Auction-based Incentive Mechanism Research Articles

Truthful double auction based incentive mechanism for participatory sensing systems

Truthful double auction based incentive mechanism for participatory sensing systems

Auction-Based Incentive Mechanism in Federated Learning Considering Communication Path Finding

Auction-Based Incentive Mechanism in Federated Learning Considering Communication Path Finding

Towards Reliable Federated Learning Using Blockchain-Based Reverse Auctions and Reputation Incentives

In recent years, the explosion of big data has presented unparalleled opportunities for the advancement of machine learning (ML). However, the vast size and sensitive nature of these datasets present significant challenges in terms of privacy and security. Federated Learning has emerged as a promising solution that enables a group of participants to train ML models without compromising the confidentiality of their raw data. Despite its potential, traditional federated learning faces challenges such as the absence of participant incentives and audit mechanisms. Furthermore, these challenges become more significant when dealing with the scale and diversity of big data, making efficient and reliable federated learning a complex task. These limitations may compromise model quality due to potential malicious nodes. To address the above issues, this paper proposes a BlockChain-based Decentralized Federated Learning (BCD-FL) model. In BCD-FL, we design a smart contract approach based on the reverse auction-based incentive mechanism and a reputation mechanism to promote the participation of reliable and high-quality data owners. Theoretical analysis shows that the BCD-FL model satisfies several desirable properties, such as individual rationality, computational efficiency, budget balance, and truthfulness. In addition, experimental results also show that the proposed model enables more efficient federated learning and provides some level of protection against malicious nodes. Therefore, the BCD-FL model presents a potential solution to the challenges in federated learning and opens up new possibilities for achieving efficient large-scale machine learning.

Competition and Cooperation: Global Task Assignment in Spatial Crowdsourcing

Online spatial crowdsourcing platforms provide popular O2O services in people's daily. Users submit real-time tasks through the Internet and require the platform to immediately assign workers to serve them. However, the imbalance distribution of tasks and workers leads to the rejection of some tasks, which reduces the profit of the platform. In this paper, we propose that similar platforms can form an alliance to make full use of the global service supply through cooperation. We name the problem as <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">G</u> lobal <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">T</u> ask <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">A</u> ssignment (GTA), in which platforms are allowed to hire idle workers from other platforms to improve the profit of all the platforms together. Different from relevant works, the decision-makers in GTA are platforms rather than individual workers, which can better assign workers in all platforms and improve the overall profit. We design an auction-based incentive mechanism (AIM), to motivate platforms to rent idle workers to other platforms so that increase their own profit. Based on the mechanism, we propose a greedy-based assignment algorithm (BaseGTA), in which platforms greedily maximizes their current profit. We further propose a prediction-based assignment algorithm (ImpGTA), in which platforms make decisions based on the spatial-temporal distribution in the future time. Experimental results show that platforms using our algorithms can achieve higher profit than the existing studies.

Reinvigorating sustainability in Internet of Things marketing: Framework for multi-round real-time bidding with game machine learning

Auction-based incentive mechanisms can satisfy the heterogeneous demands of both Demand Side Platforms (DSP) and Supply Side Platforms (SSP) in Internet of Things (IoT) marketing. However, DSP platforms often need help with two issues during the auction process: low enthusiasm and unreasonable bidding. To address these problems, we use the second-price sealed auction and propose a framework for multi-round real-time bidding with game machine learning. We introduce a multi-round advertising bidding mechanism incorporating reputation incentive rules to enhance DSP enthusiasm. The aim is to stimulate DSP participation and deter malicious DSP behavior, ensuring fairness and transparency in the bidding process. Subsequently, we design an auction screening model and adopt a multi-round auction format to ensure that only capable and willing advertising demand partners can participate, thus guaranteeing the reasonableness of DSP bids. Furthermore, we design a real-time bidding mechanism to adapt to the dynamic nature of the IoT marketing market. This mechanism transforms the problem of maximizing DSP revenue under budget constraints into a parameter adjustment problem based on a Markov Decision Process. We then utilize the Double Deep Q Network method to obtain the optimal bidding strategy for DSPs. Ultimately, the results demonstrate that our framework improves the final transaction price by 14.71%, increases the expected click-through rate by an average of 19.35%, and reduces the average cost per thousand impressions by 20.34%.

