Yanjiao Chen

TAHES: A Truthful Double Auction Mechanism for Heterogeneous Spectrums

Auction is widely applied in wireless communication for spectrum allocation. Most of prior works have assumed that all spectrums are identical. In reality, however, spectrums provided by different owners have distinctive characteristics in both spacial and frequency domains. Spectrum availability also varies in different geo-locations. Furthermore, frequency diversity may cause non-identical conflict relationships among spectrum buyers since different frequencies have distinct communication ranges. Under such a scenario, existing spectrum auction schemes cannot provide truthfulness or efficiency. In this paper, we propose a Truthful double Auction mechanism for HEterogeneous Spectrum, called TAHES, which allows buyers to explicitly express their personalized preferences for heterogeneous spectrums and also addresses the problem of interference graph variation. We prove that TAHES has nice economic properties including truthfulness, individual rationality and budget balance. Results from extensive simulation studies demonstrate the truthfulness, effectiveness and efficiency of TAHES.

TAHES: Truthful double Auction for Heterogeneous Spectrums

Auction is widely applied in wireless communication for spectrum allocation. Most of prior works have assumed that spectrums are identical. In reality, however, spectrums provided by different owners have distinctive characteristics in both spacial and frequency domains. Spectrum availability also varies in different geo-locations. Furthermore, frequency diversity may cause non-identical conflicts among spectrum buyers since different frequencies have distinct communication ranges. Under such realistic scenario, existing spectrum auction schemes cannot provide truthfulness or efficiency. In this paper, we propose a Truthful double Auction for HEterogeneous Spectrum, called TAHES. TAHES allows buyers to explicitly express their personalized preferences for heterogeneous spectrums and also addresses the problem of interference graph variation. We prove that TAHES has nice economic properties including truthfulness, individual rationality and budget balance.

A reverse auction framework for access permission transaction to promote hybrid access in femtocell network

Femtocell refers to a new class of low-power, low-cost base stations (BSs) which can provide better coverage and improved voice/data Quality of Service (QoS). Hybrid access in two-tier macro-femto network is regarded as the most ideal access control mechanism to enhance overall network performance. But the implementation of hybrid access is hindered by a lack of market that can motivate ACcess Permission (ACP) trading between Wireless Service Providers (WSPs) and private femtocell owners. In this paper, we propose a reverse auction framework for fair and efficient ACP transaction. Unlike strict outcome (the demand of bidder must be fully satisfied) in most of the existing works on auction design, the proposed auction model allows range outcome, in which WSP accepts partial demand fulfillment and femtocell owners makes best-effort selling. We first propose a Vickery-Clarke-Grove (VCG) based mechanism to maximize social welfare. As the VCG mechanism is too time-consuming, we further propose an alternative truthful mechanism (referred to as suboptimal mechanism) with acceptable polynomial computational complexity. The simulation results have shown that the suboptimal mechanism generates almost the same social welfare and the cost for WSP as VCG mechanism.

Optimal Pricing and Spectrum Allocation for Wireless Service Provider on Femtocell Deployment

Femtocell technology is regarded as a promising way to deal with poor indoor coverage and increase spectrum spatial reuse. In this paper, we focus on the scenario that macro and femto base stations are deployed by the same Wireless Service Provider (WSP), which treats the revenue maximization as its ultimate target. In such a system, there are several design factors which will affect the overall revenue, including price decision and resource allocation between macrocell and femtocell. In this paper, we propose an economic framework, where users choose either macrocell or femtocell service to optimize their own utility and the monopolistic WSP tries to maximize its revenue via pricing and spectrum allocation strategy. Theoretical results of optimal prices for macrocell and femtocell are given. Extensive theoretical analysis is carried out to determine the spectrum allocation strategy and evaluate the revenue of the WSP. The system capacity and the ratio of macrocell and femtocell users are also discussed. The results have indicated that the revenue of the WSP is significantly improved by combining the pricing strategy and the spectrum allocation strategy.

Incentive mechanism for hybrid access in femtocell network with traffic uncertainty

Femtocell refers to a new class of low-power, low-cost base stations (BSs) which can provide improved indoor coverage and higher voice/data Quality of Service (QoS). Hybrid access in two-tier macro-femto networks is regarded as the most ideal access control mechanism to help offload macrocell traffic to femtocell, thus enhancing overall network performance. However, without suitable incentive mechanism, the Femtocell Service Providers (FSPs) are not willing to share their femtocell resource with the Macrocell Service Provider (MSP). To address this problem, in this paper, we propose an ACcess Permission (ACP) transaction framework, in which a single MSP purchases ACP from multiple FSPs in various locations throughout T timeslots, and FSPs who have overlapped coverage compete with each other for selling their ACP. However, we are facing the challenge that the demand of MSP in each location dynamically changes at each timeslot. At the start of each timeslot, FSPs are unaware of the demand of MSP, which impedes them to choose an ideal strategy that yields high payoff. To address the problem of information incompleteness, we propose an adaptive strategy updating algorithm, which is based on online learning process and enables FSPs to obtain guaranteed payoff. We conduct simulations to evaluate the payoff and the payoff gap of the FSPs when the MSPs demand is constant, quasi-constant or probabilistic. We also show that the payoff of the FSPs is affected by the learning speed of the proposed algorithm.

