Proposed Incentive Mechanism Research Articles

Privacy protection-based incentive mechanism for Mobile Crowdsensing

Mobile crowdsensing (MCS) has been an emerging technology thanks to the smart devices which are capable of sensing and computing to achieve large-scale, complex sensing tasks by cooperation. However, large-scale deployment might be impeded due to that fact that the participant may face the risk of privacy leakage, and if they are not compensated favorably, they may not be willing to contribute sensing capability. To overcome the above challenges, we propose an incentive mechanism for privacy-preserving mobile crowdsensing. More specifically, we introduce a trusted third party and combine partially blind signature, which can effectively reduce the correlation between participants and data and the number of interactions between users and task platform, so as to achieve high level participant privacy. In addition, considering data quality, we define some concepts including data quality relevance, user credit, location relevance and user utility, and design a Credit-based Incentive Mechanism (CIM) based on marginal benefit density and credit, in order to obtain the maximum benefit of a task platform under given budget. Extensive simulations are carried out to show that the proposed incentive mechanism achieves superior performance compared with state-of-the-art solutions. To the existing multi-stage incentive solutions, our proposed solution can achieve higher-quality data at the expense of less time efficiency.

Game theoretical study on client-controlled cloud data deduplication

Data deduplication eliminates redundant data and is receiving increasing attention in cloud storage services due to the proliferation of big data and the demand for efficient storage. Data deduplication not only requires a consummate technological designing, but also involves multiple parties with conflict interests. Thus, how to design incentive mechanisms and study their acceptance by all relevant stakeholders remain important open issues. In this paper, we detail the payoff structure of a client-controlled deduplication scheme and analyze the feasibilities of unified discount and individualized discount under this structure. Through game theoretical study, a privacy-preserving individualized discount-based incentive mechanism is further proposed with detailed implementation algorithms for choosing strategies, setting parameters and granting discounts. After theoretical analysis on the requirements of individual rationality, incentive compatibility, and profitability, we conduct extensive experiments based on a real-world dataset to demonstrate the effectiveness of the proposed incentive mechanism.

An Incentive Mechanism Based on Bertrand Game for Opportunistic Edge Computing

In a real edge network, many nodes may be selfish and unwilling to forward messages for other nodes. In this case, an incentive mechanism is needed to encourage the selfish nodes to participate in message forwarding. In this paper, we analyze the interaction between the source node and the relay node in edge opportunistic networks, and propose an incentive mechanism based on game theory to encourage the cooperation between nodes. Firstly, we define the interaction steps between the source and the relay node, which include that the source node decides the price of forwarding a message, the relay node responses the forwarding plan to the source node, and all nodes can get reward for their participant in message forwarding. We provide two-stage incentives to nodes, that is, the nodes can get reward from both receiving and forwarding messages. Since the nodes may be selfish, both the source node and the relay node want to maximize their utilities. Then, we model the cooperation between the source and the relay node as Bertrand Game, and the utility functions of the source and the relay node are defined. Furthermore, we find that the Nash Equilibrium is existed and unique, and present the best pricing scheme for the source node and the best forwarding plan for the relay node. The simulation results show that the proposed incentive mechanism can encourage the cooperation between selfish nodes, and improve the performance of routing algorithm in terms of delivery ratio and delay.

Online Quality-Aware Incentive Mechanism for Mobile Crowd Sensing with Extra Bonus

Mobile crowd sensing is a new paradigm that enables smart mobile devices to collect and share various types of sensing data in urban environments. However, new challenges arise: one is how to evaluate the quality of data each mobile user potentially is capable of providing; another is how to allocate a satisfactory yet profitable amount of reward to mobile users in order to keep them participating in crowd sensing tasks. In this paper, we first introduce a mathematical model for characterizing quality of sensing data to be contributed by mobile users. Then, we present a utility function and formulate an optimization problem for the platform, who recruits participants to contribute sensing data, to maximize the amount of high quality sensing data under a limited task budget. We next present an effective and quality-aware incentive mechanism to solve this problem for online scenarios where participants may arrive or leave at any random time. Moreover, the proposed incentive mechanism allows the platform to provide selected participants with an extra bonus according to task completion level and their previous performance to motivate them further. We formally show the proposed mechanism has the desirable properties of truthfulness, individual rationality, budgetary feasibility, and computational efficiency. We compare the proposed scheme with existing methods via simulation using a real dataset. Extensive simulation results well justify the effectiveness and robustness of the proposed approach, e.g, compared with another online method “OMG”, the gap to the optimum for our proposed Online-QIM approach is reduce by 33.3 percent when budget $B = 1000$B=1000.

