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Recent research on WiFi sensing focuses on the identification of a wide range of human behaviors with high recognition accuracy. However, these well-studied recognition techniques can cause privacy concerns due to the ubiquity of WiFi signal and comprehensive behavior information embedded therein. In this letter, we take the first attempt to develop an adversarial deep network architecture for human behavior preservation. Our goal is to make desirable private behaviors of a human being not recognizable by general classifiers, while the recognition of other ones remaining unaffected. To achieve this, we propose a novel loss function, using which our network is capable of intentionally modifying CSI data extracted from received WiFi signal, constraining the new classification results to match the adversarial requirements. Experimental results demonstrate that, with the proposed adversarial scheme, the recognition rate of the human behaviors needed to be protected can be significantly decreased, while still maintaining the accuracy of other ones desired to be identified. Our source codes are available at https://github.com/siwangzhou/WiFi-ADG. [ABSTRACT FROM AUTHOR]

Auctions are believed to be effective methods to solve the problem of wireless spectrum allocation. Existing spectrum auction mechanisms are all centralized and suffer from several critical drawbacks of the centralized systems, which motivates the design of distributed spectrum auction mechanisms. However, extending a centralized spectrum auction to a distributed one broadens the strategy space of agents from one dimension (bid) to three dimensions (bid, communication, and computation), and thus cannot be solved by traditional approaches from mechanism design. In this paper, we propose two distributed spectrum auction mechanisms, namely distributed VCG and FAITH. Distributed VCG implements the celebrated Vickrey-Clarke-Groves mechanism in a distributed fashion to achieve optimal social welfare, at the cost of exponential communication overhead. In contrast, FAITH achieves sub-optimal social welfare with tractable computation and communication overhead. We prove that both of the two proposed mechanisms achieve faithfulness, i.e., the agents' individual utilities are maximized, if they follow the intended strategies. Besides, we extend FAITH to adapt to dynamic scenarios where agents can arrive or depart at any time, without violating the property of faithfulness. We implement distributed VCG and FAITH, and evaluate their performance in various setups. Evaluation results show that distributed VCG results in optimal allocation, while FAITH is more efficient in computation and communication. [ABSTRACT FROM AUTHOR]

In crowdsensing, appropriate rewards are always expected to compensate the participants for their consumptions of physical resources and involvements of manual efforts. While continuous low quality sensing data could do harm to the availability and preciseness of crowdsensing based services, few existing incentive mechanisms have ever addressed the issue of data quality. The design of quality based incentive mechanism is motivated by its potential to avoid inefficient sensing and unnecessary rewards. In this paper, we incorporate the consideration of data quality into the design of incentive mechanism for crowdsensing, and propose to pay the participants as how well they do, to motivate the rational participants to efficiently perform crowdsensing tasks. This mechanism estimates the quality of sensing data, and offers each participant a reward based on her effective contribution. We also implement the mechanism and evaluate its improvement in terms of quality of service and profit of service provider. The evaluation results show that our mechanism achieves superior performance when compared to general data collection model and uniform pricing scheme. [ABSTRACT FROM AUTHOR]