Improved Power Effective Node Combined Heterogeneous Path Protocol for Enhance Network Lifetime Based on Cloud Resource Management in WSN
Keywords:Energy-Efficient, WSN, cluster head, EBOCA, ACOPD, ONND, IPENCHP, Network lifetime, and Throughput
Nowadays, sensor nodes are connected wirelessly to the Wireless Sensor Network (WSN). A WSN contains frequent sensor nodes spread throughout surroundings. These nodes are responsible for identifying, approximating, and receiving information. There is a unique technology called a "sensor cloud" that combines the sensor capabilities of WSNs with the architecture of Cloud Computing (CC). The small sensor nodes can sense, process, and transmit data. However, it is difficult and expensive to extend the lifespan of WSNs. Using energy-efficient routing protocols ensures reliable data transmission and prolongs the network's lifespan. However, control limitations can have a substantial control on the overall lifetime of the network. Since batteries power nodes in WSNs, they will eventually lose all power after a certain period. Therefore, we introduced the Improved Power Effective Node Combined Heterogeneous Path (IPENCHP) protocol to solve the above problem. Initially, we use the Enhanced Butterfly Optimal Cluster Algorithm (EBOCA) to select an ideal Cluster Head (CH) from a group of nodes. Furthermore, the ONND method can enhance the network's energy efficiency of the node. The path between CHs and BSs is found using the Ant Colony Optimum Based Path Distance (ACOPD) algorithm. Finally, the IPENCHP protocol can prolong the network lifetime of cloud resource management by assessing the energy communication level within a cluster. According to the simulation results, IPENCHP outperforms regarding energy efficiency, packet loss rate, energy consumption, performance latency, and Throughput.
Jie Liu and Li Zhu "Joint Resource Allocation Optimization of Wireless Sensor Network Based on Edge Computing," Volume 2021 | Article ID 5556651 | https://doi.org/10.1155/2021/5556651.
Qianao Ding, Rongbo Zhu, Hao Liu, and Maode Ma, "An Overview of Machine Learning-Based Energy-Efficient Routing Algorithms in Wireless Sensor Networks," Electronics 2021, 10(13), 1539; https://doi.org/10.3390/electronics10131539.
Kalyan Das, Satyabrata Das, Rabi Kumar Darji and Ananya Mishra, "Survey of Energy-Efficient Techniques for the Cloud-Integrated Sensor Network," Volume 2018 | Article ID 1597089 | https://doi.org/10.1155/2018/1597089.
V. Sridhar, K. V. Ranga Rao, " A Machine Learning-Based Intelligence Approach for Multiple-Input/Multiple-Output Routing in Wireless Sensor Networks," Volume 2022 | Article ID 6391678 | https://doi.org/10.1155/2022/6391678.
Ramya Sri Valli A, Satya Surya Anirudh G, "A Review: Wireless sensor networks using machine learning and cloud computing," Quest Journals Journal of Software Engineering and Simulation Volume 9 ~ Issue 4 (2023) pp: 92-98 ISSN (Online):2321-3795 ISSN (Print):2321-3809.
C. Xu, Z. Xiong, G. Zhao, and S. Yu, "An energy-efficient region source routing protocol for lifetime maximization in WSN," IEEE Access, vol. 7, pp. 135277-135289, 2019.
H. El Alami and A. Najid, "ECH: An Enhanced Clustering Hierarchy Approach to Maximize Lifetime of Wireless Sensor Networks," in IEEE Access, vol. 7, pp. 107142-107153, 2019, doi: 10.1109/ACCESS.2019.2933052.
Y. Zhang, X. Zhang, S. Ning, J. Gao and Y. Liu, "Energy-efficient multilevel heterogeneous routing protocol for wireless sensor networks," IEEE Access, vol. 7, pp. 55873-55884, 2019.
J. Kang, J. Kim, M. Kim, and M. Sohn, "Machine Learning-Based Energy-Saving Framework for Environmental States-Adaptive Wireless Sensor Network," in IEEE Access, vol. 8, pp. 69359-69367, 2020, doi: 10.1109/ACCESS.2020.2986507.
