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.
- [1] . 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, 3450–3455.
DOI: Google ScholarDigital Library - [2] . 2014. Exploiting multichannel diversity for cooperative multicast in cognitive radio mesh networks. IEEE/ACM Trans. Netw. 22, 3 (
June 2014), 770–783. Google ScholarDigital Library - [3] . 2013. Scheduling in centralized cognitive radio networks for energy efficiency. IEEE Trans. Veh. Technol. 62, 2 (
Feb. 2013), 582–595. Google ScholarCross Ref - [4] . 2021. Recorp: Receiver-oriented policies for industrial wireless networks. ACM Trans. Sens. Netw. 17, 4 (
July 2021), 44:1–44:32. Google ScholarDigital Library - [5] . 2016. Green cooperative spectrum sensing and scheduling in heterogeneous cognitive radio networks. IEEE Trans. Cogn. Commun. Netw. 2, 3 (
Sept. 2016), 238–248. Google ScholarCross Ref - [6] . 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, 2042–2047.
DOI: Google ScholarCross Ref - [7] . 2015. Using cognitive radio for interference-resistant industrial wireless sensor networks: An overview. IEEE Trans. Ind. Informat. 11, 6 (
Dec. 2015), 1466–1481. Google ScholarCross Ref - [8] . 2020. Switching cost-aware joint frequency assignment and scheduling for industrial cognitive radio networks. IEEE Trans. Ind. Informat. 16, 7 (
July 2020), 4365–4377. Google ScholarCross Ref - [9] . 2020. Energy-efficient channel switching in cognitive radio networks: A reinforcement learning approach. IEEE Trans. Veh. Technol. 69, 10 (
Oct. 2020), 12359–12362. Google ScholarCross Ref - [10] . 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, 2633–2638.
DOI: Google ScholarCross Ref - [11] . 2020. COIN-OR Branch and Cut. (
March 2020). Retrieved November 20, 2021 from https://github.com/coin-or/Cbc.Google Scholar - [12] . 2021. A novel distributed resource allocation scheme for wireless-powered cognitive radio Internet of Things networks. IEEE Internet Things J. 8, 20 (
Oct. 2021), 15486–15499. Google ScholarCross Ref - [13] . 2018. Cross-layer lifetime optimization for practical industrial wireless networks: A petroleum refinery case study. IEEE Trans. Ind. Informat. 14, 8 (
Aug. 2018), 3559–3566. Google ScholarCross Ref - [14] . 2019. Fragmentation-based distributed control system for software-defined wireless sensor networks. IEEE Trans. Ind. Informat. 15, 2 (
Feb. 2019), 901–910. Google ScholarCross Ref - [15] . 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), 5617–5625. Google ScholarCross Ref - [16] . 2018. Optimized cooperative multiple access in industrial cognitive networks. IEEE Trans. Ind. Informat. 14, 6 (
June 2018), 2666–2676. Google ScholarCross Ref - [17] . 2015. Two-dimensional route switching in cognitive radio networks: A game-theoretical framework. IEEE/ACM Trans. Netw. 23, 4 (
Aug. 2015), 1053–1066. Google ScholarDigital Library - [18] . 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, 203–208.
