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A Brief Introduction
In this chapter, we first introduce the background regarding Mobile Crowdsensing (MCS) and present an overview of MCS. Then, we specifically state...
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Privacy-Preserving Truth Discovery with Truth Hiding
This chapter considers the participation of data requesters in mobile crowdsensing (MCS) data truth discovery scenarios and discusses how to achieve...
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A Crowdsensing Based Traffic Monitoring Approach
ThisCrowdsensing chapter presents a crowdsensing based urban traffic monitoringUrban traffic monitoring system. Different from existing works that...
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How opportunistic mobile monitoring can enhance air quality assessment?
The deteriorating air quality in urban areas, particularly in develo** countries, has led to increased attention being paid to the issue. Daily...
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Multi-dimensional Urban Sensing Using Crowdsensing Data
Chaocan **ang is an Associate Professor at the College of Computer Science, Chongqing University, China. He received his bachelor’s degree and Ph.D....
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The design of Tecnico GO!: catering for students’ well-being during the COVID-19 pandemics
Transitioning to and through University is a delicate period for students’ well-being. Moreover, the recent COVID-19 pandemic added a further toll...
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A real-time stable competitive auction-based task offloading market for heterogeneous mobile cloud (HMC)
In this paper we propose an auction-based offloading market in the HMC (Heterogeneous Mobile Cloud) environment where mobile devices, Cloudlets, and...
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Privacy-Preserving Content-Based Task Allocation
Content-based task allocation is an important part of MCS, which can effectively help data requesters find suitable sensing users to collect data....
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Completeness Assessment and Improvement in Mobile Crowd-Sensing Environments
Mobile sensors are increasingly used to monitor air quality to accurately quantify human exposure to air pollution. These sensors are subject to...
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Mobile crowd computing: potential, architecture, requirements, challenges, and applications
Owing to the enormous advancement in miniature hardware, modern smart mobile devices (SMDs) have become computationally powerful. Mobile crowd...
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Optimizing energy and latency trade-offs in mobile ultra-dense IoT networks within futuristic smart vertical networks
As the Internet of Things (IoT) evolves and is integrated into cutting-edge Smart Vertical Networks-based IoT, a plethora of IoT mobile devices (IMD)...
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Privacy-Preserving Truth Discovery with Task Hiding
Mobile crowdsensing has emerged as a popular platform for solving many challenging problems by utilizing users’ wisdom and resources. Due to the...
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Introduction
Mobile crowdsensing (MCS) is a new data sensing model that combines mobile sensing and the idea of crowdsourcing. It is centered on users with...
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URIM: Utility-Oriented Role-Centric Incentive Mechanism Design for Blockchain-Based Crowdsensing
Crowdsensing is a prominent paradigm that collects data by outsourcing to individuals with sensing devices. However, most existing crowdsensing...
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Adaptive task recommendation based on reinforcement learning in mobile crowd sensing
Adaptive task recommendation in Mobile crowd sensing (MCS) is a challenging problem, mainly because perceptual tasks are spatio-temporal in nature...
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Privacy-Preserving Location-Based Task Allocation
Chapter 3 discusses privacy-preserving task allocation based on task content. In mobile crowdsensing...
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Route selection for opportunity-sensing and prediction of waterlogging
Accurate monitoring of urban waterlogging contributes to the city’s normal operation and the safety of residents’ daily travel. However, due to...
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Incentive Mechanism Design for Mobile Crowdsourcing Without Verification
This chapter studies the design of incentive mechanisms for mobile crowdsourcing systems in which verifying the underlying ground truth is not...
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Improve Data Freshness in Mobile Crowdsensing by Task Assignment
In real-time crowdsensing, the key factor is the freshness of information. However, due to the randomness and uncertainty of mobile users, some tasks...
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