A review of industrial wireless networks in the context of Industry 4.0 | Wireless Networks Skip to main content

Advertisement

Springer Nature Link
Log in

A review of industrial wireless networks in the context of Industry 4.0

  • Published:
Wireless Networks Aims and scope Submit manuscript

Abstract

There have been many recent advances in wireless communication technologies, particularly in the area of wireless sensor networks, which have undergone rapid development and been successfully applied in the consumer electronics market. Therefore, wireless networks (WNs) have been attracting more attention from academic communities and other domains. From an industrial perspective, WNs present many advantages including flexibility, low cost, easy deployment and so on. Therefore, WNs can play a vital role in the Industry 4.0 framework, and can be used for smart factories and intelligent manufacturing systems. In this paper, we present an overview of industrial WNs (IWNs), discuss IWN features and related techniques, and then provide a new architecture based on quality of service and quality of data for IWNs. We also propose some applications for IWNs and IWN standards. Then, we will use a case from our previous achievements to explain how to design an IWN under Industry 4.0. Finally, we highlight some of the design challenges and open issues that still need to be addressed to make IWNs truly ubiquitous for a wide range of applications.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
¥17,985 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price includes VAT (Japan)

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

References

  1. Afzal, S., & Beigy, H. (2014). A localization algorithm for large scale mobile wireless sensor networks: A learning approach. Journal of Supercomputing, 69(1), 98–120.

    Article  Google Scholar 

  2. Ahmad, A., Ahmad, S., Rehmani, M. H., & Ul Hassan, N. (2015). A survey on radio resource allocation in cognitive radio sensor networks. IEEE Communications Surveys and Tutorials, 17(2), 888–917.

    Article  Google Scholar 

  3. Akerberg, J., Gidlund, M., Lennvall, T., Neander, J., Bjorkman, M. (2011). Efficient integration of secure and safety critical industrial wireless sensor networks. Eurasip Journal on Wireless Communications and Networking, Article ID: 100. doi:10.1186/1687-1499-2011-100.

  4. Anastasi, G., Marco, C., Di Francesco, M., & Passarella, A. (2009). Energy conservation in wireless sensor networks: A survey. Ad Hoc Networks, 7(3), 537–568.

    Article  Google Scholar 

  5. Azevedo, J. A. R., & Santos, F. E. S. (2012). Energy harvesting from wind and water for autonomous wireless sensor nodes. IET Circuits, Devices and Systems, 6(6), 413–420.

    Article  MathSciNet  Google Scholar 

  6. Bayindir, R., & Cetinceviz, Y. (2011). A water pumping control system with a programmable logic controller (PLC) and industrial wireless modules for industrial plants—An experimental setup. ISA Transactions, 50(2), 321–328.

    Article  Google Scholar 

  7. Chang, C. Y., Chang, C. T., Chen, Y. C., & Chang, H. R. (2009). Obstacle-resistant deployment algorithms for wireless sensor networks. IEEE Transactions on Vehicular Technology, 58(6), 2925–2954.

    Article  Google Scholar 

  8. Chen, H. Y., & Chang, J. F. (2007). Interlayer interaction in a layered error-control scheme for mobile wireless data networks. IEEE Transactions on Vehicular Technology, 56(2), 969–975.

    Article  MathSciNet  Google Scholar 

  9. Chen, M., Kwon, T., & Choi, Y. (2006). Energy-efficient differentiated directed diffusion (EDDD) for real-time traffic in wireless sensor networks. Elsevier Computer Communications, 29(2), 231–245.

    Article  Google Scholar 

  10. Chen, M., Leung, V., Mao, S., & Yuan, Y. (2007). Directional geographical routing for real-time video communications in wireless sensor networks. Elsevier Computer Communications, 30(17), 3368–3383.

    Article  Google Scholar 

  11. Chen, M., Leung, V., Mao, S., Xiao, Y., & Chlamtac, I. (2009). Hybrid geographical routing for flexible energy-delay trade-offs. IEEE Transactions on Vehicular Technology, 58(9), 4976–4988.

