scholarly journals The Truthful Evolution and Incentive for Large-Scale Mobile Crowd Sensing Networks

IEEE Access ◽  
2018 ◽  
Vol 6 ◽  
pp. 51187-51199 ◽  
Author(s):  
Yingjie Wang ◽  
Yingshu Li ◽  
Zhongyang Chi ◽  
Xiangrong Tong
Keyword(s):  
Large Scale ◽  
Mobile Crowd Sensing ◽  
Crowd Sensing ◽  
Crowd Sensing Networks ◽  
Mobile Crowd

Dynamic Participant Selection for Large-Scale Mobile Crowd Sensing

2019 ◽  
Vol 18 (12) ◽  
pp. 2842-2855 ◽  
Author(s):  
Hanshang Li ◽  
Ting Li ◽  
Weichao Wang ◽  
Yu Wang
Keyword(s):  
Large Scale ◽  
Mobile Crowd Sensing ◽  
Crowd Sensing ◽  
Participant Selection ◽  
Selection For ◽  
Mobile Crowd

scholarly journals Privacy protection in mobile crowd sensing: a survey

2019 ◽  
Vol 23 (1) ◽  
pp. 421-452 ◽  
Author(s):  
Yongfeng Wang ◽  
Zheng Yan ◽  
Wei Feng ◽  
Shushu Liu
Keyword(s):  
Privacy Protection ◽  
Data Privacy ◽  
Large Scale ◽  
System Structure ◽  
Smart Devices ◽  
Future Research ◽  
Sensitive Information ◽  
Mobile Crowd Sensing ◽  
Crowd Sensing ◽  
Mobile Crowd

AbstractThe unprecedented proliferation of mobile smart devices has propelled a promising computing paradigm, Mobile Crowd Sensing (MCS), where people share surrounding insight or personal data with others. As a fast, easy, and cost-effective way to address large-scale societal problems, MCS is widely applied into many fields, e.g., environment monitoring, map construction, public safety, etc. Despite the popularity, the risk of sensitive information disclosure in MCS poses a serious threat to the participants and limits its further development in privacy-sensitive fields. Thus, the research on privacy protection in MCS becomes important and urgent. This paper targets the privacy issues of MCS and conducts a comprehensive literature research on it by providing a thorough survey. We first introduce a typical system structure of MCS, summarize its characteristics, propose essential requirements on privacy on the basis of a threat model. Then, we survey existing solutions on privacy protection and evaluate their performances by employing the proposed requirements. In essence, we classify the privacy protection schemes into four categories with regard to identity privacy, data privacy, attribute privacy, and task privacy. Besides, we review the achievements on privacy-preserving incentives in MCS from four viewpoints of incentive measures: credit incentive, auction incentive, currency incentive, and reputation incentive. Finally, we point out some open issues and propose future research directions based on the findings from our survey.

A Mobility Model for Crowd Sensing Simulation

2017 ◽  
Vol 9 (1) ◽  
pp. 14-25
Author(s):  
Jose Mauricio Nava Auza ◽  
Jose Roberto B. de Marca
Keyword(s):  
Sensor Networks ◽  
Mobile Devices ◽  
Human Activities ◽  
Large Scale ◽  
Human Mobility ◽  
Mobility Model ◽  
Mobile Crowd Sensing ◽  
Crowd Sensing ◽  
Mobile Crowd ◽  
Specific Scenario

Mobile Crowd Sensing (MCS) is a class of sensor networks that uses mobile devices for large scale sensing. These networks have some very specific characteristics because of human (smartphone owners) involvement in its operations. Hence, it is important to have a model that takes into account the unique characteristics and opportunities of human mobility. In this paper, the authors present a Mobility Model for the ns-3 platform that considers human activities in a specific scenario and a simulation example to validate their model.

