Incentive Mechanism Design in Mobile Crowdsensing Systems

In the past few years, the popularity of Mobile Crowdsensing Systems (MCSs) has been greatly prompted, in which sensory data can be ubiquitously collected and shared by mobile devices in a distributed fashion. Typically, a MCS consists of a cloud platform, sensing tasks, and mobile users equipped with mobile devices, in which the mobile users carry out sensing tasks and receive monetary rewards as compensation for resource consumption ( e.g., energy, bandwidth, and computation) and risk of privacy leakage ( e.g., location exposure). Compared with traditional mote-class sensor networks, MCSs can reduce the cost of deploying specialized sensing infrastructures and enable many applications that require resources and sensing modalities beyond the current mote-class sensor processes as today’s mobile devices (smartphones (iPhones, Sumsung Galaxy), tablets (iPad) and vehicle-embedded sensing devices (GPS)) integrate more computing, communication, and storage resources than traditional mote-class sensors. The current applications of MCSs include traffic congestion detection, wireless indoor localization, pollution monitoring, etc . There is no doubt that one of the most significant characteristics of MCSs is the active involvement of mobile users to collect and share sensory data. In this dissertation, we study the incentive mechanism design in mobile crowdsensing system with consideration of economic properties. Firstly, we investigate the problem of joining sensing task assignment and scheduling in MCSs with the following three considerations: i) partial fulfillment, ii) attribute diversity, and iii) price diversity. Then, we design a distributed auction framework to allow each task owner to independently process its local auction without collecting global information in a MCS, reducing communication cost. Next, we propose a cost-preferred auction scheme (CPAS) to assign each winning mobile user one or more sub- working time durations and a time schedule-preferred auction scheme (TPAS) to allocate each winning mobile user a continuous working time duration. Secondly, we focus on the design of an incentive mechanism for an MCS to minimize the social cost. The social cost represents the total cost of mobile devices when all tasks published by the MCS are finished. We first present the working process of a MCS, and then build an auction market for the MCS where the MCS platform acts as an auctioneer and users with mobile devices act as bidders. Depending on the different requirements of the MCS platform, we design a Vickrey-Clarke-Groves (VCG)-based auction mechanism for the continuous working pattern and a suboptimal auction mechanism for the discontinuous working pattern. Both of them can ensure that the bidding of users are processed in a truthful way and the utilities of users are maximized. Through rigorous theoretical analysis and comprehensive simulations, we can prove that these incentive mechanisms satisfy economic properties and can be implemented in reasonable time complexcity. Next, we discuss the importance of fairness and unconsciousness of MCS surveillance applications. Then, we propose offline and online incentive mechanisms with fair task scheduling based on the proportional share allocation rules. Furthermore, to have more sensing tasks done over time dimension, we relax the truthfulness and unconsciousness property requirements and design a (ε, μ)-unconsciousness online incentive mechanism. Real map data are used to validate these proposed incentive mechanisms through extensive simulations. Finally, future research topics are proposed to complete the dissertation

An Edge Correlation Based Differentially Private Network Data Release Method

Differential privacy (DP) provides a rigorous and provable privacy guarantee and assumes adversaries’ arbitrary background knowledge, which makes it distinct from prior work in privacy preserving. However, DP cannot achieve claimed privacy guarantees over datasets with correlated tuples. Aiming to protect whether two individuals have a close relationship in a correlated dataset corresponding to a weighted network, we propose a differentially private network data release method, based on edge correlation, to gain the tradeoff between privacy and utility. Specifically, we first extracted the Edge Profile (PF) of an edge from a graph, which is transformed from a raw correlated dataset. Then, edge correlation is defined based on the PFs of both edges via Jenson-Shannon Divergence (JS-Divergence). Secondly, we transform a raw weighted dataset into an indicated dataset by adopting a weight threshold, to satisfy specific real need and decrease query sensitivity. Furthermore, we propose ϵ-correlated edge differential privacy (CEDP), by combining the correlation analysis and the correlated parameter with traditional DP. Finally, we propose network data release (NDR) algorithm based on the ϵ-CEDP model and discuss its privacy and utility. Extensive experiments over real and synthetic network datasets show the proposed releasing method provides better utilities while maintaining privacy guarantee

Multi-Scale Superpixel-Guided Structural Profiles for Hyperspectral Image Classification

