A Truthful Double Auction Mechanism for Crowdsensing Systems with Max-Min Fairness

Huang, He; Xin, Yu; Sun, Yu-E; Yang, Wenjian

doi:10.1109/wcnc.2017.7925505

Cited by 24 publications

(6 citation statements)

References 11 publications

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“…In [24], the output agreement mechanisms are used to have the true answers from the group of task executors. In [69], the set-up consists of multiple task consumers and multiple smartphone users, where each of the task consumers has multiple tasks to complete. The objective is to assign the sensing tasks to smartphone users for execution purposes.…”

Section: B Mobile Crowdsensingmentioning

confidence: 99%

A Quality Aware Multiunit Double Auction Framework for IoT-Based Mobile Crowdsensing in Strategic Setting

et al. 2022

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Crowdsourcing with the intelligent agents carrying smart devices is becoming increasingly popular in recent years. It has opened up meeting an extensive list of real-life applications such as measuring air pollution levels, road traffic information, etc. In literature, this is known as mobile crowdsourcing or mobile crowdsensing. In this paper, the discussed set-up consists of multiple task requesters (or task providers) and multiple IoT devices (as task executors), where each of the task providers has multiple homogeneous sensing tasks. Each task requester reports a bid and the number of homogeneous sensing tasks to the platform. On the other side, multiple IoT devices report the ask (the charge for imparting its services) and the number of sensing tasks they can execute. The valuations of task requesters and IoT devices are private information, and both might act strategically. One assumption that is made in this paper is that the bids and asks of the agents (task providers and IoT devices) follow decreasing marginal returns criteria. Given the above-discussed scenario, the objectives are: (1) to determine a set of quality IoT devices for each of the tasks held by the task requesters, and (2) to select a subset of quality IoT devices from among the available quality IoT devices for each of the sensing tasks. In this paper, a truthful mechanism is proposed for allocating the IoT devices to the sensing tasks carried by task requesters that also keep into account the quality of IoT devices. Through theoretical analysis, it is shown that the mechanism is truthful, budget balanced, individually rational, computationally efficient, correct, and prior-free. Further, probabilistic analysis is carried out to estimate the average number of tasks that get executed for any task requester. The simulations are carried out to measure the performance of the proposed mechanism against the benchmark mechanisms on the ground of truthfulness, budget balance, satisfaction level, average incentive, and computational efficiency.

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Section: B Mobile Crowdsensingmentioning

confidence: 99%

A Quality Aware Multiunit Double Auction Framework for IoT-Based Mobile Crowdsensing in Strategic Setting

et al. 2022

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“…In Liu and Chen (2016), the output agreement mechanisms are used to have the true answers from the group of task executors. In Huang et al (2017) the set-up consists of multiple task consumers and multiple smartphone users, where each of the task consumers have multiple tasks to complete. The objective is to assign the sensing tasks to the smartphone users for execution purpose.…”

Section: Mobile Crowdsensingmentioning

confidence: 99%

QUAD: A Quality Aware Multi-Unit Double Auction Framework for IoT-Based Mobile Crowdsensing in Strategic Setting

Singh¹,

Jasti²,

Singh³

et al. 2022

Preprint

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Crowdsourcing with the intelligent agents carrying smart devices is becoming increasingly popular in recent years. It has opened up meeting an extensive list of real life applications such as measuring air pollution level, road traffic information, and so on. In literature this is known as mobile crowdsourcing or mobile crowdsensing. In this paper, the discussed set-up consists of multiple task requesters (or task providers) and multiple IoT devices (as task executors), where each of the task providers is having multiple homogeneous sensing tasks. Each of the task requesters report bid along with the number of homogeneous sensing tasks to the platform. On the other side, we have multiple IoT devices that reports the ask (charge for imparting its services) and the number of sensing tasks that it can execute. The valuations of task requesters and IoT devices are private information, and both might act strategically. One assumption that is made in this paper is that the bids and asks of the agents (task providers and IoT devices) follow decreasing marginal returns criteria. In this paper, a truthful mechanism is proposed for allocating the IoT devices to the sensing tasks carried by task requesters, that also keeps into account the quality of IoT devices. The mechanism is truthful, budget balance, individual rational, computationally efficient, and prior-free. The simulations are carried out to measure the performance of the proposed mechanism against the benchmark mechanisms. The code and the synthetic data are available at https://github.com/Samhitha-Jasti/QUAD-Implementation.

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“…Note that fairness is the basic requirement of all those competing users in resource allocation management, and typically the criteria for fair resource allocation among different competing users is the so‐called equal fairness 7–11 . The equal fairness treats all competing users fairly and equally so that each one can get a same share of the system resource.…”

Section: Introductionmentioning

confidence: 99%

Achieving balance between economic efficiency and user fairness in traffic rate management

Pan

Wang

Li³

2020

Int J Communication

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SummaryThis paper studies achieving the desired balance between the resource economic efficiency and user fairness in network traffic rate allocation management. Fairness is a basic requirement of different competing traffic users, while maximizing the economic efficiency of traffic rate resource is desired by the network system resource provider. Hence, how to balance these two respects to achieve the desired trade‐off is a key challenging issue in real applications. Particularly, we propose a weight mechanism to specify the rate allocation weight ratio among different competing traffic users and also a trade‐off factor mechanism to clarify the extent of fairness compromise. To optimize the system rate resource economic efficiency, we formulate an objective optimization problem with consideration of both user weight diversity and fairness trade‐off as well. We also derive the balance curve, which shows the rate resource economic efficiency improved in relation to the extent of traffic user fairness trade‐off. The demonstration models are also constructed to implemented to implement both the trade‐off acceptable and unacceptable cases in practical traffic rate resource allocation applications.

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A Truthful Double Auction Mechanism for Crowdsensing Systems with Max-Min Fairness

Cited by 24 publications

References 11 publications

A Quality Aware Multiunit Double Auction Framework for IoT-Based Mobile Crowdsensing in Strategic Setting

A Quality Aware Multiunit Double Auction Framework for IoT-Based Mobile Crowdsensing in Strategic Setting

QUAD: A Quality Aware Multi-Unit Double Auction Framework for IoT-Based Mobile Crowdsensing in Strategic Setting

Achieving balance between economic efficiency and user fairness in traffic rate management

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