Artificial Intelligence Driven Power Optimization in IOT-Enabled Wireless Sensor Networks
Main Article Content
Abstract
The widespread use of Wireless Sensor Networks (WSN) in Internet of Things (IoT) causes energy efficiency issues. This paper proposes an AI-based solution to this problem. The propose an AI-Driven Power Optimization framework for IoT-enabled WSN using Deep Q-Network (DQN) and Dynamic Voltage and Frequency Scaling (DVFS). These techniques can adapt to changing network conditions and reduce power consumption when used together. Sensor nodes provide environmental parameters, battery status, and network behavior data to the AI-driven framework DQN is implemented after data preprocessing to learn and make power management decisions using reinforcement learning. Neural network-driven agent operates in a state and action space. It optimizes energy use with rewards. Real-time hardware power adjustment is done using DVFS. DVFS precise control and AI-driven decision-making create a comprehensive power optimization strategy. AI adapts to new challenges and optimizes network lifespan by improving its power management policies. Experimental implementations of the proposed framework show significant energy savings, network lifespan extension, and QoS improvements. AI-Driven Power Optimization in IoT-enabled WSN is proven effective and flexible. This study shows the potential of AI, specifically DQN and DVFS, in IoT-enabled WSN. This AI-Driven Power Optimization framework addresses energy efficiency and improves IoT sensor networks. AI improves power optimization in IoT-enabled WSN making IoT deployments more sustainable and resilient.
Article Details
This work is licensed under a Creative Commons Attribution-NoDerivatives 4.0 International License.
References
X. Zhao et al., “A detection probability guaranteed energy-efficient scheduling mechanism in large-scale WSN,” Alexandria Eng. J., vol. 71, pp. 451–462, 2023, doi: 10.1016/j.aej.2023.03.059.
R. Yadav, I. Sreedevi, and D. Gupta, “Augmentation in performance and security of WSNs for IoT applications using feature selection and classification techniques,” Alexandria Eng. J., vol. 65, pp. 461–473, 2023,
G. S. Uthayakumar, B. Jackson, C. Ramesh Babu Durai, A. Kalaimani, S. Sargunavathi, and S. Kamatchi, “Systematically efficiency enabled energy usage method for an IOT based WSN environment,” Meas. Sensors, vol. 25, no. November 2022, p. 100615, 2023.
Bharat Bhushan Jain, Nandkishor Gupta, & Ashish Raj. (2022). Numerical Simulation of Detection and Classification of Symmetrical and Unsymmetrical Faults using Improved Stockwell Transform. International Journal on Recent Technologies in Mechanical and Electrical Engineering, 9(3), 75–80. https://doi.org/10.17762/ijrmee.v9i3.376
M. Suguna and S. Sathiyabama, “Shift invariant deep convolution neural learning for resource efficient healthcare data transmission in WSN,” Meas. Sensors, vol. 25, no. November 2022, p. 100627, 2023.
A. Seyyedabbasi, F. Kiani, T. Allahviranloo, U. Fernandez-Gamiz, and S. Noeiaghdam, “Optimal data transmission and pathfinding for WSN and decentralized IoT systems using I-GWO and Ex-GWO algorithms,” Alexandria Eng. J., vol. 63, pp. 339–357, 2023.
G. Santhosh and K. V. Prasad, “Energy optimization routing for hierarchical cluster based WSN using artificial bee colony,” Meas. Sensors, vol. 29, no. May, p. 100848, 2023.
M. V. N. R. Pavan Kumar and R. Hariharan, “SPEED-UP, and energy-efficient GPSR protocol for WSNs using IOT,” Meas. Sensors, vol. 23, no. August, p. 100411, 2022, doi: 10.1016/j.measen.2022.100411.
B. H. D. D. Priyanka, P. Udayaraju, C. S. Koppireddy, and A. Neethika, “Developing a region-based energy-efficient IoT agriculture network using region- based clustering and shortest path routing for making sustainable agriculture environment,” Meas. Sensors, vol. 27, no. February, p. 100734, 2023, doi: 10.1016/j.measen.2023.100734.
Naeem, A. B. ., Senapati, B. ., Chauhan, A. S. ., Kumar, S., Orosco Gavilan, J. C. ., & Abdel-Rehim, W. M. F. . (2023). Deep Learning Models for Cotton Leaf Disease Detection with VGG-16. International Journal of Intelligent Systems and Applications in Engineering, 11(2), 550–556. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/2710
W. Li and S. Kara, “Methodology for Monitoring Manufacturing Environment by Using Wireless Sensor Networks (WSN) and the Internet of Things (IoT),” Procedia CIRP, vol. 61, pp. 323–328, 2017.
