Comparative Study Towards Energy Efficiency in Wireless Sensor Networks Using Asynchronous Duty Cycle
DOI:
https://doi.org/10.62123/enigma.v2i2.57Keywords:
Wireless Sensor Networks, Energy Efficiency, Asynchronous Duty Cycle, Multihop Broadcast, TDMA-CSMA HybridAbstract
Energy efficiency is a critical determinant in the design and operation of Wireless Sensor Networks (WSNs), as sensor nodes are typically powered by constrained battery resources. Asynchronous duty cycle mechanisms have emerged as a viable strategy to optimize energy consumption while preserving network functionality. This research presents a comparative analysis of multiple energy-efficient Medium Access Control (MAC) protocols, including Low-Energy Adaptive Clustering Hierarchy (LEACH), Energy-Efficient Sensor Routing (EESR), B-MAC, L-MAC, WiseMAC, and hybrid approaches such as TDMA-CSMA. Performance metrics such as energy efficiency, latency, throughput, and packet delivery ratio (PDR) are evaluated under varying network conditions. The findings indicate that AI-driven protocols, particularly those incorporating Artificial Neural Networks (ANN), significantly outperform conventional methodologies by enhancing cluster head selection, distributing energy load effectively, and extending network lifetime. Hybrid ADC emerges as the most robust solution, demonstrating an optimal trade-off between energy efficiency and network reliability across dynamic traffic scenarios. Furthermore, This research highlights the implications of integrating adaptive duty cycling with intelligent network optimization, underscoring its potential to enhance WSN sustainability. The results provide a comprehensive framework for refining MAC protocol architectures, offering actionable insights for optimizing next-generation WSN deployments.
Downloads
References
[1] S. Verma, S. Zeadally, S. Kaur, and A. K. Sharma, “Intelligent and Secure Clustering in Wireless Sensor Network (WSN)-Based Intelligent Transportation Systems,” IEEE Trans. Intell. Transp. Syst., vol. 23, no. 8, pp. 13473–13481, 2021.
[2] A. Lanzolla and M. Spadavecchia, “Wireless Sensor Networks for Environmental Monitoring,” Sensors, vol. 21, no. 4, p. 1172, 2021.
[3] R. Caspart et al., “Precise Energy Consumption Measurements of Heterogeneous Artificial Intelligence Workloads,” in Proc. Int. Conf. High Performance Computing, Cham: Springer International Publishing, May 2022, pp. 108–121.
[4] D. Liu, Z. Cao, H. Jiang, S. Zhou, Z. Xiao, and F. Zeng, “Concurrent Low-Power Listening: A New Design Paradigm for Duty-Cycling Communication,” ACM Trans. Sensor Netw., vol. 19, no. 1, pp. 1–24, 2022.
[5] W. B. Nedham and A. K. M. Al-Qurabat, “A Comprehensive Review of Clustering Approaches for Energy Efficiency in Wireless Sensor Networks,” Int. J. Comput. Appl. Technol., vol. 72, no. 2, pp. 139–160, 2023.
[6] A. E. H. El-Kadiry, C. Lumbao, M. Rafei, and R. Shammaa, “Autologous BMAC Therapy Improves Spinal Degenerative Joint Disease in Lower Back Pain Patients,” Front. Med., vol. 8, p. 622573, 2021.
[7] A. Gamage, J. Liando, C. Gu, R. Tan, M. Li, and O. Seller, “LMAC: Efficient Carrier-Sense Multiple Access for LoRa,” ACM Trans. Sensor Netw., vol. 19, no. 2, pp. 1–27, 2023.
[8] R. Berguerand, L. Bergamini, P. Dallemagne, and E. Franzi, “Integration of the Ultra-Low Power WiseMAC with the μ111 Real Time Operating System: A Performance Evaluation,” in Proc. 17th Int. Conf. Wireless and Mobile Computing, Networking and Communications (WiMob), Oct. 2021, pp. 301–306, IEEE.
[9] M. Mittal, C. Iwendi, S. Khan, and A. R. Javed, “Analysis of Security and Energy Efficiency for Shortest Route Discovery in Low‐Energy Adaptive Clustering Hierarchy Protocol Using Levenberg‐Marquardt Neural Network and Gated Recurrent Unit for Intrusion Detection System,” Trans. Emerg. Telecommun. Technol., vol. 32, no. 6, p. e3997, 2021.
[10] R. Kumar et al., “EESR: Energy Efficient Sector-Based Routing Protocol for Reliable Data Communication in UWSNs,” Comput. Commun., vol. 192, pp. 268–278, 2022.
[11] J. Thakur, P. Phogat, S. Shreya, R. Jha, and S. Singh, "Non-Rechargeable Batteries: A Review of Primary Battery Technology and Future Trends," Phys. Chem. Chem. Phys., vol. 2024.
[12] I. J. Habeeb, H. A. Jasim, and M. H. Hashim, "Balance Energy Based on Duty Cycle Method for Extending Wireless Sensor Network Lifetime," Bull. Electr. Eng. Inform., vol. 12, no. 5, pp. 3105–3114, 2023.
