AI-Driven Energy Management Techniques for Enhancing Network Longevity in Wireless Sensor Networks
Keywords:
Wireless Sensor Networks (WSNs), Energy Efficiency, Q-learning Algorithm, Data Transmission, Packet Delivery Ratio (PDR), Network Latency, Energy OptimizationAbstract
WSNs and mobile systems are critical for monitoring and data collection, but energy efficiency remains one of the biggest challenges due to very limited battery life in sensor nodes. The issue here is the challenge of energy management by adopting sophisticated optimization techniques and AI-driven methodologies. This research develops a Q-learning model of dynamic energy optimization. The proposed method uses MATLAB simulations and real-world testing to validate improvements. The methodology employs adaptive routing and real-time power adjustments, which optimize energy usage. The results show a 34.92% increase in energy savings compared to traditional methods, where baseline energy efficiency was 65%. The Packet Delivery Ratio (PDR) improved from a baseline of 85% to 96.38%, ensuring more reliable data communication. The network latency was reduced by 24 ms, from the initial 50 ms, thus enhancing real-time responsiveness. Q-learning approach was extended for an additional 10 hours against the 7-hour baseline established by conventional systems. These improvements are based on fully dynamic routing with online adjustments, which makes the network adaptive to changing environments. This methodology is promising for energy-efficient and high-performance communication systems in remote and critical applications. The findings contribute to sustainable network operations and reduce the maintenance costs, making WSNs viable for long-term deployments.
References
K. S. Kim, D. J. Lee, and J. A. Lee, "An energy-efficient routing protocol in wireless sensor networks using reinforcement learning," IEEE Sens. J., vol. 17, no. 5, pp. 1408-1420, Mar. 2017.
M. Di Francesco, S. K. Das, and G. Anastasi, "Data collection in wireless sensor networks with mobile elements: A survey," ACM Trans. Sens. Netw., vol. 8, no. 1, pp. 1-31, Jan. 2016.
F. A. Aderohunmu, J. D. Deng, and M. Purvis, "Enhancing lifetime of wireless sensor networks using a fuzzy logic-based clustering approach," IEEE Sens. J., vol. 16, no. 10, pp. 3896-3903, May 2016.
N. Javaid, N. Khan, M. Imran, and U. Qasim, "Designing an energy-efficient routing protocol for wireless sensor networks," IEEE Access, vol. 4, pp. 3164-3171, June 2016.
H. Yetgin, K. T. K. Cheung, M. El-Hajjar, and L. Hanzo, "A survey of network lifetime maximization techniques in wireless sensor networks," IEEE Commun. Surveys Tuts., vol. 19, no. 2, pp. 828-854, 2nd Quart., 2017.
W. Osamy, A. M. Khedr, A. Salim, A. I. Al Ali, and A. A. El-Sawy, “A review on recent studies utilizing artificial intelligence methods for solving routing challenges in wireless sensor networks,” PeerJ Computer Science, vol. 8, p. e1089, Oct. 2022, doi: 10.7717/peerj-cs.1089
Kumar and R. Mishra, "An energy-efficient cooperative spectrum sensing scheme for cognitive radio sensor networks," IEEE Sens. J., vol. 17, no. 7, pp. 2110-2121, Apr. 2017.
S. K. Singh and P. K. Singh, "Energy efficient clustering protocols in wireless sensor networks: A survey," IEEE Access, vol. 6, pp. 32689-32703, June 2018.
X. Chen, H. Zhang, C. Wu, S. Mao, and Y. Ji, "Energy-efficient offloading for mobile edge computing in 5G heterogeneous networks," IEEE Access, vol. 6, pp. 11328-11337, Feb. 2018.
M. Elhoseny, H. K. El-Minir, A. M. Riad, and X. Yuan, "A secure data routing schema for WSN based on optimized blockchain," IEEE Access, vol. 6, pp. 66576-66588, Nov. 2018.
T. M. Hoang, L. B. Le, and T. Le-Ngoc, "Energy-efficient resource allocation for D2D communications underlaying cellular networks using multi-agent Q-learning," IEEE Trans. Veh. Technol., vol. 65, no. 8, pp. 6646-6659, Aug. 2016.
S. Manogaran and D. Lopez, "A survey on deep learning techniques in wireless sensor networks," IEEE Access, vol. 7, pp. 159410-159420, Nov. 2019.
P. Rawat, K. D. Singh, H. Chaouchi, and J. M. Bonnin, "Wireless sensor networks: A survey on recent developments and potential synergies," J. Supercomput., vol. 73, no. 3, pp. 1152-1186, Mar. 2017.
