CrowdSourcing Based Online Petitioning System for Pothole Detection Using Android Platform

Enigo, V. S. Felix; Kumar, T.M. Vinoth; Vijay, Sonam; Prabu, K.G.

doi:10.1016/j.procs.2016.05.167

Cited by 15 publications

(5 citation statements)

References 2 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Hence, it is an essential work to validate the precision of the embedded sensors if conditions permitted. Subjective evaluation [14][15][16][17][18][19][20] Image and video recognition 7…”

Section: Preparatory Phasementioning

confidence: 99%

Pavement Management Utilizing Mobile Crowd Sensing

Tian

Yuan

Zhou

et al. 2020

Advances in Civil Engineering

View full text Add to dashboard Cite

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.

show abstract

“…Hence, it is an essential work to validate the precision of the embedded sensors if conditions permitted. Subjective evaluation [14][15][16][17][18][19][20] Image and video recognition 7…”

Section: Preparatory Phasementioning

confidence: 99%

Pavement Management Utilizing Mobile Crowd Sensing

Tian

Yuan

Zhou

et al. 2020

Advances in Civil Engineering

View full text Add to dashboard Cite

show abstract

“…Using a smartphone to detect road conditions is a relatively new concept but is covered by multiple papers as seen in [3], [4], [5]. In [3] they use the Android smartphone as an online petition system to gather data from users regarding the road conditions and then use an algorithm to process the data and with image processing to determine the damage. In [4] they can detect potholes in real time using the Android accelerometers.…”

Section: Chapter 2 Backgroundmentioning

confidence: 99%

“…Crowd sourcing is also present a paper as seen in [3] as stated above they use crowd sourcing to gather their data from users. They use the crowd sourcing for data collection, selection and assessment regarding the road conditions that are processed.…”

Section: Chapter 2 Backgroundmentioning

confidence: 99%

Characterizing Road Conditions Via Smart Mobile Crowd Sourcing

Shorter¹,

Alshammari

Song

2020

2020 IEEE International Smart Cities Conference (ISC2)

View full text Add to dashboard Cite

Bad road conditions can cause vehicle damage and create hazardous driving conditions.Current reporting methods for bad road conditions is a burden for the reporter, so much so that these conditions might not be reported. With current smartphone technology this reporting method can be greatly improved. Smartphones come with an accelerometer and GPS positioning on-board. Utilizing these sensors, once the application is installed and run, a smartphone user can gather bad road conditions automatically while driving. Then the user can upload the data to the server. These road conditions are then classified and optionally displayed on a map while the user is driving. The potential for finding bad road conditions is greatly increases with crowd sourcing. When the road condition data is analyzed an algorithm is used to determine the size of the bad road condition and how many samples are needed.Even though the smartphone might not have the sampling rate of a dedicated accelerometer it's potential for gather large amounts of data using crowd sourcing and ease of use outweighs this deficiency. iv APPROVAL PAGEThe faculty listed below, appointed by the Dean of the School of Computing and Engineering, have examined a thesis titled "Characterizing Road Conditions via Smart Mobile Crowd Sourcing," presented by Dustin Shorter, candidate for the Master of Science degree, and certify that in their opinion it is worth of acceptance.

show abstract

“…As the mobile users (or crowd workers) are equipped with sensing devices, so the researchers thought of utilizing these mobile users for sensing and collecting data for several real-world applications and then distributing it to the community or organization. For example, measuring the air pollution level across the cities [2] [3] [4], giving information about the road traffic [5] [6], noise pollution assessment [7] [8], information about the potholes [9] [10], and many more [11] [12]. The process of completion of task(s) by the crowd workers or group of common people equipped with sensing devices in the form of an open call give rise to a new pragmatic field of study termed as mobile crowdsensing (a.k.a.…”

Section: Introductionmentioning

confidence: 99%

A Quality Aware Multiunit Double Auction Framework for IoT-Based Mobile Crowdsensing in Strategic Setting

et al. 2022

View full text Add to dashboard Cite

Crowdsourcing with the intelligent agents carrying smart devices is becoming increasingly popular in recent years. It has opened up meeting an extensive list of real-life applications such as measuring air pollution levels, road traffic information, etc. In literature, this is known as mobile crowdsourcing or mobile crowdsensing. In this paper, the discussed set-up consists of multiple task requesters (or task providers) and multiple IoT devices (as task executors), where each of the task providers has multiple homogeneous sensing tasks. Each task requester reports a bid and the number of homogeneous sensing tasks to the platform. On the other side, multiple IoT devices report the ask (the charge for imparting its services) and the number of sensing tasks they can execute. The valuations of task requesters and IoT devices are private information, and both might act strategically. One assumption that is made in this paper is that the bids and asks of the agents (task providers and IoT devices) follow decreasing marginal returns criteria. Given the above-discussed scenario, the objectives are: (1) to determine a set of quality IoT devices for each of the tasks held by the task requesters, and (2) to select a subset of quality IoT devices from among the available quality IoT devices for each of the sensing tasks. In this paper, a truthful mechanism is proposed for allocating the IoT devices to the sensing tasks carried by task requesters that also keep into account the quality of IoT devices. Through theoretical analysis, it is shown that the mechanism is truthful, budget balanced, individually rational, computationally efficient, correct, and prior-free. Further, probabilistic analysis is carried out to estimate the average number of tasks that get executed for any task requester. The simulations are carried out to measure the performance of the proposed mechanism against the benchmark mechanisms on the ground of truthfulness, budget balance, satisfaction level, average incentive, and computational efficiency.

show abstract

CrowdSourcing Based Online Petitioning System for Pothole Detection Using Android Platform

Cited by 15 publications

References 2 publications

Pavement Management Utilizing Mobile Crowd Sensing

Pavement Management Utilizing Mobile Crowd Sensing

Characterizing Road Conditions Via Smart Mobile Crowd Sourcing

A Quality Aware Multiunit Double Auction Framework for IoT-Based Mobile Crowdsensing in Strategic Setting

Contact Info

Product

Resources

About