Abstract
We propose a Cross-layer Protocol for Sleep scheduling and Data aggregation (CPSD) in Wireless Sensor Networks (WSNs) to improve the performance in WSNs. Data aggregation and sleep scheduling have shown good performance in reducing energy consumption and improving network lifetime. A lot of past studies about sleep scheduling focused on scheduling exactly once for each node, without long-term solutions. In this paper, we combine data aggregation and sleep scheduling for improving network lifetime and supporting the long-term operation of the network. We propose the Maximum Lifetime Minimum Hop Path Aggregation Tree Problem (MLMHPATP) with the probability of each node sending in a cycle, and divide it into the Maximum Lifetime Parent-Child Matching Problem (MLPCMP). We also define the Time Slot Scheduling Problem (TSSP) in bipartite graphs based on the SINR interference model. We use the Q-learning algorithm to solve the MLPCMP and solve the MLMHPATP bottom-up based on it. As for TSSP, we propose a distributed approach for it. We propose a novel cycle structure for the data transmission phase, which staggers three kinds of time frames at nodes with adjacent levels. The simulation results show that CPSD protocol has good performance in terms of throughput, energy consumption, aggregation delay, and network lifetime.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others

References
Ali, S.S., Giweli, N., Dawoud, A., Prasad, P.W.C.: Data aggregation techniques in wireless sensors networks: a survey. In: 2021 6th International Conference on Innovative Technology in Intelligent System and Industrial Applications (CITISIA), Sydney, Australia, pp. 1–9 (2021). https://doi.org/10.1109/CITISIA53721.2021.9719939
Guo, P., Jiang, T., Zhang, Q., Zhang, K.: Sleep scheduling for critical event monitoring in wireless sensor networks. IEEE Trans. Parallel Distrib. Syst. 23(2), 345–352 (2012)
Heinzelman, W.R., Chandrakasan, A., Balakrishnan, H.: Energy-efficient communication protocol for wireless microsensor networks. In: Proceedings of the 33rd Annual Hawaii International Conference on System Sciences, Maui, HI, USA, vol. 2, p. 10 (2000)
Yun, W.-K., Yoo, S.-J.: Q-learning-based data-aggregation-aware energy-efficient routing protocol for wireless sensor networks. IEEE Access 9, 10737–10750 (2021)
Redhu, S., Garg, P., Hegde, R.: Joint mobile sink scheduling and data aggregation in asynchronous wireless sensor networks using Q-learning. In: 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Calgary, AB, Canada, pp. 6438–6442 (2018). https://doi.org/10.1109/ICASSP.2018.8461561
Abadi, A.F.E., Asghari, S.A., Marvasti, M.B., Abaei, G., Nabavi, M., Savaria, Y.: RLBEEP: reinforcement-learning-based energy efficient control and routing protocol for wireless sensor networks. IEEE Access 10, 44123–44135 (2022)
Al-Jerew, O., Bassam, N.A., Alsadoon, A.: Reinforcement learning for delay tolerance and energy saving in mobile wireless sensor networks. IEEE Access 11, 19819–19835 (2023). https://doi.org/10.1109/ACCESS.2023.3247576
Philip, S.J., Peng, C., Cao, X.: Role based medium access control in wireless sensor networks. In: 2019 IEEE 5th International Conference on Computer and Communications (ICCC), Chengdu, China, pp. 624–628 (2019). https://doi.org/10.1109/ICCC47050.2019.9064319
Singh, R., Rai, B.K., Bose, S.K.: A joint routing and mac protocol for transmission delay reduction in many-to-one communication paradigm for wireless sensor networks. IEEE Internet Things J. 4(4), 1031–1045 (2017)
Wu, Y., Li, X.-Y., Liu, Y., Lou, W.: Energy-efficient wake-up scheduling for data collection and aggregation. IEEE Trans. Parallel Distrib. Syst. 21(2), 275–287 (2010)
Chen, Q., Gao, H., Cai, Z., Cheng, L., Li, J.: Distributed low-latency data aggregation for duty-cycle wireless sensor networks. IEEE/ACM Trans. Netw. 26(5), 2347–2360 (2018)
Lin, D., Wang, Q., Min, W., Xu, J., Zhang, Z.: A survey on energy-efficient strategies in static wireless sensor networks. ACM Trans. Sen. Netw. 17(1), 1–48 (2020). Article 3
Shih, E., et al.: Physical layer driven protocol and algorithm design for energy-efficient wireless sensor networks. In: Proceedings of the 7th Annual International Conference on Mobile Computing and Networking (MobiCom 2001), pp. 272–287. Association for Computing Machinery, New York (2001). https://doi.org/10.1145/381677.381703
Halldórsson, M.M., Holzer, S., Markatou, E.A., Lynch, N.: Leader election in SINR model with arbitrary power control. Theor. Comput. Sci. 811, 21–28 (2019)
Wu, Y.-C., Chaudhari, Q., Serpedin, E.: Clock synchronization of wireless sensor networks. IEEE Signal Process. Mag. 28(1), 124–138 (2011)
Geetha, D.D., Tabassum, N.: A survey on clock synchronization protocols in wireless sensor networks. In: International Conference on Smart Technologies for Smart Nation (SmartTechCon), Bengaluru, India, pp. 504–509 (2017). https://doi.org/10.1109/SmartTechCon.2017.8358424
Huang, H., Yun, J., Zhong, Z.: Scalable clock synchronization in wireless networks with low-duty-cycle radio operations. In: 2015 IEEE Conference on Computer Communications (INFOCOM), pp. 2011–2019 (2015)
Low, C.P.: On load-balanced semi-matchings for weighted bipartite graphs. In: Cai, J.-Y., Cooper, S.B., Li, A. (eds.) TAMC 2006. LNCS, vol. 3959, pp. 159–170. Springer, Heidelberg (2006). https://doi.org/10.1007/11750321_15
Luo, D., Zhu, X., Wu, X., Chen, G.: Maximizing lifetime for the shortest path aggregation tree in wireless sensor networks. In: 2011 Proceedings IEEE INFOCOM, Shanghai, China, pp. 1566–1574 (2011). https://doi.org/10.1109/INFCOM.2011.5934947
Jang, B., Kim, M., Harerimana, G., Kim, J.W.: Q-learning algorithms: a comprehensive classification and applications. IEEE Access 7, 133653–133667 (2019). https://doi.org/10.1109/ACCESS.2019.2941229
Ma, J., Lou, W., Li, X.-Y.: Contiguous link scheduling for data aggregation in wireless sensor networks. IEEE Trans. Parallel Distrib. Syst. 25(7), 1691–1701 (2014). https://doi.org/10.1109/TPDS.2013.296
Acknowledgment
This work was supported by the Key Project of Science and Technology Innovation 2030 supported by the Ministry of Science and Technology of China (Grant No. 2018AAA0101300).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Xia, Z., Li, J. (2024). A Distributed Cross-Layer Protocol for Sleep Scheduling and Data Aggregation in Wireless Sensor Networks. In: Sun, Y., Lu, T., Wang, T., Fan, H., Liu, D., Du, B. (eds) Computer Supported Cooperative Work and Social Computing. ChineseCSCW 2023. Communications in Computer and Information Science, vol 2012. Springer, Singapore. https://doi.org/10.1007/978-981-99-9637-7_24
Download citation
DOI: https://doi.org/10.1007/978-981-99-9637-7_24
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-99-9636-0
Online ISBN: 978-981-99-9637-7
eBook Packages: Computer ScienceComputer Science (R0)