    Article  Google Scholar 

  12. Chen, M., Yang, L., Kwon, T., Zhou, L., & Jo, M. (2011). Itinerary planning for energy-efficient agent communication in wireless sensor networks. IEEE Transactions on Vehicular Technology, 60(7), 3290–3299.

    Article  Google Scholar 

  13. Chen, M., Lai, C., & Wang, H. (2011). Mobile multimedia sensor networks: Architecture and routing. EURASIP Journal on Wireless Communications and Networking. doi:10.1186/1687-1499-2011-159.

    Google Scholar 

  14. Chen, M., Gonzalez, S., Vasilakos, A., Cao, H., & Leung, V. (2011). Body area networks: A survey. ACM/Springer Mobile Networks and Applications, 16(2), 171–193.

    Article  Google Scholar 

  15. Chen, M., Ma, Y., Ullah, S., Cai, W. & Song, E. (2013). ROCHAS: Robotics and cloud-assisted healthcare system for empty nester. BodyNets 2013, Boston, USA.

  16. Chen, M., Gonzalez, S., Cao, H., Zhang, Y., & Vuong, S. (2013). Enabling low bit-rate and reliable video surveillance over practical wireless sensor networks. Journal of Supercomputing, 65(1), 287–300.

    Article  Google Scholar 

  17. Chen, Z., Zhang, D., Zhu, R., Ma, Y., Yin, P., & Xie, F. (2013). A review of automated formal verification of ad hoc routing protocols for wireless sensor networks. Sensor Letters, 11(5), 752–764.

    Article  Google Scholar 

  18. Chen, M., Wan, J., Gonzalez, S., Liao, X., & Leung, V. (2014). A survey of recent developments in home M2M networks. IEEE Communications Surveys and Tutorials, 16(1), 98–114.

    Article  Google Scholar 

  19. Chen, M., Mao, S., & Liu, Y. (2014). Big data: A survey. Mobile Networks and Applications, 19(2), 171–209.

    Article  Google Scholar 

  20. Chen, Z., Ding, L., Chen, K., & Li, R. F. (2014). The study of cooperative obstacle avoidance method for MWSN based on flocking control. The Scientific World Journal, 2014, Article ID: 614346. doi:10.1155/2014/614346

  21. Chen, F., Deng, P., Wan, J., Zhang, D., Vasilakos, A., & Rong, X. (2015). Data mining for the internet of things: Literature review and challenges. International Journal of Distributed Sensor Networks, 2015, Article ID: 431047. doi:10.1155/2015/431047

  22. Chen, M., Zhang, Y., Li, Y., Hassan, M., & Alamri, A. (2015). AIWAC: Affective interaction through wearable computing and cloud technology. IEEE Wireless Communications Magazine, 22(1), 20–27.

    Article  Google Scholar 

  23. Chen, M., Hao, Y., Li, Y., Lai, C., & Wu, D. (2015). On the computation offloading at Ad hoc cloudlet: architecture and service models. IEEE Communications, 53(6), 18–24.

    Article  Google Scholar 

  24. Chen, M., Zhang, Y., Li, Y., Mao, S., & Leung, V. (2015). EMC: Emotion-aware mobile cloud computing in 5G. IEEE Network, 29(2), 32–38.

    Article  Google Scholar 

  25. Chen, M., Zhang, Y., Hu, L., Taleb, T., & Sheng, Z. (2015). Cloud-based wireless network: Virtualized, reconfigurable, smart wireless network to enable 5G technologies. ACM/Springer Mobile Networks and Applications. doi:10.1007/s11036-015-0590-7.

    Google Scholar 

  26. Chenji, H., & Stoleru, R. (2013). Toward accurate mobile sensor network localization in noisy environments. IEEE Transactions on Mobile Computing, 12(6), 1094–1106.

    Article  Google Scholar 

  27. Chu, W., & Ssu, K. (2014). Location-free boundary detection in mobile wireless sensor networks with a distributed approach. Computer Networks, 70, 96–112.

    Article  Google Scholar 

  28. Davis, J., Edgar, T., Porter, J., Bernaden, J., & Sarli, M. (2012). Smart manufacturing, manufacturing intelligence and demand-dynamic performance. Computers and Chemical Engineering, 47, 145–156.

