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arXiv2025

Incentive Mechanism for Mobile Crowd Sensing with Assumed Bid Cost Reverse Auction

Mobile Crowd Sensing (MCS) is the mechanism wherein people can contribute in data collection process using their own mobile devices which have sensing capabilities. Incentives are rewards that individuals get in exchange for data they submit. Reverse Auction Bidding (RAB) is a framework that allows users to place bids for selling the data they collected. Task providers can select users to buy data from by looking at bids. Using the RAB framework, MCS system can be optimized for better user utility, task provider utility and platform utility. In this paper, we propose a novel approach called Reverse Auction with Assumed Bid Cost (RA-ABC) which allows users to place a bid in the system before collecting data. We opine that performing the tasks only after winning helps in reducing resource consumption instead of performing the tasks before bidding. User Return on Investment (ROI) is calculated with which they decide to further participate or not by either increasing or decreasing their bids. We also propose an extension of RA-ABC with dynamic recruitment (RA-ABCDR) in which we allow new users to join the system at any time during bidding rounds. Simulation results demonstrate that RA-ABC and RA-ABCDR outperform the widely used Tullock Optimal Prize Function, with RA-ABCDR achieving up to 54.6\% higher user retention and reducing auction cost by 22.2\%, thereby ensuring more efficient and sustainable system performance. Extensive simulations confirm that dynamic user recruitment significantly enhances performance across stability, fairness, and cost-efficiency metrics.

cs.GT↗

arXiv2021

Detection Of Primary User Emulation Attack (PUEA) In Cognitive Radio Networks Using One-Class Classification

Opportunistic usage of spectrum owned by licensed (or primary) users is the cornerstone on which the Cognitive Radio technology is built. Unlicensed (or secondary) users that thus use the spectrum rely opportunistically on spectrum sensing to determine the presence of primary user signal. In such a context, an attacker may mimic the behavior of a primary user (PU) to deceive the secondary users (SUs) into believing that a PU signal is present whereas it is not. Such an attack is known as the Primary User Emulation Attack (PUEA). A malicious user may launch a PUEA with the intention of grabbing the vacant bands for its own transmission. Another reason may be to simply disrupt the functioning of the Cognitive Radio Network (CRN). This work investigates the use of one-class classification for detecting PUEA in an infrastructure-based CRN. We opine that sensing data collected at the fusion center mainly for Collaborative Spectrum Sensing (CSS) can be exploited to characterize a PU signal. The PU signal features thus learned can aid in distinguishing a PU signal from a PU signal emulation. In particular, we investigate the use of one-class classification techniques, viz., Isolation Forest, Support Vector Machines (SVM), Minimum Covariance Determinant(MCD) and Local Outlier Factor(LOF) for detection of PUEA attacks. Simulation results support the validity of using one-class classification for detection of PUEA.

cs.NI↗