Mobile crowdsensing has become increasingly popular due to its ability to collect a massive amount of data with the help of many individual smartphone users. A crowdsensing platform can utilize the collected data to extract effective information and provide diverse services. Designing an incentive mechanism to compensate the participants for their resources consumption is critical in attracting more participation. Offline incentive mechanism design has been widely studied in various crowdsensing applications, whereas the online scenario, is much more challenging due to the unavailability of future information when the platform makes user selection decisions. In this paper, we investigate the problem of online crowdsensing by considering a critical property that the values of users’ contributions decrease as time goes by. The time-discounting property is common in inter-temporal choice scenarios but has not been carefully addressed from the perspective of mechanism design. To handle this problem, we propose a new method to select users based on a time-dependent threshold, and present a strategy-proof framework where participants prefer to submit their true types, instead of manipulating the market by misreporting their private information. We consider two cases, one is that the total value is the summation of each participant's contributing value, the other is more general that the total value function is submodular. We call these two mechanisms TDM and TDMS, respectively. We prove that our two mechanisms can achieve computational efficiency, budget feasibility, strategy-proofness, and a constant competitive ratio, in the context of time-discounting values. By comparing our mechanisms with the state-of-the-art methods, we show that our design achieves better performance in terms of the total value.

In mobile crowdsensing, finding the best match between tasks and users is crucial to ensure both the quality and effectiveness of a crowdsensing system. Existing works usually assume a centralized task assignment by the crowdsensing platform, without addressing the need of fine-grained personalized task matching. In this paper, we argue that it is essential to match tasks to users based on a careful characterization of both the users' preference and reliability. To that end, we propose a personalized task recommender system for mobile crowdsensing, which recommends tasks to users based on a recommendation score that jointly takes each user's preference and reliability into consideration. We first present a hybrid preference metric to characterize users' preference by exploiting their implicit feedback. Then, to profile users' reliability levels, we formalize the problem as a semi-supervised learning model, and propose an efficient block coordinate descent algorithm to solve the problem. For some tasks that lack users' historical information, we further propose a matrix factorization method to infer the users' reliability levels on those tasks. We conduct extensive experiments to evaluate the performance of our system, and the evaluation results demonstrate that our system can achieve superior performance to the benchmarks in both user profiling and personalized task recommendation.

Social media has become one of the main channels for people to access and consume news, due to the rapidness and low cost of news dissemination on it. However, such properties of social media also make it a hotbed of fake news dissemination, bringing negative impacts on both individuals and society. Therefore, detecting fake news has become a crucial problem attracting tremendous research effort. Most existing methods of fake news detection are supervised, which require an extensive amount of time and labor to build a reliably annotated dataset. In search of an alternative, in this paper, we investigate if we could detect fake news in an unsupervised manner. We treat truths of news and users’ credibility as latent random variables, and exploit users’ engagements on social media to identify their opinions towards the authenticity of news. We leverage a Bayesian network model to capture the conditional dependencies among the truths of news, the users’ opinions, and the users’ credibility. To solve the inference problem, we propose an efficient collapsed Gibbs sampling approach to infer the truths of news and the users’ credibility without any labelled data. Experiment results on two datasets show that the proposed method significantly outperforms the compared unsupervised methods.

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.

In the past decade, with the rapid development of wireless communication and sensor technology, ubiquitous smartphones equipped with increasingly rich sensors have more powerful computing and sensing abilities. Thus, mobile crowdsensing has received extensive attentions from both industry and academia. Recently, plenty of mobile crowdsensing applications come forth, such as indoor positioning, environment monitoring, and transportation. However, most existing mobile crowdsensing systems lack vast user bases and thus urgently need appropriate incentive mechanisms to attract mobile users to guarantee the service quality. In this paper, we propose to incorporate sensing platform and social network applications, which already have large user bases to build a three-layer network model. Thus, we can publicize the sensing platform promptly in large scale and provide long-term guarantee of data sources. Based on a three-layer network model, we design incentive mechanisms for both intermediaries and the crowdsensing platform and provide a solution to cope with the problem of user overlapping among intermediaries. We theoretically prove the properties of our proposed incentive mechanisms, including incentive compatibility, individual rationality, and efficiency. Furthermore, we evaluate our incentive mechanisms by extensive simulations. Evaluation results validate the effectiveness and efficiency of our proposed mechanisms.

