Please wait a minute...
Journal of ZheJiang University (Engineering Science)  2020, Vol. 54 Issue (10): 1915-1922    DOI: 10.3785/j.issn.1008-973X.2020.10.007
    
Design and implementation of mobile crowdsensing platform
Jia-hao LIAO1(),Zhi-wen YU2,*(),Yi-meng LIU2,Bin GUO2
1. School of Software, Northwestern Polytechnical University, Xi’an 710129, China
2. School of Computer Science, Northwestern Polytechnical University, Xi’an 710129, China
Download: HTML     PDF(795KB) HTML
Export: BibTeX | EndNote (RIS)      

Abstract  

The mobile crowdsensing platform was designed and application development work was completed using Android in order to utilize the idle sensing resources of mobile devices. The sensor type of mobile smart devices was adapted and the multi-source sensing data was obtained to complete the sensing task with the application. The task template was used to guide the user to publish the task and simplify the task publishing process. The multi-domain and multi-type sensing data were collected for tasks. The application was developed using the model-view-controller (MVC) pattern. Two user interface page levels and five functional modules were designed. The core function of this application was the task publishing and reception, which enabled users to act as task publishers and publish the current unsolved problems as sense tasks on the mobile crowdsensing platform. The user could be a task receiver, receiving and completing the sensing task. The application maintained good response speed and performance in the simulation experiment while application showed one thousand sensing tasks.



Key wordsmobile crowdsensing      sensor      software application design and development      Android application      SQLite database     
Received: 16 December 2019      Published: 28 October 2020
CLC:  TP 311  
Corresponding Authors: Zhi-wen YU     E-mail: liaojh@mail.nwpu.edu.cn;zhiwenyu@nwpu.edu.cn
Cite this article:

Jia-hao LIAO,Zhi-wen YU,Yi-meng LIU,Bin GUO. Design and implementation of mobile crowdsensing platform. Journal of ZheJiang University (Engineering Science), 2020, 54(10): 1915-1922.

URL:

http://www.zjujournals.com/eng/10.3785/j.issn.1008-973X.2020.10.007     OR     http://www.zjujournals.com/eng/Y2020/V54/I10/1915


移动群智感知平台设计与实现

为了利用移动智能设备上闲置的传感器资源,对移动群智感知平台应用进行设计,在Android系统下完成应用开发工作. 应用程序可以适配移动智能设备具备的传感器类型,获取多源传感数据完成感知任务;使用任务模版引导用户发布任务的方式,简化任务发布过程,能够面向多领域、多类型的感知任务完成传感数据收集. 框架设计模型-视图-控制器(MVC)模式,设计了2个用户界面层次和5个功能模块. 核心功能为感知任务的发布与接收,能够让使用者作为任务发布者,将当前待解决的问题作为感知任务在移动群智感知平台上发布;能够让使用者作为任务接受者领取并完成感知任务. 应用程序在1 000个感知任务显示的仿真实验下,保持良好的响应速度和性能.


