Optimized Transformer- Basde Security for Vehicular Network Communication Against Denial- of - Service Attack
DOI:
https://doi.org/10.56294/sctconf20251424Keywords:
VANET, Denial of Service, Security, Cluster Head, Packet Delivery Ratio, Dely, Network Throughput, Energy ConsumptionAbstract
Objective: A Vehicular Ad-Hoc Network (VANET) is one of the crucial elements of an Intelligent Transport System (ITS) and plays a significant role in security and communication. VANETs are susceptible to Denial of Service (DoS) attacks, which are an inherent threat to the security and performance of such networks, requiring more sophisticated detection and countermeasures.
Methods: In response to this problem, the Spatial Hyena Security Transformer Model (SHSTM) is introduced to improve the security and use of Vehicular Ad-hoc Network (VANET) communication against DoS attacks. The network nodes are set up to enable Vehicle-to-Vehicle (V2V) communication; the SHSTM constantly detects each node to detect and filter out DoS attack targets. The model includes an effective Cluster Head (CH) selection approach based on traffic patterns to enhance network security.
Results: Comparative performance measurements conducted based on network positions before and after the attacks show enhanced overall performance in terms of Packet Delivery Ratio (PDR), Network Throughput (NT), Energy Consumption (EC), End-to-End Delay (EED), and Attack Detection Ratio (ADR). The network attains an NT of 3.91 Mbps, minimal EC of 1.02 mJ, highest PDR of 99.04%, minimal EED of 0.0206 seconds, and higher ADR of 98%.
Conclusions: The design of the proposed SHSTM proved a significant improvement in security and network performance, which outperforms the existing state-of-the-art technique. Hence, it is considered a potential solution to address the DoS threat in VANET.
References
1. Ghamry, Walid K., and Suzan Shukry. "Multi-objective intelligent clustering routing schema for internet of things enabled wireless sensor networks using deep reinforcement learning." Cluster Computing, (2024):1-21.
2. Zainaddin, D. A., et al. "Recent trends and future directions of congestion management strategies for routing in IoT-based wireless sensor network: a thematic review." Wireless Networks, (2024): 1-45.
3. Priyadarshi, Rahul. "Exploring machine learning solutions for overcoming challenges in IoT-based wireless sensor network routing: a comprehensive review." Wireless Networks, (2024):1-27.
4. Tharini, V. J., & Vijayarani, S. "IoT in healthcare: Ecosystem, pillars, design challenges, applications, vulnerabilities, privacy, and security concerns.” In Incorporating the Internet of Things in healthcare applications and wearable devices, IGI Global, (2020):1-22.
5. Hu, Xi, and Rayan H. Assaad. "BIM-enabled digital twin framework for real-time indoor environment monitoring and visualization based on autonomous LIDAR-based robotic mobile mapping, IoT sensing, and indoor positioning technology." Journal of Building Engineering (2024): 108901.
6. Khan, M. A. R., et al., "Optimizing hybrid metaheuristic algorithm with cluster head to improve performance metrics on the IoT.” Theoretical Computer Science, 927, (2022): 87-97.
7. Shenbagharaman, A., and B. Paramasivan. "Trilateration method-based node localization and energy efficient routing using RSA for underwater wireless sensor network." Sustainable Computing: Informatics and Systems,41 (2024):100952.
8. Fadhil, Muthna J., Sadik Kamel Gharghan, and Thamir R. Saeed. "Path-Loss Model for Wireless Sensor Networks in Air Pollution Environments Leveraging of Drones." Arabian Journal for Science and Engineering, (2024):1-17.
9. Benyezza, Hamza, et al. "Smart platform based on IoT and WSN for monitoring and control of a greenhouse in the context of precision agriculture." Internet of Things, 23 (2023):100830.
10. Oladimeji, Damilola, et al. "Smart transportation: an overview of technologies and applications." Sensors 23, 8 (2023):3880.
