#Safe Mapping Platform: A GIS Mobile Crowd Sensing Platform for COVID-19 Self-Tracking and Self-Risk Managing

International Journal of Geoinformatics ◽

10.52939/ijg.v17i5.2009 ◽

2021 ◽

pp. 55-60

Keyword(s):

Open Data ◽

Location Based Services ◽

Health Issues ◽

Mobile Crowd Sensing ◽

The Public ◽

Related Information ◽

Crowd Sensing ◽

Sensing Platform ◽

The Social ◽

Mobile Crowd

Increases in the social sector of open data and online mapping technologies are starting new chances for interactive mapping in many research applications. Mobile crowd sensing is an application that gathers data from a network of conscientious volunteers and implements it for a public benefit which is very helpful for collecting related information during the COVID-19 situation. The paper aims to demonstrate the concept of #Safe Mapping Platform which followed a framework of opensource technology and implementation aspects. The #Safe Mapping Platform was established for self-tracking and self-risk managing by integrating GIS opensource technologies, location-based services, and LINE application. The developed platform can be adapted to the public for self-tracking and self-risk managing in any health issues in the future.

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FollowMe: a mobile crowd sensing platform for spatial-temporal data sharing

International Journal of High Performance Computing and Networking ◽

10.1504/ijhpcn.2019.102347 ◽

2019 ◽

Vol 14 (4) ◽

pp. 416

Author(s):

Mingzhong Wang

Keyword(s):

Data Sharing ◽

Temporal Data ◽

Mobile Crowd Sensing ◽

Crowd Sensing ◽

Sensing Platform ◽

Mobile Crowd

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Maximizing spatial–temporal coverage in mobile crowd-sensing based on public transports with predictable trajectory

International Journal of Distributed Sensor Networks ◽

10.1177/1550147718795351 ◽

2018 ◽

Vol 14 (8) ◽

pp. 155014771879535 ◽

Cited By ~ 3

Author(s):

Chaowei Wang ◽

Chensheng Li ◽

Cai Qin ◽

Weidong Wang ◽

Xiuhua Li

Keyword(s):

Optimization Problem ◽

System Model ◽

Trajectory Data ◽

Mobile Crowd Sensing ◽

Data Set ◽

The Public ◽

Crowd Sensing ◽

Mobile Crowd ◽

Temporal Coverage ◽

Selection Of

Mobile crowd-sensing is a prospective paradigm especially for intelligent mobile terminals, which collects ubiquitous data efficiently in metropolis. The existing crowd-sensing schemes based on intelligent terminals mainly consider the current trajectory of the participants, and the quality highly depends on the spatial-temporal coverage which is easily weakened by the mobility of participants. Nowadays, public transports are widely used and affordable in many cities around the globe. Public transports embedded with substantial sensors act as participants in crowd-sensing, but different from the intelligent terminals, the trajectory of public transports is schedulable and predictable, which sheds an opportunity to achieve high-quality crowd-sensing. Therefore, based on the predictable trajectory of public transports, we design a novel system model and formulate the selection of public transports as an optimization problem to maximize the spatial–temporal coverage. After proving the public transport selection is non-deterministic polynomial-time hardness, an approximation algorithm is proposed and the coverage is close to 1. We evaluate the proposed algorithm with samples of real T-Drive trajectory data set. The results show that our algorithm achieves a near optimal coverage and outperforms existing algorithms.

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Private Data Aggregation Based on Fog-Assisted Authentication for Mobile Crowd Sensing

Security and Communication Networks ◽

10.1155/2021/7354316 ◽

2021 ◽

Vol 2021 ◽

pp. 1-12

Author(s):

Ruyan Wang ◽

Shiqi Zhang ◽

Zhigang Yang ◽

Puning Zhang ◽

Dapeng Wu ◽

...

Keyword(s):

Spatial Data ◽

Data Aggregation ◽

Interpolation Method ◽

Homomorphic Encryption ◽

Mobile Crowd Sensing ◽

Transmission Method ◽

Crowd Sensing ◽

Sensing Platform ◽

Private Data ◽

Mobile Crowd

In mobile crowd sensing (MCS), the cloud as a single sensing platform undertakes a large number of communication tasks, leading to the reduction of sensing task execution efficiency and the risk of loss and leakage of users’ private data. In this paper, we propose a spatial ciphertext aggregation scheme with collaborative verification of fog nodes. Firstly, the cloud and fog collaboration architecture is constructed. Fog nodes are introduced for data validation and slices transmission, reducing computing cost on the sensing platform. Secondly, a multipath transmission method of slice data is proposed, in which the user identity and data are transmitted anonymously by the secret sharing method, and the data integrity is guaranteed by hash chain authentication. Finally, a spatial data aggregation method based on privacy protection is presented. The ciphertext aggregation calculation of the sensing platform is realized through Paillier homomorphic encryption, and the problem of insufficient data coverage in the sensing region is solved by the position-based weight interpolation method. The security analysis demonstrates that the scheme can achieve the expected security goal. The simulation results show the feasibility and effectiveness of the proposed scheme.

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