Mobile crowd sensing coverage and energy optimization in smart coalmine

Chaowei WANG; Ting LIU; Tianyu WANG; Weidong WANG

doi:10.11959/j.issn.2096-3750.2020.00191

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Mobile crowd sensing coverage and energy optimization in smart coalmine

Topic:IoT+Energy | 更新时间：2024-06-05

- Mobile crowd sensing coverage and energy optimization in smart coalmine
- Chinese Journal on Internet of Things Vol. 4, Issue 4, Pages: 17-25(2020)
- 作者机构：
- 作者简介：
- 基金信息：
- DOI：10.11959/j.issn.2096-3750.2020.00191
  CLC： TN92
- Published：30 December 2020，
  
  Published Online：2020-12，
- 稿件说明：
移动端阅览
CHAOWEI WANG, TING LIU, TIANYU WANG, et al. Mobile crowd sensing coverage and energy optimization in smart coalmine. [J]. Chinese journal on internet of things, 2020, 4(4): 17-25.
DOI：

CHAOWEI WANG, TING LIU, TIANYU WANG, et al. Mobile crowd sensing coverage and energy optimization in smart coalmine. [J]. Chinese journal on internet of things, 2020, 4(4): 17-25. DOI： 10.11959/j.issn.2096-3750.2020.00191.

摘要

移动群智感知（MCS

mobile crowd sensing）是一种有效利用智能移动终端协同采集环境数据的技术，集成多种传感器的移动载体（如车辆）越来越多地被当作参与者来承担各种感知任务。在智慧矿山物联网（IoT

Internet of things）中，为了更好地感知人—机—环的实时信息，支撑安全生产顺利进行，基于MCS思想对矿山环境下移动感知节点的覆盖质量和能耗优化进行研究，提出了一种综合考虑覆盖率（CP

coverage percentage）和覆盖密度（CD

coverage density）的效用函数F(Ω)来衡量MCS的覆盖质量。为了获得最优的覆盖质量，针对参与感知的车辆选择问题提出了一种改进的贪婪算法——覆盖质量优化（CQO

coverage quality optimization）算法来优化覆盖质量，并使用真实的车辆轨迹数据集对所提出的算法进行评估，研究了影响覆盖质量的几个因素。实验结果表明，该算法具有较好的覆盖质量。

Abstract

Mobile crowd sensing (MCS) is a promising diagram for the environmental information collection based on the smart mobile terminal.Nowadays

vehicles with multiple embedded sensors are increasingly being considered as participants to complete various sensing tasks.In order to better perceive the data in the coalmine environment

the coverage quality and energy consumption of the perception data of sensing terminals were studied based on MCS.A new sensing coverage quality indicator called utility function F(Ω) jointly considering the coverage percentage and coverage density was defined.The selection of vehicles as an optimization problem to improve the coverage quality was formulated

then an improved greedy algorithm-coverage quality optimization (CQO) algorithm was proposed.The proposed algorithm with the real vehicle trajectory dataset was formulated and several factors influencing the coverage quality were studied.The experiment results show that the proposed algorithm achieves a better coverage quality.

关键词

智慧矿山移动群智感知覆盖率覆盖密度

Keywords

smart coalminemobile crowd sensingcoverage percentagecoverage density

references

袁亮, 俞啸, 丁恩杰 ,等. 矿山物联网人—机—环状态感知关键技术研究[J]. 通信学报, 2020,41(2): 1-12.

YUAN L, YU X, DING E J ,et al. Research on key technologies of human-machine-environment states perception in mine Internet of things[J]. Journal on Communications, 2020,41(2): 1-12.

吴喜雄 . 通信技术在矿山无线通信的应用研究[J]. 中国战略新兴产业, 2019(24):109.

WU X X . Application research of communication technology in mine wireless communication[J]. China’s Strategic Emerging Industries, 2019(24):109.

牛志升 Sherman SHEN, 张钦宇 ,et al. 面向沉浸式体验的空天地一体化车联网体系架构与关键技术[J]. 物联网学报, 2017,1(2): 17-27.

NIU Z S, SHEN S, ZHANG Q Y ,et al. Space-air-ground integrated vehicular network for immersive driving experience[J]. Chinese Journal on Internet of Things, 2017,1(2): 17-27

CHEN Y Y, LYU P, GUO D K ,et al. Trajectory segment selection with limited budget in mobile crowd sensing[J]. Pervasive and Mobile Computing, 2017,40: 123-138.

HOWE J . The rise of crowdsourcing[EB/OL]. WIREDWIRED, 2006.

