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 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.

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 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

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 Reputation-Aware Recruitment and Credible Reporting for Platform Utility in Mobile Crowd Sensing with Smart Devices in IoT

Sensors ◽  
2018 ◽  
Vol 18 (10) ◽  
pp. 3305 ◽  
Author(s):  
Waqas Ahmad ◽  
Shengling Wang ◽  
Ata Ullah ◽  
Sheharyar ◽  
Muhammad Yasir Shabir
Keyword(s):  
Profit Maximization ◽  
Smart Devices ◽  
The Internet ◽  
Mobile Crowd Sensing ◽  
Quality Reporting ◽  
Crowd Sensing ◽  
False Reporting ◽  
The Impact ◽  
Mobile Crowd ◽  
Selection Of

The Internet of things (IoT) comprises a huge collection of electronic devices connected to the Internet to ensure the dependable exchange of sensing information. It involves mobile workers (MWs) who perform various activities to support enormous online services and applications. In mobile crowd sensing (MCS), a massive amount of sensing data is also generated by smart devices. Broadly, in the IoT, verifying the credibility and truthfulness of MWs’ sensing reports is needed for MWs to expect attractive rewards. MWs are recruited by paying monetary incentives that must be awarded according to the quality and quantity of the task. The main problem is that MWs may perform false reporting by sharing low-quality reported data to reduce the effort required. In the literature, false reporting is improved by hiring enough MWs for a task to evaluate the trustworthiness and acceptability of information by aggregating the submitted reports. However, it may not be possible due to budget constraints, or when malicious reporters are not identified and penalized properly. Recruitment is still not a refined process, which contributes to low sensing quality. This paper presents Reputation, Quality-aware Recruitment Platform (RQRP) to recruit MWs based on reputation for quality reporting with the intention of platform profit maximization in the IoT scenario. RQRP comprises two main phases: filtration in the selection of MWs and verifying the credibility of reported tasks. The former is focused on the selection of suitable MWs based on different criteria (e.g., reputation, bid, expected quality, and expected platform utility), while the latter is more concerned with the verification of sensing quality, evaluation of reputation score, and incentives. We developed a testbed to evaluate and analyze the datasets, and a simulation was performed for data collection scenario from smart sensing devices. Results proved the superiority of RQRP against its counterparts in terms of truthfulness, quality, and platform profit maximization. To the best of our knowledge, we are the first to study the impact of truthful reporting on platform utility.

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.

scholarly journals Protecting Mobile Crowd Sensing against Sybil Attacks Using Cloud Based Trust Management System

2016 ◽  
Vol 2016 ◽  
pp. 1-10 ◽  
Author(s):  
Shih-Hao Chang ◽  
Zhi-Rong Chen
Keyword(s):  
Computer Security ◽  
Trust Management ◽  
Large Scale ◽  
Multiple Identities ◽  
Sybil Attack ◽  
User Privacy ◽  
Mobile Crowd Sensing ◽  
Crowd Sensing ◽  
Mobile Crowd ◽  
Sybil Attacks

Mobile crowd sensing (MCS) arises as a new sensing paradigm, which leverages citizens for large-scale sensing by various mobile devices to efficiently collect and share local information. Unlike other MCS application challenges that consider user privacy and data trustworthiness, this study focuses on the network trustworthiness problem, namely, Sybil attacks in MCS network. The Sybil attack in computer security is a type of security attack, which illegally forges multiple identities in peer-to-peer networks, namely, Sybil identities. These Sybil identities will falsify multiple identities that negatively influence the effectiveness of sensing data in this MCS network or degrading entire network performance. To cope with this problem, a cloud based trust management scheme (CbTMS) was proposed to detect Sybil attacks in the MCS network. The CbTMS was proffered for performing active and passive checking scheme, in addition to the mobile PCS trustworthiness management, and includes a decision tree algorithm, to verify the covered nodes in the MCS network. Simulation studies show that our CbTMS can efficiently detect the malicious Sybil nodes in the network and cause 6.87 Wh power reduction compared with other malicious Sybil node attack mode.

scholarly journals Pavement Management Utilizing Mobile Crowd Sensing

2020 ◽  
Vol 2020 ◽  
pp. 1-16
Author(s):  
Boquan Tian ◽  
Yongbo Yuan ◽  
Hengyu Zhou ◽  
Zhen Yang
Keyword(s):  
Data Collection ◽  
Inertial Sensor ◽  
Pavement Management ◽  
Sensor Data ◽  
Smart Devices ◽  
Smart Device ◽  
Road Transportation ◽  
Mobile Crowd Sensing ◽  
Crowd Sensing ◽  
Mobile Crowd

Pavement management, which is vital in road transportation and maintenance, is facing some troubles, such as high costs of labors and machineries, low detecting efficiency, and low update rate of pavement conditions by means of traditional detection ways. Benefiting from the development of mobile communication, mobile computing, and mobile sensing techniques, the intelligence of mobile crowd sensing (MCS), which mainly relies on ubiquitous mobile smart devices in people’s daily lives, has overcome the above drawbacks to a large extent as one new effective and simple measure for pavement management. As a platform for data collection, processing, and visualization, a common smart device can utilize inertial sensor data, photos, videos, subjective reports, and location information to involve the public in pavement anomalies detection. This paper systematically reviewed the studies in this field from 2008 to 2018 to establish an overall knowledge. Through literature collection and screening, a database of studies was set up for analysis. As a result, the year profile of publications and distribution of research areas indicate that there has been a constant attention from researchers in various disciplines. Meanwhile, the distribution of research topic shows that inertial sensors embedded in smartphones have been the most popular data source. Therefore, the process of pavement anomalies detection based on inertial data was reviewed in detail, including preparatory, data collection, and processing phases of the previous experiments. However, some of the key issues in the experimental phases were investigated by previous studies, while some other challenges were not tackled or noticed. Hence, the challenges in both experiment and implementation stages were discussed to improve the studies and practice. Furthermore, several directions for future research are summarized from the main issues and challenges to offer potential opportunities for more relevant research studies and applications in pavement management.

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