A greedy-proof incentive-compatible mechanism for group recruitment in mobile crowd sensing

Abstract As crowd sensing gains more popularity, the issue of recruiting the best workers for a fair cost becomes more relevant. In this paper, we tackle the problem of workers’ greediness, where workers overprice their data to improve their profit, by presenting an incentive mechanism for group recruitment crowd sensing systems. The proposed incentive mechanism, which consists of a selection and a payment mechanism, considers not only the Quality of Information (QoI) of the worker and his contribution to the overall QoI of the group, but also the cost of his contribution compared with other workers. This technique avoids the selection of greedy members since their costs will be high compared to the QoI they offer, hence forcing them to align with the other members by advertising their true cost. On the other hand, the proposed payment mechanism rewards workers based on the improvement they offer to the QoI and the cost of the group. We prove that the proposed incentive mechanism is strategy-proof and demonstrate its effectiveness by adapting it to an existing group recruitment framework Azzam et al. (2016), and comparing it with the original model through experiments with real datasets.

Privacy Implication of Location-Based Service: Multi-Class Stochastic User Equilibrium and Incentive Mechanism

As a tool to assist traffic guidance and improve service quality, location-based service (LBS) platforms such as route navigation apps rely heavily on the collection and analysis of users’ location/trajectory information, which may evoke privacy concerns. Because of such privacy concerns, users may choose not to provide their information. In certain cases, this may lead to the problem of insufficient data for LBS applications (e.g., travel time estimation). To address this issue, the paper develops a modeling framework to quantify the levels of privacy for mixed user groups and proposes an incentive mechanism to encourage users to provide their location/trajectory information. It is assumed that LBS users have smaller travel time perception error but experience some extra privacy costs compared with the non-LBS users. A bi-level optimal incentive model with stochastic user equilibrium and elastic demand is developed to capture the mixed behavior of multi-class network users. The problem is solved using a meta-heuristic approach combined of genetic algorithm, successive average algorithm, and multiple behavior equilibrium assignment algorithm. The results reveal that the modeling framework can capture the mixed behavior of groups with different privacy levels. The proposed incentive mechanism is able to ensure sufficient data, and simultaneously minimize the required incentive.

An Optimization and Auction-Based Incentive Mechanism to Maximize Social Welfare for Mobile Crowdsourcing

Mobile crowdsourcing is an emerging crowdsourcing paradigm, which generates large-scale sensing tasks and sensing data. One of the major issues in mobile crowdsourcing is how to maximize social welfare through selecting appropriate sensing tasks for crowd workers and selecting appropriate workers for sensing tasks such that it can improve the effectiveness and efficiency of mobile crowdsourcing. This paper proposes an incentive mechanism to maximize social welfare for mobile crowdsourcing and, respectively, investigates worker-centric task selection and platform-centric worker selection. This paper applies an optimization algorithm in task selection for mobile crowdsourcing systems. A discrete particle swarm optimization (DPSO) algorithm for worker-centric task selection is designed to maximize the utilities of workers. In addition, a platform-centric worker selection method, which integrates multiattribute auction and two-stage auction, is proposed to maximize the utility of the platform. The performance of the proposed incentive mechanism is evaluated through experiments. The experimental results show that the proposed incentive mechanism can improve the efficiency and truthfulness of mobile crowdsourcing effectively.

Coordinating Supply-Chain Management under Stochastic Fuzzy Environment and Lead-Time Reduction

In this paper, a supply-chain (SC) coordination method based on the lead-time crashing is proposed for a seller–buyer system. By considering different transportation modes, we control the lead-time (LT) variability. For the first time, we have attempted to determine the impact of the reliable and unreliable seller in a continuous-review supply-chain model under the stochastic environment. The authors discussed two reliability cases for the seller. First, we consider the seller is unreliable and in the second case, the seller is reliable. In addition, the demand during the lead time is stochastic with the known mean and variance. The proposed approach tries to find an optimal solution that performs well without a specific probability distribution. Besides, a discrete investment is made to reduce the setup cost, which will indirectly help supply-chain members to increase the total profit of the system. In the proposed model, the seller motivates the buyer by reducing lead time to take part in coordinating decision-making for the system’s profit optimization. We derive the coordination conditions for both members, the seller and the buyer, under which they are convinced to take part in the cooperative decision-making plan. Therefore, lead-time crashing is the proposed incentive mechanism for collaborative supply-chain management. We use a fixed-charge step function to calculate the lead-time crashing cost for slow and fast shipping mode. We give two numerical examples to validate the proposed models and demonstrate the service-level enhancement under the collaborative supply-chain management in case of an unreliable seller. Concluding remarks and future extensions are discussed at the end.

