Depth-Regulated and Energy-Balanced Routing for UWSNs Using Chaotic Search and Rescue Optimization
محورهای موضوعی : پردازش چند رسانه ای، سیستمهای ارتباطی، سیستمهای هوشمند
Saif Kadhim Mutar
1
,
Azam Andalib
2
*
,
Hossein Azgomi
3
,
Seyed Ali Sharifi
4
1 - 1MSc Student, Department of Electrical and Computer Engineering, Urmia University, Urmia, Iran
2 - Assistant Professor, Department of Computer Engineering, Ra.C., Islamic Azad University, Rasht, Iran
3 - Assistant Professor, Department of Computer Engineering, Ra.C., Islamic Azad University, Rasht, Iran
4 - Assistant Professor, Department of Computer Engineering, B.C., Islamic Azad University, Bonab, Iran
کلید واژه: underwater wireless sensor networks, depth control, routing, Clustering, search and rescue optimization algorithm and multi-hop data transfer,
چکیده مقاله :
Underwater wireless sensor networks (UWSNs) employ numerous inexpensive sensor nodes deployed in deep ocean environments. These nodes, characterized by their limited transmission power, resources, and energy, serve various purposes including disaster management, underwater navigation, and environmental monitoring. In these networks it is very challenging to update their location or add new devices, and it is very important to enhance the energy performance and lifetime of the underwater wireless sensor network. Multi-hop communication can expand the range of communication in these networks and increase its connections. The use of clustering-based routing is effective for increasing energy efficiency in these networks, with the difference that, unlike conventional wireless sensor networks, they have limitations such as low bandwidth, extended persistence, underwater pressure, and higher error probability. An energy balance routing protocol with multi-hop data transmission based on search and rescue (EBMH_CSR) is proposed, which balances by adjusting the depth of less energy nodes and replacing them with more energy nodes, and by combining chaotic concepts in the search and rescue optimization algorithm and with Considering residual energy, distance and degree of sensor nodes, fitness function is calculated. Simulation results show that the EBMH_CSR algorithm has improved the packet delivery rate (PDR) in different number of nodes by 23.6 percent, the packet reception rate (NPR) in different number of loads by 26.7 percent, the energy consumption in different number of rounds by 31.2 percent, the end-to-end delay by 31.7 percent, and the network lifetime in different number of nodes by 36 percent compared to the compared algorithms.
Underwater wireless sensor networks (UWSNs) employ numerous inexpensive sensor nodes deployed in deep ocean environments. These nodes, characterized by their limited transmission power, resources, and energy, serve various purposes including disaster management, underwater navigation, and environmental monitoring. In these networks it is very challenging to update their location or add new devices, and it is very important to enhance the energy performance and lifetime of the underwater wireless sensor network. Multi-hop communication can expand the range of communication in these networks and increase its connections. The use of clustering-based routing is effective for increasing energy efficiency in these networks, with the difference that, unlike conventional wireless sensor networks, they have limitations such as low bandwidth, extended persistence, underwater pressure, and higher error probability. An energy balance routing protocol with multi-hop data transmission based on search and rescue (EBMH_CSR) is proposed, which balances by adjusting the depth of less energy nodes and replacing them with more energy nodes, and by combining chaotic concepts in the search and rescue optimization algorithm and with Considering residual energy, distance and degree of sensor nodes, fitness function is calculated. Simulation results show that the EBMH_CSR algorithm has improved the packet delivery rate (PDR) in different number of nodes by 23.6 percent, the packet reception rate (NPR) in different number of loads by 26.7 percent, the energy consumption in different number of rounds by 31.2 percent, the end-to-end delay by 31.7 percent, and the network lifetime in different number of nodes by 36 percent compared to the compared algorithms.
1. Harinder Singh, D.; Ramya, R. Saravanakumar, Nayani Sateesh, Rohit Anand, Swarnjit Singh Artificial intelligence-based quality of transmission predictive model for cognitive optical networks. Optik 2022, 257, 168789. [CrossRef]
2. Chen, Y.; Tang, Y. PB-ACR: Node Payload Balanced Ant Colony Optimal Cooperative Routing for Multi-Hop Under Water Acoustic Sensor Networks. IEEE Access 2021, 9, 57165–57178. [CrossRef]
3. Chen, Y.; Zhu, J.; Wan, L.; Huang, S.; Zhang, X.; Xu, X. ACOA-AFSA Fusion Dynamic Coded Cooperation Routing for Different Scale Multi- Hop Underwater Acoustic Sensor Networks. IEEE Access 2020, 8, 186773–186788. [CrossRef]
4. Lilhore, U. K., Khalaf, O. I., Simaiya, S., Tavera Romero, C. A., Abdulsahib, G. M., & Kumar, D. (2022). A depth-controlled and energy-efficient routing protocol for underwater wireless sensor networks. International Journal of Distributed Sensor Networks, 18(9), 15501329221117118.
