IoT Fixture Battery Management and Data Transfer Optimization
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Solution Overview
Problem
Existing IoT-enabled fixtures in facility management lack efficient battery management and data transfer optimization, leading to suboptimal performance and increased battery consumption during network connections and data uploads/downloads.
Innovation Solution
The implementation of IoT-enabled fixtures with a communication interface, battery, and processor that determine battery chemistry, measure voltage at known conditions, and optimize data transfer efficiency by using cyclical redundancy checks and non-volatile memory for resuming disrupted OTA transfers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If battery voltage is measured continuously to monitor battery status, then battery management accuracy is improved, but battery consumption increases
Solution Approach 1:
The system performs battery voltage measurements periodically rather than continuously, using event-driven triggers such as high-load events or connection events to initiate measurement cycles. This periodic measurement approach maintains adequate battery status monitoring while significantly reducing the frequency of measurements and associated power consumption.
Solution Approach 2:
The system performs preliminary assessments of battery status by evaluating whether a high-load event has occurred before initiating full battery voltage measurement. This preliminary check allows the system to skip unnecessary measurements when battery status is already known or stable, reducing overall measurement frequency and power consumption.
2Measurement precision
If battery chemistry determination is performed to optimize battery management, then battery management precision is improved, but processing time and energy consumption increase
Solution Approach 1:
The system performs preliminary battery chemistry determination during initial battery characterization or when voltage patterns clearly indicate a specific chemistry type. By establishing chemistry early in the battery lifecycle or during obvious conditions, the system avoids repeated chemistry analysis during normal operation, reducing processing time while maintaining accurate chemistry-aware battery management.
Solution Approach 2:
The system uses lightweight voltage-based chemistry identification methods rather than complex, time-consuming analysis techniques. By employing simple voltage threshold comparisons and pattern recognition that require minimal processing resources, the system achieves adequate chemistry determination without significant time or energy costs.
3Productivity
If data transfer optimization is implemented during high-load events, then data transfer efficiency is improved, but system complexity increases
Solution Approach 1:
The system dynamically adjusts data transfer operations based on real-time battery status and load conditions. During high-load events, the system temporarily modifies transfer parameters such as buffer sizes, transfer intervals, or compression levels to optimize performance under constrained conditions. This dynamic adaptation improves data transfer efficiency during critical periods without requiring permanent structural changes to the system.
Solution Approach 2:
The system changes operational parameters such as data transfer timing, compression ratios, or protocol selection based on battery status and load conditions. By adjusting these parameters rather than fundamentally changing the data transfer architecture, the system achieves efficiency improvements with minimal increase in system complexity.
4Duration of action of moving object
If battery status measurement is performed during high-load events, then real-time battery monitoring is improved, but measurement accuracy deteriorates due to voltage fluctuations
Solution Approach 1:
The system anticipates voltage fluctuations during high-load events by implementing compensation mechanisms before measurements are taken. It uses pre-established voltage correction factors, baseline voltage profiles, or predictive models to account for expected voltage drops during high-load conditions. This beforehand cushioning allows the system to maintain accurate battery status measurements even when voltage fluctuates during high-load events.
Solution Approach 2:
The system uses feedback from multiple voltage measurements taken during and after high-load events to correct and refine battery status estimates. By continuously monitoring voltage patterns and comparing them against expected behavior, the system can identify and compensate for measurement errors caused by high-load fluctuations, maintaining accurate real-time monitoring despite transient voltage variations.
Data Source
AI summary
Methods and systems of managing data transfers in a fixture. One system includes fixtures comprising: a communication interface; and a processor configured to optimize download efficiency by: receiving, from a remote device via the communication interface, a file comprising a plurality of data blocks, wherein each of the plurality of data blocks comprise a unique cyclical redundancy check (CRC) value and a payload; determining, for each of the plurality of data blocks, a check value based on a polynomial division of the respective payload; determining an error in at least one of the plurality of data blocks based on a comparison between each of the respective CRC values and the respective check values; and providing, to the remote device via the communication interface, a request for retransmission of each of the at least one of the plurality of data blocks with the determined error.