Joint Node Selection and Power Allocation for Cooperative Positioning Based on Bidding Auction in VANET

Area of Science:

• Engineering
• Computer Science
• Wireless Communications

Background:

• Traditional positioning techniques fail in GPS/Wi-Fi-denied underground environments.
• Resource constraints and lack of node cooperation hinder accurate localization.
• Agent nodes may be unwilling to expend power for others' localization.

Purpose of the Study:

• To develop a cooperative localization algorithm for resource-constrained underground parking.
• To incentivize cooperation among agent nodes through an economic model.
• To optimize power allocation for improved positioning accuracy.

Main Methods:

• Proposed a bidding-auction-based cooperative localization (BACL) algorithm.
• Quantified contributions of channel parameters and prior localization data.
• Developed a virtual currency incentive rule for energy consumption compensation.

Main Results:

• BACL algorithm significantly improved positioning accuracy compared to FPCL (10%) and NCL (65%).
• Reduced resource consumption by 50% compared to FPCL.
• Demonstrated excellent performance in resource-constrained scenarios.

Conclusions:

• The BACL algorithm effectively enhances localization accuracy in challenging underground environments.
• The proposed incentive mechanism encourages node cooperation and efficient power allocation.
• BACL offers a viable solution for accurate and resource-efficient localization in underground parking.