WANG C W, LI C S, QIN C ,et al. Maximizing spatial-temporal coverage in mobile crowd-sensing based on public transports with predictable trajectory[J]. International Journal of Distributed Sensor Networks, 2018,14(8):155014771879535.

WANG C W, GAIMU X G, LI C S ,et al. Smart mobile crowdsensing with urban vehicles:a deep reinforcement learning perspective[J]. IEEE Access, 2019,7: 37334-37341.

HE Z J, CAO J N, LIU X F . High quality participant recruitment in vehicle-based crowdsourcing using predictable mobility[C]// 2015 IEEE Conference on Computer Communications (INFOCOM). IEEE, 2015: 2542-2550.

GONG W, ZHANG B X, LI C . Location-based online task assignment and path planning for mobile crowdsensing[J]. IEEE Transactions on Vehicular Technology, 2019,68(2): 1772-1783.

MASUTANI O, . A sensing coverage analysis of a route control method for vehicular crowd sensing[C]// 2015 IEEE International Conference on Pervasive Computing and Communication Workshops (PerCom Workshops). IEEE, 2015: 396-401.

XU J L, WANG S G, ZHANG N ,et al. Reward or penalty:aligning incentives of stakeholders in crowdsourcing[J]. IEEE Transactions on Mobile Computing, 2019,18(4): 974-985.

HU A D, GU Y G . Mobile crowdsensing tasks allocation for mult-parameter bids[C]// 2017 IEEE 3rd Information Technology and Mechatronics Engineering Conference (ITOEC). IEEE, 2017: 489-493.

LUONG N C, HOANG D T, WANG P ,et al. Data collection and wireless communication in Internet of things (IoT) using economic analysis and pricing models:a survey[J]. IEEE Communications Surveys ＆ Tutorials, 2016,18(4): 2546-2590.

SURYADASARI V, POURYAZDAN M, KANTARCI B . On the impact of selective data acquisition in mobile crowd-sensing performance[C]// 2018 IEEE Canadian Conference on Electrical ＆ Computer Engineering (CCECE). IEEE, 2018: 1-4.

TRAN-THANH L, STEIN S, ROGER S A ,et al. Efficient crowdsourcing of unknown experts using bounded multi-armed bandits[J]. Artificial Intelligence, 2014,214: 89-111.

AL-TURJMAN F, KARAKOC M, GUNAY M ,et al. Routing mobile data couriers in smart-cities[C]// 2016 IEEE International Conference on Communications (ICC). IEEE, 2016: 1-6.

REN Y Y, LIU Y X, ZHANG N ,et al. Minimum-cost mobile crowdsourcing with QoS guarantee using matrix completion technique[J]. Pervasive and Mobile Computing, 2018,49: 23-44.

XIONG H Y, ZHANG D Q, CHEN G L ,et al. iCrowd:near-optimal task allocation for piggyback crowdsensing[J]. IEEE Transactions on Mobile Computing, 2016,15(8): 2010-2022.

SIM I, CHOI K, KWON K ,et al. Energy efficient cluster header selection algorithm in WSN[C]// 2009 International Conference on Complex,Intelligent and Software Intensive Systems. IEEE, 2009: 584-587.

LEE H J, WICKE M, KUSY B ,et al. Data stashing:energy-efficient information delivery to mobile sinks through trajectory prediction[C]// The 9th ACM/IEEE International Conference on Information Processing in Sensor Networks. ACM, 2010: 291-302.

ZHAO D, MA H D, LIU L . Energy-efficient opportunistic coverage for people-centric urban sensing[J]. Wireless Networks, 2014,20(6): 1461-1476.

唐志博 . 基于矿工行为分析的WMSN网络优化方法[D]. 徐州:中国矿业大学, 2015.

TANG Z B . WMSN network optimization method based on miners’ behavior analysis[D]. Xuzhou:China University of Mining and Technology, 2015.

马丰原 . 基于视觉感兴趣区域的图像质量评价算法研究[D]. 西安:西安科技大学, 2013.

MA F Y . Research on image quality assessment based on visual regions of interest[D]. Xi’an:University of Science and Technology, 2013.

KHULLER S, MOSS A, NAOR J S . The budgeted maximum coverage problem[J]. Information Processing Letters, 1999,70(1): 39-45.

SVIRIDENKO M . A note on maximizing a submodular set function subject to a knapsack constraint[J]. Operations Research Letters, 2004,32(1): 41-43.

NI L M, CHEN L, QU H ,et al. Smart city GPS data set[DB/OL]. 2007

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