Multitask Allocation to Heterogeneous Participants in Mobile Crowd Sensing

Zhu, Weiping; Guo, Wenzhong; Yu, Zhiyong; Xiong, Haoyi

doi:10.1155/2018/7218061

Cited by 33 publications

(12 citation statements)

References 21 publications

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“…In order to overcome the disappearance of gradient, transfer functions such as Relu and Maxout replace sigmoid and form the basic form of DNN (Yang and Ma, 2016). Subsequently, more and more user behavior analysis methods are proposed based on various of neural network models, such as recurrent neural network (RNN; Zhang et al, 2017), convolutional neural networks (CNN; Zhang et al, 2018;Zhu et al, 2018), long short-term memory (LSTM) networks (Borisov et al, 2016), etc.…”

Section: Measuring User Experience From Behavior Datamentioning

confidence: 99%

Measuring and Improving User Experience Through Artificial Intelligence-Aided Design

Yang

Wei

2020

Front. Psychol.

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This paper aims to propose a methodology for measuring user experience (UX) by using artificial intelligence-aided design (AIAD) technology in mobile application design. Unlike the traditional assistance design tools, AIAD focuses on the rational use of artificial intelligence (AI) technology to measure and improve UX since conventional data collection methods (such as user interview and user observation) for user behavior data are inefficient and time-consuming. We propose to obtain user behavior data from logs of mobile application. In order to protect the privacy of users, only a few dimensions of information is used in the process of browsing and operating mobile application. The goal of the proposed methodology is to make the deep neural network model simulate the user's experience in the process of operating a mobile application as much as possible. We design and use projected pages of application to train neural networks for specific tasks. These projected pages consist of the click information of all users in the process of completing a certain task. Thus, features of user behavior can be aggregated and mapped in the connection layers and the hidden layers. Finally, the optimized design is executed on the social communication application to verify the efficiency of the proposed methodology.

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Section: Measuring User Experience From Behavior Datamentioning

confidence: 99%

Measuring and Improving User Experience Through Artificial Intelligence-Aided Design

Yang

Wei

2020

Front. Psychol.

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“…Then, an iterative greedy algorithm is proposed to optimize the task allocation. In our previous work [61], we investigated a multi tasks allocation problem that considers the heterogeneity of users (including the type of sensors and the maximum workload of users). A greedy discrete particle swarm optimization with genetic algorithm operation is proposed to maximize the number of completed tasks.…”

Section: B: Workload Balancing In Mcsmentioning

confidence: 99%

“…For example, different users carry different type of devices and can complete different types of sensing task. Our previous work [61] considered the type of sensors in mobile devices. However, the human factor should be considered in practice.…”

Section: B Learning Assisted Task Allocationmentioning

confidence: 99%

A Survey of Task Allocation: Contrastive Perspectives From Wireless Sensor Networks and Mobile Crowdsensing

Guo

Zhu

et al. 2019

IEEE Access

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Wireless sensor networks (WSNs) and mobile crowdsensing (MCS) are two important paradigms in urban dynamic sensing. In both sensing paradigms, task allocation is a significant problem, which may affect the completion quality of sensing tasks. In this paper, we give a survey of task allocation in WSNs and MCS from the contrastive perspectives in terms of data quality and sensing cost, which help to better understand related objectives and strategies. We first analyze the different characteristics of two sensing paradigms, which may lead to difference in task allocation issues or strategies. Then, we present some common issues in task allocation with objectives in data quality and sensing cost. Furthermore, we provide reviews of unique task allocation issues in MCS according to its new characteristics. Finally, we identify some potential opportunities for the future research. INDEX TERMS Mobile crowdsensing (MCS), task allocation, wireless sensor networks (WSNs).

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“…The authors also made use of an attention-compensated incentive model that paid extra compensation to participants that assign more than one task type. Zhu et al [114] investigated the multitask allocation problem by considering the heterogeneity of participants. The authors designed a particle swarm optimization with a genetic algorithm to manage the number of tasks, sensing capacity, and time constraints.…”

Section: A Rq1: What Is the Strategy Used To Ensure The Data Credibimentioning

confidence: 99%

Credibility on Crowdsensing Data Acquisition

Neto

Gomes

Soares

2019

JCIS

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This paper focuses on the credibility of crowd sensed data. The ubiquity of crowdsensing platforms has enabled the capture of sensed information useful for several applications domains. However, one concern with crowdsensing is the information credibility and, over the last few years, we have seen a variety of approaches to leverage credibility on the crowdsensing platforms. Here, we carried out a systematic mapping study on 177 credibility-related articles about crowdsensing. The results show that the absence of standard models in the data capture process and the human factors such as individualism, inattention, and the possibility of errors (whether they are intentional or not), are the primary harmful to the credibility on these platforms. Additionally, we propose a three-level taxonomy that classifies crowdsensing credibility into two broad branches: direct and indirect approaches. To the best of our knowledge, we believe this is the first systematic mapping to address both credibility approaches and the factors that negatively impact the data credibility on crowdsensing platforms.

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Multitask Allocation to Heterogeneous Participants in Mobile Crowd Sensing

Cited by 33 publications

References 21 publications

Measuring and Improving User Experience Through Artificial Intelligence-Aided Design

Measuring and Improving User Experience Through Artificial Intelligence-Aided Design

A Survey of Task Allocation: Contrastive Perspectives From Wireless Sensor Networks and Mobile Crowdsensing

Credibility on Crowdsensing Data Acquisition

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