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

Zhan, Yufeng; Xia, Yuanqing; Zhang, Jinhui

doi:10.1016/j.adhoc.2018.01.009

Cited by 25 publications

(28 citation statements)

References 19 publications

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“…Wang [22]. In the incentive mechanism aspect, Zhan et al used the incentive mechanism to study the critical issue of maximizing revenue in the mobile group intelligence perception system and incorporated the personal qualities determined by the sensory platform into the incentive mechanism design to achieve the goal of obtaining high-quality sensor data at a lower cost [23]. Xiong et al proposed a task-oriented user selection incentive mechanism (TRIM), hoping to establish a task-centric design framework in the MCS system, and constructed task vectors from multiple dimensions to meet the task requirements to the greatest extent [24].…”

Section: Related Workmentioning

confidence: 99%

User Recruitment Algorithm for Maximizing Quality under Limited Budget in Mobile Crowdsensing

Jiang

Chen

Zhang

et al. 2022

Discrete Dynamics in Nature and Society

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In the mobile crowdsensing task assignment, under the premise that the data platform does not know the user’s perceived quality or cost value, how to establish a suitable user recruitment mechanism is the critical issue that this article needs to solve. It is necessary to learn the user’s perceived quality in the execution p. It also needs to try its best to ensure the efficiency and profit maximization of the mobile group intelligence perception platform. Therefore, this paper proposes a mobile crowdsensing user recruitment algorithm based on Combinatorial Multiarmed Bandit (CMAB) to solve the recruitment problem with known and unknown user costs. Firstly, the user recruitment process is modeled as a combined multiarm bandit model. Each rocker arm represents the selection of different users, and the income obtained represents the user’s perceived quality. Secondly, it proposes the upper confidence bound (UCB) algorithm, which updates the user’s perceptual quality according to the completion of the task. This algorithm sorts the users’ perceived quality values from high to low, then selects the most significant ratio of perceived quality to recruitment costs under the budget condition, assigns tasks, and updates their perceived quality. Finally, this paper introduces the regret value to measure the efficiency of the user recruitment algorithm and conducts many experimental simulations based on real data sets to verify the feasibility and effectiveness of the algorithm. The experimental results show that the recruitment algorithm with known user cost is close to the optimal algorithm, and the recruitment algorithm with unknown user cost is more than 75% of the optimal algorithm result, and the gap tends to decrease as the budget cost increases, compared with other comparisons. The algorithm is 25% higher, which proves that the proposed algorithm has good learning ability and can independently select high-quality users to realize task assignments.

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Section: Related Workmentioning

confidence: 99%

User Recruitment Algorithm for Maximizing Quality under Limited Budget in Mobile Crowdsensing

Jiang

Chen

Zhang

et al. 2022

Discrete Dynamics in Nature and Society

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show abstract

“…It is assumed that the platform revenue function to support the sensing task is the strictly concave function defined in (16), where µ is a system parameter. This logarithmic function is widely used in previous works for MCS systems (see, e.g., [25], [26], [28], [43]- [45])and it is based on the participation of users:…”

Section: B Mcs Platform Profit Modelmentioning

confidence: 99%

A Stackelberg Game Approach for Managing AI Sensing Tasks in Mobile Crowdsensing

et al. 2022

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Mobile Crowdsensing (MCS) is a new paradigm that leverages the collective sensing ability of a crowd so that a special task can be performed through the aggregation of information collected from personal mobile devices. While MCS brings several benefits, its application is prevented by challenges such as the efficient recruitment of users, effective mechanisms for rewarding users to encourage participation, and an effective and fast enough approach for managing the underlying resources that support large-scale MCS applications involving a large number of people in data collection. On the other hand, Artificial Intelligence (AI) applications, which are mostly based on Deep Neural Networks (DNN), are becoming pervasive today and are executed by the end users' mobile devices, which are characterised by limited memory and computing power, and low battery level. This paper describes and evaluates an incentive mechanism for a mobile crowdsensing system with an AI sensing task based on a one-leader multi-follower Stackelberg game. The MCS platform, as a leader, provides an AI sensing task to be executed by a DNN, which can be deployed in two different ways: fully on the user device or partially on the device and partially on edge or cloud resources. The users, as followers, make their decisions regarding their participation to the MCS system and select their desired deployment given the energy and memory available on their device and the deployment reward proposed by the MCS platform. The goals of the MCS platform are: i) to motivate the users to participate in the system, ii) to maximize its profit, and iii) to identify the optimal resources supporting the sensing task that minimizes the cost and provide performance guarantees. This problem has been formulated as a mixed integer nonlinear program and propose an efficient algorithmic approach to solve it quickly. The proposed approach has been compared with some baseline methods and with BARON state-of-the-art solver. Results show that our approach converges to the optimal solution much faster than BARON (up to orders of magnitude) especially in large scale systems. Furthermore, the comparison to the baseline methods shows that our approach always beats the best baseline method under different scenarios providing up to 16% improvement for the MCS platform profit.

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“…For the selection of participants, simple aggregation of reports is exploited rather than reputation, and weightage is not considered, which is important for credible reporting. Recent work on the idea of profit maximization for MCS is proposed in [ 58 ]. Their main focus was same as ours, but they did not consider reputation as a benchmark for selection.…”

Section: Related Workmentioning

confidence: 99%

Reputation-Aware Recruitment and Credible Reporting for Platform Utility in Mobile Crowd Sensing with Smart Devices in IoT

Ahmad

Wang

Ullah

et al. 2018

Sensors

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The Internet of things (IoT) comprises a huge collection of electronic devices connected to the Internet to ensure the dependable exchange of sensing information. It involves mobile workers (MWs) who perform various activities to support enormous online services and applications. In mobile crowd sensing (MCS), a massive amount of sensing data is also generated by smart devices. Broadly, in the IoT, verifying the credibility and truthfulness of MWs’ sensing reports is needed for MWs to expect attractive rewards. MWs are recruited by paying monetary incentives that must be awarded according to the quality and quantity of the task. The main problem is that MWs may perform false reporting by sharing low-quality reported data to reduce the effort required. In the literature, false reporting is improved by hiring enough MWs for a task to evaluate the trustworthiness and acceptability of information by aggregating the submitted reports. However, it may not be possible due to budget constraints, or when malicious reporters are not identified and penalized properly. Recruitment is still not a refined process, which contributes to low sensing quality. This paper presents Reputation, Quality-aware Recruitment Platform (RQRP) to recruit MWs based on reputation for quality reporting with the intention of platform profit maximization in the IoT scenario. RQRP comprises two main phases: filtration in the selection of MWs and verifying the credibility of reported tasks. The former is focused on the selection of suitable MWs based on different criteria (e.g., reputation, bid, expected quality, and expected platform utility), while the latter is more concerned with the verification of sensing quality, evaluation of reputation score, and incentives. We developed a testbed to evaluate and analyze the datasets, and a simulation was performed for data collection scenario from smart sensing devices. Results proved the superiority of RQRP against its counterparts in terms of truthfulness, quality, and platform profit maximization. To the best of our knowledge, we are the first to study the impact of truthful reporting on platform utility.

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

Cited by 25 publications

References 19 publications

User Recruitment Algorithm for Maximizing Quality under Limited Budget in Mobile Crowdsensing

User Recruitment Algorithm for Maximizing Quality under Limited Budget in Mobile Crowdsensing

A Stackelberg Game Approach for Managing AI Sensing Tasks in Mobile Crowdsensing

Reputation-Aware Recruitment and Credible Reporting for Platform Utility in Mobile Crowd Sensing with Smart Devices in IoT

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