# Contract-Based Incentive Mechanism for Mobile Crowdsourcing Networks
## 1 Introduction  
According to the International Data Corporation, the worldwide smartphone market will reach 1.84 billion units in 2020. With the rapid development of IT technologies, mobile devices are always equipped with powerful processors, various sensors and large memories [1]. These devices can offer a novel paradigm to collect data about individuals, human society, and environments. Numerous mobile crowdsourcing applications have been created, such as OpenStreetMap [2] for constructing an openly licensed map of the world, CrowdDB [3] for querying and answering, Honeybee [4] for face detection, SignalGuru [5] for traffic signal detection, and Medusa [6] for environment sensing and data processing. However, designing an efficient mobile crowdsourcing network (MCN) [7] is considerably challenging. First, while participating in tasks, mobile devices may consume their resources (i.e., battery, memory, and time) [1]. Mobile devices in MCNs are always controlled by rational users to maximize their own benefits. Moreover, the collected data usually contains location information with potential privacy and security threats. Mobile users (MUs) may not be willing to participate in crowdsourcing tasks without any extra incentives. Therefore, incentive mechanisms are necessary to achieve the win-win goal by considering the two parties' requirements. Recently, three primary incentive mechanisms have been suggested for MCNs, which are entertainment-based, service-based, and monetary-based mechanisms [8]. The entertainment-based incentive mechanism turns crowdsourcing tasks into playable games to attract crowdsourcingparticipants [9; 10]. The service-based incentive mechanism attracts each crowdsourcing participant to make important contributions for crowdsourcing mutually [11; 12; 13]. The monetary-based incentive mechanism offers crowdsourcing participants rewards for their efforts [14; 15; 16]. Because the former two incentive mechanisms need to obtain the domain knowledge, the third incentive mechanism is more suitable for general crowdsourcing scenarios. [PERSON] et al. [14] proposed two incentive mechanisms to attract MUs to participate in crowdsourcing tasks. [PERSON] et al. [15] designed three online incentive mechanisms for mobile crowdsourcing sensing. [PERSON] et al. [16] proposed the online incentive mechanism for crowdsourcing tasks with a budget constraint. However, most existing works have assumed that crowdsourcing participants will not deviate from the incentive mechanism. Unfortunately, because of users' mobility and mobile wireless environments' dynamicity, certain crowdsourcing information (i.e., crowdsourcing efforts of MUs) may not be available to the service provider (SP), which causes _network information asymmetry_ between the MUs and the SP. The SP may not monitor the MUs' crowdsourcing action in real-time, and the MUs may deviate from the incentive mechanism. To tackle this problem, we propose a contract-based incentive mechanism. Contract theory [17] investigates how economic parties make decisions under uncertain conditions or make contracts with asymmetric information. Recently, it has been successfully applied to many practical problems, for example, cooperative spectrum trading [18], mobile crowdsourcing [19], and cooperative relay [20; 21; 22]. [PERSON] and [PERSON] et al. considered the cooperative incentive with resource-exchange spectrum trading [18]. [PERSON] et al. investigated the adaptive contract design for crowdsourcing markets [19]. [PERSON] et al. proposed the incentive mechanism approach to solve the optimal compensation package with _moral hazard_[23]. Our prior works developed an efficient contract model for _adverse selection_ in the presence of the wireless nodes' _hidden relay information_[20] and moral hazard problems [21] caused by the wireless nodes' _hidden relay actions_. However, none of these considered the risk attitudes of crowdsourcing participants (i.e., an SP, or mobile devices). Most existing works have assumed that the MUs are risk-neutral.