A Novel Oracle-Aided Industrial IoT Blockchain: Architecture, Challenges, and Potential Solutions

Smart contract over the blockchain accelerates the deployment of decentralized Industrial Internet of Things (IIoT) applications. However, the usability of smart contracts is constrained to the data on the blockchain, and has no direct access to the off-chain environment. Consequently, smart contract cannot directly serve as a carrier for the IIoT applications that rely on intensive data exchange and task computation. To cope with this issue, we first propose an oracle-aided IIoT blockchain (OIB) system to facilitate the deployment of smart contract based IIoT applications. Specifically, a distributed oracle network is employed to not only bridge the off-chain environment and smart contracts, but also extend the computing capability of smart contracts. Then, we study important properties of the OIB system, i.e., liveness, safety, and fairness. In particular, we propose a new challenge of fairness to guarantee that each oracle can reap positive utility (i.e., individual rationality), and a new challenge of liveness to meet delay constraints of oracle services (i.e., delay-sensitivity). To address these two challenges, we develop an auction based incentive mechanism for the data-feeding oracles to achieve individual rationality as well as incentive compatibility, and a delay-sensitive matching based incentive mechanism for the computing oracles under specific delay constraints. Simulation results corroborate that the proposed auction based incentive mechanism guarantees not only truthfulness but also individual rationality, and show that the matching based incentive mechanism achieves smaller delay than the random selection scheme used in the Decentralized Oracle Service (DOS) network.

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.

RAIM: A Reverse Auction-Based Incentive Mechanism for Mobile Data Offloading Through Opportunistic Mobile Networks

Offloading cellular traffic through Opportunistic Mobile Networks (OMNs) has been an effective method to ease the traffic burden of cellular networks. However, providing data offloading services consumes a lot of resources. Since nodes in OMNs are rational and selfish, they will not be willing to provide data offloading services if they are not properly rewarded. Therefore, it is important to exploit incentive mechanisms to motivate nodes to provide data offloading services. This paper proposes a Reverse Auction-based Incentive Mechanism, named RAIM. In RAIM, reverse auction is used as the incentive mechanism, and the incentive-driven data offloading process is modeled as Non-Linear Integer Programming (NLIP) from the business point of view, aiming to minimize the cost of the Content Service Provider (CSP). Then, a heuristic method named Decay-based Helper Selection Method (DBHSM) is proposed to resolve the problem. Moreover, a payment rule based on the standard Vickrey-Clarke-Groves scheme is proposed to ensure the individual rationality and truthfulness properties of DBHSM. Finally, real mobility trace-driven simulation results show that DBHSM outperforms other baseline methods in terms of the CSP's cost and the offloading rate under different scenarios.

GAIMMO: A Grade-Driven Auction-Based Incentive Mechanism With Multiple Objectives for Crowdsourcing Managed by Blockchain

Blockchain has been applied for decentralized crowdsourcing management by deploying a number of miners to reach a consensus on crowdsourced task allocation and payment decision. In a blockchain-based crowdsourcing system (BCS), incentive becomes essential to motivate the participation and cooperation of all system entities. However, existing literature scarcely investigates how to motivate heterogeneous crowdsourcers, workers, and miners simultaneously toward satisfying multiple objectives without the support of centralized management. In this article, we propose GAIMMO, a novel grade-driven auction-based incentive mechanism for BCS with multiple objectives in mind: crowdsourcer utility maximization, social welfare maximization, social grade maximization, and social cost minimization. Concretely, we propose a grade-based task sorting (GTS) algorithm to determine the service priority of heterogeneous crowdsourcers in order to motivate their cooperative behaviors, which consequently maximizes crowdsourcer utility when combining with the carefully designed utility functions of other system entities. We propose a grade-based utility function of workers and employ a hierarchical premium-based task assignment (PTA) algorithm to realize social welfare maximization, social grade maximization, and social cost minimization. We further propose a fixed-grade-sum and grade-based reward-sharing (FGSGRS) method to encourage fast block generation and motivate high-grade miners without damaging the profits of the crowdsourcers. We conduct simulation-based experiments to show the effectiveness and advance of our proposed incentive mechanism in stimulating the participation willingness of high-grade system entities and achieving the multiple objectives.

