skip to main content
research-article

Network Lifetime Optimization in Multi-hop Industrial Cognitive Radio Sensor Networks

Authors Info & Claims
Published:08 December 2022Publication History
Skip Abstract Section

Abstract

Industrial cognitive radio sensor networks (ICRSNs) extend channel resources by occupying the vacant licensed channels in the absence of licensed users. In ICRSNs, industrial devices should switch to a common available channel to set up a communication link. However, channel switching leads to severe energy consumption. As the energy resources of battery-powered industrial devices are limited, it is crucial to carefully allocate channels to prolong the network lifetime of multi-hop ICRSNs. This paper is the first work that studies the channel allocation problem to optimize the network lifetime by considering the channel-switching (CS) energy consumption and the time-critical requirements of industrial applications. The problem is formulated to maximize the minimum residual energy at each round of data transmission, which is linearized as integer linear programming. As the channel allocation results will affect the residual energy at subsequent rounds, we propose a switching distance-optimized channel allocation (SDOCA) scheme that shortens the CS distances to improve the residual energy of each device. Moreover, we analyze the characteristics of SDOCA, i.e., convergent CS distance and guaranteed end-to-end delay. Extensive simulation results show that SDOCA can adaptively allocate channels according to the end-to-end delay requirement and significantly prolong the network lifetime.