S. Tyagi and N. Kumar, "A systematic review on clustering and routing techniques based upon LEACH protocol for wireless sensor networks," J. Netw. Comput. Appl., vol. 36, no. 2, pp. 623-645, 2016.
M. Esnaashari, A. Ghaffari, and R. Askari, "An energy-efficient routing protocol for wireless sensor networks using fuzzy C-means clustering and A-star algorithm," IEEE Access, vol. 7, pp. 122570-122580, Sept. 2019.
Z. Xu, W. Liang, and W. Xu, "Efficient deployment of wireless sensor networks for out-of-band d2d communications in cellular networks," IEEE Trans. Veh. Technol., vol. 67, no. 7, pp. 6450-6461, July 2018.
S. Sharmin, I. Ahmedy, and R. Md Noor, “An Energy-Efficient Data Aggregation Clustering Algorithm for Wireless Sensor Networks Using Hybrid PSO,” Energies, vol. 16, no. 5, p. 2487, Mar. 2023, doi: 10.3390/en16052487.
M. Y. Khan, F. Ullah, and K. H. Kim, "Energy-efficient clustering scheme for reducing the communication overhead in heterogeneous wireless sensor networks," IEEE Access, vol. 7, pp. 177829-177841, Dec. 2019.
T. Rault, A. Bouabdallah, and Y. Challal, “Energy efficiency in wireless sensor networks: A top-down survey,” Computer Networks, vol. 67, pp. 104–122, Jul. 2014, doi: 10.1016/j.comnet.2014.03.027.
K. Cengiz and T. Dag, “Improving Energy-Efficiency of WSNs through LEFCA,” International Journal of Distributed Sensor Networks, vol. 12, no. 8, p. 8139401, Aug. 2016, doi: 10.1177/155014778139401.
G. Luo, Q. Yuan, J. Li, S. Wang, and F. Yang, “Artificial Intelligence Powered Mobile Networks: From Cognition to Decision,” IEEE Network, vol. 36, no. 3, pp. 136–144, May 2022, doi: 10.1109/mnet.013.2100087.
M. del Carmen Rodríguez-Hernández and S. Ilarri, “AI-based mobile context-aware recommender systems from an information management perspective: Progress and directions,” Knowledge-Based Systems, vol. 215, p. 106740, Mar. 2021, doi: 10.1016/j.knosys.2021.106740.
H. Cai et al., “Enable Deep Learning on Mobile Devices: Methods, Systems, and Applications,” ACM Transactions on Design Automation of Electronic Systems, vol. 27, no. 3, pp. 1–50, Mar. 2022, doi: 10.1145/3486618.
Y. Zhang and B. Kantarci, “Invited Paper: AI-Based Security Design of Mobile Crowdsensing Systems: Review, Challenges and Case Studies,” 2019 IEEE International Conference on Service-Oriented System Engineering (SOSE), pp. 17–1709, Apr. 2019, doi: 10.1109/sose.2019.00014.
D. Puri and B. Bhushan, “Enhancement of security and energy efficiency in WSNs: Machine Learning to the rescue,” 2019 International Conference on Computing, Communication, and Intelligent Systems (ICCCIS), pp. 120–125, Oct. 2019, doi: 10.1109/icccis48478.2019.8974465.
H. Sarker, M. M. Hoque, Md. K. Uddin, and T. Alsanoosy, “Mobile Data Science and Intelligent Apps: Concepts, AI-Based Modeling and Research Directions,” Mobile Networks and Applications, vol. 26, no. 1, pp. 285–303, Sep. 2020, doi: 10.1007/s11036-020-01650-z.
F. Gazzawe, M. Mayouf, R. Lock, and R. Alturki, “The Role of Machine Learning in E-Learning Using the Web and AI-Enabled Mobile Applications,” Mobile Information Systems, vol. 2022, pp. 1–10, Jun. 2022, doi: 10.1155/2022/3696140.
Klimova, M. Pikhart, and J. Kacetl, “Ethical issues of the use of AI-driven mobile apps for education,” Frontiers in Public Health, vol. 10, Jan. 2023, doi: 10.3389/fpubh.2022.1118116.
H. Amri, S. Tabbane, and F. Kamoun, "Energy-efficient multilevel clustering algorithm for large-scale wireless sensor networks," IEEE Access, vol. 8, pp. 175460-175472, Sept. 2020.
P. Singh, R. Mishra, and A. Pandey, "Energy-efficient routing in wireless sensor networks using fuzzy-based optimal clustering technique," IEEE Access, vol. 8, pp. 86562-86571, Apr. 2020.
