Incentive Mechanism Design for Crowdsourcing: An All-pay Auction Approach

Crowdsourcing can be modeled as a principal-agent problem in which the principal (crowdsourcer) desires to solicit a maximal contribution from a group of agents (participants) while agents are only motivated to act according to their own respective advantages. To reconcile this tension, we propose an all-pay auction approach to incentivize agents to act in the principal\u27s interest, i.e., maximizing profit, while allowing agents to reap strictly positive utility. Our rationale for advocating all-pay auctions is based on two merits that we identify, namely all-pay auctions (i) compress the common, two-stage bid-contribute crowdsourcing process into a single bid-cum-contribute stage, and (ii) eliminate the risk of task nonfulfillment. In our proposed approach, we enhance all-pay auctions with two additional features: an adaptive prize and a general crowdsourcing environment. The prize or reward adapts itself as per a function of the unknown winning agent\u27s contribution, and the environment or setting generally accommodates incomplete and asymmetric information, risk-averse (and risk-neutral) agents, and a stochastic (and deterministic) population. We analytically derive this all-pay auction-based mechanism and extensively evaluate it in comparison to classic and optimized mechanisms. The results demonstrate that our proposed approach remarkably outperforms its counterparts in terms of the principal\u27s profit, agent\u27s utility, and social welfare

Profit-Maximizing Incentive for Participatory Sensing

Abstract—We design an incentive mechanism based on all-pay auctions for participatory sensing. The organizer (principal) aims to attract a high amount of contribution from participating users (agents) while at the same time lowering his payout, which we for-mulate as a profit-maximization problem. We use a contribution-dependent prize function in an environment that is specifically tailored to participatory sensing, namely incomplete information (with information asymmetry), risk-averse agents, and stochastic population. We derive the optimal prize function that induces the maximum profit for the principal, while satisfying strict individual rationality (i.e., strictly have incentive to participate at equilibrium) for both risk-neutral and weakly risk-averse agents. The thus induced profit is demonstrated to be higher than the maximum profit induced by constant (yet optimized) prize. We also show that our results are readily extensible to cases of risk-neutral agents and deterministic populations. Index Terms—Mechanism design, Bayesian game, all-pay auc-tion, perturbation analysis, network economics, crowdsensing

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

Studying user behavior through a participatory sensing framework in an urban context

A thesis submitted in partial fulfillment of the requirements for the degree of Doctor in Information Management, specialization in Geographic Information SystemsThe widespread use of mobile devices has given birth to participatory sensing, a data collection approach leveraging the sheer number of device users, their mobility, intelligence and device’s increasingly powerful computing and sensing capabilities. As a result, participatory sensing is able to collect various types of information at a high spatial and temporal resolution and it has many applications ranging from measuring cellular signal strength or road condition monitoring to observing the distribution of birds. However, in order to achieve better results from participatory sensing, some issues needed to be dealt with. On a high level, this thesis addressed two issues: (1) the design and development of a participatory sensing framework that allows users to flexibly create campaigns and at the same time collect different types of data and (2) the study of different aspects of the user behaviors in the context of participatory sensing. In particular, the first contribution of the thesis is the design and development of Citizense, a participatory sensing framework that facilitates flexible deployments of participatory sensing campaigns while at the same time providing intuitive interfaces for users to create sensing campaigns and collect a variety of data types. During the real-world deployments of Citizense, it has shown its effectiveness in collecting different types of urban information and subsequently received appreciation from different stakeholders. The second contribution of the thesis is the in-depth study of user behavior under the presence of different monetary incentive mechanisms and the analysis of the spatial and temporal user behavior when participants are simultaneously exposed to a large number of participatory sensing campaigns. Concerning the monetary incentive, it is observed that participants prefer fixed micro-payment to other mechanisms (i.e., lottery, variable micro-payment); their participation was increased significantly when they were given this incentive. When taking part in the participatory sensing process, participants exhibit certain spatial and temporal behaviors. They tend to primarily contribute in their free time during the working week, although the decision to respond and complete a particular participatory sensing campaign seems to be correlated to the campaign’s geographical context and/or the recency of the participants’ activities. Participants can be divided into two groups according to their behaviors: a smaller group of active participants who frequently perform participatory sensing activities and a larger group of regular participants who exhibit more intermittent behaviors

Incentive mechanism design for heterogeneous crowdsourcing using all-pay contests

Many crowdsourcing scenarios are heterogeneous in the sense that, not only the workers\u27 types (e.g., abilities or costs) are different, but the beliefs (probabilistic knowledge) about their respective types are also different. In this paper, we design an incentive mechanism for such scenarios using an asymmetric all-pay contest (or auction) model. Our design objective is an optimal mechanism, i.e., one that maximizes the crowdsourcing revenue minus cost. To achieve this, we furnish the contest with a prize tuple which is an array of reward functions each for a potential winner. We prove and characterize the unique equilibrium of this contest, and solve the optimal prize tuple. In addition, this study discovers a counter-intuitive property, called strategy autonomy (SA), which means that heterogeneous workers behave independently of one another as if they were in a homogeneous setting. In game-theoretical terms, it says that an asymmetric auction admits a symmetric equilibrium. Not only theoretically interesting, but SA also has important practical implications on mechanism complexity, energy efficiency, crowdsourcing revenue, and system scalability. By scrutinizing seven mechanisms, our extensive performance evaluation demonstrates the superior performance of our mechanism as well as offers insights into the SA property

Towards operationalizing UNESCO Recommendations on “Historic Urban Landscape”: a position paper

<p>This position paper critically analyses the process to implement the UNESCO Recommendation on the Historic Urban Landscape (2011), exploring evaluation tools, innovative business / management models and financing tools for the conservation and regeneration of Historic Urban Landscape (HUL), to make it operational in the perspective of a circular economy model of sustainable development for city / territory system regeneration. Through evaluation tools, it is possible to pass from general principles to operational practices; to produce empirical evidence of the economic, social and environmental benefits of HUL integrated conservation and regeneration. The challenge of generating a symbiosis between conservation and transformation issues requires adequate evaluation methods, business, management and financing tools, engaging civil society and local stakeholders, capturing both HUL tangible and intangible values to turn the historic urban landscape into a driver of sustainable growth. The analysis carried out in this paper shows that through the suggested tools it is possible to make operational the UNESCO Recommendations, transforming conflicts into opportunities, producing economic attractiveness and strengthen social awareness and cohesion.</p

Community Networks and Sustainability: a Survey of Perceptions, Practices, and Proposed Solutions

Community network (CN) initiatives have been around for roughly two decades, evangelizing a distinctly different paradigm for building, maintaining, and sharing network infrastructure but also defending the basic human right to Internet access. Over this time they have evolved into a mosaic of systems that vary widely with respect to their network technologies, their offered services, their organizational structure, and the way they position themselves in the overall telecommunications’ ecosystem. Common to all these highly differentiated initiatives is the sustainability challenge. We approach sustainability as a broad term with an economical, political, and cultural context. We first review the different perceptions of the term. These vary both across and within the different types of stakeholders involved in CNs and are reflected in their motivation to join such initiatives. Then, we study the diverse approaches of CN operators towards the sustainability goal. Given the rich context of the term, these range all the way from mechanisms to fund their activities, to organizational structures and social activities serving as incentives for the engagement of their members. We iterate on incentive mechanisms that have been proposed and theoretically analyzed in the literature for CNs as well as tools and processes that have been actually implemented in them. Finally, we enumerate lessons that have been learned out of these two decades of CNs’ operation and discuss additional technological and regulatory issues that are key to their longer-term sustainability