Distributed Sensor Data Processing Pipeline Energy Coordination
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Solution Overview
Problem
Existing load balancing solutions for smart wearable devices are inadequate for complex data processing pipelines, as they are designed for simple sensing tasks and do not effectively manage energy consumption and battery availability across multiple devices.
Innovation Solution
A method and system for distributed execution of sensor data processing, where a processing device obtains energy demand and battery availability data to select the most suitable device for task execution, determining execution strategies that balance energy consumption and battery life by evaluating cost functions and energy profiles, and controlling the execution of tasks across multiple devices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Use of energy by moving object
If processing tasks are distributed across multiple wearable devices, then energy consumption is reduced and battery life is extended, but system complexity increases due to the need for coordination and task management
Solution Approach 1:
The patent introduces a coordinator device that acts as an intermediary to manage task distribution and coordination between multiple wearable devices. This centralizes the complexity of task management, energy profile calculation, and execution strategy selection in a single device, while the other devices simply execute assigned tasks. This resolves the contradiction by offloading system complexity to a dedicated coordinator while enabling energy-efficient distributed processing.
Solution Approach 2:
The system dynamically adjusts task distribution strategies based on real-time battery availability data and energy demand data. Execution strategies are selected and can be changed during operation to optimize energy consumption while adapting to changing battery levels. This dynamic approach allows the system to maintain energy efficiency without requiring complex static configurations.
2Ease of manufacture
If simple load balancing techniques are used, then implementation is easier and device compatibility is better, but they are inadequate for managing complex data processing pipelines with multiple successive tasks
Solution Approach 1:
The patent segments complex data processing pipelines into discrete tasks with defined inputs and outputs. Each task in the processing pipeline can be independently analyzed for energy consumption and assigned to appropriate devices. This segmentation allows the system to handle complex pipelines by breaking them down into manageable units that can be distributed and executed independently, maintaining implementation simplicity while increasing adaptability to complex workflows.
Solution Approach 2:
The system provides a universal framework that can handle various types of processing pipelines (simple sensing tasks, complex multi-task pipelines, model training, etc.) through a common architecture. The same task distribution mechanism and execution strategy selection process work across different pipeline complexities, making the system versatile without requiring complex specialized implementations for each case.
3Speed
If tasks are executed locally on each device, then processing speed is faster and latency is reduced, but battery life is depleted faster and energy availability is not optimized
Solution Approach 1:
The system changes the parameter of task execution location based on battery availability conditions. When battery levels are high, more tasks can be executed locally for faster processing. When battery levels are low, tasks are redistributed to devices with sufficient energy. This dynamic parameter adjustment optimizes the trade-off between processing speed and energy consumption, allowing the system to adapt to changing energy conditions while maintaining overall system performance.
4Duration of action of stationary object
If distributed task execution is implemented, then battery life is extended and energy efficiency is improved, but coordination overhead and communication requirements increase
Solution Approach 1:
The coordinator device performs preliminary actions by pre-calculating execution strategies and determining task assignments before actual task execution begins. Energy profiles are computed in advance, and execution strategies are selected based on current battery availability before tasks are distributed. This preliminary planning reduces coordination overhead during actual task execution, as devices can proceed with pre-determined assignments without requiring continuous coordination communication.
Data Source
AI summary
In one embodiment, the method includes obtaining, by a first processing device, energy demand data representative of the energy consumption of respective tasks of a processing pipeline, obtaining, by the first processing device, battery availability data representative of the available energy of the batteries of other respective processing devices, for respective tasks of the processing pipeline, selecting, by the first processing device, one of the processing devices for executing the task, as a function of the energy demand data and the battery availability data, and controlling, by the first processing device, the execution of the respective tasks on the selected processing devices.