R. F. Fernandes, M. B. de Almeida, and D. Brandão, “An Energy Efficient Receiver-Initiated MAC Protocol for Low-Power WSN,” Wirel. Pers. Commun., vol. 100, no. 4, pp. 1517–1536, 2018.
A. Onasanya and M. Elshakankiri, Secured Cancer Care and Cloud Services in IoT/WSN Based Medical Systems, vol. 256. Springer International Publishing, 2019.
A. Izaddoost and M. Siewierski, “Energy efficient data transmission in IoT platforms,” Procedia Comput. Sci., vol. 175, pp. 387–394, 2020.
K. J. Co, A. V. Ong, and M. Peradilla, “WSN Data Collection and Routing Protocol with Time Synchronization in Low-cost IoT Environment,” Procedia Comput. Sci., vol. 191, no. 2019, pp. 102–110, 2021.
L. Kaur and R. Kaur, “A survey on energy efficient routing techniques in WSNs focusing IoT applications and enhancing fog computing paradigm,” Glob. Transitions Proc., vol. 2, no. 2, pp. 520–529, 2021.
D. Antony Joseph Rajan and E. R. Naganathan, “Trust based anonymous intrusion detection for cloud assisted WSN-IOT,” Glob. Transitions Proc., vol. 3, no. 1, pp. 104–108, 2022.
V. S. Badiger and T. S. Ganashree, “Data aggregation scheme for IOT based wireless sensor network through optimal clustering method,” Meas. Sensors, vol. 24, no. October, p. 100538, 2022.
A. B. Bomgni, M. L. F. Sindjoung, D. K. Tchibonsou, M. Velempini, and J. F. Myoupo, “NESEPRIN: A new scheme for energy-efficient permutation routing in IoT networks,” Comput. Networks, vol. 214, no. August 2021, p. 109162, 2022, doi: 10.1016/j.comnet.2022.109162.
P. Ajay, B. Nagaraj, R. Arunkumar, and R. Huang, “Enhancing computational energy transportation in IoT systems with an efficient wireless tree-based routing protocol,” Results Phys., vol. 51, no. May, p. 106747, 2023, doi: 10.1016/j.rinp.2023.106747.
A. Barriga, J. A. Barriga, M. J. Moñino, and P. J. Clemente, “IoT-based expert system for fault detection in Japanese Plum leaf-turgor pressure WSN,” Internet of Things (Netherlands), vol. 23, no. April, p. 100829, 2023, doi: 10.1016/j.iot.2023.100829.
S. Bouarourou, A. Zannou, E. H. Nfaoui, and A. Boulaalam, “An Efficient Model-Based Clustering via Joint Multiple Sink Placement for WSNs,” Futur. Internet, vol. 15, no. 2, 2023, doi: 10.3390/fi15020075.
Lachouri, A., & Ardjouni, A. (2022). Aeroelastic Stability of Combined Plunge-Pitch Mode Shapes in a Linear Compressor Cascade. Advances in the Theory of Nonlinear Analysis and Its Applications, 6(1), 101–117.
Panwar, A., Morwal, R., & Kumar, S. (2022). Fixed points of ρ-nonexpansive mappings using MP iterative process. Advances in the Theory of Nonlinear Analysis and Its Applications, 6(2), 229–245.
Saurabh Bhattacharya, Manju Pandey,"Deploying an energy efficient, secure & high-speed sidechain-based TinyML model for soil quality monitoring and management in agriculture", Expert Systems with Applications, Volume 242, 2024, 122735,ISSN 0957-4174.https://doi.org/10.1016/j.eswa.2023.122735.
Khetani, V., Gandhi, Y., Bhattacharya, S., Ajani, S. N., & Limkar, S. (2023). Cross-Domain Analysis of ML and DL: Evaluating their Impact in Diverse Domains. International Journal of Intelligent Systems and Applications in Engineering, 11(7s), 253-262.
Adomian polynomials method for dynamic equations on time scales. (2021). Advances in the Theory of Nonlinear Analysis and Its Application, 5(3), 300-315. https://atnaea.org/index.php/journal/article/view/204
Sherje, D. N. . (2021). Content Based Image Retrieval Based on Feature Extraction and Classification Using Deep Learning Techniques. Research Journal of Computer Systems and Engineering, 2(1), 16:22. Retrieved from https://technicaljournals.org/RJCSE/index.php/journal/article/view/14
Boutebba, H., Lakhal, H., Slimani, K., & Belhadi, T. (2023). The nontrivial solutions for nonlinear fractional Schrödinger-Poisson system involving new fractional operator. Advances in the Theory of Nonlinear Analysis and Its Applications, 7(1), 121–132.