Spatial-Temporal Value-of-Information Maximization for Mobile Crowdsensing in Wireless Sensor Networks

Mobile crowdsensing is considered as a promising technology to exploit the computing and sensing capabilities of the decentralized wireless sensor nodes. Typically, the quality of information obtained from crowdsensing is largely affected by various factors, such as the diverse requirements of crowd...

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Main Authors: Xiaoling Luo, Che Chen, Wenjie Zhang, Chunnian Zeng, Chengtao Li, Jing Xu
Format: Article
Language:English
Published: MDPI AG 2022-10-01
Series:Electronics
Subjects:
Online Access:https://www.mdpi.com/2079-9292/11/19/3224
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author Xiaoling Luo
Che Chen
Wenjie Zhang
Chunnian Zeng
Chengtao Li
Jing Xu
author_facet Xiaoling Luo
Che Chen
Wenjie Zhang
Chunnian Zeng
Chengtao Li
Jing Xu
author_sort Xiaoling Luo
collection DOAJ
description Mobile crowdsensing is considered as a promising technology to exploit the computing and sensing capabilities of the decentralized wireless sensor nodes. Typically, the quality of information obtained from crowdsensing is largely affected by various factors, such as the diverse requirements of crowdsensing tasks, the varying quality of information across different crowd workers, and the dynamic changes of channels conditions and the sensing environment. In this paper, considering the dynamics’ of the crowd workers, we focus on a spatial-temporal crowdsensing model and aim to maximize the value of information at the point of interest, by optimizing the recruiting range and time duration for the crowd workers. In particular, the crowdsensing system includes a mobile access point (MAP) and a set of wireless sensor nodes. As the information requester, the MAP can broadcast its crowdsensing task and then estimate the value of information by collecting the responses from the sensing nodes. Each sensing node in the crowdsensing task will receive a payment from the MAP. We aim to maximize the utility of the information requester by optimizing the recruiting range and waiting time for the sensing nodes. We firstly define a set of value metrics to characterize the MAP’s value of information. The optimal recruiting range can be obtained in closed-form expressions. Furthermore, considering the aging effect, we propose a gradient-based method to maximize the spatial-temporal value of information. Specifically, we first determine the optimal recruiting time for the requester and then choose the optimal recruiting range within each time slot. Via simulation, we first compare the sum, max, and min values of information at the requester, and then verify the effectiveness of the gradient-based method to optimize the recruiting time and range to maximize the value of information.
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spelling doaj.art-461fa7117d6d4d96873be2a1f01cdf9a2023-11-23T20:08:28ZengMDPI AGElectronics2079-92922022-10-011119322410.3390/electronics11193224Spatial-Temporal Value-of-Information Maximization for Mobile Crowdsensing in Wireless Sensor NetworksXiaoling Luo0Che Chen1Wenjie Zhang2Chunnian Zeng3Chengtao Li4Jing Xu5School of Information Engineering, Wuhan University of Technology, Wuhan 430070, ChinaSchool of Computer Sciences, Minnan Normal University, Zhangzhou 363000, ChinaSchool of Computer Sciences, Minnan Normal University, Zhangzhou 363000, ChinaSchool of Information Engineering, Wuhan University of Technology, Wuhan 430070, ChinaChina Three Gorges Corporation, Wuhan 430010, ChinaSchool of Electronic Information and Communications, Huazhong University of Science and Technology, Wuhan 430074, ChinaMobile crowdsensing is considered as a promising technology to exploit the computing and sensing capabilities of the decentralized wireless sensor nodes. Typically, the quality of information obtained from crowdsensing is largely affected by various factors, such as the diverse requirements of crowdsensing tasks, the varying quality of information across different crowd workers, and the dynamic changes of channels conditions and the sensing environment. In this paper, considering the dynamics’ of the crowd workers, we focus on a spatial-temporal crowdsensing model and aim to maximize the value of information at the point of interest, by optimizing the recruiting range and time duration for the crowd workers. In particular, the crowdsensing system includes a mobile access point (MAP) and a set of wireless sensor nodes. As the information requester, the MAP can broadcast its crowdsensing task and then estimate the value of information by collecting the responses from the sensing nodes. Each sensing node in the crowdsensing task will receive a payment from the MAP. We aim to maximize the utility of the information requester by optimizing the recruiting range and waiting time for the sensing nodes. We firstly define a set of value metrics to characterize the MAP’s value of information. The optimal recruiting range can be obtained in closed-form expressions. Furthermore, considering the aging effect, we propose a gradient-based method to maximize the spatial-temporal value of information. Specifically, we first determine the optimal recruiting time for the requester and then choose the optimal recruiting range within each time slot. Via simulation, we first compare the sum, max, and min values of information at the requester, and then verify the effectiveness of the gradient-based method to optimize the recruiting time and range to maximize the value of information.https://www.mdpi.com/2079-9292/11/19/3224mobile crowdsensingvalue of informationaging of informationwireless sensor network
spellingShingle Xiaoling Luo
Che Chen
Wenjie Zhang
Chunnian Zeng
Chengtao Li
Jing Xu
Spatial-Temporal Value-of-Information Maximization for Mobile Crowdsensing in Wireless Sensor Networks
Electronics
mobile crowdsensing
value of information
aging of information
wireless sensor network
title Spatial-Temporal Value-of-Information Maximization for Mobile Crowdsensing in Wireless Sensor Networks
title_full Spatial-Temporal Value-of-Information Maximization for Mobile Crowdsensing in Wireless Sensor Networks
title_fullStr Spatial-Temporal Value-of-Information Maximization for Mobile Crowdsensing in Wireless Sensor Networks
title_full_unstemmed Spatial-Temporal Value-of-Information Maximization for Mobile Crowdsensing in Wireless Sensor Networks
title_short Spatial-Temporal Value-of-Information Maximization for Mobile Crowdsensing in Wireless Sensor Networks
title_sort spatial temporal value of information maximization for mobile crowdsensing in wireless sensor networks
topic mobile crowdsensing
value of information
aging of information
wireless sensor network
url https://www.mdpi.com/2079-9292/11/19/3224
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AT chechen spatialtemporalvalueofinformationmaximizationformobilecrowdsensinginwirelesssensornetworks
AT wenjiezhang spatialtemporalvalueofinformationmaximizationformobilecrowdsensinginwirelesssensornetworks
AT chunnianzeng spatialtemporalvalueofinformationmaximizationformobilecrowdsensinginwirelesssensornetworks
AT chengtaoli spatialtemporalvalueofinformationmaximizationformobilecrowdsensinginwirelesssensornetworks
AT jingxu spatialtemporalvalueofinformationmaximizationformobilecrowdsensinginwirelesssensornetworks