Incentive Determination for IoT Data Providers
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Solution Overview
Problem
Existing methods for data collection from IoT devices, such as those disclosed in PTL 1, do not consider incentives for data providers, leading to inefficiencies in data collection.
Innovation Solution
A method and server system that records provision record information associating provider identification, data identification, and value reference information to determine incentives for data providers based on the usage of their data, using index values from various value indexes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If data collection is performed without incentive mechanisms, then system complexity is reduced, but data collection efficiency deteriorates
Solution Approach 1:
The patent introduces a server as an intermediary between data providers (home appliances) and data users. The server manages the incentive determination process by receiving usage information, calculating incentives based on pre-stored value reference information, and distributing rewards. This intermediary structure enables efficient data collection through incentives while keeping the complexity centralized in the server rather than distributed across all devices.
Solution Approach 2:
The patent implements a feedback mechanism where data providers receive incentive information based on their data usage by others. The server sends incentive information back to data providers, creating a closed-loop system that motivates continued data provision. This feedback loop drives data collection efficiency by rewarding providers according to the value their data generates.
2Measurement precision
If single-value evaluation is used for data, then evaluation process is simplified, but evaluation accuracy deteriorates due to bias
Solution Approach 1:
The patent segments the data value evaluation into multiple independent dimensions by introducing several value indexes (first value index, second value index, etc.). Each index evaluates a different aspect of data value, and the final incentive is determined by combining these segmented evaluations. This segmentation reduces bias by considering multiple factors rather than relying on a single potentially biased metric.
Solution Approach 2:
The patent changes the evaluation parameters from a single value metric to multiple value indexes. The server stores and processes multiple parameters (first value index information, second value index information) to comprehensively assess data value. This parameter expansion enables more accurate and nuanced evaluation while the server handles the computational complexity of managing multiple parameters.
Data Source
AI summary
A method includes: recording provision record information in which provider identification information which identifies a data provider, data identification information which identifies each of one or more items of provided data generated by a home appliance, and value reference information which serves as a reference for calculating a data value of each of the one or more items of provided data are associated with one another; and when the one or more items of provided data include used data that is data used by a data user, determining an incentive for the data provider of the used data based on the provision record information. The value reference information includes an index value determined by each of a plurality of value indexes.