An online auction-based incentive mechanism for soft-deadline tasks in Collaborative Edge Computing

Recently, Collaborative Edge Computing (CEC) has been proposed as an effective method to improve the performance of Mobile Edge Computing (MEC) systems by offloading workload from busy MEC servers to idle ones. Although extensive research has been conducted to study the system design and scheduling algorithms in CEC, the incentive mechanism between end-users and collaborative servers yet receives much less attention. To fill the gap, in this paper, we propose an online auction-based incentive mechanism where the task requests arrive stochastically and MEC servers need to decide whether to accept them without future information. To guarantee the applicability of our mechanism, we consider a comprehensive model where both the valuation of tasks and operation costs of edge servers are represented by general functions. Through theoretical analysis, we formally proved our mechanism achieves desirable properties, such as truthfulness and polynomial time complexity. Based on the primal–dual optimization framework, we further demonstrate the competitive ratio of our mechanism with respect to the optimal offline social welfare. Extensive simulations are conducted to verify our theoretical analysis and validate the effectiveness of our mechanism.

Bilateral Privacy-Preserving Truthful Incentive for Mobile Crowdsensing

Reverse auction-based incentive mechanisms have been widely adopted to stimulate mobile workers to participate in mobile crowdsensing (MCS), where workers need to provide location information for winner selection. However, most existing mechanisms rely on trusted platforms. The worker’s location data uploaded to platforms thus can be easily exposed. Recent works start to incorporate location privacy in designing incentive mechanisms, but they have not considered the task’s location privacy and the worker’s location privacy simultaneously. Therefore, we propose a bilateral location privacy-preserving mechanism for MCS on untrusted platforms. In our model, each worker adopts differential privacy to obfuscate his location locally and then submits the obfuscated location together with the bid information. Besides, instead of the exact location, the task requester is only required to upload the task profile and the task’s obfuscated location. Then, we propose the lowest-cost winner selection mechanism which aims to minimize the social cost of winner selection under the location constraint while ensuring task quality requirements, and adopt the critical payment determination mechanism to determine the payments for the winners, which satisfies truthfulness, individual rationality, and computational efficiency. Theoretical analysis and extensive experiments on real-world datasets show the effectiveness of the proposed mechanisms.

Incentivizing WiFi-Based Multilateration Location Verification

Due to the proliferation of WiFi devices and the high verification precision, researchers have shown interests in WiFi-based multilateration location verification (WMLV), where multiple WiFi APs (also known as verifiers) verify the location information claimed by a prover. However, it is a high expenditure for any single location-based service provider to deploy densely covered WiFi facilities. Incentivizing independent WiFi owners to corporately verify location information is thus a feasible solution to this plight, yet none of the previous research has taken this into consideration. To this point, we design a double auction-based incentive mechanism for WMLV, which motivates the participation of both provers and verifiers. More importantly, we consider practical situations, where the provers have various verification precision requirements, and different number of verifiers are required by different provers. The proposed double auction mechanism achieves desirable economical properties, including <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">truthfulness, individual rationality, computational efficiency, budget balance,</i> and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">nonnegative social welfare.</i> The desired properties are validated through both theoretical analysis and extensive simulations.

Distributed Auction-Based Incentive Mechanism for Energy Trading Between Electric Vehicles and Mobile Charging Stations

With the increasing number of electric vehicles, deploying fixed charging stations (FCSs) has been a widely adopted solution for providing charging services to EVs. However, the charging requirement of EVs being near overload FCSs and/or in areas of inadequate charging infrastructures such as highways and rural areas will surpass the capabilities of FCSs. To address this challenge, the realized peer-to-peer (P2P) energy trading between electric vehicles (EVs) and mobile charging stations (MCSs) can be utilized to relieve the overload on FCSs, leverage under-utilized energy resources among cities and achieve trading benefits. By deploying this energy trading model, EVs purchase available energy from MCSs instead of FCSs to fulfill their charging demands. However, such an energy trade requires an incentive mechanism to ensure fair trading and prevent personal gain, which motivates us to study the incentive mechanism design for the energy trading model. In this paper, first, we consider an energy trading system involving multiple MCSs and EVs. Then, we formulate the incentive mechanism between MCSs and EVs as an auction game, in which the MCSs are auctioneers and EVs are bidders. In the formulated problem, each EV secretly submits its bid to MCSs, and each MCS distributively decides the winners without the knowledge of other MCSs. To achieve this, we design a distributed action-based energy trading mechanism that ensures fairness of providing charging service, validity in trading price’s resource determination, and nothing incentivizing the EVs to cheat on payment decisions. The proposed energy trading scheme achieves certain critical properties, including truthfulness, individual rationality, budget balance, and computational efficiency for both buyers and sellers. Finally, we perform simulation experiments to verify the proposed scheme’s effectiveness compared with the baseline in the literature.

