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

PREFERENCE-AWARE TASK ASSIGNMENT IN MOBILE CROWDSENSING

Mobile crowdsensing (MCS) is an emerging form of crowdsourcing, which facilitates the sensing data collection with the help of mobile participants (workers). A central problem in MCS is the assignment of sensing tasks to workers. Existing work in the field mostly seek a system-level optimization of task assignments (e.g., maximize the number of completed tasks, minimize the total distance traveled by workers) without considering individual preferences of task requesters and workers. However, users may be reluctant to participate in MCS campaigns that disregard their preferences. In this dissertation, we argue that user preferences should be a primary concern in the task assignment process for an MCS campaign to be effective, and we develop preference-aware task assignment (PTA) mechanisms for five different MCS settings. Since the PTA problem is computationally hard in most of these settings, we present efficient approximation and heuristic algorithms. Extensive simulations performed on synthetic and real data sets validate our theoretical results, and demonstrate that the proposed algorithms produce near-optimal solutions in terms of preference-awareness, outperforming the state-of-the-art assignment algorithms by a wide margin in most cases

Differentially Private Mobile Crowd Sensing Considering Sensing Errors

An increasingly popular class of software known as participatory sensing, or mobile crowdsensing, is a means of collecting people’s surrounding information via mobile sensing devices. To avoid potential undesired side effects of this data analysis method, such as privacy violations, considerable research has been conducted over the last decade to develop participatory sensing that looks to preserve privacy while analyzing participants’ surrounding information. To protect privacy, each participant perturbs the sensed data in his or her device, then the perturbed data is reported to the data collector. The data collector estimates the true data distribution from the reported data. As long as the data contains no sensing errors, current methods can accurately evaluate the data distribution. However, there has so far been little analysis of data that contains sensing errors. A more precise analysis that maintains privacy levels can only be achieved when a variety of sensing errors are considered

Incentive mechanism design for mobile crowd sensing systems

The recent proliferation of increasingly capable and affordable mobile devices with a plethora of on-board and portable sensors that pervade every corner of the world has given rise to the fast development and wide deployment of mobile crowd sensing (MCS) systems. Nowadays, applications of MCS systems have covered almost every aspect of people's everyday living and working, such as ambient environment monitoring, healthcare, floor plan reconstruction, smart transportation, indoor localization, and many others. Despite their tremendous benefits, MCS systems pose great new research challenges, of which, this thesis targets one important facet, that is, to effectively incentivize (crowd) workers to achieve maximum participation in MCS systems. Participating in crowd sensing tasks is usually a costly procedure for individual workers. On one hand, it consumes workers' resources, such as computing power, battery, and so forth. On the other hand, a considerable portion of sensing tasks require the submission of workers' sensitive and private information, which causes privacy leakage for participants. Clearly, the power of crowd sensing could not be fully unleashed, unless workers are properly incentivized to participate via satisfactory rewards that effectively compensate their participation costs. Targeting the above challenge, in this thesis, I present a series of novel incentive mechanisms, which can be utilized to effectively incentivize worker participation in MCS systems. The proposed mechanisms not only incorporate workers' quality of information in order to selectively recruit relatively more reliable workers for sensing, but also preserve workers' privacy so as to prevent workers from being disincentivized by excessive privacy leakage. I demonstrate through rigorous theoretical analyses and extensive simulations that the proposed incentive mechanisms bear many desirable properties theoretically, and have great potential to be practically applied

Auction-Based Efficient Online Incentive Mechanism Designs in Wireless Networks

Recently, wide use of mobile devices and applications, such as YouTube and Twitter, has facilitated every aspect of our daily lives. Meanwhile, it has also posed great challenges to enable resource-demanding users to successfully access networks. Thus, in order to enlarge network capacity and fully make use of vacant resources, new communication architectures emerge, such as D2D communications, edge computing, and crowdsourcing, all of which ask for involvement of end mobile users in assisting transmission, computation, or network management. However, end mobile users are not always willing to actively provide such sharing services if no reimbursements are provided as they need to consume their own computation and communication resources. Besides, since mobile users are not always stationary, they can opt-in and opt-out the network for their own convenience. Thus, an important practical characteristic of wireless networks, i.e., the mobility of mobile users cannot be ignored, which means that the demands of mobile users span over a period of time. As one of promising solutions, the online incentive mechanism design has been introduced in wireless networks in order to motivate the participation of more mobile users under a dynamic environment. In this thesis, with the analyses of each stakeholder's economic payoffs in wireless networks, the auction-based online incentive mechanisms are proposed to achieve resource allocations, participant selections, and payment determinations in two wireless networks, i.e., Crowdsensing and mobile edge computing. In particular, i) an online incentive mechanism is designed to guarantee Quality of Information of each arriving task in mobile crowdsensing networks, followed by an enhanced online strategy which could further improves the competitive ratio; ii) an online incentive mechanism jointly considering communication and computation resource allocations in collaborative edge computing networks is proposed based on the primal-dual theory; iii) to deal with the nonlinear issue in edge computing networks, an nonlinear online incentive mechanism under energy budget constraints of mobile users is designed based on the Maximal-in-Distributional Range framework; and iv) inspired by the recent development of deep learning techniques, a deep incentive mechanism with the budget balance of each mobile user is proposed to maximize the net revenue of service providers by leveraging the multi-task machine learning model. Both theoretical analyses and numerical results demonstrate the effectiveness of the designed mechanisms