11. Abro, Ghulam E. Mustafa, et al. "Comprehensive review of recent advancements in battery technology, propulsion, power interfaces, and vehicle network systems for intelligent autonomous and connected electric vehicles." Energies 16, 6, (2023):2925.
12. Al-Ani, Ruqayah, et al. "Privacy and safety improvement of VANET data via a safety-related privacy scheme." International Journal of Information Security 22, 4 (2023):763-783.
13. choudhary, Deepak, and Roop Pahuja. "Awareness routing algorithm in vehicular ad-hoc networks (VANETs)." Journal of Big Data 10.1 (2023): 122.
14. Karabulut, Muhammet Ali, et al. "Inspecting VANET with various critical aspects–a systematic review." Ad Hoc Networks (2023): 103281.
15. Ye, Zhoujing, et al. "IoT-enhanced smart road infrastructure systems for comprehensive real-time monitoring." Internet of Things and Cyber-Physical Systems (2024).
16. Venčkauskas, Algimantas, et al. "Enhancing Communication Security an In-Vehicle Wireless Sensor Network." Electronics 13.6 (2024): 1003.
17. Alharbi, Fares, et al. "Intelligent transportation using wireless sensor networks blockchain and license plate recognition." Sensors 23.5 (2023): 2670.
18. Sachan, Smriti, Rohit Sharma, and Amit Sehgal. "SINR based energy optimization schemes for 5G vehicular sensor networks." Wireless Personal Communications 127.2 (2022): 1023-1043.
19. Khadidos, Adil O et al. "Efficient key distribution for secure and energy-optimized communication in wireless sensor network using bioinspired algorithms." Alexandria Engineering Journal 92 (2024): 63-73.
20. Lilhore, Umesh Kumar, et al. "Secure WSN Architecture Utilizing Hybrid Encryption with DKM to Ensure Consistent IoV Communication", Wireless Personal Communications (2024): 1-29.
21. Finnveden L, Jansson Y, Lindeberg T. Understanding when spatial transformer networks do not support invariance, and what to do about it. 25th IEEE International Conference on Pattern Recognition (ICPR), (2021): 3427-3434.
22. Jia H, Li J, Song W, Peng X, Lang C, Li Y. “Spotted hyena optimization algorithm with simulated annealing for feature selection.” IEEE Access., 7, (2019): 71943-62.
23. Cherappa V et al., “Energy-Efficient Clustering and Routing Using ASFO and a Cross-Layer-Based Expedient Routing Protocol for Wireless Sensor Networks.” Sensors. 23, 5, (2023):2788.
24. Ramani G, K A. “An optimized energy management and load balancing system based on cluster head selection for the vehicular network communication.” Multimedia Tools and Applications. (2024): 1-22.
25. Subasini CA, Karuppiah SP, Sheeba A, Padmakala S. “Developing an attack detection framework for wireless sensor network‐based healthcare applications using hybrid convolutional neural network.” Transactions on Emerging Telecommunications Technologies. (2021), 32(11):e4336.
26. Das R, Dwivedi M. “Cluster head selection and malicious node detection using large-scale energy-aware trust optimization algorithm for HWSN.” Journal of Reliable Intelligent Environments. (2023), 1-7.
27. Khot PS, Naik U. “Particle-water wave optimization for secure routing in wireless sensor network using cluster head selection.” Wireless Personal Communications. 119 (3), (2021):2405-29.
28. Sridhar Panneerselvam et al., “Federated learning-based fire detection method using local MobileNet.” Scientific Reports, 14, 30388, (2024): 1-24.
29. Asir Chandra Shinoo, Robert Vincent, and Sudhakar Sengan. “Edge computing-based ensemble learning model for health care decision systems.” Sci Rep., 14(26997) (2024).
30. Asir Chandra Shinoo, Robert Vincent and Sudhakar Sengan, “Effective clinical decision support implementation using a multi-filter and wrapper optimisation model for Internet of Things based healthcare data.” Sci Rep, 14, 21820 (2024).