关键词: 移动群智感知,  传感器,  软件设计与开发,  Android应用,  SQLite数据库 
Fig.1 Application’s framework design
Fig.2 Functions design of mobile crowdsensing platform application
Fig.3 Application’s page-hierarchy design
Fig.4 Encapsulation class of application’s sensing function
Fig.5 Types of sensing task releasing templates
Fig.6 MainController’s function
名称 定义
NETWORK_PROVIDER 根据蜂窝网络和WiFi接入点确定位置数据
GPS_PROVIDER 使用卫星提供位置数据
PASSIVE_PROVIDER 通过其他应用发送位置请求,被动接收位置数据
FUSED_PROVIDER 混合模式
Tab.1 Position sensor Provider types
性能指标 CPU占比 内存/MB 耗能
测试1 5%~10% 188±2 中低
测试2 6%~15% 185±5
测试3 7%~10% 187±2
测试4 7%~11% 188±4
综合 5%~10% 180~190
Tab.2 Pressure test results of showing 10 sense tasks in app
性能指标 CPU占比 内存/MB 耗能
测试1 6%~11% 185±2
测试2 6%~10% 186±3
测试3 7%~12% 187±2
测试4 5%~11% 185±4 中低
综合 6%~12% 181~189
Tab.3 Pressure test results of showing 100 sense tasks in app
性能指标 CPU占比 内存/MB 耗能
测试1 6%~13% 187±3
测试2 8%~10% 185±2 中低
测试3 6%~17% 185±5
测试4 8%~12% 187±3 中低
综合 6%~13% 183~190
Tab.4 Pressure test results of showing 1 000 sense tasks in app
[1]   GANTI R K, YE F, LEI H Mobile crowdsensing: current state and future challenges[J]. IEEE Communications Magazine, 2011, 49 (11): 32- 39
doi: 10.1109/MCOM.2011.6069707
[2]   GUO B, WANG Z, YU Z W, et al Mobile crowd sensing and computing: the review of an emerging human-powered sensing paradigm[J]. ACM Computing Surveys, 2015, 48 (1): 1- 31
[3]   WANG L, ZHANG D, WANG Y, et al Sparse mobile crowdsensing: challenges and opportunities[J]. IEEE Communications Magazine, 2016, 54 (7): 161- 167
doi: 10.1109/MCOM.2016.7509395
[4]   陈荟慧, 郭斌, 於志文 移动群智感知应用[J]. 中兴通讯技术, 2014, 20 (1): 35- 37
CHEN Hui-hui, GUO Bin, YU Zhi-wen Mobile crowd-sensing application[J]. ZTE Technology Journal, 2014, 20 (1): 35- 37
[5]   KIM S, ROBSON C, ZIMMERMAN T, et al. Creek watch: pairing usefulness and usability for successful citizen science [C] // Proceedings of the SIGCHI Conference on Human Factors in Computing Systems. New York: ACM, 2011: 2125–2134.
[6]   DUTTA P, AOKI P M, KUMAR N, et al. Common sense: participatory urban sensing using a network of handheld air quality monitors [C] // Proceedings of the 7th ACM Conference on Embedded Networked Sensor Systems. Berkeley, California: ACM, 2009: 349-350.
[7]   MATHUR S, JIN T, KASTURIRANGAN N, et al. Parknet: drive-by sensing of road-side parking statistics [C] // Proceedings of the ACM International Conference on Mobile Systems, Applications, and Services. San Francisco: ACM, 2010: 123-136.
[8]   CHEN L, CAI Y, DING Y, et al. Spatially ?ne-grained urban air quality estimation using ensemble semi-supervised learning and pruning [C] // Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing. Heidelberg: ACM, 2016: 1076–1087.
[9]   DEMIBAS M, RUDRA C, RUDRA A, et al. Imap: indirect measurement of air pollution with cellphones [C] // Proceedings of the IEEE International Conference on Pervasive Computing and Communications. Galveston: IEEE, 2009: 1-6.
[10]   MOHAN P, PADMANABHAN V N, RAMJEE R. Nericell: rich monitoring of road and traffic conditions using mobile smartphones [C] // Proceedings of the 6th ACM Conference on Embedded Network Sensor Systems. New York: ACM, 2008: 323-336.
[11]   LIU S, LIU Y, NI L, et al Detecting crowdedness spot in city transportation[J]. IEEE Transactions on Vehicular Technology, 2013, 62 (4): 1527- 1539
doi: 10.1109/TVT.2012.2231973
[12]   CHEN H, GUO B, YU Z W, et al. Toward real-time and cooperative mobile visual sensing and sharing [C] // Proceedings of the 35th Annual IEEE International Conference on Computer Communications. San Francisco: IEEE, 2016: 1-9.
[13]   LIU Y M, YU Z W, GUO B, et al. CrowdOS: a ubiquitous operating system for crowdsourcing and mobile crowd sensing [EB/OL]. [2020-08-11]. https://doi.org/10.1109/TMC.2020.3015750.
