Context-Aware Cargo Labels for Battery-Limited Transit Tracking
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Solution Overview
Problem
Existing IoT devices for tracking and monitoring cargo face challenges due to limited battery capacity and power management, leading to potential loss of tracking data during transit, with existing solutions lacking accuracy and failing to consider contextual information.
Innovation Solution
A system and method for contextual tracking of label units that optimize energy consumption by determining contextual transit parameters, adjusting communication and sensor operations, and replacing units with sufficient power reserves when needed, using sensors and processors to manage power supply units.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Weight of moving object
If smaller and lower capacity batteries are used to keep IoT devices compact and light weight, then device portability is improved, but battery life becomes insufficient to complete the transit duration
Solution Approach 1:
The system dynamically adjusts the operational parameters of the IoT device based on real-time battery state-of-charge levels and contextual transit information. When battery charge drops below thresholds, the system automatically reduces sensor sampling frequency, decreases communication intervals, and adjusts processor activity to extend battery life dynamically throughout the transit journey.
Solution Approach 2:
The system changes operational parameters such as sensor sampling rate, communication frequency, and processor performance levels based on battery state-of-charge conditions. By adjusting these parameters dynamically, the system optimizes the balance between device functionality and power consumption to ensure complete transit coverage.
2Duration of action of moving object
If battery capacity is increased to ensure complete transit tracking, then battery life is improved, but device size and weight increase
Solution Approach 1:
Instead of using a large static battery, the system employs dynamic power management that adjusts consumption based on real-time conditions. The device monitors battery state-of-charge continuously and adapts its operational intensity to match available energy, allowing smaller batteries to last longer by reducing power demand during critical low-charge periods.
Solution Approach 2:
The system performs preliminary assessments of transit duration, route, and environmental conditions to predict power requirements before deployment. Based on this preliminary analysis, the system pre-configures optimal power management strategies and adjusts operational parameters in advance to ensure complete transit coverage with minimal battery capacity.
3Measurement precision
If tracking data collection frequency is increased to improve monitoring accuracy, then measurement precision is improved, but energy consumption increases
Solution Approach 1:
The system dynamically adjusts sensor sampling frequency and data collection intervals based on battery state-of-charge levels and transit phase. During high-charge periods, the system collects data at higher frequencies for improved accuracy. When charge drops below thresholds, the system automatically reduces sampling frequency while maintaining critical tracking capabilities, thus balancing accuracy with energy conservation.
Solution Approach 2:
The system applies partial action by collecting full-resolution tracking data only when necessary (e.g., during critical transit phases or when battery charge is sufficient), and uses reduced-frequency or lower-resolution monitoring during other periods. This selective data collection maintains adequate tracking accuracy while significantly reducing overall energy consumption.
4Reliability
If communication frequency with cloud network is increased to improve tracking reliability, then reliability is improved, but energy consumption increases
Solution Approach 1:
The system dynamically adjusts communication frequency with the cloud network based on battery state-of-charge levels. When battery charge is high, the system maintains frequent communication for improved tracking reliability. When charge drops below predetermined thresholds, the system automatically reduces communication frequency, batches data transmissions, and uses lower-power communication modes to extend battery life while maintaining adequate tracking reliability.
Solution Approach 2:
The system implements periodic communication cycles with variable intervals based on battery conditions. Instead of continuous or fixed-frequency communication, the system uses adaptive periodic transmissions that extend intervals during low-charge periods while maintaining regular updates during high-charge periods, thus balancing reliability with energy conservation.
Data Source
AI summary
The present disclosure provides a system (100) and a method for contextual tracking of label units (110) and optimizing energy consumption thereof. The system (100) includes one or more label units (110) being attached to a corresponding object (106), each label unit (110) having one or more sensors (116) that collect one or more tracking data, and being powered by a corresponding power supply unit (118). A tracking system (102) receives the tracking data from each of the label units (110) via a first set of signals, and determines a state-of-charge value of the power supply unit (118) therewith. When the state-of-charge value is less than a transit duration value associated with the object (106), the tracking system (102) determines one or more contextual transit parameters, and transmits a second set of signals to the label units (110) to optimize energy consumption thereof based on the contextual transit parameters.