31. Gulista Khan et al., “Energy-Efficient Routing Algorithm for Optimizing Network Performance in Underwater Data Transmission Using Gray Wolf Optimization Algorithm.” Journal of Sensors, 2024(2288527), (2024):1-15.
32. Madhubala, P. et al., “Bridging the gap in biomedical information retrieval: Harnessing machine learning for enhanced search results and query semantics.” Journal of Intelligent & Fuzzy Systems, (2024): 1-20, 2024, DOI: 10.3233/JIFS-237056.
33. Sudhakar Sengan et al., “Improved LSTM-Based Anomaly Detection Model with Cybertwin Deep Learning to Detect Cutting-Edge Cybersecurity Attacks.” Human-centric Computing and Information Sciences, 13(55), (2023).
34. Mohammad Khalid Imam Rahmani et al., “Early Pathogen Prediction in Crops Using Nano Biosensors and Neural Network-Based Feature Extraction and Classification,” Big Data Research, 38 (100412), (2023).
35. Sudhakar Sengan et al., “Fake News Detection Using Stance Extracted Multimodal Fusion-Based Hybrid Neural Network.” IEEE Transactions on Computational Social Systems, DOI:10.1109/TCSS.2023.3269087.
36. Arodh Lal Karn et al., “IoT Based Smart Framework Monitoring System for Power Station.” Computers, Materials & Continua, 74(3), (2023): 6019-6037.
37. Eman S. Sabry et al., “Sketch-Based Retrieval Approach Using Artificial Intelligence Algorithms for Deep Vision Feature Extraction.” Axioms, 11 (12), (2022): 663; DOI:10.3390/axioms11120663.
38. Prabu Selvam et al., “A Transformer-Based Framework for Scene Text Recognition.” IEEE Access, 10, (2022): 100895-100910, DOI:10.1109/ACCESS.2022.3207469, 2022.
39. Arodh Lal Karn et al., “ICACIA: An Intelligent Context-Aware framework for COBOT in defense industry using ontological and deep learning models, Robotics and Autonomous Systems.” (2022), 104234, DOI:10.1016/j.robot.2022.104234.
40. Tribhuwan Kumar et al., “Fuzzy Logic and Machine Learning-Enabled Recommendation System to Predict Suitable Academic Program for Students.” Mathematical Problems in Engineering, 2022 (5298468), (2022):1-7, DOI:10.1155/2022/5298468.
41. Surbhi Bhatia et al., “An efficient modular framework for automatic LIONC classification of MedIMG using unified medical language.” Frontiers in Public Health, (2022) DOI:10.3389/fpubh.2022.926229.
42. Mahrukh Mansoor et al., “A machine learning approach for non-invasive fall detection using Kinect, Springer-Multimedia Tools, and Applications.” 2022, DOI:10.1007/s11042-022-12113-w.
43. Thirumoorthy Palanisamy et al., “Improved Energy Based Multi-Sensor Object Detection in Wireless Sensor Networks.” Intelligent Automation & Soft Computing, 33(1), (2022):227-244, DOI:10.32604/iasc.2022.023692.
44. Abolfazl Mehbodniya et al., “Fetal health classification from cardiotocographic data using machine learning.” Expert Systems Wiley, (2021), DOI:10.1111/exsy.12899.
45. Vasanthi Raghupathy et al., “Interactive Middleware Services for Heterogeneous Systems.” Computer Systems Science and Engineering, 41, 3, (2022):1241-1253, DOI:10.32604/csse.2022.021997.
46. Abolfazl Mehbodniya et al., “Proportional Fairness Based Energy Efficient Routing in Wireless Sensor Network.” Computer Systems Science and Engineering, 41(3), (2022): 1071-1082, DOI:10.32604/csse.2022.021529.
