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Incentive Mechanisms for Crowdsensing: Crowdsourcing With Incentive Mechanisms for Crowdsensing: Crowdsourcing with Smartphones Dejun Yang, Member, IEEE, Guoliang Xue, Fellow, IEEE, Xi Fang, and Jian Tang, Senior Member, IEEE Abstract—Smartphones are programmable and equipped with range of human activities and the surrounding environment. a set of cheap but powerful embedded sensors, such as ac- Smartphones are undoubtedly revolutionizing many sectors of celerometer, digital compass, gyroscope, GPS, microphone, and our life, including social networks, environmental monitoring, camera. These sensors can collectively monitor a diverse range of human activities and the surrounding environment. Crowdsens- business, healthcare, and transportation [21]. ing is a new paradigm which takes advantage of the pervasive If all the smartphones on the planet together constitute a smartphones to sense, collect, and analyze data beyond the single sensing network, it would form the largest sensing scale of what was previously possible. With the crowdsensing network ever. One can leverage millions of personal smart- system, a crowdsourcer can recruit smartphone users to provide phones and a near-pervasive wireless network infrastructure to sensing service. Existing crowdsensing applications and systems lack good incentive mechanisms that can attract more user sense, collect, and analyze data far beyond the scale of what participation. To address this issue, we design incentive mech- was possible before, without the need to deploy thousands of anisms for crowdsensing. We consider two system models: the static sensors. This new paradigm is commonly referred as crowdsourcer-centric model where the crowdsourcer provides crowdsensing. a reward shared by participating users, and the user-centric Realizing the great potential of crowdsensing, many re- model where users have more control over the payment they will receive. For the crowdsourcer-centric model, we design searchers have developed various applications and systems, an incentive mechanism using a Stackelberg game, where the such as Sensorly [36] for making cellular/WiFi network cov- crowdsourcer is the leader while the users are the followers. erage maps, Nericell [27] and VTrack [42] for providing traffic We show how to compute the unique Stackelberg Equilibrium, at information, PIER [28] for calculating personalized environ- which the utility of the crowdsourcer is maximized, and none mental impact and exposure, and Ear-Phone [33] for creating of the users can improve its utility by unilaterally deviating from its current strategy. For the user-centric model, we design noise maps. For more details on crowdsensing applications, an auction-based incentive mechanism, which is computationally we refer interested readers to the survey paper [21]. efficient, individually rational, profitable, and truthful. Through extensive simulations, we evaluate the performance and validate Sensing Task Description the theoretical properties of our incentive mechanisms. Sensing Plan Cloud Index Terms—Crowdsourcing, crowdsensing, incentive mech- anism, Stackelberg game User Selection Sensed Data Payment 1. INTRODUCTION Smartphone Users Crowdsourcer The past few years have witnessed the proliferation of smartphones in people’s daily lives. With the advent of 4G Fig. 1. Crowdsensing system networks and more powerful processors, the needs for laptops In the crowdsensing system, as shown in Fig. 1, there is a in particular have begun to fade. Smartphone sales passed PCs crowdsourcer and a large group of smartphone users connected for the first time in the final quarter of 2010 [13]. According with the crowdsourcer via the cloud. These smartphone users to the International Data Corporation (IDC), there were more act as sensing service providers. The crowdsourcer recruits than 1.3 billion smartphones shipped worldwide in 2014 [17]. smartphone users to provide sensing services. It is estimated that the smartphone users worldwide will total 2.5 billion in 2015 [41]. Although there are many applications and systems based Nowadays, smartphones are programmable and equipped on crowdsensing [27, 28, 33, 36, 42], most of them require with a set of cheap but powerful embedded sensors, such as voluntary participation. While participating in a crowdsensing accelerometer, digital compass, gyroscope, GPS, microphone, task, smartphone users consume their own resources such as and camera. These sensors can collectively monitor a diverse battery and computing power. In addition, users also expose themselves to potential privacy threats by sharing their sensed This is an extended and enhanced version of the paper [45] that appeared data with location tags. Therefore a user would not be in- in ACM MobiCom 2012. Yang is with Colorado School of Mines, Golden, terested