N. Aslam, K. Xia and M. U. Hadi, "Optimal Wireless Charging Inclusive of Intellectual Routing Based on SARSA Learning in Renewable Wireless Sensor Networks," in IEEE Sensors Journal, vol. 19, no. 18, pp. 8340-8351, 15 Sept.15, 2019, doi: 10.1109/JSEN. 2019.2918865.
D. P. Kumar, T. Amgoth and C. S. R. Annavarapu, "Machine learning algorithms for wireless sensor networks: A survey," Inf. Fusion, vol. 49, pp. 1-25, Jun. 2018.
M. Kozlowski, R. McConville, R. Santos-Rodriguez and R. Piechocki, "Energy efficiency in reinforcement learning for wireless sensor networks," arXiv: 1812.02538, 2018, [online] Available: https://arxiv.org/abs/1812.02538.
A. Padhy, S. Joshi, S. Bitragunta, V. Chamola, and B. Sikdar, "A survey of energy and spectrum harvesting technologies and protocols for next-generation wireless networks," IEEE Access, vol. 9, pp. 1737-1769, 2021.
L. Wang, H. Shao, J. Li, X. Wen, and Z. Lu, "Optimal multi-user computation offloading strategy for wireless powered sensor networks," IEEE Access, vol. 8, pp. 35150-35160, 2020.
T. Wang, Y. Li, G. Wang, J. Cao, M. Z. A. Bhuiyan and W. Jia, "Sustainable and Efficient Data Collection from WSNs to Cloud," in IEEE Transactions on Sustainable Computing, vol. 4, no. 2, pp. 252-262, 1 April-June 2019, doi: 10.1109/TSUSC.2017.2690301.
Z. Sun et al., "CSR-IM: Compressed Sensing Routing-Control- Method With Intelligent Migration-Mechanism Based on Sensing Cloud-Computing," in IEEE Access, vol. 8, pp. 28437-28449, 2020, doi: 10.1109/ACCESS.2020.2971537.
Wang, X., Chen, H. & Li, S. A reinforcement learning-based sleep scheduling algorithm for compressive data gathering in wireless sensor networks. J Wireless Com Network 2023, 28 (2023). https://doi.org/10.1186/s13638-023-02237-4
Chowdhury, S.M.; Hossain, A. Different energy saving schemes in wireless sensor networks: A survey. Wirel. Pers. Commun. 2020, 114, 2043–2062.
Saba, T.; Haseeb, K.; Ud Din, I.; Almogren, A.; Altameem, A.; Fati, S.M. EGCIR: Energy-aware graph clustering and intelligent routing using supervised system in wireless sensor networks. Energies 2020, 13, 4072.
P. S. Banerjeea, S. N. Mandala, D. Deb, and B. Maiti, “RL-sleep: temperature adaptive sleep scheduling using Reinforcement learning for sustainable connectivity in wireless sensor networks,” Sustainable Computing: Informatics and Systems, vol. 26, p. 100380, 2020.
M. E. Haque and U. Baroudi, “Dynamic energy efficient routing protocol in wireless sensor networks,” Wireless Networks, vol. 26, no. 5, pp. 3715–3733, 2020.
F. N. Al-Wesabi, M. Obayya, M. A. Hamza, J. S. Alzahrani, D. Gupta and S. Kumar, "Energy-aware resource optimization using unified metaheuristic optimization algorithm allocation for a cloud computing environment," Sustain. Comput. Informat. Syst., vol. 35, Sep. 2022.
Z. Gao, D. Chen and H. -C. Wu, "Energy Loss Minimization for Wireless Power Transfer Based Energy Redistribution in WSNs," in IEEE Transactions on Vehicular Technology, vol. 68, no. 12, pp. 12271-12285, Dec. 2019, doi: 10.1109/TVT.2019.2946631.
K. Zaimen, M. -E. -A. Brahmia, L. Moalic, A. Abouaissa, and L. Idoumghar, "A Survey of Artificial Intelligence Based WSNs Deployment Techniques and Related Objectives Modeling," in IEEE Access, vol. 10, pp. 113294-113329, 2022, doi: 10.1109/ACCESS. 2022.3217200.3
A. Srivastava and P. K. Mishra, "A survey on WSN issues with its heuristics and meta-heuristics solutions," Wireless Pers. Commun., vol. 121, no. 1, pp. 745-814, Nov. 2021.