47. D. Stalin David et al., “Cloud Security Service for Identifying Unauthorized User Behaviour.” Computers, Materials & Continua, 70, 2, (2022): 2581-2600, DOI:10.32604/cmc.2022.020213.
48. K. Rajakumari et al., “Fuzzy Based Ant Colony Optimization Scheduling in Cloud Computing.” Computer Systems Science and Engineering, 40(2), (2022): 581-592, DOI:32604/csse.2022.019175.
49. R. Nithya et al., “An Optimized Fuzzy Based Ant Colony Algorithm for 5G-MANET.” Computers, Materials & Continua, 70(1), (2022): 1069–1087, DOI:10.32604/cmc.2022.019221.
50. Razia Sulthana Abdul Kareem et al., “Multilabel land cover aerial image classification using convolutional neural networks.” Springer Arabian Journal of Geosciences, 14(2021) DOI:10.1007/s12517-021-07791-z.
51. Keerthana Nandakumar et al., “Securing data in transit using data-in-transit defender architecture for cloud communication.” Soft Computing, (2021), DOI:10.1007/s00500-021-05928-6.
52. Sudhakar Sengan et al., “Cost-effective and efficient 3D human model creation and re-identification application for human digital twins.” Multimedia Tools and Applications, (2021). DOI:10.1007/s11042-021-10842-y.
53. Roy Setiawan et al., “Encrypted Network Traffic Classification and Resource Allocation with Deep Learning in Software Defined Network.” Wireless Personal Communication, (2021), DOI;10.1007/s11277-021-08403-5.
54. Omnia Saidani Neffati et al.,” Migrating from traditional grid to smart grid in smart cities promoted in developing country.” Sustainable Energy Technologies and Assessments, 45 (2021), DOI:10.1016/j.seta.2021.101125.
55. Prabhakaran Narayanan et al., “Novel Collision Detection and Avoidance System for Mid-vehicle Using Offset-Based Curvilinear Motion.” Wireless Personal Communication, (2021). DOI:10.1007/s11277-021-08333-2.
56. Balajee Alphonse et al., “Modeling and multi-class classification of vibroarthographic signals via time domain curvilinear divergence random forest.” J Ambient Intell Human Comput, (2021), DOI:10.1007/s12652-020-02869-0.
57. Ganesh Kumar, K, and Sudhakar Sengan, “Improved Network Traffic by Attacking Denial of Service to Protect Resource Using Z-Test Based 4-Tier Geomark Traceback (Z4TGT).” Wireless Personal Communications, (2020), DOI:10.1007/s11277-020-07546-1.
58. Sudhakar Sengan et al., “Enhancing cyber-physical systems with hybrid smart city cyber security architecture for secure public data-smart network.” Future Generation Computer Systems, (2020), DOI:10.1016/j.future.2020.06.028.
59. Sudhakar Sengan and Chenthur Pandian S, “Hybrid Cluster-based Geographical Routing Protocol to Mitigate Malicious Nodes in Mobile Ad Hoc Network.” International Journal of Ad Hoc and Ubiquitous Computing, 21,4, (2016): 224-236. DOI:10.1504/IJAHUC.2016.076358.
60. T. Gopalakrishnan et al., “Leveraging blockchain technology to combat deception, deepfake, and counterfeit system.” Journal of Discrete Mathematical Sciences and Cryptography, 27:7, (2024), 2143–2154, DOI: 10.47974/JDMSC-2087
61. Madan Mohan Tito Ayyalasomayajula et al., “Enhanced network security through algebraic cryptanalysis of elliptic curve cryptography.” Journal of Discrete Mathematical Sciences and Cryptography, 27, 7, (2024): 2219–2230, DOI: 10.47974/JDMSC-2093.
62. Milton Lopez-Cueva et al., “A Multi-Moving Target Localization in Agricultural Farmlands by Employing Optimized Cooperative Unmanned Aerial Vehicle Swarm.” Scalable Computing: Practice and Experience, 25, 6, (2024): 4647–4660, DOI 10.12694/scpe.v25i6.3130.
