Skip to main content

Advertisement

SpringerLink
Log in

C-EEUC: a Cluster Routing Protocol for Coal Mine Wireless Sensor Network Based on Fog Computing and 5G

  • Published:
Mobile Networks and Applications Aims and scope Submit manuscript

Abstract

In underground coal mine, the routing protocol in Wireless Sensor Network (WSN) based on fog computing can effectively achieve combination the monitoring task with the computing task, and provide the correct data forwarding path to meet the requirements of the aggregation and transmission of sensed information. However, the energy efficiency is still taken into account, especially, the unbalance of energy consumption. 5G is a technical system of high frequency and low frequency mixing, with characteristics of large capacity, low energy consumption and low cost. With the formal freeze on 5G NSA standards, 5G networks are one step closer to our lives. In this paper, a centralized non-uniform clustering routing protocol C-EEUC based on the residual energy and communication cost. The C-EEUC protocol considers all nodes as candidate cluster heads in the clustering stage and defines a weight matrix P. The value of the matrix elements takes into account the residual energy of nodes and the cost of communication between nodes and cluster heads, selected as the basis for the cluster head. When selecting a cluster head, each time a node with the largest weight is selected from a set of candidate cluster heads, other candidate cluster heads within the competition range abandon competition, and then updates the candidate cluster head set. Experimental results show that the protocol optimized in this paper can effectively extend the network life cycle.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11

Similar content being viewed by others

References

  1. Cheng-Fa L, Gui-Hai C, Mao Y, Jie W (2007) Uneven cluster-based routing protocol for wireless sensor networks. Chin J Comput 30(1):27–36

    Google Scholar 

  2. Suto K, Nishiyama H, Kato N, Huang C (2015) An energy-efficient and delay-aware wireless computing system for industrial wireless sensor networks. IEEE Access 3:1026–1035

    Article  Google Scholar 

  3. Vaquero L, Rodero-Merino L (2014) Finding your way in the definition fog: towards a comprehensive of fog computing. ACM Sigcomm Comput Commun Rev 44:27–32

    Article  Google Scholar 

  4. Zeng J, Wang T, Lai Y, Liang J, Chen H (2016) Data delivery from WSNs to cloud based on a fog structure. In: Fourth international conference on advanced cloud and big data (Cbd 2016), p 104–109

  5. Sharma S, Puthal D, Tazeen S, Prasad M, Zomaya AY (2017) MSGR: a mode-switched grid-based sustainable routing protocol for wireless sensor networks. IEEE Access 5:19864–19875

    Article  Google Scholar 

  6. Li T, Liu YX, Gao LX, Liu AF (2017) A cooperative-based model for smart-sensing tasks in fog computing. IEEE Access 5:21296–21311

    Article  Google Scholar 

  7. Lee W, Nam K, Roh H G, Kim S H (2016) A gateway based fog computing architecture for wireless sensors and actuator networks. In: 2016 18th international conference on advanced communications technology, IEEE, p 210–213

  8. Naranjo P G V, Shojafar M, Abraham A, Baccarelli E (2016) A new stable election-based routing algorithm to preserve aliveness and energy in fog-supported wireless sensor networks. In: IEEE international conference on systems, man, and cybernetics, IEEE, p 2413–2418

  9. Chiti F, Fantacci R, Tani A (2017) Performance evaluation of an adaptive channel allocation technique for cognitive wireless sensor networks. IEEE Trans Veh Technol 66(6):5351–5363

    Article  Google Scholar 

  10. Ni JB, Zhang AQ, Lin XD, Shen XM (2017) Ecurity, privacy, and fairness in fog-based vehicular Crowdsensing. IEEE Commun Mag 55(6):146–152

    Article  Google Scholar 

  11. Liu Q, Li P, Zhao WT, Cai W, Yu S, Leung VCM (2018) A survey on security threats and defensive techniques of machine learning: a data driven view. IEEE Access 6:12103–12117

    Article  Google Scholar 

  12. Cheng JR, Zhou JH, Liu Q, Tang XY, Guo YX (2018) A DDoS detection method for socially aware networking based on forecasting fusion feature sequence. Comput J 61(7):959–970

    Article  Google Scholar 

  13. Feng JY, Liu Z, Wu C, Ji YS (2019) Mobile edge computing for the internet of vehicles: offloading framework and job scheduling. IEEE Veh Technol Mag 14(1):28–36

    Article  Google Scholar 

  14. Feng JY, Liu Z, Wu C, Ji YS (2017) AVE: autonomous vehicular edge computing framework with ACO-based scheduling. IEEE Trans Veh Technol 66(12):10660–10675

    Article  Google Scholar 

  15. Wu C, Liu Z, Zhang D, Yoshinaga T, Ji YS (2018) Spatial intelligence toward trustworthy vehicular iot. IEEE Commun Mag 56(10):22–27

    Article  Google Scholar 

  16. Jutila M (2016) An adaptive edge router enabling internet of things. IEEE Internet Things J 3(6):1061–1069

    Article  Google Scholar 

  17. Yangui S, Ravindran P, Bibani O, Glitho R H, Ben Hadj-Alouane N, Morrow M J, Polakos P A (2016) A platform as-a-service for hybrid cloud/fog environments. In: 22nd IEEE international symposium on local and metropolitan area networks, IEEE, p 1–7