Collusion-Resistant Worker Recruitment in Crowdsourcing Systems

In the wake of the Web 2.0, crowdsourcing has emerged as a promising approach to maintain a flexible workforce for human intelligence tasks. To stimulate worker participation, many reverse auction-based incentive mechanisms have been proposed. Designing auctions that discourage workers from cheating and instead encouraging them to reveal their true cost information has drawn significant attention. However, the existing efforts have been focusing on tackling individual cheating misbehaviors, while the scenarios that workers strategically form collusion coalitions and rig their bids together to manipulate auction outcomes have received little attention. To fill this gap, in this work we develop a <inline-formula><tex-math notation="LaTeX">$(t,p)$</tex-math></inline-formula> -collusion resistant scheme that ensures no coalition of <i>weighted cardinality</i> <inline-formula><tex-math notation="LaTeX">$t$</tex-math></inline-formula> can improve its group utility by coordinating the bids at a probability of <inline-formula><tex-math notation="LaTeX">$p$</tex-math></inline-formula> . This paper takes into account the unique features of crowdsourcing, such as diverse worker types and reputations, in the design. The proposed scheme can suppress a broad spectrum of collusion strategies. Besides, desirable properties, including <inline-formula><tex-math notation="LaTeX">$p$</tex-math></inline-formula> -truthfulness and <inline-formula><tex-math notation="LaTeX">$p$</tex-math></inline-formula> -individual rationality, are also achieved. To provide a comprehensive evaluation, we first analytically prove our scheme's collusion resistance and then experimentally verify our analytical conclusion using a real-world dataset. Our experimental results show that the baseline scheme, where none of the critical properties is guaranteed, costs up to 20.1 times the optimal payment in an ideal case where no collusion exists, while our final scheme is merely 4.9 times the optimal payment.

Differentially Private Double Auction with Reliability-Aware in Mobile Crowd Sensing

With the unprecedented proliferation of mobile devices, Mobile Crowd Sensing (MCS) emerges as a promising computing paradigm which utilizes sensor-embedded smart devices to collect sensory data. Recently, a number of privacy-preserving auction-based incentive mechanisms have been proposed. However, none of them guarantees the quality of sensing data in double-side auction scenarios. In this paper, we propose a Differentially Private Double Auction With Reliability-Aware in Mobile Crowd Sensing (DPDR). Specifically, we design the incentive mechanism by employing the exponential mechanism in double-side auction to select the clearing price tuple. Moreover, to collect precise sensory data, we heuristically choose more reliable workers as candidates for each clearing price tuple. We further improve the social welfare of the mechanism by designing the utility function with less sensitivity, or adopting a more practical pricing strategy. Through theoretical analysis, we demonstrate that our mechanisms can guarantee both differential privacy and economic properties, including individual rationality, budget balance, approximate truthfulness and approximate maximal social welfare. Extensive experimental results show that the improved mechanisms can achieve better performance than DPDR in term of social welfare, and all proposed mechanisms can produce high-quality data.

ATM: Attribute-Based Privacy-Preserving Task Assignment and Incentive Mechanism for Crowdsensing

Crowdsensing is a practical component in Internet-of-Things, in which task requesters outsource sensing tasks to workers by a crowdsensing server (CS). Task assignment and incentive design are two essential parts of crowdsensing. However, due to the semi-honest CS and malicious workers, there exist several security and fairness issues. First, during task assigning and reward distributing, CS can get the private information of both task requester and workers such as identity, attributes. Second, malicious workers who are unable to execute tasks can get the task content, which leaks task privacy. Third, most auction-based incentive mechanism only considers the biddings of workers and ignores the real capabilities of workers. To this end, we propose a privacy-preserving fine-grained task assignment scheme for the crowdsensing system. Moreover, we also design a novel incentive mechanism based on the capabilities of workers. To be specific, a ciphertext-policy attribute-based encryption (CP-ABE) scheme with the characteristics of hidden policy is adopted to select workers and protect the privacy that of both requirements and workers’ attributes. We quantify the capabilities of workers based on the workers’ attributes and further determine the workers’ reward according to their capabilities. Detailed security analysis demonstrates that our proposed scheme can protect workers’ identity privacy, attribute privacy, and data confidentiality. Besides, the extensive experiments show that our incentive mechanism can effectively motivate workers.

