User Rewarding and Distributed Payment Platformsfor Mobile Crowdsensing Systems

Andrea CapponiUniversity of Luxembourg

andrea.capponi@uni.lu

ABSTRACTMobile Crowdsensing (MCS) has become in the last years one ofthe most prominent paradigms for urban sensing. In MCS systems,citizens actively participate in the sensing process by contributingdata from their mobile devices. To make e�ective a MCS cam-paign, large participation is fundamental. Users sustain costs tocontribute data and they may be reluctant in joining the sensingprocess. Hence, it is essential to incentivize participants. Severalincentive mechanisms have been investigated, such as monetaryrewarding. In this context, distributed payment platforms based oncustom built blockchains assume a fundamental role. We aim to de-velop a platform to distribute micro-payments following rewardingschemes. The key idea is to di�erentiate between users throughseveral parameters, such as the amount of acquired data and theQuality of Information (QoI), according to the particular campaignand the need of the organizers.

1 INTRODUCTIONNowadays, the unprecedented growth of population living in urbanareas calls for a sustainable urban development. To this end, sens-ing is fundamental to monitor the current status of infrastructuresand the resource utilization [1]. Deploying sensing infrastructuresis typically expensive, while including citizens in the loop throughmobile crowdsensing (MCS) has been proven as a win-win strat-egy [2]. Indeed, it allows to exploit already deployed infrastructureswith no need of further investments. Mobility and intelligence ofhuman participants guarantee higher coverage and better contextawareness, if compared to traditional sensor networks. In addition,users ensure self-maintenance and recharge of the devices thatact as sensor and communication mobile nodes, unprecedentelyfrom other paradigms. Smartphones are equipped with a rich setof sensors suitable in multiple domains, such as environmentalmonitoring, health care, public safety and intelligent transporta-tion systems. Available communication technologies deliver theacquired data to a collector, typically located in the cloud, for dataprocessing and analysis.

MCS follows a Sensing as a Service (S2aaS) business model, whichmakes data collected from sensors available to cloud users. IoT andMCS are key enablers in the S2aaS models, whose e�ciency isde�ned in terms of the revenues obtained through data selling andthe costs. The success of crowdsensing campaigns relies on largeparticipation of citizens, who sustain costs to contribute data (e.g.,the battery drain of their devices or monthly data plans for cellulardata connectivity). Hence, it is fundamental to devise e�cientdata collection frameworks (DCFs) and to reward users for theircontribution. Consequently, the organizer of a sensing campaign(e.g., a government agency, an academic institution or a business

corporation) sustains costs to recruit and compensate users for theirinvolvement [3].

Several incentive mechanisms, e.g., social or monetary, havebeen and are currently under investigation. Many researchers arechallenging the issues to design practical data collection schemesfor MCS data markets [4]. Our aim is to exploit distributed pay-ment platforms that run applications on custom built blockchainsto provide incentives in MCS campaigns, according to rewardingschemes. The motivation is twofold. First, these platforms enableorganizers to design custom user rewarding schemes that coulddi�erentiate between contributions according the need of the appli-cation, taking into account several parameters (e.g., quantity andquality of contributed data). Second, they provide the possibility tomove funds without the need of a central authority or counterpartyrisk.

2 BACKGROUND ON OUR PREVIOUS WORKSDevising energy e�cient DCFs is crucial to foster citizens’ partici-pation. To this end, in the early-stage of our work we investigatedthe costs users sustain for sensing and reporting operations and weproposed a distributed energy e�cient DCF [5]. It aims to minimizethe battery drain for sensing and reporting, while maximizing theutility of data collection and, as a result, the quality of contributedinformation. Then, we proposed a methodology to investigate theperformance of several DCFs and compare them from an energyperspective, but also considering amount of collected data andfairness among participants. To this end, we developed a customAndroid application for smartphones that can implement di�erentdata reporting mechanisms and we performed energy and network-related measurements with a power monitor and Wireshark. AsMCS systems require large participation to be e�ective, performingexperiments on real testbeds is not often feasible. To this end, sim-ulations are a valid alternative and CrowdSenSim [6] is a customsimulator we speci�cally designed to assess the performance ofcrowdsensing activities in large urban areas. CrowdSenSim sup-ports pedestrian mobility in citywide scenarios and is composedby independent modules representing inputs of the particular MCScampaign (see Fig. 1(a)). Modeling the urban environment withhigh precision is a key ingredient to obtain e�ective results andCrowdSenSim provides the street network graph at any desiredlevel of precision (see Fig. 1(b)) through an algorithm running on itsbackground. All the individual walking paths are obtained beforesimulation runtime to ensure the scalability of the platform. Duringruntime, users move following their predetermined trajectories andcontribute data according to the implemented DCF, exploiting sen-sors typically available in mobile devices. The simulator computesthe amount of gathered data for each user and the associated bat-tery drain for sensing and reporting. After the runtime it is possible