QoI-aware incentive for multimedia crowdsensing enabled learning system

While much research has been devoted to algorithm improvement of the machine learning model for multimedia applications, relatively little research has focused on the acquisition of massive multimedia datasets with strict data demands for model training. In this paper, we propose a Quality-of-Information (QoI) aware incentive mechanism in multimedia crowdsensing, with the objective of promoting the growth of an initial training model. We begin with a reverse auction incentive model to maximize social welfare while meeting the requirements in quality, timeliness, correlation, and coverage. Then, we discuss how to achieve the optimal social welfare in the presence of an NP-hard winner determination problem. Lastly, we design an effective incentive mechanism to solve the auction problem, which is shown to be truthful, individually rational and computationally efficient. Our evaluation study is carried out using a real multimedia dataset. Extensive simulation results demonstrate that the proposed incentive mechanism produces close-to-optimal social welfare noticeably, while accompanied by accelerating the growth of the machine learning model with a high-QoI dataset.

Participant Incentive Mechanism Toward Quality-Oriented Sensing

The ubiquity of ever-more-capable mobile devices, especially smartphones, brings forth participatory sensing to collect and interpret information. It can achieve unprecedented quantity of data. However, it is arduous to guarantee quality of data because everyone can contribute data without scrutinization. It is an important issue in quality-oriented participatory sensing. Our idea to address this issue is motivating participants to contribute accurate data for improving data quality directly. In this article, we propose a reputation-based incentive mechanism, RIM, to realize the idea. More specifically, we identify the participants who collect the accurate data and regard them as the reputable ones. Then, the reputable participants are granted a higher chance to obtain rewards so that other people will try to follow such users and become reputable as well. Namely, RIM can encourage and steer users to collect accurate data in the long term. We analyze our incentive mechanism by formalization and premise implications. For a feasibility study of participatory sensing and verification of the implications, we implement and deploy a participatory sensing application focusing on monitoring environmental noise in a specific location as a case study and conduct a simulation based on the case study to further evaluate the proposed incentive mechanism. The results from the case study and the simulation present that RIM can remarkably increase the quality of collected data in participatory sensing while corroborating our theoretical implications.

Designing Incentive Mechanisms for Mobile Crowdsensing with Intermediaries

In the past decade, with the rapid development of wireless communication and sensor technology, ubiquitous smartphones equipped with increasingly rich sensors have more powerful computing and sensing abilities. Thus, mobile crowdsensing has received extensive attentions from both industry and academia. Recently, plenty of mobile crowdsensing applications come forth, such as indoor positioning, environment monitoring, and transportation. However, most existing mobile crowdsensing systems lack vast user bases and thus urgently need appropriate incentive mechanisms to attract mobile users to guarantee the service quality. In this paper, we propose to incorporate sensing platform and social network applications, which already have large user bases to build a three-layer network model. Thus, we can publicize the sensing platform promptly in large scale and provide long-term guarantee of data sources. Based on a three-layer network model, we design incentive mechanisms for both intermediaries and the crowdsensing platform and provide a solution to cope with the problem of user overlapping among intermediaries. We theoretically prove the properties of our proposed incentive mechanisms, including incentive compatibility, individual rationality, and efficiency. Furthermore, we evaluate our incentive mechanisms by extensive simulations. Evaluation results validate the effectiveness and efficiency of our proposed mechanisms.

Contract-Based Incentive Mechanism for Cooperative NOMA Systems

In the emerging cooperative non-orthogonal multiple access (NOMA) systems, an important design issue is to incentivize mobile relays (MRs) to participate in the cooperative process and achieve a win–win situation to both the base station (BS) and MRs under asymmetric information, where the BS does not know the channel state information of MRs and NOMA users. As a promising remedy, we apply the adverse selection model from contract theory and derive the optimal feasible contract set for MRs with different wireless characteristics (types). Based on the contracts accepted by all the MRs, we further propose an efficient MR selection scheme for the original combinatorial optimization. The efficiency of the proposed incentive mechanism is verified through the simulation results.