63. Firas Tayseer Ayasrah et al., “Strategizing Low-Carbon Urban Planning through Environmental Impact Assessment by Artificial Intelligence-Driven Carbon Foot-Print Forecasting.” Journal of Machine and Computing, 4, 4, (2024), doi: 10.53759/7669/jmc202404105.
64. Roberto E. Roque-Claros et al., “UAV Path Planning Model Leveraging Machine Learning and Swarm Intelligence for Smart Agriculture.” Scalable Computing: Practice and Experience, 25, 5, (2024): 3752–3765, DOI 10.12694/scpe.v25i5.3131.
65. Hayder M. A. Ghanimi et al., “Smart Fertilizing Using IOT Multi-Sensor and Variable Rate Sprayer Integrated UAV.” Scalable Computing: Practice and Experience, 25, 5, (2024): 3766–3777, DOI 10.12694/scpe.v25i5.3132.
66. Bernabe Canqui-Flores et al., “Echocardiographic cardiac views classification using whale optimization and weighted support vector machine.” Vessel Plus. 8,29, (2024), http://dx.doi.org/10.20517/2574-1209.2023.140.
67. Shaymaa Hussein Nowfal et al., “Genetic Algorithms for Optimized Selection of Biodegradable Polymers in Sustainable Manufacturing Processes.” Journal of Machine and Computing, 4, 3, (2024): 563-574, https://doi.org/10.53759/7669/jmc202404054.
68. V. Jeevika Tharini et al., “Business Decision-Making Using Hybrid LSTM for Enhanced Operational Efficiency.” In: Vimal, V., Perikos, I., Mukherjee, A., Piuri, V. (eds) Multi-Strategy Learning Environment. ICMSLE Algorithms for Intelligent Systems. Springer, Singapore, (2024) https://doi.org/10.1007/978-981-97-1488-9_12.
69. A. Jermanshiyamala et al., “ACO-Optimized DRL Model for Energy-Efficient Resource Allocation in High-Performance Computing. In: Vimal, V., Perikos, I., Mukherjee, A., Piuri, V. (eds) Multi-Strategy Learning Environment.” ICMSLE Algorithms for Intelligent Systems. Springer, Singapore. (2024), https://doi.org/10.1007/978-981-97-1488-9_11.
70. Hayder M. A. Ghanimi et al., ”An open-source MP + CNN + BiLSTM model-based hybrid model for recognizing sign language on smartphones.” Int. J. Syst. Assur. Eng. Manag. (2024). https://doi.org/10.1007/s13198-024-02376-x.
71. Ghanimi, Hayder M. A., et al., “Chebyshev polynomial approximation in CNN for zero-knowledge encrypted data analysis.” Journal of Discrete Mathematical Sciences and Cryptography, 27:2-A, (2024): 203–214, DOI: 10.47974/JDMSC-1880, 2024.
72. Ghanimi, Hayder M. A., et al., “Merkle-Damg hash functions and blockchains: Securing electronic health records.” Journal of Discrete Mathematical Sciences and Cryptography, 27, 2-A, (2024): 237–248, DOI: 10.47974/JDMSC-1878, 2024.
73. Sagar, P. Vidya, et al., “Secure multi-party computation in deep learning: Enhancing privacy in distributed neural networks.” Journal of Discrete Mathematical Sciences and Cryptography, 27, 2-A, (2024):249–259, DOI: 10.47974/JDMSC-1879, 2024.
74. Rao, Ganga Rama Koteswara, et al., “Enhanced security in federated learning by integrating homomorphic encryption for privacy-protected, collaborative model training.” Journal of Discrete Mathematical Sciences and Cryptography, 27, 2-A, (2024), 361–370, DOI: 10.47974/JDMSC-1891, 2024.