Fast participant recruitment algorithm for large-scale Vehicle-based Mobile Crowd Sensing

2017 ◽  
Vol 38 ◽  
pp. 188-199 ◽  
Author(s):  
Kefu Yi ◽  
Ronghua Du ◽  
Li Liu ◽  
Qingying Chen ◽  
Kai Gao
Keyword(s):  
Large Scale ◽  
Mobile Crowd Sensing ◽  
Participant Recruitment ◽  
Crowd Sensing ◽  
Mobile Crowd

scholarly journals Radio Environment Map Construction by Kriging Algorithm Based on Mobile Crowd Sensing

2019 ◽  
Vol 2019 ◽  
pp. 1-12 ◽  
Author(s):  
Zhifeng Han ◽  
Jianxin Liao ◽  
Qi Qi ◽  
Haifeng Sun ◽  
Jingyu Wang
Keyword(s):  
Large Scale ◽  
Kriging Interpolation ◽  
Smart Devices ◽  
Prototype System ◽  
Interpolation Algorithm ◽  
Mobile Crowd Sensing ◽  
Crowd Sensing ◽  
Environment Map ◽  
Mobile Crowd ◽  
Radio Environment Map

In the IoT era, 5G will enable various IoT services such as broadband access everywhere, high user and devices mobility, and connectivity of massive number of devices. Radio environment map (REM) can be applied to improve the utilization of radio resources for the access control of IoT devices by allocating them reasonable wireless spectrum resources. However, the primary problem of constructing REM is how to collect the large scale of data. Mobile crowd sensing (MCS), leveraging the smart devices carried by ordinary people to collect information, is an effective solution for collecting the radio environment information for building the REM. In this paper, we build a REM collecting prototype system based on MCS to collect the data required by the radio environment information. However, limited by the budget of the platform, it is hard to recruit enough participants to join the sensing task to collect the radio environment information. This will make the radio environment information of the sensing area incomplete, which cannot describe the radio information accuracy. Considering that the Kriging algorithm has been widely used in geostatistics principle for spatial interpolation for Kriging giving the best unbiased estimate with minimized variance, we utilize the Kriging interpolation algorithm to infer complete radio environment information from collected sample radio environment information data. The interpolation performance is analyzed based on the collected sample radio environment information data. We demonstrate experiments to analyze the Kriging interpolation algorithm interpolation results and error and compared them with the nearest neighbor (NN) and the inverse distance weighting (IDW) interpolation algorithms. Experiment results show that the Kriging algorithm can be applied to infer radio environment information data based on the collected sample data and the Kriging interpolation has the least interpolation error.

scholarly journals Design of a Local Information Incentive Mechanism for Mobile Crowdsensing

Sensors ◽  
2019 ◽  
Vol 19 (11) ◽  
pp. 2532 ◽  
Author(s):  
Jose Mauricio Nava Auza ◽  
Jose Roberto Boisson de Marca ◽  
Glaucio Lima Siqueira
Keyword(s):  
Large Scale ◽  
Incentive Mechanism ◽  
Network Connectivity ◽  
Physical World ◽  
Local Information ◽  
Mobile Crowd Sensing ◽  
Great Opportunity ◽  
Crowd Sensing ◽  
The Incentive Mechanism ◽  
Mobile Crowd

The world of telecommunications has seen the growing popularity of mobile devices and its massive technological advancements and innovations (e.g., smartphones, smart watches, among others). One critical particularity is that these devices have a series of built-in sensors and continuous network connectivity. Therefore, they present a great opportunity to perform large-scale sensing of different activities in the physical world. This new sensor application, better known as Mobile crowd-sensing (MCS), has lately become a focus of research. One of the challenges when developing a MCS-based network is to attract and convince users to participate. In this paper, we present a framework for MCS that includes a model to represent the behavior of the users and a novel incentive mechanism. The model aims to characterize the behavior of users considering the availability of their resources and the non-homogeneity of their responses. The incentive mechanism proposed assigns different values of incentives and in it the users only consider their local information to decide their participation in the framework. The performance of the proposed framework is evaluated through simulations. The results allow us to prove the uncertainty of participation of the users and that they react in different ways to the incentives offered. They also prove that the incentive mechanism estimates satisfactorily the type of users and the incentive that will be offered to each user. In addition, we show the advantages of an incentive mechanism that considers different values of payments.

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