[13] R. S. Dhaka, M. Khurana, and S. Lamba, "Comparison of Sensor-MAC (S-MAC), Timeout-MAC (T-MAC), Berkeley-MAC (B-MAC), and Modified ML-MAC (M2L-MAC) for Wireless Sensor Networks," in Proc. Int. Conf. Deep Learning, Artif. Intell. Robot., Cham: Springer Nature Switzerland, Dec. 2023, pp. 454–471.
[14] R. Berguerand, L. Bergamini, P. Dallemagne, and E. Franzi, "Integration of the Ultra-Low Power WiseMAC with the μ111 Real Time Operating System: A Performance Evaluation," in Proc. 17th Int. Conf. Wireless Mobile Comput., Netw. Commun. (WiMob), Oct. 2021, pp. 301–306, IEEE.
[15] A. A. El-Hinnawey and A. H. Ismail, "A Design Aid Tool for Asynchronous SAR ADCs," in Proc. 14th Int. Conf. Electr. Eng. (ICEENG), May 2024, pp. 412–416, IEEE.
[16] A. Kirmaz, D. S. Michalopoulos, I. Balan, and W. Gerstacker, "Mobile Network Traffic Forecasting Using Artificial Neural Networks," in Proc. 28th Int. Symp. Model., Anal., Simul. Comput. Telecommun. Syst. (MASCOTS), Nov. 2020, pp. 1–7, IEEE.
[17] F. Amin, R. Abbasi, S. Khan, and M. A. Abid, "An Overview of Medium Access Control and Radio Duty Cycling Protocols for Internet of Things," Electronics, vol. 11, no. 23, p. 3873, 2022.
[18] B. Krishna and V. Karthikeyan, "Active Switched-Inductor Network Step-Up DC–DC Converter With Wide Range of Voltage-Gain at the Lower Range of Duty Cycles," IEEE J. Emerg. Sel. Top. Ind. Electron., vol. 2, no. 4, pp. 431–441, 2021.
[19] N. V. Martyushev et al., "Review of Methods for Improving the Energy Efficiency of Electrified Ground Transport by Optimizing Battery Consumption," Energies, vol. 16, no. 2, p. 729, 2023.
[20] M. Amirinasab Nasab, S. Shamshirband, A. T. Chronopoulos, A. Mosavi, and N. Nabipour, "Energy-Efficient Method for Wireless Sensor Networks Low-Power Radio Operation in Internet of Things," Electronics, vol. 9, no. 2, p. 320, 2020.
[21] F. Afroz and R. Braun, "Energy-Efficient MAC Protocols for Wireless Sensor Networks: A Survey," Int. J. Sensor Netw., vol. 32, no. 3, pp. 150–173, 2020.
[22] P. Maheshwari, A. K. Sharma, and K. Verma, "Energy Efficient Cluster-Based Routing Protocol for WSN Using Butterfly Optimization Algorithm and Ant Colony Optimization," Ad Hoc Netw., vol. 110, p. 102317, 2021.
[23] F. L. Fan, J. Xiong, M. Li, and G. Wang, "On Interpretability of Artificial Neural Networks: A Survey," IEEE Trans. Radiat. Plasma Med. Sci., vol. 5, no. 6, pp. 741–760, 2021.
[24] R. F. T. Ceskoutsé, A. B. Bomgni, D. R. G. Zanfack, D. D. Agany, T. B. Bouetou, and E. G. Zohim, "Sub-Clustering Based Recommendation System for Stroke Patient: Identification of a Specific Drug Class for a Given Patient," Comput. Biol. Med., vol. 171, p. 108117, 2024.
[25] A. M. Bedewy, Y. Sun, R. Singh, and N. B. Shroff, "Low-Power Status Updates via Sleep-Wake Scheduling," IEEE/ACM Trans. Netw., vol. 29, no. 5, pp. 2129–2141, 2021.
[26] M. B. Dowlatshahi, M. K. Rafsanjani, and B. B. Gupta, "An Energy-Aware Grouping Memetic Algorithm to Schedule the Sensing Activity in WSNs-Based IoT for Smart Cities," Appl. Soft Comput., vol. 108, p. 107473, 2021.
[27] B. Choi, Introduction to Python Network Automation Volume I - Laying the Groundwork: The Essential Skills for Growth, Berkeley, CA: Apress, 2024.
[28] X. Chen, W. Wan, and J. Li, "OMNeT++ Based on Simulation of InfiniBand Interconnection Networks," in Proc. 4th Int. Conf. Internet of Things Smart City (IoTSC), vol. 13224, Aug. 2024, pp. 407–418, SPIE.
[29] K. Yuntao, N. M. Phuong, T. Racharak, T. Le, and L. M. Nguyen, "An Effective Method to Answer Multi-Hop Questions by Single-Hop QA System," in Proc. Int. Conf. Agents Artif. Intell. (ICAART), vol. 2, 2022, pp. 244–253.
[30] H. Qiu, Q. Zheng, M. Msahli, G. Memmi, M. Qiu, and J. Lu, "Topological Graph Convolutional Network-Based Urban Traffic Flow and Density Prediction," IEEE Trans. Intell. Transp. Syst., vol. 22, no. 7, pp. 4560–4569, 2020.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2025 Indah Pratiwi Putri
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
