Pavement Management Utilizing Mobile Crowd Sensing

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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IoT-Based Smart Surveillance: Role of Sensor Data Analytics and Mobile Crowd Sensing in Crowd Behavior Analysis

Crowd-Assisted Networking and Computing ◽

10.1201/9780429453731-3 ◽

2018 ◽

pp. 45-76

Author(s):

Sabu M. Thampi ◽

Elizabeth B. Varghese

Keyword(s):

Behavior Analysis ◽

Data Analytics ◽

Sensor Data ◽

Crowd Behavior ◽

Mobile Crowd Sensing ◽

Crowd Behavior Analysis ◽

Crowd Sensing ◽

Smart Surveillance ◽

Mobile Crowd

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A Cost-Effective Distributed Framework for Data Collection in Cloud-Based Mobile Crowd Sensing Architectures

IEEE Transactions on Sustainable Computing ◽

10.1109/tsusc.2017.2666043 ◽

2017 ◽

Vol 2 (1) ◽

pp. 3-16 ◽

Cited By ~ 40

Author(s):

Andrea Capponi ◽

Claudio Fiandrino ◽

Dzmitry Kliazovich ◽

Pascal Bouvry ◽

Stefano Giordano

Keyword(s):

Data Collection ◽

Cost Effective ◽

Mobile Crowd Sensing ◽

Crowd Sensing ◽

Distributed Framework ◽

Mobile Crowd

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Privacy protection in mobile crowd sensing: a survey

World Wide Web ◽

10.1007/s11280-019-00745-2 ◽

2019 ◽

Vol 23 (1) ◽

pp. 421-452 ◽

Cited By ~ 1

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.

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LCBPA: two-stage task allocation algorithm for high-dimension data collecting in mobile crowd sensing network

EURASIP Journal on Wireless Communications and Networking ◽

10.1186/s13638-019-1610-2 ◽

2019 ◽

Vol 2019 (1) ◽

Author(s):

Ning Zhou ◽

Jianhui Zhang ◽

Binqiang Wang ◽

Jia Xiao

Keyword(s):

Data Collection ◽

High Dimension ◽

Task Allocation ◽

High Dimensional Data ◽

High Dimensional ◽

Mobile Crowd Sensing ◽

Allocation Algorithm ◽

Crowd Sensing ◽

High Dimension Data ◽

Mobile Crowd

AbstractMobile crowd sensing (MCS) is a novel emerging paradigm that leverages sensor-equipped smart mobile terminals (e.g., smartphones, tablets, and intelligent wearable devices) to collect information. Compared with traditional data collection methods, such as construct wireless sensor network infrastructures, MCS has advantages of lower data collection costs, easier system maintenance, and better scalability. However, the limited capabilities make a mobile crowd terminal only support limited data types, which may result in a failure of supporting high-dimension data collection tasks. This paper proposed a task allocation algorithm to solve the problem of high-dimensional data collection in mobile crowd sensing network. The low-cost and balance-participating algorithm (LCBPA) aims to reduce the data collection cost and improve the equality of node participation by trading-off between them. The LCBPA performs in two stages: in the first stage, it divides the high-dimensional data into fine-grained and smaller dimensional data, that is, dividing an m-dimension data collection task into k sub-task by K-means, where (k < m). In the second stage, it assigns different nodes with different sensing capability to perform sub-tasks. Simulation results show that the proposed method can improve the task completion ratio, minimizing the cost of data collection.

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

Wireless Communications and Mobile Computing ◽

10.1155/2019/4064201 ◽

2019 ◽

Vol 2019 ◽

pp. 1-12 ◽

Cited By ~ 3

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.

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Participant Service Ability Aware Data Collecting Mechanism for Mobile Crowd Sensing

Sensors ◽

10.3390/s18124219 ◽

2018 ◽

Vol 18 (12) ◽

pp. 4219

Author(s):

Jing Yang ◽

Jialiang Xu

Keyword(s):

Data Collection ◽

Stackelberg Game ◽

Task Complexity ◽

Game Model ◽

Mobile Crowd Sensing ◽

Sensing Data ◽

Crowd Sensing ◽

Data Collecting ◽

Mutual Choice ◽

Mobile Crowd

To collect data efficiently and reliably in Mobile Crowd Sensing (MCS), a Participant Service Ability Aware (PSAA) data collecting mechanism is proposed. First, participants select the best sensing task according to the task complexity and desired reward in the multitasking scenario. Second, the Stackelberg Game model is established based on the mutual choice of participants and platform to maximize their utilities to evaluate the service ability of participants. Finally, participants transmit data to platform directly or indirectly through the best relay and the sensing data from the participants with better service ability is selected to complete sensing tasks accurately and efficiently with the minimum overall reward expense. The numerical results show that the proposed data collection mechanism can maximize the utility of participants and platform, efficiently accomplish sensing tasks and significantly reduce the overall reward expense.

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

Sensors ◽

10.3390/s18103305 ◽

2018 ◽

Vol 18 (10) ◽

pp. 3305 ◽

Cited By ~ 1

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.

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