Efficient Routing for Delay-energy Tradeoff in Event-based Wireless Sensor Networks

• Autorzy: Benaouda Nadjib

Identyfikatory

DOI

10.26636/jtit.2024.4.1833

• Języki publikacji: EN

Abstrakty

EN

Wireless sensor networks (WSNs) play a crucial role in the Internet of Things (IoT) by providing a foundation for collecting, transmitting and processing data from the physical world. Beyond the necessity of proposing solutions that are in line with the constrained resources of sensor nodes, particularly their limited energy capacity, the consideration of real-time data collection becomes essential. This is particularly vital due to the fact that many IoT applications require timely data collection. However, the need to establish energy-efficient routes contradicts the requirement to guarantee timely data collection. Hence, achieving an equilibrium and striking, subsequently, a trade-off between these two issued becomes imperative. To answer this question, a localized delay-bounded and energy-efficient routing protocol (abbreviated as LDER) is presented. It is based on another protocol, namely DEDA, aimed at achieving a higher energy conservation degree. To validate the efficacy of LDER, simulations were conducted using the J-sim simulator. The results demonstrate the ability of LDER to achieve the desired equilibrium and prove its superiority over DEDA.

Słowa kluczowe

EN

delay-bounded path; energy efficiency; LMST; WSN

• Wydawca: Instytut Łączności - Państwowy Instytut Badawczy
• Czasopismo: Journal of Telecommunications and Information Technology
• Rocznik: 2024
• Tom: nr 4
• Strony: 69--76
• Opis fizyczny: Bibliogr. 21 poz., rys., wykr.

Twórcy

autor

Benaouda Nadjib

• Computer Science Department University of Mohamed El Bachir El Ibrahimi, Bordj Bou Arreridj, Algeria