L. Yang, D. Zhang, L. Li, and Q. He, “Energy efficient cluster-based routing protocol for WSN using multi-strategy fusion snake optimizer and minimum spanning tree,” Scientific Reports, vol. 14, no. 1, Jul. 2024, doi: 10.1038/s41598-024-66703-9.
V. Ponnusamy, A. Abdullah, and A. G. Downe, “Energy Efficient Routing Protocols in Wireless Sensor Networks,” Wireless Sensor Networks and Energy Efficiency, pp. 237–261, 2012, doi: 10.4018/978-1-4666-0101-7.ch010.
T. E. Djahel, H. M. Ammar, and Z. Zhang, "Optimizing energy consumption in WSNs for IoT-based applications using deep learning techniques," IEEE Access, vol. 8, pp. 155987-155998, Nov. 2020.
S. Das, A. K. Balmiki, and K. Mazumdar, “A Review on AI-ML Based Cyber-Physical Systems Security for Industry 4.0,” Intelligent Cyber-Physical Systems Security for Industry 4.0, pp. 203–216, Nov. 2022, doi: 10.1201/9781003241348-11.
Rachna, Y. Chhabra, and P. Bhambri, “Various Approaches and Algorithms for Monitoring Energy Efficiency of Wireless Sensor Networks,” Sustainable Development Through Engineering Innovations, pp. 761–770, 2021, doi: 10.1007/978-981-15-9554-7_68.
J. Yu, L. Liu, W. Wang, and X. Gu, "Energy-efficient clustering routing algorithm based on the edge computing in wireless sensor networks," IEEE Access, vol. 6, pp. 101-111, Dec. 2018.
Liu, K. Wu, M. Tao, and M. Yang, "Energy-aware adaptive clustering protocol based on Cauchy–Gaussian distribution for WSNs," IEEE Sens. J., vol. 19, no. 22, pp. 10984-10995, Nov. 2019.
X. Tang, W. Yan, and Y. Yang, "An efficient energy-aware routing algorithm for wireless sensor networks," IEEE Access, vol. 8, pp. 122450-122460, Aug. 2020.
M. G. Kibria, S. Das, M. Yusuf, and J. Choi, "An energy-efficient medium access control protocol for wireless sensor networks using intelligent scheduling with deep reinforcement learning," IEEE Access, vol. 7, pp. 150387-150398, Oct. 2019.
M. A. Razzaque, M. M. Hassan, and S. K. Uddin, "QoS-aware data aggregation in wireless sensor networks: A comprehensive survey," IEEE Sens. J., vol. 19, no. 10, pp. 4067-4086, May 2019.
A. K. Raji and F. A. Taofeek-Ibrahim, "Enhancing the Lifetime and Efficiency of Wireless Sensor Networks Using AI-Integrated CRT-LEACH Protocols," Journal of Engineering Research and Reports, vol. 23, no. 5, pp. 48-56, 2024.
O. I. Akinola, "Adaptive Location-Based Routing Protocols for Dynamic Wireless Sensor Networks in Urban Cyber-Physical Systems," Journal of Engineering Research and Reports, vol. 18, no. 2, pp. 150-162, 2024.
V. N. Sankaran, M. Sivasankari, and R. T. S. Babu, "Machine Learning in Wireless Networks: Optimizing Data Flow and Connectivity," ESP Journal of Engineering and Technology Advances, vol. 3, no. 3, pp. 109-120, 2023.
E. Dvir, M. Shifrin, and O. Gurewitz, "Cooperative Multi-Agent Reinforcement Learning for Data Gathering in Energy-Harvesting Wireless Sensor Networks," Mathematics, vol. 12, no. 13, 2024, doi: 10.3390/math12132102.
S. Anslam Sibi, "Optimizing Energy Resources in WSNs: ARIMA Feature Selection Meets Adaptive Reinforcement Learning," Fiber and Integrated Optics, vol. 43, no. 2, pp. 123-135, 2024, doi: 10.1080/01468030.2024.2342269.
R. Priyadarshi, "Energy-Efficient Routing in Wireless Sensor Networks: A Meta-Heuristic and Artificial Intelligence-Based Approach," Archives of Computational Methods in Engineering, vol. 31, no. 1, pp. 243-259, 2024, doi: 10.1007/s11831-023-10039-6.
B. Balkhande, G. Ghule, and V. H. Meshram, "Artificial Intelligence Driven Power Optimization in IoT-Enabled Wireless Sensor Networks," Journal of Electrical Engineering, vol. 74, no. 6, pp. 435-447, 2023.
