Maximizing Clearance Rate of Reputation-aware Auctions in Mobile Crowdsensing
Maggie Ezzat Gaber Gendy, Ahmad Al-Kabbany, Ehab Farouk Badran · 2019
This research is concerned with maximizing the clearance rate (CR) of reputation-aware (RA) auctions for assigning tasks in mobile crowdsensing (MCS) systems-CR refers to the percentage of items that are sold over the duration of the auction. Towards maximizing CR during task allocation, we propose two new bidding procedures. Through simulations under varying system parameters, we demonstrate the effectiveness of the suggested methods through consistent and considerable increases in the CR compared to the state-of-the-art.