• https://orcid.org/0000-0002-4361-9597

Bibliografia

• [1] A.M.K. Abdulzahra, A.K.M. Al-Qurabat, and S.A. Abdulzahra, “Optimizing Energy Consumption in WSN-based IoT Using Unequal Clustering and Sleep Scheduling Methods”, Internet of Things, vol. 22, art. no. 100765, 2023 (https://doi.org/10.1016/j.iot.2023.100765).
• [2] T. Fang and Y. Yang, “Distributed Communication Protocol in Wireless Sensor Network Based on Internet of Things Technology”, Wireless Personal Communications, vol. 126, no. 3, pp. 2361–2377, 2022 (https://doi.org/10.1007/s11277-021-09203-7).
• [3] B. A. Begum and S. V. Nandury, “Data Aggregation Protocols for WSN and IoT Applications – A Comprehensive Survey”, Journal of King Saud University – Computer and Information Sciences, vol. 35, no. 2, pp. 651–681, 2023 (https://doi.org/10.1016/j.jksuci.2023.01.008).
• [4] J. Bian et al., “Machine Learning in Real-time Internet of Things (IoT) Systems: A Survey”, IEEE Internet of Things Journal, vol. 9, no. 11, pp. 8364–8386, 2022 (https://doi.org/10.1016/10.1109/JIOT.2022.3161050).
• [5] R. Kavra, A. Gupta, and S. Kansal, “Optimization of Energy and Delay on Interval Data Based Graph Model of Wireless Sensor Networks”, Wireless Networks, vol. 29, no. 5, pp. 2293–2311, 2023 (https://doi.org/10.1007/s11276-023-03292-x).
• [6] A. Hassan, A. Anter, and M. Kayed, “A Survey on Extending the Lifetime for Wireless Sensor Networks in Real-time Applications”, International Journal of Wireless Information Networks, vol. 28, no. 1, pp. 77–103, 2021 (https://doi.org/10.1007/s10776-020-00502-7).
• [7] N. Benaouda and A. Lahlouhi, “Ant-based Delay-bounded and Powerefficient Data Aggregation in Wireless Sensor Networks”, International Journal of Pervasive Computing and Communications, vol. 15, no. 2, pp. 97–119, 2019 (https://doi.org/10.1108/IJPCC-04-2019-0037).
• [8] X. Li et al., “Localized Delay-bounded and Energy-efficient Data Aggregation in Wireless Sensor and Actor Networks”, Wireless Communications and Mobile Computing, vol. 11, no. 12, pp. 1603–1617, 2011 (https://doi.org/10.1002/wcm.1222).
• [9] A. Sobeih et al., “J-sim: a Simulation and Emulation Environment for Wireless Sensor Networks”, IEEE Wireless Communications, vol. 13, no. 4, pp. 104–119, 2006 (https://doi.org/10.1109/MWC.2006.1678171).
• [10] A. Sarkar and T.S. Murugan, “Cluster Head Selection for Energy Efficient and Delay-less Routing in Wireless Sensor Network”, Wireless Networks, vol. 25, no. 1, pp. 303–320, 2019 (https://doi.org/10.1007/s11276-017-1558-2).
• [11] M. Selvi et al., “A Rule Based Delay Constrained Energy Efficient Routing Technique for Wireless Sensor Networks”, Cluster Computing, vol. 22, no. 5, pp. 10839–10848, 2019 (https://doi.org/10.1007/s10586-017-1191-y).
• [12] J. Agarkhed, P.Y. Dattatraya, and S. Patil, “Multi-QoS Constraint Multipath Routing in Cluster-based Wireless Sensor Network”, International Journal of Information Technology, vol. 13, no. 3, pp. 865–876, 2021 (https://doi.org/10.1007/s41870-020-00461-5).
• [13] E.D. Tita, W.-P. Nwadiugwu, J.M. Lee, and D.-S. Kim, “Real-time Optimizations in Energy Profiles and End-to-end Delay in WSN Using Two-hop Information”, Computer Communications, vol. 172, pp. 169–182, 2021 (https://doi.org/10.1016/j.comcom.2021.02.007).
• [14] X. Liu et al., “Intelligent Data Fusion Algorithm Based on Hybrid Delay-aware Adaptive Clustering in Wireless Sensor Networks”, Future Generation Computer Systems, vol. 104, pp. 1–14, 2020 (https://doi.org/10.1016/j.future.2019.10.001).
• [15] S. Yahiaoui et al., “An Energy Efficient and QoS Aware Routing Protocol for Wireless Sensor and Actuator Networks”, AEU – International Journal of Electronics and Communications, vol. 83, pp. 193–203, 2018 (https://doi.org/10.1016/j.aeue.2017.08.045).
• [16] G. Shah, M. Bozyigit, and F. Hussain, “Cluster-based Coordination and Routing Framework for Wireless Sensor and Actor Networks”, 76 Efficient Routing for Delay-energy Tradeoff in Event-based Wireless Sensor Networks Wireless Communications and Mobile Computing, vol. 11, pp. 1140–1154, 2011 (https://doi.org/10.1002/wcm.885).
• [17] T. Melodia, D. Pompili, V.C. Gungor, and I.F. Akyildiz, “Communication and Coordination in Wireless Sensor and Actor Networks”, IEEE Transactions on Mobile Computing, vol. 6, no. 10, pp. 1116–1129, 2007 (https://doi.org/10.1109/TMC.2007.1009).
• [18] H. Bagci, I. Korpeoglu, and A. Yazıcı, “A Distributed Fault-tolerant Topology Control Algorithm for Heterogeneous Wireless Sensor Networks”, IEEE Transactions on Parallel and Distributed Systems, vol. 26, no. 4, pp. 914–923, 2015 (https://doi.org/10.1109/TPDS.2014.2316142).
• [19] A. Mehto, S. Tapaswi, and K.K. Pattanaik, “Virtual Grid-based Rendezvous Point and Sojourn Location Selection for Energy and Delay Efficient Data Acquisition in Wireless Sensor Networks with Mobile Sink”, Wireless Networks, vol. 26, pp. 3763–3779, 2020 (https://doi.org/10.1007/s11276-020-02293-4).
• [20] K. Li and C.-C. Shen, “Balancing Transmission Power and Hop Count in ad hoc Unicast Routing with Swarm Intelligence”, 2008 IEEE Swarm Intelligence Symposium, SIS 2008, St. Louis, USA, 2008, (https://doi.org/10.1109/SIS.2008.4668323).
• [21] N. Li, J.C. Hou, and L. Sha, “Design and Analysis of an MSTbased Topology Control Algorithm”, IEEE Transactions on Wireless Communications, vol. 4, no. 3, pp. 1195–1206, 2005 (https://doi.org/10.1109/TWC.2005.846971).