REFERENCES

  1. [1] Al-Dabbagh Ahmad W. and Chen Tongwen. 2016. A fixed structure topology for wireless networked control systems. In 55th IEEE Conference on Decision and Control (CDC’16). IEEE, Las Vegas, NV, USA, 34503455. DOI:Google ScholarGoogle ScholarDigital LibraryDigital Library
  2. [2] Almasaeid Hisham M. and Kamal Ahmed E.. 2014. Exploiting multichannel diversity for cooperative multicast in cognitive radio mesh networks. IEEE/ACM Trans. Netw. 22, 3 (June2014), 770783. Google ScholarGoogle ScholarDigital LibraryDigital Library
  3. [3] Bayhan Suzan and Alagöz Fatih. 2013. Scheduling in centralized cognitive radio networks for energy efficiency. IEEE Trans. Veh. Technol. 62, 2 (Feb.2013), 582595. Google ScholarGoogle ScholarCross RefCross Ref
  4. [4] Brummet Ryan, Hossain Md. Kowsar, Chipara Octav, Herman Ted, and Stewart David E.. 2021. Recorp: Receiver-oriented policies for industrial wireless networks. ACM Trans. Sens. Netw. 17, 4 (July2021), 44:1–44:32. Google ScholarGoogle ScholarDigital LibraryDigital Library
  5. [5] Celik Abdulkadir and Kamal Ahmed E.. 2016. Green cooperative spectrum sensing and scheduling in heterogeneous cognitive radio networks. IEEE Trans. Cogn. Commun. Netw. 2, 3 (Sept.2016), 238248. Google ScholarGoogle ScholarCross RefCross Ref
  6. [6] Chiwewe Tapiwa M. and Hancke Gerhard P.. 2016. Cognitiva - A cognitive industrial wireless network protocol: Protocol design and testbed implementation. In IEEE International Conference on Industrial Technology (ICIT’16). IEEE, Taipei, Taiwan, 20422047. DOI:Google ScholarGoogle ScholarCross RefCross Ref
  7. [7] Chiwewe Tapiwa M., Mbuya Colman F., and Hancke Gerhard P.. 2015. Using cognitive radio for interference-resistant industrial wireless sensor networks: An overview. IEEE Trans. Ind. Informat. 11, 6 (Dec.2015), 14661481. Google ScholarGoogle ScholarCross RefCross Ref
  8. [8] Demirci Sercan and Gözüpek Didem. 2020. Switching cost-aware joint frequency assignment and scheduling for industrial cognitive radio networks. IEEE Trans. Ind. Informat. 16, 7 (July2020), 43654377. Google ScholarGoogle ScholarCross RefCross Ref
  9. [9] Ding Haichuan, Li Xuanheng, Ma Ying, and Fang Yuguang. 2020. Energy-efficient channel switching in cognitive radio networks: A reinforcement learning approach. IEEE Trans. Veh. Technol. 69, 10 (Oct.2020), 1235912362. Google ScholarGoogle ScholarCross RefCross Ref
  10. [10] Eryigit Salim, Bayhan Suzan, and Tugcu Tuna. 2013. Channel switching cost aware and energy-efficient cooperative sensing scheduling for cognitive radio networks. In Proceedings of IEEE International Conference on Communications (ICC’13). IEEE, Budapest, Hungary, 26332638. DOI:Google ScholarGoogle ScholarCross RefCross Ref
  11. [11] Forrest John. 2020. COIN-OR Branch and Cut. (March2020). Retrieved November 20, 2021 from https://github.com/coin-or/Cbc.Google ScholarGoogle Scholar
  12. [12] He Tengjiao, Chin Kwan-Wu, Soh Sieteng, and Zhang Zhen. 2021. A novel distributed resource allocation scheme for wireless-powered cognitive radio Internet of Things networks. IEEE Internet Things J. 8, 20 (Oct.2021), 1548615499. Google ScholarGoogle ScholarCross RefCross Ref
  13. [13] Herrmann Michael J. and Messier Geoffrey G.. 2018. Cross-layer lifetime optimization for practical industrial wireless networks: A petroleum refinery case study. IEEE Trans. Ind. Informat. 14, 8 (Aug.2018), 35593566. Google ScholarGoogle ScholarCross RefCross Ref
  14. [14] Kobo Hlabishi Isaac, Abu-Mahfouz Adnan M., and Hancke Gerhard Petrus. 2019. Fragmentation-based distributed control system for software-defined wireless sensor networks. IEEE Trans. Ind. Informat. 15, 2 (Feb.2019), 901910. Google ScholarGoogle ScholarCross RefCross Ref
  15. [15] Künzel Gustavo, Indrusiak Leandro Soares, and Pereira Carlos Eduardo. 2020. Latency and lifetime enhancements in industrial wireless sensor networks: A Q-learning approach for graph routing. IEEE Trans. Ind. Informat. 16, 8 (Aug.2020), 56175625. Google ScholarGoogle ScholarCross RefCross Ref
  16. [16] Li Nan, Xiao Ming, and Rasmussen Lars K.. 2018. Optimized cooperative multiple access in industrial cognitive networks. IEEE Trans. Ind. Informat. 14, 6 (June2018), 26662676. Google ScholarGoogle ScholarCross RefCross Ref