Truthful Incentive Mechanism for Nondeterministic Crowdsensing with Vehicles

In this paper, we focus on the incentive mechanism design for a vehicle-based, nondeterministic crowdsensing system. In this crowdsensing system, vehicles move along their trajectories and perform corresponding sensing tasks with different probabilities. Each task may be performed by multiple vehicles jointly so as to ensure a high probability of success. Designing an incentive mechanism for such a crowdsensing system is challenging since it contains a non-trivial set cover problem. To solve this problem, we propose a truthful, reverse-auction-based incentive mechanism that includes an approximation algorithm to select winning bids with a nearly minimum social cost and a payment algorithm to determine payments for all participants. Moreover, we extend the problem to a more complex case in which the Quality of sensing Data (QoD) of each vehicle is taken into consideration. For this problem, we propose a QoD-aware incentive mechanism, which consists of a QoD-aware winning-bid selection algorithm and a QoD-aware payment determination algorithm. We prove that the proposed incentive mechanisms have truthfulness, individual rationality, and computational efficiency. Moreover, we analyze the approximation ratios of the winning-bid selection algorithms. The simulations, based on a real vehicle trace, also demonstrate the significant performances of our incentive mechanisms.

REAP: An Efficient Incentive Mechanism for Reconciling Aggregation Accuracy and Individual Privacy in Crowdsensing

Incentive mechanism plays a critical role in privacy-aware crowdsensing. Most previous studies assume a trustworthy fusion center (FC) in their co-design of incentive mechanism and privacy preservation. Very recent work has taken the step to relax the assumption on trustworthy FC and allowed participatory users (PUs) to randomly report their binary sensing data, whereas the focus is to examine PUs' equilibrium behavior. Making a paradigm shift, this paper aims to study the privacy compensation for continuous data sensing while allowing FC to directly control PUs. There are two conflicting objectives in such a scenario: FC desires better quality data in order to achieve higher aggregation accuracy whereas PUs prefer injecting larger noises for higher privacy-preserving levels (PPLs). To strike a good balance therein, we propose an efficient incentive mechanism named REAP to reconcile FC's aggregation accuracy and individual PU's data privacy. Specifically, we adopt the celebrated notion of differential privacy to quantify PUs' PPLs and characterize their impacts on FC's aggregation accuracy. Then, appealing to contract theory, we design an incentive mechanism to maximize FC's aggregation accuracy under a given budget. The proposed incentive mechanism offers different contracts to PUs with different privacy preferences, by which FC can directly control them. It can further overcome the information asymmetry problem, i.e., FC typically does not know each PU's precise privacy preference. We derive closed-form solutions for the optimal contracts in both complete information and incomplete information scenarios. Further, the results are generalized to the continuous case where PUs' privacy preferences take values in a continuous domain. Extensive simulations are provided to validate the feasibility and advantages of our proposed incentive mechanism.

Incentive mechanisms for mobile crowd sensing based on supply-demand relationship

Mobile crowd sensing has become an efficient paradigm for performing large scale sensing tasks. An incentive mechanism is important for the mobile crowd sensing system to stimulate participants, and to achieve good service quality. In this paper, we design the incentive mechanisms for mobile crowd sensing, where the price and supply of the resource contributed by the smartphone users are determined by the supply-demand relationship of market. We present two models of mobile crowd sensing: the resource model and the budget model. In the resource model, each sensing task has the least resource demand. In the budget model, each task has a budget constraint. We design an incentive mechanism for each of the two models. Through both rigorous theoretical analysis and extensive simulations, we demonstrate that the proposed incentive mechanisms achieve computational efficiency, profitability, individual rationality, and truthfulness. Moreover, the designed mechanisms can satisfy the properties of non-monopoly and constant discount under certain conditions.

An Incentive Mechanism for Crowdsensing Markets With Multiple Crowdsourcers

In this paper, we design an incentive mechanism for data collection in smart cities. We propose an incentive mechanism for crowdsensing with multiple crowdsourcers. We model the incentive mechanism as a noncooperative game. We consider two different pricing mechanisms when the crowdsourcers fixed the rewards in advance, and when the crowdsourcers dynamically set the rewards in order to maximize their own utilities. A discrete time dynamic inspired by the well known best response dynamic, called elite strategy dynamics, is proposed to compute a Nash equilibrium of the modeled game. Comprehensive simulations were presented to evaluate the performance of the proposed incentive mechanism.

