Truthful Incentive Mechanism Research Articles

Tensor-Based Secure Truthful Incentive Mechanism for Mobile Crowdsourcing in IoT-Enabled Maritime Transportation Systems

Tensor-Based Secure Truthful Incentive Mechanism for Mobile Crowdsourcing in IoT-Enabled Maritime Transportation Systems

Truthful Incentive Mechanism Design via Internalizing Externalities and LP Relaxation for Vertical Federated Learning

Truthful Incentive Mechanism Design via Internalizing Externalities and LP Relaxation for Vertical Federated Learning

Towards Privacy-Driven Truthful Incentives for Mobile Crowdsensing Under Untrusted Platform

Reverse auction-based incentive mechanisms have been commonly proposed to stimulate mobile users to participate in crowdsensing, where users submit bids to the platform to compete for interested tasks. Recent works pointed out that bid is a private information which can reveal sensitive information of users (e.g., location privacy), and proposed bidding-preserving mechanisms with differential privacy against inference attack. However, all these mechanisms rely on a trusted platform, and would fail in bid protection completely when the platform is untrusted. In this paper, we design novel privacy-preserving incentive mechanisms to protect users’ true bid information against the honest-but-curious platform while minimizing the social cost of winner selection. To this end, instead of uploading the true bid to the platform, a differentially private bid obfuscation function is designed with the exponential mechanism, which helps each user to obfuscate bids locally and submit obfuscated bids to the platform. Two solutions are proposed for the platform to solve the winner selection problem with the obfuscated information, which is proved to be NP-hard. Moreover, we further propose a novel task-bid pair protection truthful incentive mechanism to further prevent privacy leakage from the set of interested tasks, where each user encrypts his interested tasks via homomorphic encryption locally, and an encrypted task clustering method is proposed to group users with the same interested tasks into the same cluster for winner selection with users’ encrypted task-bid pairs. Both of theoretical analysis and extensive experiments demonstrate the effectiveness of proposed mechanisms against the untrusted platform.

Truthful Incentive Mechanism for Budget-Constrained Online User Selection in Mobile Crowdsensing

Mobile crowdsensing (MCS) has attained much attention for gathering distributed mobile users to complete large-scale sensing tasks. To ensure the task completion, enough users should be motivated to participate in sensing tasks. Thus, much research in MCS focuses on proposing incentive mechanism, and these works usually focus on the offline scenario, where the information of all users is available to the platform. However, we argue that the actual MCS is usually an online scenario, where the platform does not know the user's information until they establish connections with the platform. Meanwhile, mobile users connect to the platform randomly and will cut off the connection at any time. Hence, when accessed by a user, the platform needs to make an irrevocable decision instantly about whether to select the user or not, and decides a remuneration for the user without knowing future information. In this paper, we first propose a reverse-auction framework to model the interaction between the platform and mobile users. Then, we present an online truthful incentive mechanism (OTIM) to motivate users, including online winner selection and remuneration determination strategies. Finally, massive simulations are conducted based on three real traces, and the simulation results illustrate that OTIM achieves truthfulness, individual rationality, computational efficiency and an approximately full budget utilization.

Blockchain-Empowered Collaborative Task Offloading for Cloud-Edge-Device Computing

How to enable high-performance task offloading and preserve the trust between participants is imperative yet nontrivial to the Cloud-Edge-Device (CED) computing, mainly because the resources are geo-distributed and operated by different parties. Also, the CED participants are highly dynamic and heterogeneous in resource provision and may conflict in interest. This paper proposes BlockChain-empowered CED (BC-CED), a blockchain-empowered collaborative task offloading for CED computing. In BC-CED, blockchain plays a central role in the main functionality of CED, including task offloading, brokerage of resource usage, and incentives. We distinguish the BC-CED from the existing solutions by modifying the blockchain consensus process, enabling the participants to reach an agreement via solving the task offloading problem. For this purpose, we formulate the offloading problem by considering the computation capabilities of candidate nodes and the network performance. BC-CED allows each participant to apply reinforcement learning-based methods to solve this problem and compete for the right of block output by comparing the offloading policy performance and accepting the best policy as the offloading scheme within the next period. We also propose a truthful incentive mechanism to encourage resource contributions in BC-CED and force them to be honest. Extensive tests by implementing our solutions in a commercialized blockchain platform have shown how BC-CED achieves a superior performance in task offloading and blockchain maintenance.

