Service Allocation Selection Method for IoT Networks
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Solution Overview
Problem
In network systems, the distance between computers and data generation devices can lead to increased network traffic and load, and the selection of inappropriate devices can result in prolonged processing times and ineffective service execution due to varying device characteristics over time and location.
Innovation Solution
A service allocation selection method that calculates an evaluation value F(x, y, z) based on data effectiveness and computer load to optimize the combination of application software, computers, and IoT devices, reducing the need for frequent device changes and improving service efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If the distance between the computer executing application software and the data generation device is significantly long, then the service can be provided using distributed resources, but the network traffic and network load increase
Solution Approach 1:
The patent applies local quality by selecting devices based on their local characteristics and proximity to the executing computer. The evaluation function considers the distance between computers and data generation devices, prioritizing locally available resources to reduce network traffic while maintaining service distribution capability.
Solution Approach 2:
The patent performs preliminary action by pre-calculating and storing evaluation values for device combinations before actual service execution. The system evaluates multiple device combinations in advance, storing these evaluations in a database, so that when a service request arrives, the optimal device can be quickly selected without real-time calculation delays.
2Productivity
If a computer with high operating rate is selected to execute application software, then resource utilization is improved, but the processing duration time increases
Solution Approach 1:
The patent applies dynamics by making the device selection adaptable to changing system conditions. The evaluation function dynamically considers the operating rate of computers and adjusts device selection based on current load conditions, allowing the system to balance resource utilization and processing speed according to real-time status.
Solution Approach 2:
The patent implements feedback by using the operating rate of computers as a parameter in the evaluation function. The system continuously monitors computer performance metrics and feeds this information back into the device selection process, ensuring that overloaded computers are not selected for new tasks.
3Ease of operation
If a device is selected whose output information type or characteristics are not effective for the search service, then device selection is simplified, but the service purpose cannot be achieved and device changes are required
Solution Approach 1:
The patent applies parameter changes by incorporating multiple evaluation parameters into the device selection process, including data effectiveness expectation values. The evaluation function considers not only device availability but also the effectiveness of device output for the specific service purpose, ensuring reliable service execution while maintaining automated selection.
Solution Approach 2:
The patent uses an evaluation function as an intermediary between the service requirements and device selection. This evaluation function acts as a mediator that translates service needs into device selection criteria, automatically assessing both the effectiveness and suitability of candidate devices without manual intervention.
4Adaptability or versatility
If device characteristics vary significantly over time and location, then device versatility is improved, but the ability to determine appropriate devices in advance deteriorates
Solution Approach 1:
The patent applies preliminary action by pre-calculating evaluation values for device combinations and storing them in a database before actual service execution. This allows the system to account for varying device characteristics in advance while maintaining the ability to quickly select appropriate devices when service requests arrive.
Solution Approach 2:
The patent implements feedback mechanisms that continuously update device evaluation information based on actual performance and changing conditions. This feedback loop allows the system to adapt to varying device characteristics over time and location while maintaining accurate evaluation data for future selections.
Data Source
AI summary
When selecting a combination of application software, a computer, and a device in executing various services, even in a situation in which an environment greatly varies, the probability to achieve the purpose of the service is increased to shorten the duration time required to achieve the purpose of the service.Data effectiveness expectation value J(x, z) regarding a combination of a specific application x and a specific device z on a communication network is calculated, an evaluation value F(x, y, z) including the data effectiveness expectation value and a specific computer y as parameters is calculated, and an appropriate combination (x, y, z) is selected based on the result. The data effectiveness expectation value is calculated from actual record data. The data effectiveness expectation value is calculated by weighting old data and new data. The data effectiveness expectation value is calculated by using actual record values of other applications similar to the specific application x.