5. Bhargava, S.; Mohan, K.; Robert, N.R.; Upadhye, S. Optimal Stacked Sparse Autoencoder Based Traffic Flow Prediction in Intelligent Transportation Systems. Innov. Vis. Appl. Stud. Syst. Decis. Control. 2022, 412, 111–127. [CrossRef]
6. Jayapradha, J.; Prakash, M.; Alotaibi, Y.; Khalaf, O.I.; Alghamdi, S. Heap Bucketization Anonymity-An Efficient Privacy-Preserving Data Publishing Model for Multiple Sensitive Attributes. IEEE Access 2022, 10, 28773–28791. [CrossRef]
7. Rawat, S.S.; Alghamdi, S.; Kumar, G.; Alotaibi, Y.; Khalaf, O.I.; Verma, L.P. Infrared Small Target Detection Based on Partial Sum Minimization and Total Variation. Mathematics 2022, 10, 671. [CrossRef]
8. Gola, Gupta, et al. Networks of underwater sensor wireless systems: latest problems and threats. Int J Wirel Netw Broadband Technol 2021; 10(1): 59–69.
9. Goyal, Guo R, Qin D, Zhao M, et al. Mobile target localization based on iterative tracing for underwater wireless sensor networks. Int J Distrib Sens Netw 2020; 16(7): 1550147720940634.
10. Nazareth, Chandavarkar, Alotaibi, Y.; Khalaf, O.I.; Alghamdi, S. Authentication and Resource Allocation Strategies during Handoff for 5G IoVs Using Deep Learning. Energies 2022, 15, 2006. [CrossRef]
11. Goyal, Alotaibi, Y. A New Meta-Heuristics Data Clustering Algorithm Based on Tabu Search and Adaptive Search Memory. Symmetry 2022, 14, 623. [CrossRef]
12. Choudhary, Kavitha, T.; Mathai, P.P.; Karthikeyan, C.; Ashok, M.; Kohar, R.; Avanija, J.; Neelakandan, S. Deep Learning Based Capsule Neural Network Model for Breast Cancer Diagnosis Using Mammogram Images. Interdiscip. Sci. Comput. Life Sci. 2021, 14, 113–129. [CrossRef]
13. Al-Bukhari, Bouabdullah Sunitha, G.; Geetha, K.; Neelakandan, S.; Pundir, A.K.; Hemalatha, S.; Kumar, V. Intelligent deep learning-based ethnicity recognition and classification using facial images. Image Vis. Comput. 2022, 121, 104404. [CrossRef]
14. Durrani, Su, Y.; Wang, M.L. Optimal cooperative relaying and power control for IoUT networks with reinforcement learning. IEEE Internet Things J. 2021, 8, 791–801. [CrossRef]
15. NR, Khater E, El-Fishawy N, Tolba M, et al. Buffering_Slotted_ALOHA protocol for underwater acoustic sensor networks based on the slot status. Wirel Netw 2021; 27(5): 3127–3145.
16. Haque KF, Kabir KH and Abdelgawad A. Advancement of routing protocols and applications of underwater wireless sensor network (UWSN)-a survey. J Sens Actuator Netw 2020; 9(2): 19–31.
17. Beulah, J.R.; Prathiba, L.; Murthy, G.L.N.; Fantin Irudaya Raj, E.; Arulkumar, N. Blockchain with deep learning-enabled secure healthcare data transmission and diagnostic model. Int. J. Modeling Simul. Sci. Computing. 2021, 15, 1–14. [CrossRef]
18. Rajeswari, Venu, D.; Mayuri, A.V.R.; Murthy, G.L.N.; Arulkumar, N.; Shelke, N. An efficient low complexity compression based optimal homomorphic encryption for secure fiber optic communication. Optik 2022, 252, 168545. [CrossRef]
19. Jiang Jin, Reshma, G.; Al-Atroshi, C.; Nassa, V.K.; Geetha, B. Deep Learning-Based Skin Lesion Diagnosis Model Using Dermoscopic Images. Intell. Autom. Soft Comput. 2022, 31, 621–634. [CrossRef]
20. Yugan Chen, Arun, A.; Bhukya, R.R.; Hardas, B.M.; Anil Kumar, C.T.; Ashok, M. An Automated Word Embedding with Parameter Tuned Model for Web Crawling. Intell. Autom. Soft Comput. 2022, 32, 1617–1632.