A Tensor-Based Truthful Incentive Mechanism for Blockchain-Enabled Space-Air-Ground Integrated Vehicular Crowdsensing

Space-Air-Ground Integrated Network (SAGIN) as an efficient newly integration network could provide more comprehensive network services to meet the multifarious quality of service requirements in different Intelligent Transportation Systems (ITS). By taking advantage of SAGIN, Space-Air-Ground Integrated Vehicular Crowdsensing (SAGI-VCS) would have great potential and the services regarding ITS could be facilitated. However, centralized SAGI-VCS is usually vulnerable to malicious attacks and the trust issues are one of the main reasons that hinder its further development. Blockchain as a distributed hyperledger shows a vital potential to solve the trust problem of multiple participants who do not trust each other and tackle the security issues in SAGI-VCS. Additionally, selfishness is another factor that prevents vehicles from participating in SAGI-VCS. The vast majority of existing incentives for vehicular crowdsensing only focus on the terrestrial networks which cannot be directly used in SAGI-VCS. Meanwhile, the redundant winner phenomenon and the multi-attributes of participants are less considered by them. Toward this end, we first illustrate a blockchain-enabled service architecture for SAGI-VCS and then construct a unified representation model. Afterwards, a tensor computing based truthful incentive mechanism TensorBC for blockchain-enabled SAGI-VCS is proposed to motivate vehicles to participate in completing tasks, ensure the security of the whole process and maximize the social welfare. TensorBC not only can eliminate the redundant winner phenomenon, but also can guarantee the economic properties such as truthfulness, individual rationality and profitability. Finally, both the rigorous theoretical analysis and extensive experimental results show that TensorBC could achieve a better performance.

Auction design for cross-edge task offloading in heterogeneous mobile edge clouds

Task offloading is a promising technology to exploit the available resources in edge cloud efficiently. Many incentive mechanisms for offloading systems have been proposed. However, most of existing works study the centralized incentive mechanisms under the assumption that all mobile edge infrastructures are operated by a central cloud. In this paper, we aim to design the auction-based truthful incentive mechanisms for heavily loaded task offloading system in heterogeneous MECs. We first study the homogeneous MEC situation and present a global auction executed in the central cloud as a benchmark. For the heterogeneous MEC situation, we model the system as a dual auction framework, which enables the heterogeneous MECs to perform cross-edge task offloading without the participation of central servers. Specifically, we design two dual auction models: secondary auction-based model, which enables the system to offload tasks from a large-scale region in a single auction, and double auction-based model, which is suitable for the time sensitive tasks. Then the auctions for these two dual auction models are proposed. Through rigorous theoretical analysis, we demonstrate that the proposed auctions achieve desirable properties of computational efficiency, individual rationality, budget balance, truthfulness, and guaranteed approximation. The simulation results show that the secondary auction and double auction can obtain 14.5% and 4.2% more social welfare than comparison algorithm on average, respectively. In addition, the double auction has great advantage in terms of computation efficiency.