    Article  Google Scholar 

  29. Ding, Y., & Hong, S. H. (2013). CFP scheduling for real-time service and energy efficiency in the industrial applications of IEEE 802.15.4. Journal of Communications and Networks, 15(1), 87–101.

    Article  Google Scholar 

  30. Di-Pietro, R., Guarino, S., Verde, N. V., & Domingo-Ferrer, J. (2014). Security in wireless ad-hoc networks: A survey. Computer Communications, 51, 1–20.

    Article  Google Scholar 

  31. Drath, R., & Horch, A. (2014). Industry 4.0: Hit or hype? IEEE Industrial Electronics Magazine, 8(2), 56–58.

    Article  Google Scholar 

  32. Eskola, M., & Heikkila, T. (2015). Detection of short-term radio signal disturbances in industrial wireless sensor networks. Journal of Networks, 10(4), 201–208.

    Article  Google Scholar 

  33. Fontana, E., Martins, J. F., Oliveira, S. C., Cavalcanti, F. J. M. M., Lima, R. A., Cavalcanti, G. O., et al. (2012). Sensor network for monitoring the state of pollution of high-voltage insulators via satellite. IEEE Transactions on Power Delivery, 27(2), 953–962.

    Article  Google Scholar 

  34. Gorecky, D., Schmitt M., Loskyll M., & Zuhlke, D. (2014). Human-machine-interaction in the industry 4.0 era. In 12th IEEE International Conference on Industrial Informatics (INDIN) (pp. 289–294).

  35. Gungor, V. C., & Hancke, G. P. (2009). Industrial wireless sensor networks: Challenges, design principles, and technical approaches. IEEE Transactions on Industrial Electronics, 56(10), 4258–4265.

    Article  Google Scholar 

  36. Gungor, V. C., Khan, O. B., & Akyildiz, I. F. (2008). A real-time and reliable transport (RT)(2) protocol for wireless sensor and actor networks. IEEE-ACM Transactions on Networking, 16(2), 359–370.

    Article  Google Scholar 

  37. Han, G., Jiang, J., Shu, L., Niu, J., & Chao, H. C. (2014). Management and applications of trust in wireless sensor networks: A survey. Journal of Computer and System Sciences, 80(3), 602–617.

    Article  MATH  Google Scholar 

  38. Hou, L., & Bergmann, N. W. (2012). Novel industrial wireless sensor networks for machine condition monitoring and fault diagnosis. IEEE Transactions on Instrumentation and Measurement, 61(11), 2787–2789.

    Article  Google Scholar 

  39. Jazdi, N. (2014). Cyber physical systems in the context of Industry 4.0. In IEEE International Conference on Quality and Testing, Robotics, Automation (pp. 1–4).

  40. Jeong, Y., Kim, J., & Han, S. J. (2011). Interference mitigation in wireless sensor networks using dual heterogeneous radios. Wireless Networks, 17(7), 1699–1713.

    Article  Google Scholar 

  41. Kang, H., Hong, H., Sung, S., & Kim, K. (2008). Interference and sink capacity of wireless CDMA sensor networks with layered architecture. ETRI Journal, 30(1), 13–20.

    Article  Google Scholar 

  42. Kausar, A. S. M. Z., Reza, A. W., Saleh, M. U., & Ramiah, H. (2014). Energizing wireless sensor networks by energy harvesting systems: Scopes, challenges and approaches. Renewable and Sustainable Energy Reviews, 38, 973–989.

    Article  Google Scholar 

  43. Khan, J. A., Qureshi, H. K., & Iqbal, A. (2015). Energy management in wireless sensor networks: A survey. Computers and Electrical Engineering, 41, 159–176.

    Article  Google Scholar 

  44. Ko, H. S., Lim, H., Jeong, W., & Nof, S. Y. (2010). A statistical analysis of interference and effective deployment strategies for facility-specific wireless sensor networks. Computers in Industry, 61(5), 472–479.

    Article  Google Scholar 

  45. Kreibich, O., Neuzil, J., & Smid, R. (2014). Quality-based multiple-sensor fusion in an industrial wireless sensor network for MCM. IEEE Transactions on Industrial Electronics, 61(9), 4903–4911.