Location reliability and gamification mechanisms for mobile crowd sensing

People-centric sensing with smart phones can be used for large scale sensing of the physical world by leveraging the sensors on the phones. This new type of sensing can be a scalable and cost-effective alternative to deploying static wireless sensor networks for dense sensing coverage across large areas. However, mobile people-centric sensing has two main issues: 1) Data reliability in sensed data and 2) Incentives for participants. To study these issues, this dissertation designs and develops McSense, a mobile crowd sensing system which provides monetary and social incentives to users. This dissertation proposes and evaluates two protocols for location reliability as a step toward achieving data reliability in sensed data, namely, ILR (Improving Location Reliability) and LINK (Location authentication through Immediate Neighbors Knowledge). ILR is a scheme which improves the location reliability of mobile crowd sensed data with minimal human efforts based on location validation using photo tasks and expanding the trust to nearby data points using periodic Bluetooth scanning. LINK is a location authentication protocol working independent of wireless carriers, in which nearby users help authenticate each other’s location claims using Bluetooth communication. The results of experiments done on Android phones show that the proposed protocols are capable of detecting a significant percentage of the malicious users claiming false location. Furthermore, simulations with the LINK protocol demonstrate that LINK can effectively thwart a number of colluding user attacks. This dissertation also proposes a mobile sensing game which helps collect crowd sensing data by incentivizing smart phone users to play sensing games on their phones. We design and implement a first person shooter sensing game, “Alien vs. Mobile User”, which employs techniques to attract users to unpopular regions. The user study results show that mobile gaming can be a successful alternative to micro-payments for fast and efficient area coverage in crowd sensing. It is observed that the proposed game design succeeds in achieving good player engagement

Failure of Equilibrium Selection Methods for Multiple-Principal, Multiple-Agent Problems with Non-Rivalrous Goods: An Analysis of Data Markets

The advent of machine learning tools has led to the rise of data markets. These data markets are characterized by multiple data purchasers interacting with a set of data sources. Data sources have more information about the quality of data than the data purchasers; additionally, data itself is a non-rivalrous good that can be shared with multiple parties at negligible marginal cost. In this paper, we study the multiple-principal, multiple-agent problem with non-rivalrous goods. Under the assumption that the principal's payoff is quasilinear in the payments given to agents, we show that there is a fundamental degeneracy in the market of non-rivalrous goods. Specifically, for a general class of payment contracts, there will be an infinite set of generalized Nash equilibria. This multiplicity of equilibria also affects common refinements of equilibrium definitions intended to uniquely select an equilibrium: both variational equilibria and normalized equilibria will be non-unique in general. This implies that most existing equilibrium concepts cannot provide predictions on the outcomes of data markets emerging today. The results support the idea that modifications to payment contracts themselves are unlikely to yield a unique equilibrium, and either changes to the models of study or new equilibrium concepts will be required to determine unique equilibria in settings with multiple principals and a non-rivalrous good

Edge Intelligence : Empowering Intelligence to the Edge of Network

Edge intelligence refers to a set of connected systems and devices for data collection, caching, processing, and analysis proximity to where data are captured based on artificial intelligence. Edge intelligence aims at enhancing data processing and protects the privacy and security of the data and users. Although recently emerged, spanning the period from 2011 to now, this field of research has shown explosive growth over the past five years. In this article, we present a thorough and comprehensive survey of the literature surrounding edge intelligence. We first identify four fundamental components of edge intelligence, i.e., edge caching, edge training, edge inference, and edge offloading based on theoretical and practical results pertaining to proposed and deployed systems. We then aim for a systematic classification of the state of the solutions by examining research results and observations for each of the four components and present a taxonomy that includes practical problems, adopted techniques, and application goals. For each category, we elaborate, compare, and analyze the literature from the perspectives of adopted techniques, objectives, performance, advantages and drawbacks, and so on. This article provides a comprehensive survey of edge intelligence and its application areas. In addition, we summarize the development of the emerging research fields and the current state of the art and discuss the important open issues and possible theoretical and technical directions.Peer reviewe

Edge Intelligence : Empowering Intelligence to the Edge of Network

Edge intelligence refers to a set of connected systems and devices for data collection, caching, processing, and analysis proximity to where data are captured based on artificial intelligence. Edge intelligence aims at enhancing data processing and protects the privacy and security of the data and users. Although recently emerged, spanning the period from 2011 to now, this field of research has shown explosive growth over the past five years. In this article, we present a thorough and comprehensive survey of the literature surrounding edge intelligence. We first identify four fundamental components of edge intelligence, i.e., edge caching, edge training, edge inference, and edge offloading based on theoretical and practical results pertaining to proposed and deployed systems. We then aim for a systematic classification of the state of the solutions by examining research results and observations for each of the four components and present a taxonomy that includes practical problems, adopted techniques, and application goals. For each category, we elaborate, compare, and analyze the literature from the perspectives of adopted techniques, objectives, performance, advantages and drawbacks, and so on. This article provides a comprehensive survey of edge intelligence and its application areas. In addition, we summarize the development of the emerging research fields and the current state of the art and discuss the important open issues and possible theoretical and technical directions.Peer reviewe