Constraint-Aware Data Stream Assembly for Low-Power IoT Sources
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Solution Overview
Problem
Existing technologies face challenges in efficiently assembling data streams from disparate sources while adhering to constraints such as time, resource, and power consumption requirements, particularly in the Internet of Things (IoT) devices where low-cost, low-power operation is crucial.
Innovation Solution
A machine-implemented method for a data-source device that selects appropriate accessors and transformers based on estimated constraints, modifies system state, and communicates data streams efficiently, ensuring compliance with data stream constraints by acquiring and transforming data items while optimizing resource usage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If highly sophisticated devices with advanced data processing capabilities are used, then data stream assembly compliance and processing quality improve, but manufacturing costs and power consumption increase
Solution Approach 1:
The patent implements dynamic selection of accessors and transformers based on runtime constraint estimates. The system adapts its processing approach by selecting different data access methods and transformation techniques depending on the estimated time and power constraints, rather than using a fixed sophisticated approach. This dynamic adaptation allows the system to maintain compliance while reducing power consumption when constraints permit simpler methods.
Solution Approach 2:
The system changes operational parameters by estimating constraints and selecting accessors/transformers accordingly. It modifies its behavior by adjusting the complexity of data access and transformation operations based on the estimated available time and power resources, thereby optimizing the balance between compliance and power consumption.
2Productivity
If sophisticated data processing devices are deployed, then data stream assembly capability improves, but manufacturing costs and resource consumption increase
Solution Approach 1:
The patent segments the data processing function into multiple accessors and transformers that can be independently selected. Rather than using a single complex processing unit, the system divides the work into discrete data access operations and transformation operations, each with its own set of selectable implementations. This segmentation allows the system to achieve high productivity through careful selection of simple, specialized components rather than relying on a single complex device.
Solution Approach 2:
The system implements a universal framework that can perform multiple data access and transformation functions through a common selection mechanism. The same constraint estimation and selection logic handles diverse data sources and transformation requirements, making the system highly productive across different scenarios without requiring specialized complex hardware for each case.
3Reliability
If data is accessed and transformed with strict constraint compliance, then data stream quality improves, but processing time and resource usage increase
Solution Approach 1:
The patent performs preliminary estimation of time and power constraints before actually accessing or transforming data. By evaluating the constraints in advance and selecting appropriate accessors and transformers based on these estimates, the system avoids unnecessary processing steps that would waste time. This preliminary action ensures constraint compliance while minimizing actual processing time by choosing the most efficient path upfront.
Solution Approach 2:
The system uses feedback from constraint estimation to guide accessor and transformer selection. The estimated constraints feed into the selection logic, which then chooses accessors and transformers that are most likely to meet those constraints. This feedback loop ensures that the selected processing methods will achieve compliance while optimizing processing time based on the estimated available resources.
Data Source
AI summary
Technology for operating a data-source device for assembling a data stream compliant with a data stream constraint. The technology comprises acquiring a plurality of data items by accessing data in a memory and/or transforming data. Prior to completion of the accessing data in a memory, an accessor is selected based on an estimate of access constraint. Prior to completion of the transforming data, a transformer is selected based on an estimate of transformation constraint, wherein the transportation constraint comprises any data acquisition constraint. The access and transformation constraints are dependent upon system state it the data-source system. The data items are positioned in the data stream, and, responsive to achieving compliance with the data stream constraint, the data strewn is communicated.