M. S. Basingab, H. Bukhari, and S. H. Serbaya, "AI-Based Decision Support System Optimizing Wireless Sensor Networks for Consumer Electronics in E-Commerce," Applied Sciences, vol. 14, no. 12, Article 4960, 2024, doi: 10.3390/app14124960.
B. Muniandi, K. J. Raut, and P. G. Gawande, "AI-Driven Energy Efficient Routing Protocols for Wireless Sensor Networks," Naturalista Siciliano, vol. 48, no. 2, pp. 156-168, 2024.
A. Cengiz, B. Kantarci, and T. Soyata, "Sensor Network Optimization for Smart Grid Applications," IEEE Transactions on Smart Grid, vol. 7, no. 1, pp. 47-57, Jan. 2016, doi: 10.1109/TSG.2015.2467212.
S. Kim, J. Lee, and H. Cho, "Energy-Efficient Routing in Wireless Sensor Networks Using Reinforcement Learning," Sensors, vol. 17, no. 5, May 2017, doi: 10.3390/s17051043.
S. Manogaran and D. Lopez, "A Deep Learning Algorithm for Energy-Efficient Data Routing in Wireless Sensor Networks," Future Generation Computer Systems, vol. 92, pp. 892-899, March 2019, doi: 10.1016/j.future.2018.10.029.
C. A. Alabi, M. A. Idakwo, and A. L. Imoize, "AI for Spectrum Intelligence and Adaptive Resource Management," in Artificial Intelligence for Wireless Networks, CRC Press, pp. 35-57, 2024.
N. Senthil and A. Sumathi, "Intelligent Routing Algorithms for 6G Wireless Networks: A Review and Analysis," International Journal of Advanced Networking and Applications, vol. 16, no. 2, pp. 210-220, 2024.
X. Cao et al., "AI-Empowered Multiple Access for 6G: A Survey of Spectrum Sensing, Protocol Designs, and Optimizations," arXiv preprint arXiv:2406.13335, 2024, doi: 10.48550/arXiv.2406.13335.
P. Mishra and G. Singh, "Energy Management Systems in Sustainable Smart Cities Based on the Internet of Energy: A Technical Review," Energies, vol. 16, no. 19, 2023, doi: 10.3390/en16196903.
J. Anand and J. R. P. Perinbam, "Q-Learning-Based Optimized Routing in Biomedical Wireless Sensor Networks," IETE Journal of Research, vol. 63, no. 5, pp. 485-495, 2017.
C. Han, X. Wang, and H. Zhu, "Security Enhancement for Deep Reinforcement Learning-Based Strategy in Energy-Efficient Wireless Sensor Networks," Sensors, vol. 24, no. 6, 2024.
B. Mohammed, "AI-Empowered Flying Ad-Hoc Networks for Dynamic Connectivity," International Journal of Computing and Digital Systems, vol. 15, no. 3, pp. 320-332, 2024.
L. Hu, C. Han, and H. Zhu, "Dynamic Power Allocation and Adaptive Routing in Wireless Sensor Networks," IEEE Transactions on Networking, vol. 32, no. 4, pp. 870-882, 2024, doi: 10.1109/TNET.2024.3301234.
R. Priyadarshi, "AI-Based Routing Algorithms for Enhancing Wireless Sensor Network Lifespan," Journal of Network Optimization, vol. 29, no. 2, pp. 215-228, 2024.
B. Balkhande, G. Ghule, and V. H. Meshram, "AI-Driven Dynamic Power Management in IoT Networks," Journal of Internet of Things Research, vol. 10, no. 1, pp. 45-58, 2024.
X. Yang, J. Yan, D. Wang, Y. Xu, and G. Hua, "WOAD3QN-RP: An Intelligent Routing Protocol in Wireless Sensor Networks—A Swarm Intelligence and Deep Reinforcement Learning-Based Approach," Expert Systems with Applications, vol. 234, 2024, doi: 10.1016/j.eswa.2023.120433.
D. Godfrey, B. K. Suh, B. H. Lim, K. C. Lee, and K. I. Kim, "An Energy-Efficient Routing Protocol with Reinforcement Learning in Software-Defined Wireless Sensor Networks," Sensors, vol. 23, no. 20, 2023, doi: 10.3390/s23208435.
S. K. Gupta, S. Patel, and P. K. Mannepalli, "Designing Dense-Healthcare IoT Networks for Industry 4.0 Using AI-Based Energy Efficient Reinforcement Learning Protocol," in Industry 4.0 and Healthcare, pp. 45-60, 2023, doi: 10.1007/978-981-99-1949-9_3.
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