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Reinforcement learning for energy optimization in IoT based landslide early warning systems

Yıl 2024, Sayı: 059, 32 - 57, 31.12.2024

Seyfullah Arslan , Safa Dörterler , Fırat Aydemir

Öz

This study introduces a novel energy management model based on Deep Reinforcement Learning for IoT-based landslide early warning systems, aiming to achieve energy neutrality and enhance system resilience, efficiency, and sustainability. Unlike traditional energy optimization methods, the proposed model employs a Deep Q-Network (DQN) to dynamically optimize the duty cycle of sensor nodes by leveraging real-time energy availability. By adaptively balancing energy harvesting and consumption, sensor nodes can maintain continuous operation even under highly variable environmental conditions, maximizing their performance during high-energy periods while preserving battery life when energy is limited. Extensive simulations using real-world solar radiation data demonstrate the model's superior capability in extending system longevity and operational stability compared to existing approaches. Addressing critical energy management challenges in landslide monitoring systems, this work enhances system reliability, scalability, and adaptability, offering a robust foundation for broader IoT applications deployed in energy-limited and dynamic environments. The proposed method represents a significant improvement over conventional techniques, as it autonomously optimizes energy resources to ensure the continuous and sustainable operation of IoT ecosystems

Anahtar Kelimeler

Reinforcement learning internet of things energy management optimization landslide early warning systems

Kaynakça

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Yıl 2024, Sayı: 059, 32 - 57, 31.12.2024