  17. [17] Liang Qingkai, Wang Xinbing, Tian Xiaohua, Wu Fan, and Zhang Qian. 2015. Two-dimensional route switching in cognitive radio networks: A game-theoretical framework. IEEE/ACM Trans. Netw. 23, 4 (Aug.2015), 10531066. Google ScholarGoogle ScholarDigital LibraryDigital Library
  18. [18] Lin Feilong, Chen Cailian, Li Liran, Xu Honghua, and Guan Xinping. 2014. A novel spectrum sharing scheme for industrial cognitive radio networks: From collective motion perspective. In IEEE International Conference on Communications (ICC’14). IEEE, Sydney, NSW, Australia, 203208. DOI:Google ScholarGoogle ScholarCross RefCross Ref
  19. [19] Lin Feilong, Dai Wenbin, Li Wenbai, Xu Zhezhuang, and Yuan Liyong. 2020. A framework of priority-aware packet transmission scheduling in cluster-based industrial wireless sensor networks. IEEE Trans. Ind. Informat. 16, 8 (Aug.2020), 55965606. Google ScholarGoogle ScholarCross RefCross Ref
  20. [20] Liu Chin-Jung and Xiao Li. 2019. Building k-protected routes in multi-hop cognitive radio networks. IEEE Trans. Cogn. Commun. Netw. 5, 4 (Dec.2019), 976989. Google ScholarGoogle ScholarCross RefCross Ref
  21. [21] Liu Mingqian, Liao Guiyue, Zhao Nan, Song Hao, and Gong Fengkui. 2021. Data-driven deep learning for signal classification in industrial cognitive radio networks. IEEE Trans. Ind. Informat. 17, 5 (May2021), 34123421. Google ScholarGoogle ScholarCross RefCross Ref
  22. [22] Liu Mingqian, Liu Lingjia, Song Hao, Hu Yaohua, Yi Yang, and Gong Fengkui. 2020. Signal estimation in underlay cognitive networks for Industrial Internet of Things. IEEE Trans. Ind. Informat. 16, 8 (Aug.2020), 54785488. Google ScholarGoogle ScholarCross RefCross Ref
  23. [23] Liu Xin, Hu Su, Li Ming, and Lai Biaojun. 2021. Energy-efficient resource allocation for cognitive Industrial Internet of Things with wireless energy harvesting. IEEE Trans. Ind. Informat. 17, 8 (Aug.2021), 56685677. Google ScholarGoogle ScholarCross RefCross Ref
  24. [24] Liu Xin and Zhang Xueyan. 2020. NOMA-based resource allocation for cluster-based cognitive Industrial Internet of Things. IEEE Trans. Ind. Informat. 16, 8 (Aug.2020), 53795388. Google ScholarGoogle ScholarCross RefCross Ref
  25. [25] Liu Yongkang, Kashef Mohamed, Lee Kang B., Benmohamed Lotfi, and Candell Richard. 2019. Wireless network design for emerging IIoT applications: Reference framework and use cases. Proc. IEEE 107, 6 (June2019), 11661192. Google ScholarGoogle ScholarCross RefCross Ref
  26. [26] Mao Wenliang, Zhao Zhiwei, Chang Zheng, Min Geyong, and Gao Weifeng. 2021. Energy-efficient Industrial Internet of Things: Overview and open issues. IEEE Trans. Ind. Informat. 17, 11 (Nov.2021), 72257237. Google ScholarGoogle Scholar
  27. [27] Mo Lei, Cao Xianghui, Song Yeqiong, and Kritikakou Angeliki. 2018. Distributed node coordination for real-time energy-constrained control in wireless sensor and actuator networks. IEEE Internet Things J. 5, 5 (Oct.2018), 41514163.Google ScholarGoogle ScholarCross RefCross Ref
  28. [28] Oyewobi Stephen S. and Hancke Gerhard P.. 2017. A survey of cognitive radio handoff schemes, challenges and issues for industrial wireless sensor networks (CR-IWSN). J. Netw. Comput. Appl. 97 (2017), 140156. Google ScholarGoogle ScholarDigital LibraryDigital Library
  29. [29] Oyewobi Stephen S., Hancke Gerhard P., Abu-Mahfouz Adnan M., and Onumanyi Adeiza J.. 2019. A delay-aware spectrum handoff scheme for prioritized time-critical industrial applications with channel selection strategy. Comput. Commun. 144 (2019), 112123. Google ScholarGoogle ScholarDigital LibraryDigital Library
  30. [30] Park Pan Gun, Marco Piergiuseppe Di, and Johansson Karl Henrik. 2017. Cross-layer optimization for industrial control applications using wireless sensor and actuator mesh networks. IEEE Trans. Ind. Electron. 64, 4 (April2017), 32503259. Google ScholarGoogle ScholarCross RefCross Ref
  31. [31] Qu Yuben, Dong Chao, Dai Haipeng, Wu Fan, Tang Shaojie, Wang Hai, and Tian Chang. 2017. Multicast in multihop CRNs under uncertain spectrum availability: A network coding approach. IEEE/ACM Trans. Netw. 25, 4 (Aug.2017), 20262039. Google ScholarGoogle ScholarDigital LibraryDigital Library
  32. [32] Raptis Theofanis P., Passarella Andrea, and Conti Marco. 2020. Distributed data access in industrial edge networks. IEEE J. Sel. Areas Commun. 38, 5 (May2020), 915927. Google ScholarGoogle ScholarCross RefCross Ref