    Article  Google Scholar 

  46. Lee, S., Lee, I., Kim, S., Lee, S., & Bovik, A. C. (2014). A pervasive network control algorithm for multicamera networks. IEEE Sensors Journal, 14(4), 1280–1294.

    Article  Google Scholar 

  47. Li, Y., & Bartos, R. (2014). A survey of protocols for intermittently connected delay-tolerant wireless sensor networks. Journal of Network and Computer Applications, 41, 411–423.

    Article  Google Scholar 

  48. Li, N., Zhang, N., Das, S. K., & Thuraisingham, B. (2009). Privacy preservation in wireless sensor networks: A state-of-the-art survey. Ad Hoc Networks, 7(8), 1501–1514.

    Article  Google Scholar 

  49. Liang, W., Zhang, X. L., Xiao, Y., Wang, F. Q., Zeng, P., & Yu, H. B. (2011). Survey and experiments of WIA-PA specification of industrial wireless network. Wireless Communications and Mobile Computing, 11(8), 1197–1212.

    Article  Google Scholar 

  50. Liao, Z. F., Wang, J. X., Zhang, S. G., Cao, J. N., & Min, G. (2015). Minimizing movement for target coverage and network connectivity in mobile sensor networks. IEEE Transactions on Parallel and Distributed Systems, 26(7), 1971–1983.

    Article  Google Scholar 

  51. Liu, J., Wang, Q., Wan, J., & Xiong, J. (2012). Towards real-time indoor localization in wireless sensor networks. In Proceedings of the 12th IEEE International Conference on Computer and Information Technology (pp. 877–884).

  52. Liu, Q., Wan, J., & Zhou, K. (2014). Cloud manufacturing service system for industrial-cluster-oriented application. Journal of Internet Technology, 15(3), 373–380.

    Google Scholar 

  53. Liu, L., Zhang, N., & Liu, Y. (2015). Topology control models and solutions for signal irregularity in mobile underwater wireless sensor networks. Journal of Network and Computer Applications, 51, 68–90.

    Article  Google Scholar 

  54. Ma, C., Xue, K., & Hong, P. (2014). Distributed access control with adaptive privacy preserving property for wireless sensor networks. Security and Communication Networks, 7(4), 759–773.

    Article  Google Scholar 

  55. Maadani, M., Motamedi, S. A., Safdarkhani, H., & Parsa, M. (2014). Saturated distributed coordination function Markov model for noisy soft-real-time industrial wireless networks. IET Communications, 8(10), 1724–1735.

    Article  Google Scholar 

  56. Mahmood, M. A., Seah, W. K. G., & Welch, I. (2015). “Reliability in wireless sensor networks: A survey and challenges ahead. Computer Networks, 79, 166–187.

    Article  Google Scholar 

  57. Miso, K., Dugundji, J., & Wardle, B. L. (2015). Effect of electrode configurations on piezoelectric vibration energy harvesting performance. Smart Materials and Structures, 24(4), Article ID: 045026.

  58. Mostafa, A., Vegni, A. M., & Agrawal, D. P. (2014). A probabilistic routing by using multi-hop retransmission forecast with packet collision-aware constraints in vehicular networks. Ad Hoc Networks, 14, 118–129.

    Article  Google Scholar 

  59. Oualha, N., & Olivereau, A. (2011). Sensor and data privacy in industrial wireless sensor networks. In Conference on Network and Information Systems Security (SAR-SSI 2011).

  60. Perera, C., Liu, C., Jayawardena, S., & Chen, M. (2014). A survey on internet of things from industrial market perspective. IEEE Access, 2, 1660–1679.

    Article  Google Scholar 

  61. Pham, T. A. Q., & Kim, D. S. (2012). Enhancing real-time delivery of gradient routing for industrial wireless sensor networks. IEEE Transactions on Industrial Informatics, 8(1), 61–68.

    Article  MathSciNet  Google Scholar 

  62. Posada, J., Toro, C., Barandiaran, I., Oyarzun, D., Stricker, D., de Amicis, R., et al. (2015). Visual computing as a key enabling technology for industrie 4.0 and industrial internet. IEEE Computer Graphics and Applications, 35(2), 26–40.