Hyperspectral image classification has received a lot of attention in the remote sensing field. However, most classification methods require a large number of training samples to obtain satisfactory performance. In real applications, it is difficult for users to label sufficient samples. To overcome this problem, in this work, a novel multi-scale superpixel-guided structural profile method is proposed for the classification of hyperspectral images. First, the spectral number (of the original image) is reduced with an averaging fusion method. Then, multi-scale structural profiles are extracted with the help of the superpixel segmentation method. Finally, the extracted multi-scale structural profiles are fused with an unsupervised feature selection method followed by a spectral classifier to obtain classification results. Experiments on several hyperspectral datasets verify that the proposed method can produce outstanding classification effects in the case of limited samples compared to other advanced classification methods. The classification accuracies obtained by the proposed method on the Salinas dataset are increased by 43.25%, 31.34%, and 46.82% in terms of overall accuracy (OA), average accuracy (AA), and Kappa coefficient compared to recently proposed deep learning methods

Truthful Incentive Mechanisms for Social Cost Minimization in Mobile Crowdsourcing Systems

With the emergence of new technologies, mobile devices are capable of undertaking computational and sensing tasks. A large number of users with these mobile devices promote the formation of the Mobile Crowdsourcing Systems (MCSs). Within a MCS, each mobile device can contribute to the crowdsourcing platform and get rewards from it. In order to achieve better performance, it is important to design a mechanism that can attract enough participants with mobile devices and then allocate the tasks among participants efficiently. In this paper, we are interested in the investigation of tasks allocation and price determination in MCSs. Two truthful auction mechanisms are proposed for different working patterns. A Vickrey–Clarke–Groves (VCG)-based auction mechanism is proposed to the continuous working pattern, and a suboptimal auction mechanism is introduced for the discontinuous working pattern. Further analysis shows that the proposed mechanisms have the properties of individual rationality and computational efficiencies. Experimental results suggest that both mechanisms guarantee all the mobile users bidding with their truthful values and the optimal maximal social cost can be achieved in the VCG-based auction mechanism

Long-term follow-up of donor-derived CD7 CAR T-cell therapy in patients with T-cell acute lymphoblastic leukemia

Abstract Background Donor-derived CD7-directed chimeric antigen receptor (CAR) T cells showed feasibility and early efficacy in patients with refractory or relapsed T-cell acute lymphoblastic leukemia (r/r T-ALL), in a previous phase I trial report, at a median follow-up of 6.3 months. Here we report long-term safety and activity of the therapy after a 2-year follow-up. Methods Participants received CD7-directed CAR T cells derived from prior stem cell transplantation (SCT) donors or from HLA-matched new donors after lymphodepletion. The target dose was 1 × 106 (± 30%) CAR T cells per kg of patient weight. The primary endpoint was safety with efficacy secondary. This report focuses on the long-term follow-up and discusses them in the context of previously reported early outcomes. Results Twenty participants were enrolled and received infusion with CD7 CAR T cells. After a median follow-up time of 27.0 (range, 24.0–29.3) months, the overall response rate and complete response rate were 95% (19/20 patients) and 85% (17/20 patients), respectively, and 35% (7/20) of patients proceeded to SCT. Six patients experienced disease relapse with a median time-to-relapse of 6 (range, 4.0–10.9) months, and 4 of these 6 patients were found to have lost CD7 expression on tumor cells. Progression-free survival (PFS) and overall survival (OS) rates 24 months after treatment were respectively 36.8% (95% CI, 13.8–59.8%) and 42.3% (95% CI, 18.8–65.8%), with median PFS and OS of respectively 11.0 (95% CI, 6.7–12.5) months and 18.3 (95% CI, 12.5–20.8) months. Previously reported short-term adverse events ( 30 days after treatment included five infections and one grade 4 intestinal GVHD. Despite good CD7 CAR T-cell persistence, non-CAR T and natural killer cells were predominantly CD7-negative and eventually returned to normal levels in about half of the participants. Conclusions In this 2-year follow-up analysis, donor-derived CD7 CAR T-cell treatment demonstrated durable efficacy in a subset of patients with r/r T-ALL. Disease relapse was the main cause of treatment failure, and severe infection was a noteworthy late-onset adverse event. Trial registration ChiCTR2000034762