  33. [33] Ren Ju, Zhang Yaoxue, Zhang Kuan, Liu Anfeng, Chen Jianer, and Shen Xuemin Sherman. 2016. Lifetime and energy hole evolution analysis in data-gathering wireless sensor networks. IEEE Trans. Ind. Informat. 12, 2 (April2016), 788800. Google ScholarGoogle ScholarCross RefCross Ref
  34. [34] Ren Ju, Zhang Yaoxue, Zhang Ning, Zhang Deyu, and Shen Xuemin. 2016. Dynamic channel access to improve energy efficiency in cognitive radio sensor networks. IEEE Trans. Wirel. Commun. 15, 5 (May2016), 31433156. Google ScholarGoogle ScholarDigital LibraryDigital Library
  35. [35] Sahoo Prasan Kumar, Mohapatra Sulagna, and Sheu Jang-Ping. 2018. Dynamic spectrum allocation algorithms for industrial cognitive radio networks. IEEE Trans. Ind. Informat. 14, 7 (July2018), 30313043. Google ScholarGoogle ScholarCross RefCross Ref
  36. [36] Shi Junyang, Sha Mo, and Yang Zhicheng. 2019. Distributed graph routing and scheduling for industrial wireless sensor-actuator networks. IEEE/ACM Trans. Netw. 27, 4 (Aug.2019), 16691682. Google ScholarGoogle ScholarDigital LibraryDigital Library
  37. [37] Singh Jai Sukh Paul and Rai Mritunjay Kumar. 2018. CROP: Cognitive radio routing protocol for link quality channel diverse cognitive networks. J. Netw. Comput. Appl. 104 (Feb.2018), 4860. Google ScholarGoogle ScholarCross RefCross Ref
  38. [38] Sisinni Emiliano, Saifullah Abusayeed, Han Song, Jennehag Ulf, and Gidlund Mikael. 2018. Industrial Internet of Things: Challenges, opportunities, and directions. IEEE Trans. Ind. Informat. 14, 11 (Nov.2018), 47244734. Google ScholarGoogle ScholarCross RefCross Ref
  39. [39] Song Hao, Fang Xuming, and Wang Cheng-Xiang. 2017. Cost-reliability tradeoff in licensed and unlicensed spectra interoperable networks with guaranteed user data rate requirements. IEEE J. Sel. Areas Commun. 35, 1 (Jan.2017), 200214. DOI:Google ScholarGoogle ScholarDigital LibraryDigital Library
  40. [40] Valls Victor, Iosifidis George, and Salonidis Theodoros. 2019. Maximum lifetime analytics in IoT networks. In 2019 IEEE Conference on Computer Communications (INFOCOM’19). IEEE, Paris, France, 13691377. DOI:Google ScholarGoogle ScholarDigital LibraryDigital Library
  41. [41] Xu Zhezhuang, Chen Liquan, Chen Cailian, and Guan Xinping. 2016. Joint clustering and routing design for reliable and efficient data collection in large-scale wireless sensor networks. IEEE Internet Things J. 3, 4 (Aug.2016), 520532. Google ScholarGoogle ScholarCross RefCross Ref
  42. [42] Yetgin Halil, Cheung Kent Tsz Kan, El-Hajjar Mohammed, and Hanzo Lajos. 2017. A survey of network lifetime maximization techniques in wireless sensor networks. IEEE Commun. Surv. Tut. 19, 2 (2017), 828854. Google ScholarGoogle ScholarDigital LibraryDigital Library
  43. [43] Zhao Guodong, Imran Muhammad Ali, Pang Zhibo, Chen Zhi, and Li Liying. 2019. Toward real-time control in future wireless networks: Communication-control co-design. IEEE Commun. Mag. 57, 2 (Feb.2019), 138144. Google ScholarGoogle ScholarDigital LibraryDigital Library

Index Terms

  1. Network Lifetime Optimization in Multi-hop Industrial Cognitive Radio Sensor Networks

    Recommendations

    Comments

    Login options

    Check if you have access through your login credentials or your institution to get full access on this article.

    Sign in

    Full Access

    • Published in

      cover image ACM Transactions on Sensor Networks
      ACM Transactions on Sensor Networks  Volume 19, Issue 1
      February 2023
      565 pages
      ISSN:1550-4859
      EISSN:1550-4867
      DOI:10.1145/3561987
      Issue’s Table of Contents

      Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

      Publisher

      Association for Computing Machinery

      New York, NY, United States

      Publication History

      • Published: 8 December 2022
      • Online AM: 27 July 2022
      • Accepted: 1 July 2022
      • Revised: 10 May 2022
      • Received: 22 January 2022
      Published in tosn Volume 19, Issue 1

      Permissions

      Request permissions about this article.

      Request Permissions

      Check for updates

      Qualifiers

      • research-article
      • Refereed

    PDF Format

    View or Download as a PDF file.

    PDF

    eReader

    View online with eReader.

    eReader

    Full Text

    View this article in Full Text.

    View Full Text

    HTML Format

    View this article in HTML Format .

    View HTML Format