    Article  Google Scholar 

  63. Qiu, J., Chen, H. G., Wen, Y. M., & Li, P. (2015). Magnetoelectric and electromagnetic composite vibration energy harvester for wireless sensor networks. Journal of Applied Physics, 117, 17A331. doi:10.1063/1.4918688.

    Article  Google Scholar 

  64. Radi, M., Dezfouli, B., Abu Bakar, K., Abd Razak, S., & Hwee-Pink, T. (2014). IM2PR: Interference-minimized multipath routing protocol for wireless sensor networks. Wireless Networks, 20(7), 1807–1823.

    Article  Google Scholar 

  65. Rashid, H., & Turuk, A. K. (2015). Dead reckoning localisation technique for mobile wireless sensor networks. IET Wireless Sensor Systems, 5(2), 87–96.

    Article  Google Scholar 

  66. Rault, T., Bouabdallah, A., & Challal, Y. (2014). Energy efficiency in wireless sensor networks: A top-down survey. Computer Networks, 67, 104–122.

    Article  Google Scholar 

  67. Sha, K., Gehlot, J., & Greve, R. (2013). Multipath routing techniques in wireless sensor networks: A survey. Wireless Personal Communications, 70(2), 807–829.

    Article  Google Scholar 

  68. Sharma, S., & Jena, S. K. (2015). Cluster based multipath routing protocol for wireless sensor networks. ACM SIGCOMM Computer Communication Review, 45(2), 14–20.

    Article  Google Scholar 

  69. Shen, W., Zhang, T. T., Gidlund, M., & Dobslaw, F. (2013). SAS-TDMA: A source aware scheduling algorithm for real-time communication in industrial wireless sensor networks. Wireless Networks, 19(6), 1155–1170.

    Article  Google Scholar 

  70. Varghese, A., & Tandur, D. (2014). Wireless requirements and challenges in Industry 4.0. In 2014 International Conference on Contemporary Computing and Informatics (IC3I) (pp.634–638).

  71. Viegas, R., Guedes, L. A., Vasques, F., Portugal, P., & Moraes, R. (2013). A new MAC scheme specifically suited for real-time industrial communication based on IEEE 802.11e. Computers and Electrical Engineering, 39(6), 1648–1704.

    Article  Google Scholar 

  72. Vuran, M. C., & Akyildiz, I. F. (2009). Error control in wireless sensor networks: A cross layer analysis. IEEE-ACM Transactions on Networking, 17(4), 1186–1199.

    Article  Google Scholar 

  73. Wan, Y., Wang Q., Duan, S., & Zhang, X. (2009). RAFH: Reliable aware frequency hopping method for industrial wireless sensor networks. In 5th International Conference on Wireless Communications, Networking and Mobile Computing (WiCOM) (pp. 1–4).

  74. Wan, J., Li, D., Yan, H., & Zhang, P. (2010). Fuzzy feedback scheduling algorithm based on central processing unit utilization for a software-based computer numerical control system. Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture, 224(7), 1133–1143.

    Article  Google Scholar 

  75. Wan, J., Zou, C., Ullah, S., Lai, C., Zhou, M., & Wang, X. (2013). Cloud-enabled wireless body area networks for pervasive healthcare. IEEE Network, 27(5), 56–61.

    Article  Google Scholar 

  76. Wan, J., Zhang, D., Zhao, S., Yang, L. T., & Lloret, J. (2014). Context-aware vehicular cyber-physical systems with cloud support: Architecture, challenges and solutions. IEEE Communications Magazine, 52(8), 106–113.

    Article  Google Scholar 

  77. Wan, J., Cai, H., & Zhou, K. (2015). Industrie 4.0: Enabling technologies. In International Conference on Intelligent Computing and Internet of Things (ICIT), 2014 International Conference on 17–18 Jan, 2015 (pp. 135–140). IEEE.

  78. Wang, L., Ye, W., Wang, H. K., Fu, X. P., Fei, M. P., & Menhas, M. I. (2012). Optimal node placement of industrial wireless sensor networks based on adaptive mutation probability binary particle swarm optimization algorithm. Computer Science and Information Systems, 9(4), 1553–1576.