[14]   RANA R K, CHOU C T, KANHERE S S, et al. Ear-phone: an end-to-end participatory urban noise mapping system [C] // Proceedings of the 9th ACM/IEEE International Conference on Information Processing in Sensor Networks. Stockholm, Sweden: ACM, 2010: 105-116.
[15]   WHITE J, THOMPSON C, TURNER H, et al Wreckwatch: automatic traf?c accident detection and noti?cation with smart phones[J]. Mobile Networks and Applications, 2011, 16 (3): 285- 303
doi: 10.1007/s11036-011-0304-8
[1] Ming-hui YOU,Ya-feng YIN,Lei XIE,Sang-lu LU. User profiling based on activity sensing[J]. Journal of ZheJiang University (Engineering Science), 2021, 55(4): 608-614.
[2] Teng ZHANG,Xin-long JIANG,Yi-qiang CHEN,Qian CHEN,Tao-mian MI,Piu CHAN. Wrist attitude-based Parkinson's disease ON/OFF state assessment after medication[J]. Journal of ZheJiang University (Engineering Science), 2021, 55(4): 639-647.
[3] Zhi-qiang WU,Jun WEI,Rong-zhen DONG. Graphene-based piezoresistive composite and application in crack monitoring[J]. Journal of ZheJiang University (Engineering Science), 2020, 54(2): 233-240.
[4] Ji-jun TONG,Yan-jie BAI,Jian-wei PAN,Jia-feng YANG,Lu-rong JIANG. Ballistocardiogram and respiratory signal separation based on variational mode decomposition[J]. Journal of ZheJiang University (Engineering Science), 2020, 54(10): 2058-2066.
[5] Hao TIAN,Yu-ren ZHAO. Optical measurement of high-speed solenoid valve switching-on characteristics[J]. Journal of ZheJiang University (Engineering Science), 2020, 54(1): 17-22.
[6] Chun-sheng LIU,Hong SHAN,Bin WANG,Jun HUANG. Wireless sensor network localization via Bregman divergence[J]. Journal of ZheJiang University (Engineering Science), 2019, 53(8): 1525-1535.
[7] Yan LV,Meng ZHANG,Wu-hao JIANG,Yi-hua NI,Xiao-hong QIAN. Design of elderly fall detection system using CNN[J]. Journal of ZheJiang University (Engineering Science), 2019, 53(6): 1130-1138.
[8] YUAN Qi, MA Chun-guang, YAO Jian-sheng, YU Hai-tao. w-balanced incomplete block design method for key pre-distribution scheme in heterogeneous wireless sensor network[J]. Journal of ZheJiang University (Engineering Science), 2019, 53(1): 126-136.
[9] LIU Zhou-zhou, LI Shi-ning, LI Bin, WANG Hao, ZHANG Qian-yun, ZHENG Ran. New elastic collision optimization algorithm and its application in sensor cloud resource scheduling[J]. Journal of ZheJiang University (Engineering Science), 2018, 52(8): 1431-1443.
[10] KE Xian-xin, ZHANG Wen-zhen, YANG Yang, WEN Lei. Multi-sensor positioning system for humanoid robot[J]. Journal of ZheJiang University (Engineering Science), 2018, 52(7): 1247-1252.
[11] WEI Xin-wei, GAO Qing, SU Kai-qi, QIN Zhen, PAN Yu-xiang, HE Yong, WANG Ping. Three-dimensional cardiomyocyte-based biosensor with tissue engineering scaffold[J]. Journal of ZheJiang University (Engineering Science), 2018, 52(7): 1415-1422.
[12] ZHOU Yu, WANG Hong-jun, SHAO Fu-cai, SHA Wen-hao. Signal coverage detection algorithm for electromagnetic situation generation in wireless communication networks[J]. Journal of ZheJiang University (Engineering Science), 2018, 52(6): 1088-1096.
[13] LAI Xiao-han, WEN Hao-xiang, CHEN Long-dao. Energy efficient routing for wireless sensor networks in intertidal environment[J]. Journal of ZheJiang University (Engineering Science), 2018, 52(12): 2414-2422.
[14] QI Xiao-gang, WANG Zhen-yu, LIU Li-fang, LIU Xing-cheng, MA Jiu-long. Reliable and efficient routing of wireless sensors and actuator networks[J]. Journal of ZheJiang University (Engineering Science), 2018, 52(10): 1964-1972.
[15] XIAO Jing-bo, CHEN Min, LIU Yun-tao, LIU Yun-chao, CHEN Jie. Design and implementation of sensor data acquisition node for water monitoring[J]. Journal of ZheJiang University (Engineering Science), 2017, 51(7): 1446-1452.