Adaptive Sampling for Vehicular Crowd Sensing
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Solution Overview
Problem
In ad hoc communication networks, the limited bandwidth of communication channels leads to reduced data communication rates due to congestion, hindering the transmission of important information among vehicles.
Innovation Solution
A method for adaptively controlling the number of remote entities queried by a central entity in crowd sensing applications, where the number is adjusted based on the similarity or difference between current and previous data, increasing or decreasing the number of samples to verify accuracy and minimize data flow.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If each vehicle transmits data continuously, then data communication completeness is improved, but communication channel congestion increases and data communication rate decreases
Solution Approach 1:
Instead of every vehicle transmitting data continuously (excessive action), the system selectively queries a subset of remote entities (partial action) based on statistical analysis. The central entity determines an optimal query probability that balances data completeness with communication efficiency, querying only when necessary to maintain information accuracy while avoiding unnecessary transmissions that cause congestion.
Solution Approach 2:
The system dynamically adjusts the query probability parameter based on statistical values calculated from received data. When data variability is low, the query probability is reduced; when variability increases, the probability is increased to ensure data accuracy. This parameter adaptation resolves the contradiction by optimizing the balance between information completeness and communication rate under different conditions.
2Measurement precision
If the number of queried remote entities is increased, then data accuracy is improved, but data flow over the communication channel increases
Solution Approach 1:
The central entity calculates statistical values from received data and uses this feedback to dynamically adjust the query probability. When the statistical analysis indicates high data accuracy with current sampling, the query probability is reduced, decreasing data flow. When accuracy is insufficient, the probability is increased to query more entities, ensuring data precision while minimizing unnecessary data transmission.
3Productivity
If the number of queried remote entities is decreased, then communication efficiency is improved, but data accuracy may be compromised
Solution Approach 1:
The system dynamically changes the query probability parameter based on statistical analysis of received data. When statistical values indicate sufficient data accuracy, the query probability is decreased to improve communication efficiency. When accuracy thresholds are not met, the probability is increased to ensure precision. This adaptive parameter adjustment resolves the contradiction by optimizing the trade-off between efficiency and accuracy based on real-time conditions.
Data Source
AI summary
A crowd sensing system includes a central entity and remote entities. The remote entities receive queries from the central entity and transmit data to the central entity in response to the query. The sample data obtained from the queried remote entity is analyzed and an average value for the sample data is determined for a current time interval. The central entity determines whether a difference between the average values for the current time interval and a previous time interval is greater than a predetermined threshold. The central entity increases the number of entities in response to the difference being greater than the predetermined threshold, and decreases the respective number of remote entities sampled in response to the difference being less than the predetermined threshold. The central entity queries a number of remote entities in the plurality of regions equal to the adjusted number of samples identified by the central entity.