In mobile crowdsensing, finding the best match between tasks and users is crucial to ensure both the quality and effectiveness of a crowdsensing system. Existing works usually assume a centralized task assignment by the platform, without addressing the need of fine-grained personalized task matching. In this paper, we argue that it is essential to match tasks to users based on a careful characterization of both the users' preferences and reliability levels. To that end, we propose a personalized task recommender system for mobile crowdsensing, which recommends tasks to users based on a recommendation score that jointly takes each user's preference and reliability into consideration. We first present a simple but effective method to profile the users' preferences by exploiting the implicit feedback from their historical performance. Then, to profile the users' reliability levels, we formalize the problem as a semi-supervised learning model, and propose an efficient block coordinate descent algorithm to solve the problem. For some tasks that lack historical information, we further propose a matrix factorization method to infer the users' reliability on those tasks. We conduct extensive experiments to evaluate the performance of our system, and the evaluation results demonstrate that our system can achieve superior performance to our benchmarks in both user profiling and personalized task matching.

Mobile crowdsensing has become a novel and effective way to collect sensing data of people's surrounding environment. Among the data collected from multiple contributors, inconsistency often occurs due to noise, different sensor precision, or contributors' heterogeneous sensing behaviors. To tackle the data inconsistency, the problem of truth discovery has been widely studied to jointly infer the underlying ground truths and the contributors' data qualities. Existing truth discovery algorithms are based on the aggregation of large amounts of data so as to generate accurate estimations. However, in mobile crowdsensing, the collected data are usually sparsely distributed among a large sensing area, where each point of interest (PoI) may receive only a few sensing reports. In this case, traditional truth discovery algorithms may not provide an accurate truth estimation for each PoI. To tackle this challenge, in this paper, we propose an effective truth discovery method, namely Holmes, which takes advantage of the spatial correlations of the monitored phenomena by reusing each contributor's data for multiple nearby PoIs. We also take the issue of long-tail data phenomenon into the estimation of contributors' data quality levels, and proposed Holmes-LT. We further propose Holmes-OL to address the online streaming data scenarios. We evaluate the performance of our proposed algorithms on both real and synthetic datasets. The evaluation results demonstrate that our algorithms achieve significant performance improvements in terms of estimation accuracy over the existing truth discovery algorithms.

Mobile crowdsensing (MCS), as a novel and promising sensing paradigm, can utilize people's mobile devices to gather large amounts of data, such as environment information, traffic conditions, and human movements. The users of mobile crowdsensing are usually more capable than traditional sensors, and can reach locations that cannot be easily covered by static sensors, achieving more comprehensive coverage than traditional sensor networks. However, the uncertainty of the users' behaviors, as well as their uneven levels of qualities of contributed data, may also bring challenges to the coordination and supervision of mobile crowdsensing, causing the effectiveness of crowdsensing platform to significantly deviate from the theoretical optimum. In this paper, we address the users' uncertain behaviors by considering a quality- aware user steering problem, and propose to design user coordination algorithms so as to improve the mobile crowdsensing system's overall effectiveness. We jointly take two issues into account, i.e., data quality and coverage of sensing area, and propose a characterization of the system's effectiveness based on the two factors. Next, we consider optimizing the system's effectiveness in three different practical crowdsensing scenarios, and prove the NP-hardness of each of them. Given the infeasibility of calculating the global optimum in polynomial time, we propose three efficient algorithms to achieve suboptimal solutions to the three problems respectively. We extensively evaluate our proposed algorithms based on both real and synthetic datasets. The evaluation results show that our proposed algorithms can dramatically improve the crowdsensing system's effectiveness.

Due to the rapid proliferation of mobile devices, mobile crowdsensing (MCS) has become a novel and effective approach to collect large amounts of data. A critical problem in crowdsensing is to decide which mobile device users to select to perform sensing tasks, in order to maximize the platform's total obtained utility, within a fixed budget and limited time. Since the users' utilities are unknown a priori, the platform is facing a challenge of how to effectively learn the users' utilities and make optimal user selection decisions. Previous works usually considered a budgeted multi-armed bandit (MAB) approaches, but they cannot effectively address the joint constraints of fixed budget and limited time. In this paper, we build a Budgeted and Time-Limited Combinatorial Multi-Armed Bandit (BT-CMAB) model to tackle this problem, and propose an effective UCB-based user selection algorithm, namely BT- CUCB. We prove that the BT-CUCB algorithm can achieve a zero beta-regret. The evaluation results show that our proposed algorithm effectively utilize budget and time to achieve a low regret.

In the past decade, with the rapid development of wireless communication and sensor technology, ubiquitous smartphones equipped with increasingly rich sensors have more powerful computing and sensing abilities. Thus, mobile crowdsensing has received extensive attentions from both industry and academia. Recently, plenty of mobile crowdsensing applications come forth, such as indoor positioning, environment monitoring, transportation, and so on. However, most existing mobile crowdsensing systems lack of vast user bases, and thus urgently need appropriate incentive mechanisms to attract mobile users to guarantee the service quality. In this paper, we propose to incorporate sensing platform and social network applications, which already have large user bases to build a three-layer network model. Thus, we can publicize the sensing platform promptly in large scale, and provide longterm guarantee of data sources. Based on a three-layer network model, we design incentive mechanisms for both intermediaries and the crowdsensing platform, and provide a solution to cope with the problem of user overlapping among intermediaries. We indicate the properties of our proposed incentive mechanisms, including incentive compatibility, individual rationality, and efficiency.