J. Amutha, S. Sharma and J. Nagar, "WSN strategies based on sensors deployment sensing models coverage and energy efficiency: Review approaches and open issues," Wireless Pers. Commun., vol. 111, no. 2, pp. 1089-1115, Mar. 2020.
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, Jan. 2021.
T. Reis, M. Teixeira, J. Almeida, and A. Paiva, "A Recommender for Resource Allocation in Compute Clouds Using Genetic Algorithms and SVR," in IEEE Latin America Transactions, vol. 18, no. 06, pp. 1049-1056, Jun 2020, doi: 10.1109/TLA.2020.9099682.
A. Moazeni, R. Khorsand, and M. Ramezanpour, "Dynamic Resource Allocation Using an Adaptive Multi-Objective Teaching-Learning Based Optimization Algorithm in Cloud," in IEEE Access, vol. 11, pp. 23407-23419, 2023, doi: 10.1109/ACCESS.2023.3247639.
Yang, J., Xiang, Z., Mou, L. et al. Multimedia resource allocation strategy of wireless sensor networks using distributed heuristic algorithm in a cloud computing environment. Multimed Tools Appl 79, 35353–35367 (2020). https://doi.org/10.1007/s11042-019-07759-y.
Hemanand, D., Reddy, G. ., Babu, S. S. ., Balmuri, K. R. ., Chitra, T., & Gopalakrishnan, S. (2022). An Intelligent Intrusion Detection and Classification System using the CSGO-LSVM Model for Wireless Sensor Networks (WSNs). International Journal of Intelligent Systems and Applications in Engineering, 10(3), 285–293. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/2167.
P. Satyanarayana, U. D. Yalavarthi, Y. S. S. Sriramam, M. Arun, V. G. Krishnan and S. Gopalakrishnan, "Implementation of Enhanced Energy Aware Clustering Based Routing (EEACBR)Algorithm to Improve Network Lifetime in WSN’s," 2022 IEEE 2nd International Conference on Mobile Networks and Wireless Communications (ICMNWC), Tumkur, Karnataka, India, 2022, pp. 1-6, doi: 10.1109/ICMNWC56175.2022.10031991.
Turaka, R., Chand, S. R., Anitha, R., Prasath, R. A., Ramani, S., Kumar, H., Gopalakrishnan, S., & Farhaoui, Y. (2023). A novel approach for design energy-efficient inexact reverse carry select adders for IoT applications. Results in Engineering, 18, 101127. https://doi.org/10.1016/j.rineng.2023.101127.
Gopalakrishnan Subburayalu, Hemanand Duraivelu, Arun Prasath Raveendran, Rajesh Arunachalam, Deepika Kongara & Chitra Thangavel (2023) Cluster Based Malicious Node Detection System for Mobile Ad-Hoc Network Using ANFIS Classifier, Journal of Applied Security Research, 18:3, 402-420, DOI: 10.1080/19361610.2021.2002118.
Satyanarayana, P., Diwakar, G., Subbayamma, B. V., Phani Sai Kumar, N. V., Arun, M., & Gopalakrishnan, S. (2023). Comparative analysis of new meta-heuristic-variants for privacy preservation in wireless mobile ad-hoc networks for IoT applications. Computer Communications, 198, 262–281. https://doi.org/10.1016/j.comcom. 2022.12.006.
Tadepalli, S. K. ., & Lakshmi, P. V. . (2023). A Comparative Study on Prediction of Endometriosis Causing Infertility Using Machine Learning Techniques: in Detail. International Journal on Recent and Innovation Trends in Computing and Communication, 11(4), 131–140. https://doi.org/10.17762/ijritcc.v11i4.6396
Kamau, J., Goldberg, R., Oliveira, A., Seo-joon, C., & Nakamura, E. Improving Recommendation Systems with Collaborative Filtering Algorithms. Kuwait Journal of Machine Learning, 1(3). Retrieved from http://kuwaitjournals.com/index.php/kjml/article/view/134
Sherje, N. P., Agrawal, S. A., Umbarkar, A. M., Dharme, A. M., & Dhabliya, D. (2021). Experimental evaluation of mechatronics based cushioning performance in hydraulic cylinder. Materials Today: Proceedings, doi:10.1016/j.matpr.2020.12.1021
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