    Article  Google Scholar 

  79. Wang, W., Wang, H. R., Wang, B. Z., Wang, Y. P., & Wang, J. J. (2013). Energy-aware and self-adaptive anomaly detection scheme based on network tomography in mobile ad hoc networks. Information Sciences, 220, 580–602.

    Article  Google Scholar 

  80. Wang, S., Wan, J., Li, D., & Zhang, C. (2015). Implementing smart factory of industrie 4.0: An outlook. International Journal of Distributed Sensor Networks, Article ID: 681806.

  81. Willig, A., Matheus, K., & Wolisz, A. (2005). Wireless technology in industrial networks. Proceedings of IEEE, 93(6), 1130–1151.

    Article  Google Scholar 

  82. Wu, F. J., Kao, Y. F., & Tseng, Y. C. (2011). From wireless sensor networks towards cyber physical systems. Pervasive and Mobile Computing, 7(4), 397–413.

    Article  Google Scholar 

  83. Wu, C., Ji, Y. S., Xu, J., Ohzahata, S., & Kato, T. (2014). Coded packets over lossy links: A redundancy-based mechanism for reliable and fast data collection in sensor networks. Computer Networks, 70, 179–191.

    Article  Google Scholar 

  84. Wu, Z. L., Jiang, L. H., Ren, G. H., Zhao, N., & Zhao, Y. Q. (2015). A novel joint spatial-code clustered interference alignment scheme for large-scale wireless sensor networks. Sensors, 15(1), 1964–1997.

    Article  Google Scholar 

  85. Xia, F., Wang, L., Zhang, D., He, D., & Kong, X. (2015). An adaptive MAC protocol for real-time and reliable communications in medical cyber-physical systems. Telecommunication Systems, 58(2), 125–138.

    Article  Google Scholar 

  86. Industrial ethernet: A control engineer’s guide. http://www.cisco.com/web/strategy/docs/manufacturing/industrial_ethernet.pdf

  87. Introduction to real-time ethernet. http://www.ccontrols.com/pdf/Extv5n3.pdf

  88. Industrial wireless networks application in cement production. http://www.saiyuan.net/P/p154.html

  89. Wireless roaming networking for automated guided vehicles. http://www.moxa.com/applications/Wireless_Roaming_Networking_for_Automated_Guided_Vehicles.htm

  90. Yang, Y. Y., Fonoage, M. I., & Cardei, M. (2010). Improving network lifetime with mobile wireless sensor networks. Computer Communications, 33(4), 409–419.

    Article  Google Scholar 

  91. Yang, D., Xu, Y., Wang, H., Zheng, T., Zhang, H., Zhang, H. K., & Gidlund, M. (2015). Assignment of segmented slots enabling reliable real-time transmission in industrial wireless sensor networks. IEEE Transactions on Industrial Electronics, 62(6), 3966–3977.

    Google Scholar 

  92. Yen, K. T., & Panda, S. K. (2011). Energy Harvesting from hybrid indoor ambient light and thermal energy sources for enhanced performance of wireless sensor nodes. IEEE Transactions on Industrial Electronics, 58(9), 4424–4435.

    Article  Google Scholar 

  93. Yin Zhang, Y., Qiu, M., Tsai, C. W., Hassan, M. M., & Alamri, A. (2015). Health-CPS: Healthcare cyber-physical system assisted by cloud and big data. IEEE Systems Journal. doi:10.1109/JSYST.2015.2460747.

    Google Scholar 

  94. Yoo, S. E., Chong, P. K., Kim, D., Doh, Y., Pham, M. L., Choi, E., & Huh, J. (2010). Guaranteeing real-time services for industrial wireless sensor networks with IEEE 802.15.4. IEEE Transactions on Industrial Electronics, 57(11), 3868–3876.

    Article  Google Scholar 

  95. Yoon, S., Ye, W., Heidemann, J., Littlefield, B., & Shahabi, C. (2011). SWATS: Wireless sensor networks for steamflood and waterflood pipeline monitoring. IEEE Network, 25(1), 50–56.

