CENTURION: Incentivizing Multi-Requester Mobile Crowd Sensing

The recent proliferation of increasingly capable mobile devices has given rise to mobile crowd sensing (MCS) systems that outsource the collection of sensory data to a crowd of participating workers that carry various mobile devices. Aware of the paramount importance of effectively incentivizing participation in such systems, the research community has proposed a wide variety of incentive mechanisms. However, different from most of these existing mechanisms which assume the existence of only one data requester, we consider MCS systems with multiple data requesters, which are actually more common in practice. Specifically, our incentive mechanism is based on double auction, and is able to stimulate the participation of both data requesters and workers. In real practice, the incentive mechanism is typically not an isolated module, but interacts with the data aggregation mechanism that aggregates workers' data. For this reason, we propose CENTURION, a novel integrated framework for multi-requester MCS systems, consisting of the aforementioned incentive and data aggregation mechanism. CENTURION's incentive mechanism satisfies truthfulness, individual rationality, computational efficiency, as well as guaranteeing non-negative social welfare, and its data aggregation mechanism generates highly accurate aggregated results. The desirable properties of CENTURION are validated through both theoretical analysis and extensive simulations

Cheating-Resilient Incentive Scheme for Mobile Crowdsensing Systems

Mobile Crowdsensing is a promising paradigm for ubiquitous sensing, which explores the tremendous data collected by mobile smart devices with prominent spatial-temporal coverage. As a fundamental property of Mobile Crowdsensing Systems, temporally recruited mobile users can provide agile, fine-grained, and economical sensing labors, however their self-interest cannot guarantee the quality of the sensing data, even when there is a fair return. Therefore, a mechanism is required for the system server to recruit well-behaving users for credible sensing, and to stimulate and reward more contributive users based on sensing truth discovery to further increase credible reporting. In this paper, we develop a novel Cheating-Resilient Incentive (CRI) scheme for Mobile Crowdsensing Systems, which achieves credibility-driven user recruitment and payback maximization for honest users with quality data. Via theoretical analysis, we demonstrate the correctness of our design. The performance of our scheme is evaluated based on extensive realworld trace-driven simulations. Our evaluation results show that our scheme is proven to be effective in terms of both guaranteeing sensing accuracy and resisting potential cheating behaviors, as demonstrated in practical scenarios, as well as those that are intentionally harsher

Incentive Mechanisms for Participatory Sensing: Survey and Research Challenges

Participatory sensing is a powerful paradigm which takes advantage of smartphones to collect and analyze data beyond the scale of what was previously possible. Given that participatory sensing systems rely completely on the users' willingness to submit up-to-date and accurate information, it is paramount to effectively incentivize users' active and reliable participation. In this paper, we survey existing literature on incentive mechanisms for participatory sensing systems. In particular, we present a taxonomy of existing incentive mechanisms for participatory sensing systems, which are subsequently discussed in depth by comparing and contrasting different approaches. Finally, we discuss an agenda of open research challenges in incentivizing users in participatory sensing.Comment: Updated version, 4/25/201

MODELING AND RESOURCE ALLOCATION IN MOBILE WIRELESS NETWORKS

We envision that in the near future, just as Infrastructure-as-a-Service (IaaS), radios and radio resources in a wireless network can also be provisioned as a service to Mobile Virtual Network Operators (MVNOs), which we refer to as Radio-as-a-Service (RaaS). In this thesis, we present a novel auction-based model to enable fair pricing and fair resource allocation according to real-time needs of MVNOs for RaaS. Based on the proposed model, we study the auction mechanism design with the objective of maximizing social welfare. We present an Integer Linear Programming (ILP) and Vickrey-Clarke-Groves (VCG) based auction mechanism for obtaining optimal social welfare. To reduce time complexity, we present a polynomial-time greedy mechanism for the RaaS auction. Both methods have been formally shown to be truthful and individually rational. Meanwhile, wireless networks have become more and more advanced and complicated, which are generating a large amount of runtime system statistics. In this thesis, we also propose to leverage the emerging deep learning techniques for spatiotemporal modeling and prediction in cellular networks, based on big system data. We present a hybrid deep learning model for spatiotemporal prediction, which includes a novel autoencoder-based deep model for spatial modeling and Long Short-Term Memory units (LSTMs) for temporal modeling. The autoencoder-based model consists of a Global Stacked AutoEncoder (GSAE) and multiple Local SAEs (LSAEs), which can offer good representations for input data, reduced model size, and support for parallel and application-aware training. Mobile wireless networks have become an essential part in wireless networking with the prevalence of mobile device usage. Most mobile devices have powerful sensing capabilities. We consider a general-purpose Mobile CrowdSensing(MCS) system, which is a multi-application multi-task system that supports a large variety of sensing applications. In this thesis, we also study the quality of the recruited crowd for MCS, i.e., quality of services/data each individual mobile user and the whole crowd are potentially capable of providing. Moreover, to improve flexibility and effectiveness, we consider fine-grained MCS, in which each sensing task is divided into multiple subtasks and a mobile user may make contributions to multiple subtasks. More specifically, we first introduce mathematical models for characterizing the quality of a recruited crowd for different sensing applications. Based on these models, we present a novel auction formulation for quality-aware and fine- grained MCS, which minimizes the expected expenditure subject to the quality requirement of each subtask. Then we discuss how to achieve the optimal expected expenditure, and present a practical incentive mechanism to solve the auction problem, which is shown to have the desirable properties of truthfulness, individual rationality and computational efficiency. In a MCS system, a sensing task is dispatched to many smartphones for data collections; in the meanwhile, a smartphone undertakes many different sensing tasks that demand data from various sensors. In this thesis, we also consider the problem of scheduling different sensing tasks assigned to a smartphone with the objective of minimizing sensing energy consumption while ensuring Quality of SenSing (QoSS). First, we consider a simple case in which each sensing task only requests data from a single sensor. We formally define the corresponding problem as the Minimum Energy Single-sensor task Scheduling (MESS) problem and present a polynomial-time optimal algorithm to solve it. Furthermore, we address a more general case in which some sensing tasks request multiple sensors to re- port their measurements simultaneously. We present an Integer Linear Programming (ILP) formulation as well as two effective polynomial-time heuristic algorithms, for the corresponding Minimum Energy Multi-sensor task Scheduling (MEMS) problem. Numerical results are presented to confirm the theoretical analysis of our schemes, and to show strong performances of our solutions, compared to several baseline methods