Seyfullah Arslan , Safa Dörterler , Fırat Aydemir

Öz

Kaynakça

  • [1] R. Steen, E. Roud, T. M. Torp, and T.-A. Hansen, “The impact of interorganizational collaboration on the viability of disaster response operations: The Gjerdrum landslide in Norway,” Saf Sci, vol. 173, p. 106459, 2024.
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  • [5] M. A. Jamshed, K. Ali, Q. H. Abbasi, M. A. Imran, and M. Ur-Rehman, “Challenges, applications, and future of wireless sensors in Internet of Things: A review,” IEEE Sens J, vol. 22, no. 6, pp. 5482–5494, 2022.
  • [6] N. Casagli, E. Intrieri, V. Tofani, G. Gigli, and F. Raspini, “Landslide detection, monitoring and prediction with remote-sensing techniques,” Nat Rev Earth Environ, vol. 4, no. 1, pp. 51–64, 2023.
  • [7] M. Esposito, L. Palma, A. Belli, L. Sabbatini, and P. Pierleoni, “Recent advances in internet of things solutions for early warning systems: A review,” Sensors, vol. 22, no. 6, p. 2124, 2022.
  • [8] J. Singh, R. Kaur, and D. Singh, “Energy harvesting in wireless sensor networks: A taxonomic survey,” Int J Energy Res, vol. 45, no. 1, pp. 118–140, 2021.
  • [9] A. J. Williams, M. F. Torquato, I. M. Cameron, A. A. Fahmy, and J. Sienz, “Survey of energy harvesting technologies for wireless sensor networks,” IEEE Access, vol. 9, pp. 77493–77510, 2021.
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  • [11] S. Shresthamali, M. Kondo, and H. Nakamura, “Multi-objective reinforcement learning for energy harvesting wireless sensor nodes,” in 2021 IEEE 14th International Symposium on Embedded Multicore/Many-core Systems-on-Chip (MCSoC), IEEE, 2021, pp. 98–105.
  • [12] A. Karras, C. Karras, I. Karydis, M. Avlonitis, and S. Sioutas, “An Adaptive, Energy-Efficient DRL-Based and MCMC-Based Caching Strategy for IoT Systems,” in International Symposium on Algorithmic Aspects of Cloud Computing, Springer, 2023, pp. 66–85.
  • [13] E. O. Arwa and K. A. Folly, “Reinforcement learning techniques for optimal power control in grid-connected microgrids: A comprehensive review,” Ieee Access, vol. 8, pp. 208992–209007, 2020.
  • [14] L. M. Highland and P. Bobrowsky, The landslide handbook-A guide to understanding landslides, no. 1325. US Geological Survey, 2008.
  • [15] A. Tekerek and M. Dörterler, “The adaptation of gray wolf optimizer to data clustering,” Politeknik Dergisi, p. 1, 2020.
  • [16] S. Dörterler, “Kanser Hastalığı Teşhisinde Ölüm Oyunu Optimizasyon Algoritmasının Etkisi,” Mühendislik Alanında Uluslararası Araştırmalar VIII, p. 15, 2023.
  • [17] İ. Akgül and V. Kaya, “Learning performance of optimization algorithms in convolutional neural networks: An application,” in INSAC Scientific Researches in Natural and Engineering Sciences, 1st ed., vol. 1, Duvar, 2022, ch. 11, pp. 215–236.
  • [18] N. Yağmur, H. Temurtaş, and İ. Dağ, “Anemi hastalığının yapay sinir ağları yöntemleri kullanılarak sınıflandırılması,” Journal of Scientific Reports-B, no. 008, pp. 20–34, 2023.
  • [19] N. N. Arslan, E. Şahin, and M. Akçay, “Deep learning-based isolated sign language recognition: a novel approach to tackling communication barriers for individuals with hearing impairments,” Journal of Scientific Reports-A, no. 055, pp. 50–59, 2023.
  • [20] V. Kaya, I. Akgül, and Ö. Tanır, “A novel hybrid model based on machine and deep learning techniques for the classification of microalgae.,” Phyton-International Journal of Experimental Botany, vol. 92, no. 9, pp. 2519-2534, 2023.
  • [21] G. Arslan, F. Aydemir, and S. Arslan, “Enhanced license plate recognition using deep learning and block-based approach,” Journal of Scientific Reports-A, no. 058, pp. 57–82, 2023.
  • [22] F. Aydemir and S. Arslan, “A System Design With Deep Learning and IoT to Ensure Education Continuity for Post-COVID,” IEEE Transactions on Consumer Electronics, 2023.
  • [23] S. Dörterler, H. Dumlu, D. Özdemir, and H. Temurtaş, “Hybridization of Meta-heuristic Algorithms with K-Means for Clustering Analysis: Case of Medical Datasets,” Gazi Mühendislik Bilimleri Dergisi, pp. 1–23.
  • [24] E. Şahin, D. Özdemir, and H. Temurtaş, “Multi-objective optimization of ViT architecture for efficient brain tumor classification,” Biomed Signal Process Control, vol. 91, p. 105938, 2024.
  • [25] F. Aydemir and S. Arslan, “Covid-19 pandemi sürecinde çocukların el yıkama alışkanlığının nesnelerin interneti tabanlı sistem ile izlenmesi,” Mühendislik Bilimleri ve Araştırmaları Dergisi, vol. 3, no. 2, pp. 161–168, 2021.
  • [26] M. C. Özbalcı, H. Şahin, and T. T. Bilgin, “Makine Öğrenmesi Yöntemleri ile GTZAN Veri Kümesine Ait Müzik Türlerinin Sınıflandırılması,” Mühendislik Bilimleri ve Araştırmaları Dergisi, vol. 6, no. 1, pp. 77–87.
  • [27] E. Şahin and M. F. Talu, “WY-NET: A NEW APPROACH to IMAGE SYNTHESIS with GENERATIVE ADVERSARIAL NETWORKS,” Journal of Scientific Reports-A, no. 050, pp. 270–290, 2022.
  • [28] E. Şahin and M. F. Talu, “Automatic Mustache Pattern Production on Denim Fabric with Generative Adversarial Networks,” Computer Science, vol. 7, no. 1, pp. 1–9, 2022.
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Toplam 83 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Derin Öğrenme, Nöral Ağlar, Takviyeli Öğrenme, Modelleme ve Simülasyon
Bölüm Research Articles
Yazarlar

Seyfullah Arslan 0000-0002-2573-273X

Safa Dörterler 0000-0001-8778-081X

Fırat Aydemir 0000-0002-8965-1429

Yayımlanma Tarihi 31 Aralık 2024
Gönderilme Tarihi 20 Kasım 2024
Kabul Tarihi 27 Aralık 2024
Yayımlandığı Sayı Yıl 2024 Sayı: 059

Kaynak Göster

IEEE S. Arslan, S. Dörterler, ve F. Aydemir, “Reinforcement learning for energy optimization in IoT based landslide early warning systems”, JSR-A, sy. 059, ss. 32–57, Aralık 2024.
 Kapak Resmi İndir