Pre-computed models and pre-processed data are stored for later use during energy-constrained operations, reducing real-time computational requirements and energy consumption during critical operational phases.
4Productivity
If advanced AI and machine learning systems are deployed for energy and data optimization, then optimization capability improves, but device complexity and initial energy requirements worsen
Solution Approach 1:
The system implements a nested architecture where lightweight machine learning models are embedded within the mobile device, while more complex models and additional data sources are accessed through cloud-based services and edge computing infrastructure. This nested structure enables advanced optimization capabilities without requiring the full computational complexity to be present in the mobile device itself, reducing device complexity while maintaining high optimization performance.
Data Source
AI summary
A system and method for artificial intelligence enhanced mobile energy and data and network control, planning and optimization. The present invention relates to a system and method for optimizing energy and data and network use in mobile platforms, such as facilities, vehicles, tools, and devices. The system leverages AI, data analytics, and context-aware techniques to collect, process, and analyze data from various sources, including sensors, weather data, and spatial and temporal data with locality aware computing, transport, storage and networking across assets that may be owned or operated by multiple stakeholders. By considering a variety of factors, the system generates optimized recommendations for data storage, compute, transmission, device settings, fleet management, and physical and virtual route planning and logic locality planning. The invention offers benefits, including improved energy efficiency, enhanced data and network management, increased operational efficiency, and cost savings.