Soldier PAN Power Tracking Middleware for Faster Runtime Prediction
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Solution Overview
Problem
Existing soldier-ported power tracking systems using Android OS-based processors suffer from slow information update speeds due to individual data gathering from multiple power devices, leading to latency in information flow and inaccurate power predictions.
Innovation Solution
A middleware messaging system for a soldier-ported personal area network (PAN) that includes a power application on an end user device (EUD) to track instantaneous power usage and availability, generate power information products, and provide alerts, utilizing a hub for message routing and a historical power data store for accurate predictions based on real-time and historical data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If individual data gathering from multiple power devices is used, then device complexity is reduced, but information update speed deteriorates
Solution Approach 1:
The system segments power tracking into two layers: (1) individual power devices report to a centralized power tracking service, and (2) the service aggregates data and publishes to power applications. This segmentation allows parallel data collection from multiple devices without sequential processing delays, improving update speed while maintaining manageable complexity through clear interface definitions.
Solution Approach 2:
A centralized power tracking service acts as an intermediary between power devices and power applications. This mediator collects, validates, and aggregates power data from multiple sources, then publishes unified power information to applications. The intermediary pattern resolves the contradiction by centralizing complex aggregation logic while keeping individual device and application sides simple.
2Measurement precision
If real-time power data aggregation is implemented, then power prediction accuracy is improved, but information flow latency increases
Solution Approach 1:
The power tracking service continuously collects power data from all connected devices without interruption, maintaining a persistent data stream. This continuous action ensures prediction accuracy by always having current power consumption information available, while the asynchronous publishing mechanism prevents latency by not blocking data flow for aggregation operations.
Solution Approach 2:
The system pre-aggregates power data in the tracking service before applications request it. By maintaining pre-processed aggregated power information ready in the service layer, the system eliminates real-time computation delays when applications need power data, thus reducing latency while preserving prediction accuracy through continuous pre-aggregation.
3Measurement precision
If historical power data is stored and analyzed, then power prediction accuracy is improved, but device complexity increases
Solution Approach 1:
The system extracts historical power data analysis functionality from the power application and places it in the power tracking service. The service maintains a historical database and performs predictive analytics, while applications simply request predictions. This extraction reduces application complexity while improving prediction accuracy through dedicated data analysis infrastructure.
4Loss of information
If centralized power tracking service is used, then power information aggregation is improved, but information flow latency increases
Solution Approach 1:
The power tracking service implements a publish-subscribe feedback mechanism where aggregated power information is automatically published to all subscribed applications. This feedback loop ensures complete power information aggregation from all devices while reducing latency through event-driven notifications that immediately push updates to applications without polling delays.
Data Source
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AI summary
A power tracking system includes a power application that receives live power data from multiple power devices that are operably connected to a personal area network (PAN) during operation of the PAN. The system stores the live power data and uses historical power data that includes previously collected live power data corresponding to the multiple power devices that was collected during previous operations to generate prediction data, including a predicted runtime of the PAN. The live power data and prediction data are displayed to a user on a user interface and are used by components of the system to model and predict power requirements and power usage of the PAN components.