Towards Flexible and Truthful Incentive for Multichannel Allocation in DSA

Due to its potential of increasing spectrum utilization, dynamic spectrum access (DSA) is a promising platform to solve the so-called spectrum shortage problem, caused by a huge proliferation of wireless applications and services. Because of good allocation efficiency and fairness, auction based mechanisms have been extensively studied in DSA, in which truthfulness is a critical property to guarantee that each bidder can obtain her maximal utility by bidding with her true valuation of bidding spectrum. However, existing spectrum auction mechanisms on homogenous channels strictly require bidders to bid one channel or multiple channels with continuous demands, and does not consider the noncontinuous demands for channels. In this paper, we propose THIMBLE, the first TrutHful Incentive Mechanism for flexiBle muLtichannEl allocation by considering bidders’ continuous/noncontinuous demands for channels and spatial reusability. Specifically, bidders using the same channel without interference are formulated to a group, and a group bid recalculation method is presented for noncontinuous group bids. Next, a winner selection and charging method based on group bids is designed to achieve truthfulness. Extensive simulation results show that THIMBLE achieves not only truthfulness in continuous and noncontinuous channel demand cases, but also as good spectrum allocation efficiency as existing spectrum auction mechanisms.

Incentive Mechanisms for Large-Scale Crowdsourcing Task Diffusion Based on Social Influence

Crowdsourcing has become an effective tool to utilize human intelligence to perform tasks that are challenging for machines. Many incentive mechanisms for crowdsourcing systems have been proposed. However, most of existing mechanisms assume that there are enough participants to perform the crowdsourcing tasks. This assumption may not be true in large-scale crowdsourcing scenarios. To address this issue, we diffuse the crowdsourcing tasks via the social network. We study two task diffusion models, and formulate the problem of minimizing the total cost such that all tasks can be completed in expectation for each model. The topology based influence estimation and history based influence estimation based on the limited knowledge of social network are presented in this paper. Further, we present the global influence estimation method to measure the influence over the whole community with the full knowledge of social network. We design two sealed reverse auction based truthful incentive mechanisms, MTD-L and MTD-IC, for both diffusion models. Through both rigorous theoretical analysis and extensive simulations, we demonstrate that the proposed mechanisms achieve computational efficiency, individual rationality, truthfulness, and guaranteed approximation. Moreover, the global influence estimation based mechanisms always output the least social cost and overpayment ratio, and the history influence estimation based mechanisms show significant superiority in terms of task completion rate.

Incentive Mechanism for Mobile Devices in Dynamic Crowd Sensing System

Mobile crowdsensing (MCS) has gained much attention due to the proliferation of smart devices equipped with powerful sensors. Large-scale users are the foundation of MCS, so designing incentive mechanisms to motivate users to participate in MCS is necessary. Existing works on incentive mechanisms usually assume a scenario where a group of tasks arrive at the platform at the same time and are immediately assigned to users. We argue that a more realistic MCS scenario can delay a task, which is called the assignment duration time, to wait for appropriate users. In this scenario, we focus on proposing a truthful incentive mechanism to reduce the overall social cost. Due to the uncertainty of coming users, the problems of selecting the appropriate users and calculating the payment for each recruited user (winner) are more complicated. To overcome these challenges, we design a dynamic truthful incentive mechanism (DTIM) including winner selection and payment decision processes. The former uniformly recruits users before the assignment deadline of tasks and dynamically readjusts the recruiting frequency of other tasks to select winners iteratively, which achieves an approximation ratio. Furthermore, the latter determines truthful payment for each winner to encourage user participation as well as avoid being deceived, which achieves truthfulness, individual rationality, and computational efficiency. Finally, massive simulations based on a real dataset roma/taxi validate the DTIM, which can effectively reduce the overall social cost and make a truthful payment for each winner.

Incentive Mechanism for Multiple Cooperative Tasks with Compatible Users in Mobile Crowd Sensing via Online Communities

Mobile crowd sensing emerges as a new paradigm which takes advantage of the pervasive sensor-embedded smartphones to collect data. Many incentive mechanisms for mobile crowd sensing have been proposed. However, none of them takes into consideration the cooperative compatibility of users for multiple cooperative tasks. In this paper, we design truthful incentive mechanisms to minimize the social cost such that each of the cooperative tasks can be completed by a group of compatible users. We study two bid models and formulate the Social Optimization Compatible User Selection (SOCUS) problem for each model. We also define three compatibility models and use real-life relationships from social networks to model the compatibility relationships. We design two incentive mechanisms, MCT - M and MCT - S, for the compatibility cases. Both of MCT - M and MCT - S consist of two steps: compatible user grouping and reverse auction. We further present a user grouping method through neural network model and clustering algorithm. Through both rigorous theoretical analysis and extensive simulations, we demonstrate that the proposed mechanisms achieve computational efficiency, individual rationality, and truthfulness. Moreover, MCT - M can output the optimal solution. By using neural network and clustering algorithm for user grouping, the proposed incentive mechanisms can reduce the social cost and overpayment ratio further with less grouping time.