    Article  Google Scholar 

  96. Yu, K., Pang, Z. B., Gidlund, M., Akerberg, J., & Bjorkman, A. (2014). REALFLOW: Reliable real-time flooding-based routing protocol for industrial wireless sensor networks. International Journal of Distributed Sensor Networks, 2014, Article ID: 936379. doi:10.1155/2014/936379.

  97. Yuan, P. Y., & Liu, P. (2015). Data fusion prolongs the lifetime of mobile sensing networks. Journal of Network and Computer Applications, 49, 51–59.

    Article  Google Scholar 

  98. Zhang, Y. (2010). Handoff performance in wireless mobile networks with unreliable fading channel. IEEE Transactions on Mobile Computing, 9(2), 188–200.

    Article  MathSciNet  Google Scholar 

  99. Zhang, D., Wan, J., Liu, Q., Guan, X., & Liang, X. (2012). A Taxonomy of agent technologies for ubiquitous computing environments. KSII Transactions on Internet and Information Systems, 6(2), 547–565.

    Google Scholar 

  100. Zhang, X. H., Fang, J. L., Meng, F. F., & Wei, X. L. (2014). A novel self-powered wireless sensor node based on energy harvesting for mechanical vibration monitoring. Mathematical Problems in Engineering, 2014, Article ID: 642365.

  101. Zhang, Y., Yang, W., Han, D. S., & Kim, Y. I. (2014). An integrated environment monitoring system for underground coal mines-wireless sensor network subsystem with multi-parameter monitoring. Sensors, 14(7), 13149–13170.

    Article  Google Scholar 

  102. Zhang, Y., Chen, M., Mao, S., Hu, L., & Leung, V. (2014). CAP: Crowd activity prediction based on big data analysis. IEEE Network, 28(4), 52–57.

    Article  Google Scholar 

  103. Zhang, X. M., Zhang, Y., Yan, F., & Vasilakos, A. V. (2015). Interference-based topology control algorithm for delay-constrained mobile Ad hoc networks. IEEE Transactions on Mobile Computing, 14(4), 742–754.

    Article  Google Scholar 

  104. Zhang, Y., Zhang, D., Hassan, M. M., Alamri, A., & Peng, L. (2015). CADRE: Cloud-assisted drug recommendation service for online pharmacies. Mobile Networks and Applications, 20(3), 348–355.

    Article  Google Scholar 

  105. Zhou, G., Wu, Y. F., Yan, T., He, T., Huang, C. D., Stankovic, J. A., & Abdelzaher, T. F. (2010). A multifrequency MAC specially designed for wireless sensor network applications. ACM Transactions on Embedded Computing Systems, 9(4), 1–41.

    Article  Google Scholar 

Download references

Acknowledgments

This work is partially supported by National Natural Science Foundations of China (Nos. 61572220, 61262013, and 51575194), the Fundamental Research Funds for the Central Universities (No. 2015ZZ079), the National Key Technology Research and Development Program of the Ministry of Science and Technology of China (No. 2015BAF20B01), the Natural Science Foundation of Guangdong Province, China (2015A030308002), the Science and Technology Planning Project of Guangdong Province, China (Nos. 2015B010101005, 2012A010702004, and 2012A090100012), and Science and Technology Planning Project of Guangzhou City (No. 201508030007).

Author information

Authors and Affiliations

  1. School of Mechanical and Automotive Engineering, South China University of Technology, Guangzhou, China

    Xiaomin Li, Di Li, Jiafu Wan & Shiyong Wang

  2. Department of Computer Science, Electrical and Space Engineering, Luleå University of Technology, 97187, Luleå, Sweden

    Athanasios V. Vasilakos

  3. Department of Computer Science and Information Engineering, National Chung Cheng University, Jiayi, Taiwan

    Chin-Feng Lai

Authors
  1. Xiaomin Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Di Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jiafu Wan
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Athanasios V. Vasilakos
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Chin-Feng Lai
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Shiyong Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Jiafu Wan.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Li, X., Li, D., Wan, J. et al. A review of industrial wireless networks in the context of Industry 4.0. Wireless Netw 23, 23–41 (2017). https://doi.org/10.1007/s11276-015-1133-7

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11276-015-1133-7

Keywords

Search

Navigation