User MobilityList of Events

City Layout

SIMULATORCrowdSensing

Inputs

Results

(a) Main modules of CrowdSenSim (b) The green dots are lanes where users can walk0

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(c) Normalized distribution of contributed data

Figure 1: CrowdSenSim modules, walking paths and possible results

to obtain di�erent results, such as heatmaps with the normalizedamount of gathered data (see Fig. 1(c)).

3 DISTRIBUTED PAYMENTS FOR MOBILECROWDSENSING

The organizers of crowdsensing campaigns (e.g., a company, a mu-nicipality, or an academic institution) have no longer the need toacquire an infrastructure to perform a campaign, but exploit alreadydeployed ones in a pay-as-you-go basis. Many researchers are inves-tigating on several incentive mechanisms, mainly monetary onesbecause they result more e�ective than others in fostering citizens’participation. In this Section we will analyze user rewarding anddecentralized payment platforms that enable developers at runningsmart contracts on custom built blockchains.

3.1 User rewardingThe success of a MCS campaign relies on large participation ofcitizens and on their willingness to contribute data. To this end,rewarding users is essential. Fig. 2 shows the process of user recruit-ment, selection and rewarding in a typical crowdsensing campaign.First, the organizer of the campaign needs to recruit participantsfrom citizens. Then, a central authority allocates tasks to the re-cruited users and selects contributors amongst the group accordingto policies speci�cally designed from the organizer. In other words,the user selection consists in choosing participants who are moresuitable for given reasons to accomplish sensing tasks. Selectionis totally dependent on context-awareness and can be based ondi�erent strategies, such as spatial or temporal sensing coverage,energy costs or technologies needed for delivering data. Finally,selected users contribute data to the central collector, which re-wards them according to some criteria that depend on the sensingcampaign design, such as amount of collected data and quality ofinformation. Research works based on monetary incentives usuallyfocus on �nding an optimal task policy under a limited budget byconsidering that each task has di�erent requirements. Leveragingthe vast adoption in crowd applications of multiple sensors, currentrewarding strategies are suboptimal as fail to di�erentiate betweenactive versus non-active user involvement, type and quality of re-ported data. Our aim is to exploit several criteria to develop a user

rewarding scheme based on a strong di�erentiation between par-ticipants who contribute in di�erent ways, compensating the mostactive ones.

3.2 Decentralized platforms to distributepayment

Rewarding users implies the need to distribute micro-payments tothem according to the criteria of each sensing campaign. Delivermicro-payments through participants requires distributed and re-liable platforms, which could run smart contracts to respect thedesigned rewarding schemes between the parties. A smart contractis a self-executing digital contract veri�ed through peers, withoutthe need of a central authority. In this context, blockchain technol-ogy is fundamental by providing a transparent, unalterable, orderedledger and by enabling a decentralized and secure environment.Hyperledger1 is an open-source project to develop and improveblockchain frameworks and platforms. It is based on the idea thatonly collaborative software development can guarantee the inter-operability and transparency to adopt blockchain tecnologies inthe commercial mainstream. Ethereum2 is a decentralized platformthat allows developers to distribute payments on the basis of previ-ously chosen instructions without a counterpart risk or the need ofa middle authority. Developers have the opportunity to run applica-tions with their rewarding rules on a custom built blockchain witha shared global infrastructure to distribute payments. Exploitingthese platforms could allow the organizer of a sensing campaignto decide how to reward citizens according to several parameterpreviously decided on application-basis (e.g., amount of data, typeof sensor, QoI, time of reporting).