Promoting Users’ Participation in Mobile Crowdsourcing: A Distributed Truthful Incentive Mechanism (DTIM) Approach

With the advancement of smartphones, mobile crowdsourcing has become a new computing paradigm to efficiently support novel mobile applications. Achieving good service quality of these applications, however, necessitates the participation of large number of smartphones, which can be obtained via providing suitable incentives to smartphone users. Nonetheless, most existing incentive mechanisms assume a centralized platform for recruiting smartphones, which is prone to expose the privacy of both smartphones and task requesters. In this context, this article studies a distributed truthful incentive mechanism (DTIM) for mobile crowdsourcing, where multiple auction rounds can be conducted locally in each smartphone and task requester. Specifically, in each auction round, the participating smartphones act as the sellers and submit bids to compete for their intended crowdsourcing tasks. The task requesters, on the other hand, act as the buyers that decide on the sellers and corresponding payments, depending on their submitted bids. Finally, based on the offered payment, each smartphone selects a buyer for trading to optimize its utility. It is shown that the proposed incentive mechanism is strategy-proof, budget balanced, individually rational, and computationally efficient. Numerical results provided corroborate the beneficial properties of the scheme.

Pay as How You Behave: A Truthful Incentive Mechanism for Mobile Crowdsensing

Mobile crowdsensing (MCS) is widely applied in large-scale distributed networks for collecting sensing data from workers. In an MCS system, workers are recruited to complete tasks for data requesters, and they will get profits. Accordingly, how to establish an effective incentive mechanism has become an important issue to consider. Since workers are naturally selfish, they try to maximize individual benefits while minimize costs. In this article, we propose a truthful incentive mechanism which pays for the workers by the workers’ performance in the task just completed and the reputation. For each worker, through the future prediction function, we get the reputation of the worker by utilizing the previous performances. In the proposed scheme, partial payment for the workers is distributed depending on workers’ reputation. The final payment is based on punishments and rewards according to the performances. Moreover, data accuracy and response time are introduced to evaluate the worker performance in the task. It can be demonstrated that the mechanism provides continuous incentives to workers compared to the single ex-ante and ex-post pricing schemes. The experimental results show that our mechanism is effective.

Efficient Path Planning and Truthful Incentive Mechanism Design for Mobile Crowdsensing.

Mobile crowdsensing (MCS) is a promising paradigm for large-scale sensing. A group of users are recruited as workers to accomplish various sensing tasks and provide data to the platform and requesters. A key problem in MCS is to design the incentive mechanism, which can attract enough workers to participate in sensing activities and maintain the truthfulness. As the main advantage of MCS, user mobility is a factor that must be considered. We make an attempt to build a technical framework for MCS, which is associated with a truthful incentive mechanism taking the movements of numerous workers into account. Our proposed framework contains two challenging problems: path planning and incentive mechanism design. In the path planning problem, every worker independently plans a tour to carry out the posted tasks according to its own strategy. A heuristic algorithm is proposed for the path planning problem, which is compared with two baseline algorithms and the optimal solution. In the incentive mechanism design, the platform develops a truthful mechanism to select the winners and determine their payments. The proposed mechanism is proved to be computationally efficient, individually rational, and truthful. In order to evaluate the performance of our proposed mechanism, the well-known Vickrey–Clarke–Groves (VCG) mechanism is considered as a baseline. Simulations are conducted to evaluate the performance of our proposed framework. The results show that the proposed heuristic algorithm for the path planning problem outperforms the baseline algorithms and approaches the optimal solution. Meanwhile, the proposed mechanism holds a smaller total payment compared with the VCG mechanism when both mechanisms achieve the same performance. Finally, the utility of a selected winner shows the truthfulness of proposed mechanism by changing its bid.

