Asset Tag Wake Scheduling for Low-Power Error Recovery
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Solution Overview
Problem
Existing asset tracking systems face inefficiencies in power management and error recovery, particularly in managing battery life and accurately locating assets with clock drift or malfunctioning tags.
Innovation Solution
A method involving asset tag scheduling with wake windows and unicast/multicast communication modes to optimize power usage and recover from errors, using motion sensors and response schedules to enhance localization accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If multiple separate systems are used for asset tracking, grouping, and error handling, then functional versatility is improved, but device complexity increases
Solution Approach 1:
The patent combines asset tracking, asset grouping, and error recovery functions into a single integrated system. The server system processes tracking data, performs grouping operations, and handles errors through unified data structures and processing pipelines, eliminating the need for multiple separate systems while maintaining all required functionalities.
Solution Approach 2:
The server system is designed as a multi-functional platform that simultaneously performs asset tracking, grouping by various criteria (customer, location, time), and error recovery. The same infrastructure and processing mechanisms serve multiple purposes, reducing overall system complexity while providing versatile capabilities.
2Measurement precision
If manual error checking and data correction is performed, then measurement precision is improved, but productivity decreases
Solution Approach 1:
The system implements automatic error detection and correction mechanisms that operate without manual intervention. The error recovery module automatically identifies data quality issues, corrects errors, and validates results, allowing the system to self-correct while maintaining high processing throughput and eliminating manual labor bottlenecks.
Solution Approach 2:
The system incorporates continuous feedback loops where processing results are automatically validated against expected patterns and constraints. When errors are detected, the system automatically triggers correction procedures and re-validation, ensuring data accuracy is maintained through automated feedback mechanisms rather than manual checking.
3Reliability
If comprehensive error recovery mechanisms are implemented, then reliability is improved, but device complexity increases
Solution Approach 1:
The system pre-defines error handling procedures, validation rules, and recovery protocols before errors occur. Data structures are designed with built-in error checking capabilities, and the system establishes correction workflows in advance, allowing rapid automated response to errors without requiring complex real-time decision-making or additional complexity during error events.
Data Source
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AI summary
A method includes, generating a schedule for an asset tag, the schedule defining: a unicast trigger time for transmission of a unicast trigger to the asset tag; a transmit time succeeding receipt of the multicast trigger; and a wake window intersecting the transmit time and the receipt of the multiact trigger. The method also includes transmitting the schedule from a node network to the asset tag and configuring the asset tag based on the schedule. The method also includes, at the node network, broadcasting the unicast trigger and, at an asset tag: entering a wake mode; receiving the unicast trigger; transmitting a ranging signal; and entering the sleep mode. The method further includes, at the node network: deriving location of the asset tag based on instance of the ranging signal received by nodes in the node network.