Incentivizing crowdsourcing systems with network effects

In a crowdsourcing system, it is important for the crowdsourcer to engineer extrinsic rewards to incentivize the participants. With mobile social networking, a user enjoys an intrinsic benefit when she aligns her behavior with the behavior of others. Referred to as network effects, such an intrinsic benefit becomes more significant as the number of users grows in the crowdsourcing system. But should a crowdsourcer design her extrinsic rewards differently when such network effects are taken into account? In this paper, we, for the first time, consider network effects as a contributing factor to intrinsic rewards, and study its influence on the design of extrinsic rewards. Rather than assuming a fixed participant population, we show that the number of participating users evolves to a steady equilibrium, thanks to subtle interactions between intrinsic rewards due to network effects and extrinsic rewards offered by the crowdsourcer. Taken network effects into consideration, we design progressively more sophisticated extrinsic reward mechanisms, and propose new and optimal strategies for a crowdsourcer to obtain a higher utility. Via extensive simulations, we demonstrate that with our new strategies, a crowdsourcer is able to attract more participants with higher contributed efforts; and participants gain higher utilities from both intrinsic and extrinsic rewards.

Understanding viewer engagement of video service in Wi-Fi network

With the dramatic growth of online video services and video traffic, video service providers and network operators have keen interest in improving viewer engagement. Viewer engagement is mainly influenced by four aspects: service quality metrics (e.g., rebuffer time), network quality metrics (e.g., physical-layer data rate), video content (e.g., video length) and viewer demography. Previous works only partially consider some of these factors due to limitation of the dataset. In this paper, we develop an experimental platform with more than 50 self-deployed routers in our university campus, collecting information regarding all four aspects of engagement-related factors. Correlation and information gain analysis show that different viewer groups and video content types have different engagement patterns. Furthermore, we analyze each factors significance in determining viewer engagement. Finally, we propose to build personalized models to better predict viewer engagement, with bootstrapping customized models for new viewers.

Convergence Dynamics of Graphical Congestion Games

Graphical congestion games provide powerful models for a wide range of scenarios where spatially distributed individuals share resources. Understanding when graphical congestion game dynamics converge to pure Nash equilibria yields important engineering insights into when spatially distributed individuals can reach a stable resource allocation. In this paper, we study the convergence dynamics of graphical congestion games where players can use multiple resources simultaneously. We show that when the players are free to use any subset of resources the game always converges to a pure Nash equilibrium in polynomial time via lazy best response updates. When the collection of sets of resources available to each player is a matroid, we show that pure Nash equilibria may not exist in the most general case. However, if the resources are homogenous, the game can converge to a Nash equilibrium in polynomial time.

Heart rate estimation using wrist-acquired photoplethysmography under different types of daily life motion artifact

Reflective wrist photoplethysmograph (PPG), obtained by a watch or wristband, can provide a natural and unconstrained way for daily life heart rate monitoring. However, reflective wrist PPG often suffers from poor signal quality and various distortions due to daily life motion artifact. In this paper, we analyze the influence of motion artifact on reflective wrist PPG signals, and propose a method to extract reliable heart rate from such distorted PPG signals. The proposed method consists of adaptive filtering, heart rate selection, and motion identification. Experimental results show that our proposed method can generate reliable heart rate values from wrist PPG signals with different types of motion artifact.

A privacy-aware framework for online advertisement targeting

With the prosperity of the Internet, many advertisers choose to deliver their advertisements by online targeting, where the ad broker is responsible for matching advertisements with users who are likely to be interested in the underlying products or services. However, this online advertisement targeting system requires user profile information and may fail due to privacy issues. In light of growing privacy concerns, we propose a privacy-aware framework for online advertisement targeting, where users are compensated for their privacy leakage and motivated to click more advertisements. In the framework, an ad broker pays a varying amount of money to users for clicking different advertisements due to distinct privacy leakage. Meanwhile advertisers send advertisements to the ad broker and determine the price per user click they need to pay. We model the interactions among advertisers, the ad broker and users as a three-stage game, where every player aims at maximizing its own utility, and Nash Equilibrium is achieved by backward induction. We further analyze the optimal strategies for advertisers, the ad broker and users. Numerical results have shown that the proposed privacy-aware framework is effective as it enables all advertisers, the ad broker and users to maximize their utilities in case of different levels of user privacy sensitivities. In addition, the proposed framework produces higher profits for advertisers and the ad broker than the traditional “paid to click” system.