3.3 Application for rewarding citizensExisting distributed payment platforms allow to run custom appli-cations to move funds in accordance with instructions, representinga perfect match with rewarding schemes for crowdsensing systems.The key idea is to distribute payments di�erentiating between cit-izens that actively perform tasks and report highly accurate dataand others that passively perform data reporting, often resultingin low-quality contributions. To this end, payment platforms run-ning smart contracts could reward users according to chosen cri-teria, which vary on di�erent campaigns’ requirements. We aim1https://www.hyperledger.org/2https://www.ethereum.org/

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Figure 2: User selection and rewarding in MCS systems

to develop a custom application that runs on a blockchain-baseddistributed payment platform, investigating how to design a re-warding scheme that could di�erentiate between users according totheir contribution. First, rewarding strategies should consider qual-ity and quantity of contributed information. Then, a sustainablescheme should also consider to award in a better way a user whohas a lower level of battery or will contribute for a higher energyconsuming task. Finally, available communication technologiesshould be considered because typically WiFi entails lower coststhan cellular data connectivity (e.g., a user could pay for a monthlydata plan). In other words, active citizens sending a large amountof data with a high QoI and low level of remaining battery shouldbe rewarded better than passive ones with full battery and sendinglow-quality data.

4 RELATEDWORKSIncentives for MCS frameworks are typically either monetary orsocial. The latter include research works that focus on citizens whoexpress their availability to be recruited for contributing informa-tion during a MCS campaign and establishing social interactions.To give a few examples, Xiao et al. [7] analyze the task assign-ment problem in mobile social networks and propose a scenario inwhich users send tasks to others who accomplish the task, in orderto minimize the average makespan. Chessa et al. [8] investigatemobility and interaction patterns between volunteers to increaseperformance of MCS campaigns, exploiting existing social ties andintroducing community-based tasks to share social relationships.The former are research works that investigate how to e�cientlyallocate tasks under a limited budget and how to foster user par-ticipation with monetary rewarding in order to achieve the goalsof the sensing campaign. Zheng et al. [9] investigate the busi-ness paradigm of data trading designing a data collection schemefor crowdsensed data markets. They propose a framework calledVENUS, which aims to maximize the pro�t and minimize the pay-ment. Xu et al. [10] propose a distributed active learning frameworkthat aims to minimize prediction errors under a limited amount ofbudget, focusing on trade-o�s between inference performance andcosts of data acquisition. Chen et al. [11] present a payment schemewhich aims to maximize pro�t and to select quali�ed contributors.

To the best of our knowledge, currently there are no works that ex-ploit existing distributed payment platforms running smart contractbased on rewarding schemes to foster user participation in MCSsystems. The closest work to our idea is [12], where the authorsinvestigate incentives and preservation of privacy and propose adual-anonymous reward distribution scheme to achieve reward-sharing incentive and privacy protection for both customers andcontributing users. This mechanism allows a campaign organizerto motivate user contribution by claiming electronic coins from abank, while protecting the identities of both sides.

5 CONCLUSIONActive citizen participation in MCS campaigns is becoming a com-mon practice. Devising e�cient DCFs and having a large partici-pation of users is essential for the success of a campaign. In ourearly-stage work, we �rst proposed an energy e�cient DCF whichminimizes the costs user sustain, while maximizing the utility ofcollected information. Then, we developed an Android applica-tion that can implement DCFs and we pro�led real energy andnetwork measurements exploiting a power monitor and Wireshark.Then, these results have been included in the CrowdSenSim simu-lator, speci�cally designed to assess the performance in real urbanenvironments. Next step of our work is to exploit existing reward-ing platforms based on custom built blockchains that run smartcontracts and develop on it a custom application to di�erentiatepayments between users that actively send high-quality data andpassive low-quality contributors.

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