Mobile crowdsensing has become a novel and promising paradigm in collecting, analyzing, and exploiting massive amounts of data. However, the issue of data quality has not been carefully addressed. Low quality data contributions undermine the effectiveness and prospects of crowdsensing, and thus motivate the need for approaches to guarantee the high quality of the contributed data. In this paper, we integrate quality estimation and monetary incentive, and propose a quality-based truth estimation and surplus sharing method for crowdsensing. Specifically, we design an unsupervised learning approach to quantify the users' data qualities and long-term reputations, and exploit an outlier detection technique to filter out anomalous data items. Furthermore, we model the process of surplus sharing as a co-operative game, and propose a Shapley value-based method to determine each user's payment. We have conducted a real crowdsensing experiment and a large-scale simulation to evaluate our method. The evaluation results show that our approach achieves good performance in terms of both quality estimation and surplus sharing.

Crowdsensing has become increasingly popular due to its ability to collect a massive amount of real-time data with the help of many individual smartphone users. A crowdsensing platform can utilize the collected data to extract effective information and provide services to service requesters. Due to the rationality of smartphone users, designing an incentive mechanism to compensate the participants for their resources consumption is critical in attracting more participation. Offline incentive mechanism design has been widely studied in various crowdsensing applications, whereas the online scenario, is much more challenging due to the unavailability of future information when the platform has to make user selection decisions. In this paper, we investigate the problem of online crowdsensing by considering a critical property that the values of users' contributions decrease as time goes by. The time- discounting property is common in inter-temporal choice scenarios but has not been carefully addressed in mechanism design perspective. To handle this problem, we propose a new method to select users based on a time-related threshold, and present a strategyproof framework where participants prefer to submit their true types, instead of manipulating the market by misreporting their private information. We prove that our mechanism can achieve computational efficiency, budget feasibility, strategy-proofness, and a constant competitive ratio. By comparing our mechanism with two heuristic benchmarks, we show that our design achieves great performance in terms of the total obtained value.

Online advertising has become a tremendous business, and continues to grow rapidly. There are mainly two ways to distribute ads in online advertising: contracts and auctions. Previous researches usually focus on ad allocation through either contracts or auctions, while the problem of allocating ads simultaneously through both methods has not been carefully addressed. Jointly considering these two methods enables the publisher to globally allocates ads in a more efficient way, conforming to the actual needs of online advertising, but rises several critical challenges that cannot be solved by existing approaches. Our work aims to fill this gap and takes a deep investigation into this problem. In this paper, we consider the revenue maximization problem in online advertising, where the publisher sells ads to advertisers and also needs to satisfy the demands of the contracts. We discuss and tackle the emerged challenges based on a more practical contract model, and propose a bidding strategy for the publisher to satisfy the contract and maximize her total revenue. Our evaluation results demonstrate the good performance of our algorithm in fulfilling the contract and maximizing the revenue.

Mobile crowdsensing has become a novel and promising paradigm in collecting environmental data. A critical problem in improving the QoS of crowdsensing is to decide which users to select to perform sensing tasks, in order to obtain the most informative data, while maintaining the total sensing costs below a given budget. The key challenges lie in (i) finding an effective measure of the informativeness of users' data, (ii) learning users' sensing costs which are unknown a priori, and (iii) designing efficient user selection algorithms that achieve low-regret guarantees. In this paper, we build Gaussian Processes (GPs) to model spatial locations, and provide a mutual information-based criteria to characterize users' informativeness. To tackle the second and third challenges, we model the problem as a budgeted multi-armed bandit (MAB) problem based on stochastic assumptions, and propose an algorithm with theoretically proven low-regret guarantee. Our theoretical analysis and evaluation results both demonstrate that our algorithm can efficiently select most informative users under stringent constraints.

Participatory sensing has become a novel and promising paradigm in environmental data collection. However, the issue of data quality has not been carefully addressed. Low quality data contributions may undermine the effectiveness and prospects of participatory sensing, and thus motivates the need for approaches to guarantee the high quality of the contributed data. In this paper, we integrate quality estimation and monetary incentive, and propose a quality-based surplus sharing method for participatory sensing. Specifically, we design an unsupervised learning approach to quantify the users' data qualities and long-term reputations, and exploit an outlier detection technique to filter out anomalous data items. Furthermore, we model the process of surplus sharing as a cooperative game, and propose a Shapley value-based method to determine each user's payment. We have conducted a participatory sensing experiment, and the experiment results show that our approach achieves good performance in terms of both quality estimation and surplus sharing.

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. We also implement FAITH and evaluate its performance in various setups. Evaluation results show that FAITH achieves superior performance compared with the Nash equilibrium based approach.