  18. Yaseen Q, AlBalas F, Jararweh Y, Al-Ayyoub M (2016) A fog computing based system for selective forwarding detection in mobile wireless sensor networks. In: 1st international workshops on foundations and applications of self * systems (Fas*W), IEEE, p 256–262

  19. Gebre-Amlak H, Lee S, Jabbari A M A, Chen Y, Choi B Y, Huang C T, Song S (2017) MIST: mobility-inspired SofTware- defined fog system. IEEE international conference on consumer electronics (ICCE), IEEE

  20. Huang L, Li G L, Wu J, Li L, Li J H, Morello R (2016) Software-defined QoS provisioning for fog computing advanced wireless sensor networks. In: 2016 proceedings of IEEE sensors, IEEE, p 1–3

  21. Pacheco J, Hariri S (2016) IoT security framework for smart cyber infrastructures. In: IEEE 1st international workshops on foundations and applications of self * systems (Fas*W), IEEE, p 242–247

  22. Liu Q, Hu XP, Ngai ECH, Liang M, Leung VCM, Cai ZP, Yin JP (2016) A security patch addressing bandwidth request vulnerabilities in the IEEE 802.16 standard. IEEE Netw 30(5):26–34

    Article  Google Scholar 

  23. Liu Z, Tsuda T, Watanabe H, Ryuo S, Iwasawa N (2018) Data driven cyber-physical system for landslide detection. Mobile networks and applications. https://doi.org/10.1007/s11036-018-1031-1

  24. Handy M J, Haase M, Timmermann D (2002) Low energy adaptive clustering hierarchy with deterministic cluster-head selection. In: 4th International workshop on mobile and wireless communications network, MWCN 2002, IEEE, p 368–372

  25. Jiang CJ, Shi WR, Tang XL, Wang P, Xiang M (2012) Energy-balanced unequal clustering routing protocol for wireless sensor networks. J Software 23(5):1222–1232

    Article  Google Scholar 

  26. Ye M, Li CF, Chen GH, Wu J (2007) An energy efficient clustering scheme in wireless sensor networks. Ad Hoc Sens Wireless Netw 3(2–3):99–119

    Google Scholar 

  27. Stanislava S, Wendi B H (2005) Prolonging the lifetime of wireless sensor networks via unequal clustering. In: 19th IEEE international parallel and distributed processing symposium, IPDPS 2005, IEEE, p 1–8

  28. Sun LM, Li JZ, Chen Y, Zhu HS (2005) Wireless sensor networks. Tsinghua University Press, Peiking

    Google Scholar 

  29. Liu AF, He H, Wu XY, Chen ZG (2009) Optimization of parameter selection for wireless sensor network with mobile base station. J Centr South Univ Sci Technol 40(5):1336–1344

    Google Scholar 

  30. Wendi R H, Anantha C, Hari B (2000) Energy-efficient communication protocol for wireless microsensor networks. In: the 33rd annual Hawaii international conference on system sciences, IEEE 2, 1-10

Download references

Acknowledgements

The research is supported by National Natural Science Foundation of China(Grant No.51874300), the National Natural Science Foundation of China and Shanxi Provincial People’s Government Jointly Funded Project of China for Coal Base and Low Carbon(Grant No.U1510115), National Natural Science Foundation of China(Grant Nos. 51874299, 51104157),the Qing Lan Project, the Postdoctoral Research Foundation of China (CN) (Grant No.2013 T60574), the Ph.D. Programs Foundation of Ministry of Education of China (Grant No.20110095120008), the Shanghai Natural Science Foundation (Grant No. 17ZR1428900), the China Postdoctoral Science Foundation (Grant No 20100481181) and the Scientific Instrument Developing Project of the Chinese Academy of Sciences (No. YJKYYQ20170074), and the Open Research Fund of Key Laboratory of Wireless Sensor Network & Communication, Shanghai Institute of Microsystem and Information Technology, Chinese Academy of Sciences, grant numbers 20190902 and 20190913.

Author information

Author notes

  1. Wei Chen and Bobin Zhang contributed equally to this work and should be considered co-first authors.

Authors and Affiliations

  1. School of Computer Science and Technology, China University of Mining and Technology, Xuzhou, 221116, China

    Wei Chen, Bobin Zhang, Xiao Yang & Xiaorong Jiang

  2. Mine Digitization Engineering Research Center of the Ministry of Education, China University of Mining and Technology, Xuzhou, 221116, China

    Wei Chen, Bobin Zhang, Xiao Yang & Xiaorong Jiang

  3. Key Laboratory of Wireless Sensor Network & Communication, Shanghai Institute of Microsystem and Information Technology, Chinese Academy of Sciences, Shanghai, 201899, China

    Wei Chen, Weidong Fang & Wuxiong Zhang

  4. School of Earth and Space Sciences, Peking University, Beijing, 100871, China

    Wei Chen

  5. Shanghai Research Center for Wireless Communications, Shanghai, 201210, China

    Wuxiong Zhang

Authors
  1. Wei Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Bobin Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Xiao Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Weidong Fang
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Wuxiong Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Xiaorong Jiang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Weidong Fang.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Chen, W., Zhang, B., Yang, X. et al. C-EEUC: a Cluster Routing Protocol for Coal Mine Wireless Sensor Network Based on Fog Computing and 5G. Mobile Netw Appl 27, 1853–1866 (2022). https://doi.org/10.1007/s11036-019-01401-9

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11036-019-01401-9

Keywords

Search

Navigation