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.

Towards secure and truthful task assignment in spatial crowdsourcing

The ubiquity of mobile device and wireless networks flourishes the market of spatial crowdsourcing, in which location constrained tasks are sent to workers and expected to be performed in some designated locations. To obtain a global optimal task assignment scheme, the platform usually needs to collect location information of all workers. During this process, there is a significant security concern, that is, the platform may not be trustworthy, so it brings about a threat to workers location privacy. In this paper, to tackle the privacy-preserving task assignment problem, we propose a privacy-preserving reverse auction based assignment model which consists of two key parts. In the first part, we generalize private location to travel cost and protect it by an anonymity based data aggregation protocol. In the second part, we propose a reverse auction task assignment algorithm, which is a truthful incentive mechanism, to encourage workers to offer authentic data. We theoretically show that the proposed model is secure against semi-honest adversaries. Experimental results show that our model is efficient and can scale to real SC applications.

Truthful incentive mechanisms for mobile crowd sensing with dynamic smartphones

The emergence of ubiquitous mobile devices has given rise to mobile crowd sensing, as a new data collection paradigm to potentially produce enormous economic value. Fully aware of the paramount importance to incentivize smartphone users’ participation, a wide variety of incentive mechanisms have been proposed, however, most of which have made the impractical assumption that smartphones remain static in the system and sensing tasks are known in advance. Designing truthful incentive mechanisms for mobile crowd sensing system has to address four major challenges, i.e., dynamic smartphones, uncertain arrivals of tasks, strategic behaviors, and private information of smartphones. To jointly address these four challenges, we propose two truthful auction mechanisms, OT-OFMCS and NOT-ONMCS, with respect to the offline and online case of mobile crowd sensing, aiming at selecting an optimal set of winning bids with low costs for maximizing the social welfare. The OT-OFMCS mechanism features an optimal task allocation algorithm with the polynomial-time computational complexity where the information of all smartphones and tasks are known a priori. The NOT-ONMCS mechanism is comprised of a critical payment scheme and an online allocation algorithm with a 12-competitive ratio, where the real-time allocation decisions are made based on current active smartphones. To improve the theoretical competitive ratio, we investigate a modified online approximation algorithm RWBD with the ratio of (1−1e). Rigorous theoretical analysis and extensive simulations have been performed, and the results demonstrate our proposed auction mechanisms achieve truthfulness, individual rationality and computational efficiency.

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.

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.

A truthful incentive mechanism for mobile crowd sensing with location-Sensitive weighted tasks

Mobile crowd sensing has emerged as an appealing paradigm to provide sensing data for its efficient economy. A large number of incentive mechanisms has been proposed for stimulating smartphone users to participate in mobile crowd sensing applications. Different from existing work, in addition to sensing tasks with diverse weights, we uniquely take into consideration the crucial dimension of location information when performing sensing tasks allocation. However, the location-sensitive weighted tasks are more vulnerable to the real life where each sensing task has the evident distinction. Meanwhile, the location sensitiveness leads to the increase of theoretical and computational complexity. In this paper, we investigate a truthful incentive mechanism which consists of two main components, winning bids determination algorithm and critical payment scheme. Since optimally determining the winning bids is NP hard, a near-optimal algorithm with polynomial-time computation complexity is proposed, which further approximates the optimal solution within a factor of 1+ln(n), where n is the maximum number of sensing tasks that a smartphone can accommodate. To guarantee the truthfulness, a critical payment scheme is proposed to induce smartphones to disclose their real costs. Through both rigid theoretical analysis and extensive simulations, we demonstrate that the proposed mechanism achieves truthfulness, individual rationality and high computation efficiency.