Crowd-Sourced Network Mapping for Data Transaction Scheduling
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Solution Overview
Problem
Existing network transactions are hindered by physical limitations such as channel availability, quality, download speeds, upload speeds, bandwidth, and latency, leading to inefficient use of network resources and user dissatisfaction.
Innovation Solution
Utilizing a crowd-sourced network map to identify and schedule data transactions on networks with desirable properties, offloading transactions to better-suited networks when the device comes into connection range.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If data transactions are executed on currently connected networks, then immediate data transmission is achieved, but network load and computational burden increase on networks with limited bandwidth and resources
Solution Approach 1:
The system performs preliminary actions by identifying upcoming data transactions before they are executed, analyzing their parameters, and determining target network properties in advance. This allows the device to proactively schedule transactions on optimal networks rather than reactively transmitting on currently connected networks, thereby reducing immediate network load while maintaining transmission speed requirements.
Solution Approach 2:
The system introduces an intermediary scheduling mechanism that acts between the application layer and the network transmission layer. This intermediary analyzes transaction parameters, matches them with target network properties, and routes transactions through appropriate networks, thereby optimizing resource distribution across multiple networks and reducing burden on any single network.
2Productivity
If data transactions are scheduled based on target network properties, then network traffic is optimized and user experience improves, but additional computational processing is required to analyze transaction parameters and match networks
Solution Approach 1:
The system segments the transaction scheduling process into distinct functional modules: identifying upcoming transactions, determining transaction parameters, selecting target network properties, and scheduling transmissions. This segmentation allows each module to be independently optimized and managed, reducing overall system complexity while maintaining high network utilization efficiency.
Solution Approach 2:
The system implements self-service mechanisms where the scheduling algorithm automatically adapts to network conditions and transaction requirements without requiring manual configuration or complex external control. The system monitors network properties and autonomously makes scheduling decisions, simplifying the overall architecture while achieving optimal network utilization.
3Reliability
If the system identifies and schedules transactions on networks with desirable properties, then network load is reduced on currently connected networks, but time is required to determine predicted connection times and schedule transactions
Solution Approach 1:
The system performs preliminary identification of upcoming data transactions and determines their parameters before actual transmission needs occur. By analyzing transaction characteristics in advance and matching them with target network properties, the system can proactively schedule transmissions to minimize delays while ensuring reliable network selection, rather than making rushed decisions when transmission is urgently needed.
Solution Approach 2:
The system implements dynamic scheduling that adapts to both network conditions and transaction characteristics. The scheduling mechanism continuously monitors network properties and adjusts transaction timing and routing decisions in real-time, balancing the need for reliable network selection with the requirement to minimize scheduling delays based on current system state.
Data Source
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AI summary
Techniques for efficiently performing data transactions are disclosed herein. In an example, crowd-sourced data is used to generate a network map that includes network properties mapped to one or more geographical locations. The network map is used to determine if a network that has one or more target properties is within a threshold range of a computing device. The target properties are based on one or more parameters of an upcoming data transaction. If a respective network with one or more target properties is within range of the computing device, the upcoming data transaction can be scheduled for initiation when the computing device is connected to the respective network. If no networks have the one or more target properties, the upcoming transaction can be initiated using a current network connection.