DOI: Google ScholarCross Ref - [19] . 2020. A framework of priority-aware packet transmission scheduling in cluster-based industrial wireless sensor networks. IEEE Trans. Ind. Informat. 16, 8 (
Aug. 2020), 5596–5606. Google ScholarCross Ref - [20] . 2019. Building k-protected routes in multi-hop cognitive radio networks. IEEE Trans. Cogn. Commun. Netw. 5, 4 (
Dec. 2019), 976–989. Google ScholarCross Ref - [21] . 2021. Data-driven deep learning for signal classification in industrial cognitive radio networks. IEEE Trans. Ind. Informat. 17, 5 (
May 2021), 3412–3421. Google ScholarCross Ref - [22] . 2020. Signal estimation in underlay cognitive networks for Industrial Internet of Things. IEEE Trans. Ind. Informat. 16, 8 (
Aug. 2020), 5478–5488. Google ScholarCross Ref - [23] . 2021. Energy-efficient resource allocation for cognitive Industrial Internet of Things with wireless energy harvesting. IEEE Trans. Ind. Informat. 17, 8 (
Aug. 2021), 5668–5677. Google ScholarCross Ref - [24] . 2020. NOMA-based resource allocation for cluster-based cognitive Industrial Internet of Things. IEEE Trans. Ind. Informat. 16, 8 (
Aug. 2020), 5379–5388. Google ScholarCross Ref - [25] . 2019. Wireless network design for emerging IIoT applications: Reference framework and use cases. Proc. IEEE 107, 6 (
June 2019), 1166–1192. Google ScholarCross Ref - [26] . 2021. Energy-efficient Industrial Internet of Things: Overview and open issues. IEEE Trans. Ind. Informat. 17, 11 (
Nov. 2021), 7225–7237. Google Scholar - [27] . 2018. Distributed node coordination for real-time energy-constrained control in wireless sensor and actuator networks. IEEE Internet Things J. 5, 5 (
Oct. 2018), 4151–4163.Google ScholarCross Ref - [28] . 2017. A survey of cognitive radio handoff schemes, challenges and issues for industrial wireless sensor networks (CR-IWSN). J. Netw. Comput. Appl. 97 (2017), 140–156. Google ScholarDigital Library
- [29] . 2019. A delay-aware spectrum handoff scheme for prioritized time-critical industrial applications with channel selection strategy. Comput. Commun. 144 (2019), 112–123. Google ScholarDigital Library
- [30] . 2017. Cross-layer optimization for industrial control applications using wireless sensor and actuator mesh networks. IEEE Trans. Ind. Electron. 64, 4 (
April 2017), 3250–3259. Google ScholarCross Ref - [31] . 2017. Multicast in multihop CRNs under uncertain spectrum availability: A network coding approach. IEEE/ACM Trans. Netw. 25, 4 (
Aug. 2017), 2026–2039. Google ScholarDigital Library - [32] . 2020. Distributed data access in industrial edge networks. IEEE J. Sel. Areas Commun. 38, 5 (
May 2020), 915–927. Google ScholarCross Ref - [33] . 2016. Lifetime and energy hole evolution analysis in data-gathering wireless sensor networks. IEEE Trans. Ind. Informat. 12, 2 (
April 2016), 788–800. Google ScholarCross Ref - [34] . 2016. Dynamic channel access to improve energy efficiency in cognitive radio sensor networks. IEEE Trans. Wirel. Commun. 15, 5 (
May 2016), 3143–3156. Google ScholarDigital Library - [35] . 2018. Dynamic spectrum allocation algorithms for industrial cognitive radio networks. IEEE Trans. Ind. Informat. 14, 7 (
July 2018), 3031–3043. Google ScholarCross Ref - [36] . 2019. Distributed graph routing and scheduling for industrial wireless sensor-actuator networks. IEEE/ACM Trans. Netw. 27, 4 (
Aug. 2019), 1669–1682. Google ScholarDigital Library - [37] . 2018. CROP: Cognitive radio routing protocol for link quality channel diverse cognitive networks. J. Netw. Comput. Appl. 104 (
Feb. 2018), 48–60. Google ScholarCross Ref - [38] . 2018. Industrial Internet of Things: Challenges, opportunities, and directions. IEEE Trans. Ind. Informat. 14, 11 (
Nov. 2018), 4724–4734. Google ScholarCross Ref - [39] . 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), 200–214.DOI: Google ScholarDigital Library - [40] . 2019. Maximum lifetime analytics in IoT networks. In 2019 IEEE Conference on Computer Communications (INFOCOM’19). IEEE, Paris, France, 1369–1377.
DOI: Google ScholarDigital Library - [41] . 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), 520–532. Google ScholarCross Ref - [42] . 2017. A survey of network lifetime maximization techniques in wireless sensor networks. IEEE Commun. Surv. Tut. 19, 2 (2017), 828–854. Google ScholarDigital Library
- [43] . 2019. Toward real-time control in future wireless networks: Communication-control co-design. IEEE Commun. Mag. 57, 2 (
Feb. 2019), 138–144. Google ScholarDigital Library
Index Terms
- Network Lifetime Optimization in Multi-hop Industrial Cognitive Radio Sensor Networks
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