A Reverse Auction-Based Incentive Mechanism for Mobile Crowdsensing

Incentive mechanism has been an important research direction in mobile crowdsensing. An effective incentive mechanism is critical to ensure the adequate number of participants/workers by providing them proper rewards. However, existing incentive mechanisms lack consideration on potential contributions of individual workers when recruiting new workers and retaining existing workers in the system. In this article, we propose a reverse auction-based incentive mechanism (RAIN), which considers participants' potential contributions when recruiting new workers, performing reverse auctions, and retaining existing workers. The design objective is to optimize the worker composition in the system while reducing the system cost. In RAIN, the potential contribution of a user to the system is measured as the degree at which the user's joining or staying in the system can remedy the inadequacy of workers for task auction/execution at the frequently visited locations of the user. We present design details of RAIN which includes selective worker recruitment, reverse auction based on biased bids, and selective retaining of auction losers, all based on individual users' potential contributions to the system. Extensive simulation results show that RAIN can effectively optimize the worker composition in a system and also effectively reduce the system cost.

DRAIM: A Novel Delay-Constraint and Reverse Auction-Based Incentive Mechanism for WiFi Offloading

Offloading cellular traffic through WiFi Access Points (APs) has been a promising way to relieve the overload of cellular networks. However, data offloading process consumes a lot of resources (e.g., energy, bandwidth, etc.). Given that the owners of APs are rational and selfish, they will not participate in the data offloading process without receiving the proper reward. Hence, there is an urgent need to develop an effective incentive mechanism to stimulate APs to take part in the data offloading process. This paper proposes a novel Delay-constraint and Reverse Auction-based Incentive Mechanism, named DRAIM. In DRAIM, we model the reverse auction-based incentive problem as a nonlinear integer problem from the business perspective, aiming to maximize the revenue of the Mobile Network Operator (MNO), and jointly consider the delay constraint of different applications in the optimization problem. Then, two low-complexity methods: Greedy Winner Selection Method (GWSM), and Dynamic Programming Winner Selection Method (DPWSM) are proposed to solve the optimization problem. Furthermore, an innovative standard Vickrey-Clarke-Groves scheme-based payment rule is proposed to guarantee the individual rationality and truthfulness properties of DPWSM. At last, extensive simulation results show that the proposed DPWSM is superior to the proposed GWSM and the Random Winner Selection Method in terms of the MNO’s utility and traffic load under different scenarios.

Privacy-Aware Sensing-Quality-Based Budget Feasible Incentive Mechanism for Crowdsourcing Fingerprint Collection

Mobile crowdsourcing (MCS) has shown great potential in received signal strength (RSS) fingerprint collection, in which an incentive mechanism plays a critical role to motivate users’ participation. However, how to quantify the quality of the gathered fingerprint data is still not addressed well in the design of incentive mechanism for MCS-based fingerprint collection. In this paper, a sensing quality metric is proposed to characterize the joint impact of users’ privacy protection and the spatial coverage of the submitted data. Given a limited budget, a basic incentive mechanism is devised to recruit appropriate users to maximize sensing quality. Considering that the cost of each user is regarded as private information and users may be attempted to misreport their costs to increase the revenue. Hence, an auction-based incentive mechanism is proposed to achieve the truthfulness of users’ costs, which is truthful, individually rational, computationally efficient and budget feasible. Simulation results show that our proposed schemes outperform the baseline schemes and the experiment with real-world data is carried out to evaluate the performance of our proposed basic incentive mechanism.

A Truthful Online Mechanism for Collaborative Computation Offloading in Mobile Edge Computing

Collaborative computation offloading in mobile edge computing where edge users offload tasks opportunistically to resourceful neighboring mobile devices (MDs), offers a promising solution to satisfy low-latency requirements. However, most existing works assume that those MDs volunteer to help edge users without an incentive mechanism. In this article, we propose an auction-based incentive mechanism, where users and MDs participate in the system dynamically. Our auction mechanism runs in the online fashion and optimizes the long-term system welfare without knowledge of future information, e.g., task start time, task length, resource demand, and valuation, etc. We prove that the proposed online mechanism achieves the desired properties, including individual rationality, truthfulness, and computational tractability. Moreover, the theoretical competitive ratio shows that our online mechanism achieves near-optimal long-term social welfare close to the offline optimum. Extensive experiments based on real-world traces demonstrate the efficiency of the proposed online mechanism.