M2M Resource Acquisition via Consumer-Triggered Updates
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Solution Overview
Problem
In conventional M2M systems, producer applications frequently update data even when no consumer applications are interested, leading to redundant message transmission and waste, especially in resource-constrained devices like battery-powered sensors, due to a mismatch between data generated and data used.
Innovation Solution
A resource acquiring method where the platform receives a request from a consumer application to acquire producer data, updates the producer data resource with current moment data, and returns the updated resource, ensuring that only latest data is transmitted, and using subscription resources to notify the producer application of consumer interest.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If the producer application continuously sends update requests to ensure data is available, then the data freshness is improved, but the energy consumption and network usage increase
Solution Approach 1:
The system implements feedback mechanisms where consumer applications indicate their data acquisition needs to the platform. The platform then selectively triggers producer applications to update data only when there is confirmed consumer interest, creating a closed-loop control system that optimizes update frequency based on actual demand.
Solution Approach 2:
Consumer applications actively manage their own data acquisition by sending indication messages to the platform when they need data. This self-service approach allows consumers to control the update trigger rather than relying on continuous producer-initiated updates, reducing unnecessary energy consumption.
2Loss of information
If the producer application frequently updates data to ensure consumers get latest information, then the data availability is improved, but the network transmission overhead increases
Solution Approach 1:
The platform receives indication messages from consumer applications and uses this feedback to selectively trigger data updates. This feedback-driven approach ensures network transmission occurs only when necessary, eliminating redundant data transmissions while maintaining data availability for interested consumers.
Solution Approach 2:
Instead of continuous periodic updates by the producer application, the system transitions to event-driven periodic action where updates occur only when triggered by consumer indication messages. This reduces network transmission overhead by aligning update frequency with actual consumer demand.
3Loss of information
If the consumer application continuously sends acquiring requests to ensure latest data is obtained, then the data freshness is improved, but the mismatch between produced and consumed data increases
Solution Approach 1:
The system uses feedback from consumer applications (indication messages) to coordinate with producer applications. This ensures that data production and consumption are synchronized, reducing mismatch between produced and consumed data while maintaining freshness through on-demand updates.
Solution Approach 2:
Consumer applications send indication messages in advance to express their data acquisition needs. This preliminary action allows the platform to coordinate updates before consumers actually request data, ensuring data freshness is achieved through efficient coordination rather than continuous polling.
4Loss of information
If the producer application updates data continuously to maintain data currency, then the data currency is improved, but the operational time of resource-constrained devices decreases
Solution Approach 1:
Consumer applications self-manage their data acquisition by sending indication messages when needed. This eliminates the need for continuous producer-initiated updates, allowing resource-constrained producer devices to remain dormant longer and extend their operational time while still providing currency data when consumers actually need it.
Solution Approach 2:
The feedback mechanism from consumers to the platform enables selective triggering of producer updates. This ensures data currency is maintained only when there is confirmed consumer interest, significantly reducing the operational burden on resource-constrained devices and extending their battery life.
Data Source
AI summary
A resource acquiring method relates to the field of machine-to-machine communications (M2M) technologies, where the method is used by a consumer application to acquire a producer data resource generated by a producer application, and includes receiving a request message that is used to acquire the producer data resource and that is sent by the consumer application, where the request message carries indication information that is used to instruct to update the producer data resource and return an updated producer data resource, acquiring current moment data from the producer application, updating the producer data resource using the current moment data, and returning the updated producer data resource to the consumer application. Hence, a problem of mismatching between data generated by the producer application and data used by the consumer application is resolved.