in participating in crowdsensing, unless it receives a CO 80401. Xue is with Arizona State University, Tempe, AZ 85287. Tang is with Syracuse University, Syracuse, NY 13244. Email: [email protected], satisfying reward to compensate its resource consumption and [email protected], [email protected]. This research was supported in part potential privacy breach. Without adequate user participation, by NSF grants 1217611, 1218203, 1420881, 1421685, and 1444059, and it is impossible for the crowdsensing applications to achieve ARO grants W911NF-12-1-0470 and W911NF-09-1-0467. The information reported here does not reflect the position or the policy of the federal good service quality, since sensing services are truly dependent government. on users’ sensed data. While many researchers have developed different crowdsensing applications [8, 22], they either do not participants for sensing services. However, they focused only consider the design of incentive mechanisms or have neglected on the user selection, not the incentive mechanism design. some critical properties of incentive mechanisms. To fill this In [8], Danezis et al. developed a sealed-bid second-price void, we design several incentive mechanisms to motivate auction to motivate user participation. However, the utility of users to participate in crowdsensing applications. the crowdsourcer was neglected in the design of the auction. In We consider two types of incentive mechanisms for a crowd- [22], Lee and Hoh designed and evaluated a reverse auction sensing system: crowdsourcer-centric incentive mechanisms based dynamic price incentive mechanism, where users can and user-centric incentive mechanisms. In a crowdsourcer- sell their sensed data to the service provider with users’ centric incentive mechanism, the crowdsourcer has the ab- claimed bid prices. However, the authors failed to consider solute control over the total payment to users, and users the truthfulness in the design of the mechanism. In [11], Duan can only tailor their actions to cater for the crowdsourcer. et al. studied two applications, data acquisition and distributed Whereas in a user-centric incentive mechanism, the roles of computing. For data acquisition, they considered a thresh- the crowdsourcer and users are reversed. To assure itself of old revenue model, where a certain number of smartphone the bottom-line benefit, each user announces a reserve price, users are required to successfully build the data base. The the lowest price at which it is willing to sell a service. The total reward is shared equally among all participating users. crowdsourcer then selects a subset of users and pay each of For distributed computing, they designed a contract-based them an amount that is no lower than the user’s reserve price. mechanism to decide different task-reward combinations for heterogeneous users. In addition to incentives, Li and Cao [23] A. Summary of Key Contributions considered the privacy protection in the incentive mechanism design. Different objectives have also been considered. For The following is a list of our main contributions. example, Koutsopoulos [19] developed a randomized incentive We design incentive mechanisms for crowdsensing, a new • mechanism to minimize the total payment to the participating sensing paradigm that takes advantage of the pervasive users while guaranteeing certain quality of service level. smartphones to scale up the sensed data collection and Assuming that the cost distribution is known, Luo et al. [26] analysis to a level of what was previously impossible. designed an all-pay auction based incentive mechanism such We consider two system models from two different • that the expected profit is maximized and the individual perspectives: the crowdsourcer-centric model where the rationality is satisfied. The aforementioned mechanisms are all crowdsourcer provides a fixed reward to participating off-line, the authors in [48] and [50] designed online incentive users, and the user-centric model where users can have mechanisms where the crowdsourcer makes decisions instantly their reserve prices for the sensing service. upon the user’s arrival. In this paper, we do not require the For the crowdsourcer-centric model, we design an incen- • knowledge of the cost distribution, and focus on designing tive mechanism using a Stackelberg game. We present deterministic off-line incentive mechanisms. an efficient algorithm to compute the unique Stackelberg Through experiments with real data, Musthag et al. [29] Equilibrium, at which the utility of the crowdsourcer is empirically compared three different incentive mechanisms maximized, and none of the users can improve its utility and revealed several interesting observations which are bene- by unilaterally deviating from its current strategy. ficial for
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