Mobile sensing has become a promising paradigm for mobile users to obtain information by task crowd- sourcing. However, due to the social preferences of mobile users, the quality of sensing reports may be impacted by the underlying social attributes and selfishness of individuals. Therefore, it is crucial to con- sider the social impacts and trustworthiness of mobile users when selecting task participants in mobile sensing. In this paper, we propose a Social Aware Crowdsourcing with Reputation Management (SACRM) scheme to select the well-suited participants and allocate the task rewards in mobile sensing. Specifically, we consider the social attributes, task delay and reputation in crowdsourcing and propose a participant selection scheme to choose the well-suited participants for the sensing task under a fixed task budget. A report assessment and rewarding scheme is also introduced to measure the quality of the sensing reports and allocate the task rewards based the assessed report quality. In addition, we develop a reputation management scheme to evaluate the trustworthiness and cost performance ratio of mobile users for par- ticipant selection. Theoretical analysis and extensive simulations demonstrate that SACRM can efficiently improve the crowdsourcing utility and effectively stimulate the participants to improve the quality of their sensing reports.
As an emerging information collection mechanism, crowdsourcing has been extensively studied in mobile sensing. Most of the related works focus on studying the incentive mechanisms to stimulate the participation of mobile users for crowdsourcing @cite_21 @cite_4 @cite_1 @cite_10 @cite_11 @cite_12 .
@cite_4 @cite_21 @cite_1 @cite_10 @cite_12 @cite_11
2037892770 2029396991 1552963716 2122005484
Crowd sensing is a new paradigm which leverages the ubiquity of sensor-equipped mobile devices to collect data. To achieve good quality for crowd sensing, incentive mechanisms are indispensable to attract more participants. Most of existing mechanisms focus on the expected utility prior to sensing, ignoring the risk of low quality solution and privacy leakage. Traditional incentive mechanisms such as the Vickrey-Clarke-Groves (VCG) mechanism and its variants are not applicable here. In this paper, to address these challenges, we propose a behavior based incentive mechanism for crowd sensing applications with budget constraints by applying sequential all-pay auctions in mobile social networks (MSNs), not only to consider the effects of extensive user participation, but also to maximize high quality of sensing data submission for the platform (crowd sensing organizer) under the budget constraints, where users arrive in a sequential order. Through an extensive simulation, results indicate that incentive mechanisms in our proposed framework outperform existing solutions. Mobile crowdsourced sensing (MCS) is a new paradigm which takes advantage of pervasive smartphones to efficiently collect data, enabling numerous novel applications. To achieve good service quality for a MCS application, incentive mechanisms are necessary to attract more user participation. Most of existing mechanisms apply only for the offline scenario where all users' information are known a priori. On the contrary, we focus on a more realistic scenario where users arrive one by one online in a random order. Based on the online auction model, we investigate the problem that users submit their private types to the crowdsourcer when arrive, and the crowdsourcer aims at selecting a subset of users before a specified deadline for maximizing the value of services (assumed to be a non-negative monotone submodular function) provided by selected users under a budget constraint. We design two online mechanisms, OMZ and OMG, satisfying the computational efficiency, individual rationality, budget feasibility, truthfulness, consumer sovereignty and constant competitiveness under the zero arrival-departure interval case and a more general case, respectively. Through extensive simulations, we evaluate the performance and validate the theoretical properties of our online mechanisms. Mobile phone sensing has become increasingly popular since it can collect and analyze real-time data anywhere anytime, especially with the help of mobile phone users via crowdsourcing. In order to stabilize the mobile crowdsourcing at a massive scale, incentive mechanisms are needed not only to stimulate the users of mobile phones to participate in sensing, but also to incentivize the organizer of the sensing tasks with maximum service time and payoff. In this paper, we study a practical problem in mobile sensing, i.e., sensing time uncertainty, which may lead to failures of existing incentive mechanisms. In particular, we model this problem as a perturbed Stackelberg game in which mobile phone users may actually conduct sensing tasks during different periods of time rather than what they intend, like through a trembling hand. We find that there exist Trembling-Hand Perfect Equilibria (THP) given proper rewards. After characterizing THPs in this game with rigorous analysis, we design incentive mechanisms that can achieve a THP with the maximum system wide total sensing time and maximum platform utility. We finally verify the correctness and efficiency of our proposed incentive mechanisms and algorithms through extensive experiments. Fueled by the widespread adoption of sensor-enabled smartphones, mobile crowdsourcing is an area of rapid innovation. Many crowd-powered sensor systems are now part of our daily life -- for example, providing highway congestion information. However, participation in these systems can easily expose users to a significant drain on already limited mobile battery resources. For instance, the energy burden of sampling certain sensors (such as WiFi or GPS) can quickly accumulate to levels users are unwilling to bear. Crowd system designers must minimize the negative energy side-effects of participation if they are to acquire and maintain large-scale user populations. To address this challenge, we propose Piggyback CrowdSensing (PCS), a system for collecting mobile sensor data from smartphones that lowers the energy overhead of user participation. Our approach is to collect sensor data by exploiting Smartphone App Opportunities -- that is, those times when smartphone users place phone calls or use applications. In these situations, the energy needed to sense is lowered because the phone need no longer be woken from an idle sleep state just to collect data. Similar savings are also possible when the phone either performs local sensor computation or uploads the data to the cloud. To efficiently use these sporadic opportunities, PCS builds a lightweight, user-specific prediction model of smartphone app usage. PCS uses this model to drive a decision engine that lets the smartphone locally decide which app opportunities to exploit based on expected energy quality trade-offs. We evaluate PCS by analyzing a large-scale dataset (containing 1,320 smartphone users) and building an end-to-end crowdsourcing application that constructs an indoor WiFi localization database. Our findings show that PCS can effectively collect large-scale mobile sensor datasets (e.g., accelerometer, GPS, audio, image) from users while using less energy (up to 90 depending on the scenario) compared to a representative collection of existing approaches.