21. Asha, P.; Natrayan, L.; Geetha, B.T.; Beulah, J.R.; Sumathy, R.; Varalakshmi, G. IoT enabled environmental toxicology for air pollution monitoring using AI techniques. Environ. Res. 2022, 205, 112574. [CrossRef] [PubMed]
22. Paulraj, D. A gradient boosted decision tree-based sentiment classification of twitter data. Int. J. Wavelets. Multiresolut. Inf. Processing 2020, 18, 205027. [CrossRef]
23. Houssein, E.H.; Saber, E.; Ali, A.A.; Wazery, Y.M. Centroid mutation-based Search and Rescue optimization algorithm for feature selection and classification. Expert Syst. Appl. 2022, 191, 116235. [CrossRef]
24. Choudhary M and Goyal N. Node deployment strategies in underwater wireless sensor network. In: 2021 International Conference on Advance Computing and Innovative Technologies in Engineering (ICACITE), Greater Noida, India,4-5 March 2021, pp. 2212–2231. New York: IEEE.
25. Satpathy, S.; Debbarma, S.; Sengupta Aditya, S.C.; Bhattacaryya Bidyut, K.D. Design a FPGA, fuzzy based, insolent method for prediction of multi-diseases in rural area. J. Intell. Fuzzy Syst. 2019, 37, 7039–7046. [CrossRef]
26. Li, M.; Xu, G.; Lai, Q.; Chen, J. A chaotic strategy-based quadratic Opposition-Based Learning adaptive variable-speed whale optimization algorithm. Math. Comput. Simul. 2022, 193, 71–99. [CrossRef]
27. V. K. Kamboj, C. L. Kumari, S. K. Bath, D. Prashar, M. Rashid, S. S. Alshamrani, and A. S. AlGhamdi, ‘‘A cost-effective solution for nonconvex economic load dispatch problems in power systems using slime mould algorithm,’’ Sustainability, vol. 14, no. 5, p. 2586, Feb. 2022.
28. A. Srivastava and D. K. Das, ‘‘An adaptive chaotic class topper optimization technique to solve economic load dispatch and emission economic dispatch problem in power system,’’ Soft Comput., vol. 2022, pp. 1–22, Jan. 2022
29. Subramani, N.; Mohan, P.; Alotaibi, Y.; Alghamdi, S.; Khalaf, O.I. An Efficient Metaheuristic-Based Clustering with Routing Protocol for Underwater Wireless Sensor Networks. Sensors 2022, 22, 415. [CrossRef] [PubMed]
30. Simaiya S. EEPSA: energy efficiency priority scheduling algorithm for cloud computing. In: 2021 2nd international conference on smart electronics and communication (ICOSEC), Trichy, India, 7–9 October 2021. New York: IEEE.
31. Guleria K, Prasad D, Lilhore UK, et al. Asynchronous media access control protocols and cross-layer optimizations for wireless sensor networks: an energy-efficient perspective. J Comput Theor Nanosci 2020; 17(6): 2531–2538.
32. Srilakshmi, U.; Alghamdi, S.; Ankalu, V.V.; Veeraiah, N.; Alotaibi, Y. A secure optimization routing algorithm for mobile ad hoc networks. IEEE Access 2022, 10, 14260–14269. [CrossRef]
33. Choudhary, M.; Goyal, N. A rendezvous point-based data gathering in underwater wireless sensor networks for monitoring applications. Int. J. Commun. Syst. 2022, 14, e5078. [CrossRef]
34. Alotaibi, Y.; Subahi, A.F. New goal-oriented requirements extraction framework for e-health services: A case study of diagnostic testing during the COVID-19 outbreak. Bus. Process Manag. J. 2021, 38, 1–19. [CrossRef]
35. Suryanarayana, G.; Chandran, K.; Khalaf, O.I.; Alotaibi, Y.; Alsufyani, A.; Alghamdi, S.A. Accurate Magnetic Resonance Image Super-Resolution Using Deep Networks and Gaussian Filtering in the Stationary Wavelet Domain. IEEE Access 2021, 9, 71406–71417. [CrossRef]
36. Mohan, P.; Subramani, N.; Alotaibi, Y.; Alghamdi, S.; Khalaf, O.I.; Ulaganathan, S. Improved Metaheuristics-Based Clustering with Multihop Routing Protocol for Underwater Wireless Sensor Networks. Sensors 2022, 22, 1618. [CrossRef] [PubMed]
37. Chithambaramani, R.; Prakash, M. An Efficient Applications Cloud Interoperability Framework UsingI-Anfis. Symmetry 2021, 13, 268.