Truthful incentive mechanisms for mobile crowd sensing with dynamic smartphones

Cai, Hongbin; Zhu, Yanmin; Feng, Zhenni; Zhu, Hongzi; Yu, Jiadi; Cao, Jian

doi:10.1016/j.comnet.2018.05.016

Cited by 17 publications

(8 citation statements)

References 12 publications

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“…Similarly, our model adopts a simple incentive mechanism. On the other hand, research works on incentive mechanism design [56], [57] mainly focus on designing diverse thorough mechanisms to provide ordinary mobile users with sufficient incentives, and they use scenarios which do not require efficient task allocation methods. In the future work, we will investigate incorporating a more thorough incentive mechanism to the task allocation model.…”

Section: Discussionmentioning

confidence: 99%

Multi-Task Allocation Under Time Constraints in Mobile Crowdsensing

Zhang

2021

IEEE Trans. on Mobile Comput.

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Mobile crowdsensing (MCS) is a popular paradigm to collect sensed data for numerous sensing applications. With the increment of tasks and workers in MCS, it has become indispensable to design efficient task allocation schemes to achieve high performance for MCS applications. Many existing works on task allocation focus on single-task allocation, which is inefficient in many MCS scenarios where workers are able to undertake multiple tasks. On the other hand, many tasks are time-limited, while the available time of workers is also limited. Therefore, time validity is essential for both tasks and workers. To accommodate these challenges, this paper proposes a multi-task allocation problem with time constraints, which investigates the impact of time constraints to multi-task allocation and aims to maximize the utility of the MCS platform. We first prove that this problem is NP-complete. Then two evolutionary algorithms are designed to solve this problem. Finally, we conduct the experiments based on synthetic and real-world datasets under different experiment settings. The results verify that the proposed algorithms achieve more competitive and stable performance compared with baseline algorithms.

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Section: Discussionmentioning

confidence: 99%

Multi-Task Allocation Under Time Constraints in Mobile Crowdsensing

Zhang

2021

IEEE Trans. on Mobile Comput.

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“…Other similar studies on multiagent systems have focused on resource allocation [22][23][24][25][26] and single agent task allocation [27][28][29][30][31][32] . The difference between resource allocation and task allocation in the coalition skill game is that the resources owned by the service agent can be transferred, while the skills owned by the service agent in the coalition skill game cannot be transferred between the service agents [33] .…”

Section: Related Workmentioning

confidence: 99%

Task Allocation and Utility Distribution Algorithms Based on Nash Bargaining Solution

Chen

Wan

et al. 2021

IEEE Access

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In practical applications, there are many task allocation problems involving the participation of self-interested agents, including Witkey, crowdsourcing and electronic markets. In these cases, to improve the efficiency of task allocation, a reasonable distribution of utilities is critical. To the best of our knowledge, few studies have examined the complex task allocation and utility distribution of self-interested agents, and good solutions are lacking. To address this issue, the following works are done in this paper: first, based on a task allocation model for self-interested agents and by studying the Nash bargaining solution and the bargaining characteristics of the agents, an efficiency utility distribution algorithm satisfying individual rationality and budget effectiveness is proposed. Second, based on the best response strategy of the self-interested agent, a complex task allocation algorithm for multiple self-interested agents is proposed. Finally, the effectiveness of the proposed algorithm is verified by comparing the system revenues with other utility distribution and task allocation algorithms.

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“…The heterogeneous sensing capabilities pose fundamental challenges to MCS systems by affecting their two main operations truth discovery and reward distribution. The truth discovery refers to the process of aggregating and analyzing the crowd-sensed data to estimate the ground truth [28], while a reward distribution scheme is required to reward participants according to their effort levels in truth discovery process [13]. The task of sensing on the phones can be triggered manually, automatically or based on the current context.…”

Section: A Sensorsmentioning

confidence: 99%

“…Currently, smart mobile devices are becoming increasingly popular everywhere and are equipped with very powerful sensors that have been pervasively applied in crowdsensing as effective tools to solve large-scale sensing tasks in urban areas. The group owner (GO) performs the coordination work by establishing contracts with mobile user devices to specify the expected results and their corresponding incentive payments [13]. These incentives can take various forms i.e.…”

Section: Introductionmentioning

confidence: 99%

Simulation of Trust-Based Mechanism for Enhancing User Confidence in Mobile Crowdsensing Systems

et al. 2020

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With the rapid development of mobile technology and subsequent mass adoption of mobile devices, mobile crowdsensing (MCS) has gained a lot of research attention. In MCS systems, trust is a key focus in the overall improvement in the participant uptake of the sensing tasks. The trust-based scheme of MCS is studied to predict the damage level, the scores of quality-of-service (QoS), and the levels of qualityof-data (QoD) of MCS systems. Users can participate in MCS sensing based on trustworthy indicators that are related to user experience and system reputation, as well as the knowledge obtained about the MCS systems. This paper illustrates the establishment of user confidence during recruitment in MCS as it is very critical for the success of MCS systems and proposes a simulation trust-based mechanism (SiTBaM) approach. The level of MCS security is enhanced to protect the privacy of participants, so that participants can be assured that the MCS system they are working with during sensing moment is trustworthy. The application of SiTBaM in MCS is verified to yield better results as the simulations show that it offers higher QoS levels, QoD scores, as well as low damage levels in the presence of any task or many malicious users. These results were validated through comparisons with other schemes. INDEX TERMS Mobile crowdsensing, quality-of-data, quality-of-service, security, trust-based scheme.

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Truthful incentive mechanisms for mobile crowd sensing with dynamic smartphones

Cited by 17 publications

References 12 publications

Multi-Task Allocation Under Time Constraints in Mobile Crowdsensing

Multi-Task Allocation Under Time Constraints in Mobile Crowdsensing

Task Allocation and Utility Distribution Algorithms Based on Nash Bargaining Solution

Simulation of Trust-Based Mechanism for Enhancing User Confidence in Mobile Crowdsensing Systems

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