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

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

Vehicle as a Service (VaaS): Leverage Vehicles to Build Service Networks and Capabilities for Smart Cities

Smart cities demand resources for rich immersive sensing, ubiquitous communications, powerful computing, large storage, and high intelligence (SCCSI) to support various kinds of applications, such as public safety, connected and autonomous driving, smart and connected health, and smart living. At the same time, it is widely recognized that vehicles such as autonomous cars, equipped with significantly powerful SCCSI capabilities, will become ubiquitous in future smart cities. By observing the convergence of these two trends, this article advocates the use of vehicles to build a cost-effective service network, called the Vehicle as a Service (VaaS) paradigm, where vehicles empowered with SCCSI capability form a web of mobile servers and communicators to provide SCCSI services in smart cities. Towards this direction, we first examine the potential use cases in smart cities and possible upgrades required for the transition from traditional vehicular ad hoc networks (VANETs) to VaaS. Then, we will introduce the system architecture of the VaaS paradigm and discuss how it can provide SCCSI services in future smart cities, respectively. At last, we identify the open problems of this paradigm and future research directions, including architectural design, service provisioning, incentive design, and security & privacy. We expect that this paper paves the way towards developing a cost-effective and sustainable approach for building smart cities.Comment: 32 pages, 11 figure

Security and Privacy in Dynamic Spectrum Access: Challenges and Solutions

abstract: Dynamic spectrum access (DSA) has great potential to address worldwide spectrum shortage by enhancing spectrum efficiency. It allows unlicensed secondary users to access the under-utilized spectrum when the primary users are not transmitting. On the other hand, the open wireless medium subjects DSA systems to various security and privacy issues, which might hinder the practical deployment. This dissertation consists of two parts to discuss the potential challenges and solutions. The first part consists of three chapters, with a focus on secondary-user authentication. Chapter One gives an overview of the challenges and existing solutions in spectrum-misuse detection. Chapter Two presents SpecGuard, the first crowdsourced spectrum-misuse detection framework for DSA systems. In SpecGuard, three novel schemes are proposed for embedding and detecting a spectrum permit at the physical layer. Chapter Three proposes SafeDSA, a novel PHY-based scheme utilizing temporal features for authenticating secondary users. In SafeDSA, the secondary user embeds his spectrum authorization into the cyclic prefix of each physical-layer symbol, which can be detected and authenticated by a verifier. The second part also consists of three chapters, with a focus on crowdsourced spectrum sensing (CSS) with privacy consideration. CSS allows a spectrum sensing provider (SSP) to outsource the spectrum sensing to distributed mobile users. Without strong incentives and location-privacy protection in place, however, mobile users are reluctant to act as crowdsourcing workers for spectrum-sensing tasks. Chapter Four gives an overview of the challenges and existing solutions. Chapter Five presents PriCSS, where the SSP selects participants based on the exponential mechanism such that the participants' sensing cost, associated with their locations, are privacy-preserved. Chapter Six further proposes DPSense, a framework that allows the honest-but-curious SSP to select mobile users for executing spatiotemporal spectrum-sensing tasks without violating the location privacy of mobile users. By collecting perturbed location traces with differential privacy guarantee from participants, the SSP assigns spectrum-sensing tasks to participants with the consideration of both spatial and temporal factors. Through theoretical analysis and simulations, the efficacy and effectiveness of the proposed schemes are validated.Dissertation/ThesisDoctoral Dissertation Electrical Engineering 201