Selective Sensing Mechanism in Vehicular Crowd-Sensing Systems
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Solution Overview
Problem
Current vehicular crowd sensing systems face challenges in efficiently collecting and processing environmental data from multiple vehicles while minimizing costs and maintaining accurate event detection without degrading performance.
Innovation Solution
A processor-implemented method for selective crowd sourcing, which calculates a contribution-to-cost ratio utility (CCRU) for each vehicle to determine the most cost-effective vehicles to transmit data, using a greedy algorithm to select vehicles with the highest CCRU and instructing them to report data to a central repository, while vehicles assess their contribution factor, effectiveness factor, and reputation score to decide on data transmission.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If data is collected from all vehicles in the geographical area, then the quantity of environmental data is increased, but the transmission cost and system complexity increase significantly
Solution Approach 1:
The system segments the fleet of vehicles into different groups based on their sensing contributions and transmission costs. Instead of treating all vehicles uniformly, the controller calculates a greedy parameter for each vehicle and selectively activates only those with the highest contribution-to-cost ratios, thereby reducing overall system complexity while maintaining data quantity.
Solution Approach 2:
The system changes the operational parameter of each vehicle from a binary state (transmit or not) to a graded state based on the calculated greedy parameter value. By dynamically adjusting which vehicles transmit data based on their individual contribution-to-cost ratios, the system optimizes the balance between data quantity and transmission cost.
2Measurement precision
If data is transmitted by all vehicles, then the accuracy of environmental event detection is improved, but the transmission cost increases
Solution Approach 1:
The system calculates a greedy parameter that represents the ratio of a vehicle's contribution to detection accuracy versus its transmission cost. By changing the operational parameter from uniform transmission to selective transmission based on this ratio, the system maintains detection accuracy while minimizing transmission costs.
Solution Approach 2:
Each vehicle's controller independently evaluates its own greedy parameter value and makes autonomous decisions about whether to transmit data, based on pre-established thresholds. This self-service mechanism eliminates the need for centralized coordination of each transmission decision, reducing overall system overhead and cost.
3Productivity
If selective sensing is implemented to reduce transmission cost, then the system efficiency is improved, but the event detection accuracy may be degraded
Solution Approach 1:
The system establishes a feedback mechanism where the controller continuously monitors detection accuracy and adjusts the greedy parameter thresholds accordingly. When accuracy degradation is detected, the system dynamically adjusts which vehicles are selected for transmission, ensuring that efficiency gains do not come at the cost of detection accuracy.
Solution Approach 2:
The system dynamically changes the selection parameters based on environmental conditions and detection requirements. By adjusting the greedy parameter thresholds and selection criteria in real-time, the system optimizes the balance between efficiency and accuracy for different operational scenarios.
Data Source
AI summary
Systems and method are provided for implementing selective crowd sourcing. In one embodiment, a processor-implemented method for obtaining data from vehicles includes calculating a greedy parameter value for each of a plurality of vehicles in a geographical area; selecting no more than a predetermined number of the plurality of vehicles having a greedy parameter value in a greedy parameter threshold range; instructing the selected vehicles to transmit data while in the geographical area; and receiving the data from the selected vehicles. In another embodiment, a vehicle including a crowd sourcing selection module is provided. The crowd sourcing selection module is configured to retrieve consensus information from a central repository; calculate greedy parameter information regarding the vehicle using the consensus information; and determine whether to transmit an event observation to the central repository based on the greedy parameter information.