Multi-Dimensional Incentive Mechanism in Mobile Crowdsourcing with Moral Hazard

In current wireless communication systems, there is a rapid development of location based services, which will play an essential role in the future 5G networks. One key feature in providing the service is the mobile crowdsourcing in which a central cloud node denoted as the principal collects location based data from a large group of users. In this paper, we investigate the problem of how to provide continuous incentives based on user's performances to encourage users' participation in the crowdsourcing, which can be referred to the moral hazard problem in the contract theory. We not only propose the one-dimensional performance-reward related contract, but also extend this basic model into the multi-dimensional contract. First, an incentive contract which rewards users by evaluating their performances from multiple dimensions is proposed. Then, the utility maximization problem of the principal in both one-dimension and multi-dimension are formulated. Furthermore, we detailed the analysis of the multi-dimensional contract to allocate incentives. Finally, we use the numerical results to analyze the optimal reward package, and compare the principal's utility under the different incentive mechanisms. Results demonstrate that by using the proposed incentive mechanism, the principal successfully maximizes the utilities, and the users obtain continuous incentives to participate in the crowdsourcing activity.

Truthful incentive mechanism with location privacy-preserving for mobile crowdsourcing systems

With the rapid development of mobile devices, mobile crowdsourcing has become an important research focus. In order to improve the efficiency and truthfulness of mobile crowdsourcing systems, this paper proposes a truthful incentive mechanism with location privacy-preserving for mobile crowdsourcing systems. The improved two-stage auction algorithm based on trust degree and privacy sensibility (TATP) is proposed. In addition, the k−ɛ-differential privacy-preserving is proposed to prevent users’ location information from being leaked. Through comparison experiments, the effectiveness of the proposed incentive mechanism is verified. The proposed incentive mechanism with location privacy-preserving can inspire users to participate sensing tasks, and protect users’ location privacy effectively.

Quality-aware incentive mechanism based on payoff maximization for mobile crowdsensing

Recent years, we have witnessed the explosion of smart devices. These smart devices are more and more powerful with a set of built in sensor devices, such as GPS, accelerometer, gyroscope, camera, etc. The large scale and powerful smart devices make the mobile crowdsensing applications which leverage public crowd equipped with various mobile devices for large scale sensing tasks be possible. In this paper, we study a critical problem of payoff maximization in mobile crowdsensing system with incentive mechanism. Due to the influence of various factors (e.g. sensor quality, noise, etc.), the quality of the sensed data contributed by individual users varies significantly. Obtaining the high quality sensed data with less expense is the ideal of sensing platforms. Therefore, we take the quality of individuals which is determined by the sensing platforms into incentive mechanism design. We propose to maximize the social welfare of the whole system, due to that the private parameters of the mobile users are unknown to the sensing platforms. It is impossible to solve the problem in a central manner. Then a dual decomposition method is employed to divide the social welfare maximization problem into sensing platforms’ local optimization problems and mobile users’ local optimization problems. Finally, distributed algorithms based on an iterative gradient descent method are designed to achieve the close-to-optimal solution. Extensive simulations demonstrate the effectiveness of the proposed incentive mechanism.

A Blockchain Based Truthful Incentive Mechanism for Distributed P2P Applications

In distributed peer-to-peer (P2P) applications, peers self-organize and cooperate to effectively complete certain tasks such as forwarding files, delivering messages, or uploading data. Nevertheless, users are selfish in nature and they may refuse to cooperate due to their concerns on energy and bandwidth consumption. Thus each user should receive a satisfying reward to compensate its resource consumption for cooperation. However, suitable incentive mechanisms that can meet the diverse requirements of users in dynamic and distributed P2P environments are still missing. On the other hand, we observe that Blockchain is a decentralized secure digital ledger of economic transactions that can be programmed to record not just financial transactions and Blockchain-based cryptocurrencies get more and more market capitalization. Therefore in this paper, we propose a Blockchain based truthful incentive mechanism for distributed P2P applications that applies a cryptocurrency such as Bitcoin to incentivize users for cooperation. In this mechanism, users who help with a successful delivery get rewarded. As users and miners in the Blockchain P2P system may exhibit selfish actions or collude with each other, we propose a secure validation method and a pricing strategy, and integrate them into our incentive mechanism. Through a game theoretical analysis and evaluation study, we demonstrate the effectiveness and security strength of our proposed incentive mechanism.