Crowdsensing with Social Network-Aided Collaborative Trust Scores

Kantarcı, Burak; Glasser, Philip M.

doi:10.1109/glocom.2015.7417008

Cited by 30 publications

(16 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Fig 3 (b) is depicts an overcrowding scenario and requires attention. Fig 3 (c) and Fig 3 (d) show a very dense crowd with a serious threat to public safety [9] that can result in stampedes like the one that happened during the stoning ritual in 2015 [10], or create a human catastrophe in case of fire or proliferation of a pandemic [10] [11][12] [13]. Considering all the challenges of the diversified crowd, the authorities connected with Hajj event are constantly working on their respective portfolios to understand, design, implement and provide the necessary services such as pilgrim identification, mobility tracking, navigation to points of interests, and health, to manage the Haj crowd in an efficient manner [1].…”

Section: Hajj Crowd Statistics and Challengesmentioning

confidence: 99%

See 1 more Smart Citation

Digital Revolution for Hajj Crowd Management: A Technology Survey

et al. 2020

View full text Add to dashboard Cite

Section: Hajj Crowd Statistics and Challengesmentioning

confidence: 99%

“…Hajj AR 9 is a mobile phone app, which uses augmented reality and provides pilgrims with a unique and diverse experience for identification and directions of the holy sites and can guide the pilgrims stepwise to perform Hajj related rituals accurately, utilizing 3D maps.…”

Section: A2 Augmented Reality and Hajjmentioning

confidence: 99%

Digital Revolution for Hajj Crowd Management: A Technology Survey

et al. 2020

View full text Add to dashboard Cite

“…Also, user trustworthiness refers to the average reputation of a user over a certain period of time [19]. Data trustworthiness in user incentivization was studied in Kantarci et al [20]. The authors used both statistical and recommendation-based user reputation to ensure data trustworthiness.…”

Section: Data Trustworthiness and User Reputationmentioning

confidence: 99%

Security analysis of mobile crowd sensing applications

Owoh

Singh

2020

ACI

View full text Add to dashboard Cite

The proliferation of mobile phones with integrated sensors makes large scale sensing possible at low cost. During mobile sensing, data mostly contain sensitive information of users such as their real-time location. When such information are not effectively secured, users’ privacy can be violated due to eavesdropping and information disclosure. In this paper, we demonstrated the possibility of unauthorized access to location information of a user during sensing due to the ineffective security mechanisms in most sensing applications. We analyzed 40 apps downloaded from Google Play Store and results showed a 100% success rate in traffic interception and disclosure of sensitive information of users. As a countermeasure, a security scheme which ensures encryption and authentication of sensed data using Advanced Encryption Standard 256-Galois Counter Mode was proposed. End-to-end security of location and motion data from smartphone sensors are ensured using the proposed security scheme. Security analysis of the proposed scheme showed it to be effective in protecting Android based sensor data against eavesdropping, information disclosure and data modification.

show abstract

“…A different thread of research has focused on evaluating the quality of crowdsourced data or trustworthiness of crowdworkers so that more informed decisions can be made (e.g., on data or worker selection). Kantarci et al [10] treated crowdworkers to be part of a social network (based on the commonality of tasks assigned) to assess the trustworthiness of workers and data. Wu et al [11] proposed a reputation system that not only assesses but also predicts the trustworthiness of crowdworkers without requiring their prior contributions.…”

Section: Related Workmentioning

confidence: 99%

Reshaping Mobile Crowd Sensing Using Cross Validation to Improve Data Credibility

Luo

Zeynalvand

2017

GLOBECOM 2017 - 2017 IEEE Global Communications Conference

View full text Add to dashboard Cite

Data credibility is a crucial issue in mobile crowd sensing (MCS) and, more generally, people-centric Internet of Things (IoT). Prior work takes approaches such as incentive mechanism design and data mining to address this issue, while overlooking the power of crowds itself, which we exploit in this paper. In particular, we propose a cross validation approach which seeks a validating crowd to verify the data credibility of the original sensing crowd, and uses the verification result to reshape the original sensing dataset into a more credible posterior belief of the ground truth. Following this approach, we design a specific cross validation mechanism, which integrates four sampling techniques with a privacy-aware competency-adaptive push (PACAP) algorithm and is applicable to time-sensitive and quality-critical MCS applications. It does not require redesigning a new MCS system but rather functions as a lightweight "plug-in", making it easier for practical adoption. Our results demonstrate that the proposed mechanism substantially improves data credibility in terms of both reinforcing obscure truths and scavenging hidden truths.

show abstract

Crowdsensing with Social Network-Aided Collaborative Trust Scores

Cited by 30 publications

References 19 publications

Digital Revolution for Hajj Crowd Management: A Technology Survey

Digital Revolution for Hajj Crowd Management: A Technology Survey

Security analysis of mobile crowd sensing applications

Reshaping Mobile Crowd Sensing Using Cross Validation to Improve Data Credibility

Contact Info

Product

Resources

About