75. Krishna, Raguru Jaya, et al., “Security and privacy concerns in social networks mathematically modified metaheuristic-based approach.” Journal of Discrete Mathematical Sciences and Cryptography, 27,2-A, (2024): 371–382, DOI: 10.47974/JDMSC-1892.
76. Swapna Siddamsetti et al., “Modular metric spaces: Some fixed-point theorems and application of secure dynamic routing for WSN.” Journal of Interdisciplinary Mathematics, 27, 2, (2024): 393–401, DOI: 10.47974/JIM-1890
77. Shaymaa Hussein Nowfal et al., “Advancing viscoelastic material modeling: Tackling time-dependent behavior with fractional calculus.” Journal of Interdisciplinary Mathematics, 27, 2, (2024): 307–316, DOI: 10.47974/JIM-1827.
78. P. Vidya Sagar et al., “Utilizing stochastic differential equations and random forest for precision forecasting in stock market dynamics.” Journal of Interdisciplinary Mathematics, 27, 2, (2024): 285–298, DOI: 10.47974/JIM-1822.
79. Ghayth Almahadin et al., “Enhancing Video Anomaly Detection Using Spatio-Temporal Autoencoders and Convolutional LSTM Networks.” SN COMPUT. SCI. 5,190, (2024). https://doi.org/10.1007/s42979-023-02542-1.
80. Senthil Kumar Nramban Kannan et al., “Analysis of COVID-19 Datasets Using Statistical Modelling and Machine Learning Techniques to Predict the Disease.” SN COMPUT. SCI. 5,181, (2024). https://doi.org/10.1007/s42979-023-02464-y.
81. Nayer Tumi-Figueroa E, et al., “Adaptive Approach To Anomaly Detection In Internet of Things Using Autoencoders And Dynamic Thresholds.” Journal of Machine and Computing, doi: 10.53759/7669/jmc202404001.
82. Rasheed Abdulkader et al., “Optimizing student engagement in edge-based online learning with advanced analytics.” ARRAY, (2023), doi: https://doi.org/10.1016/j.array.2023.100301.
83. Arokia Jesu Prabhu Lazar et al., “Gaussian Differential Privacy Integrated Machine Learning Model for Industrial Internet of Things.” SN Computer Science, 4, 454, (2023), https://doi.org/10.1007/s42979-023-01820-2.
84. Kalaivani Pachiappan et al., “Quality of Smart Health Service for Enhancing the Performance of Machine Learning-Based Secured Routing on MANET.” EAI/Springer Innovations in Communication and Computing. Springer, Cham. (2023), DOI:10.1007/978-3-031-23602-0_17.
85. Maheswari Subburaj et al., “Artificial Intelligence for Smart in Match Winning Prediction in Twenty20 Cricket League Using Machine Learning Model.” EAI/Springer Innovations in Communication and Computing. Springer, Cham. (2023), DOI:10.1007/978-3-031-23602-0_3.
86. Joel Sunny Deol Gosu et al., “Comparative Analysis of Handwritten Digit Recognition Investigation Using Deep Learning Model.” EAI/Springer Innovations in Communication and Computing. Springer, Cham, (2023), DOI:10.1007/978-3-031-23602-0_3.
87. M. Anto Bennet et al., “Classification and Localization of COVID-19 based on a Pneumonia Radiograph using a Deep Learning Approach.” In Proceedings of the 4th International Conference on Information Management & Machine Intelligence (ICIMMI '22). Association for Computing Machinery, New York, NY, USA, 20, (2023):1-6, DOI:10.1145/3590837.3590857.
88. Abolfazl Mehbodniya et al., “Classification of Cervical Cells Using Deep Learning Feature Extraction.” Innovations in Computer Science and Engineering. ICICSE 2022. Lecture Notes in Networks and Systems, Springer, Singapore, 565, (2022): 27–41, DOI:10.1007/978-981-19-7455-7_3.
89. Julian L. Webber et al., “Hybrid Power Generation Forecasting Using an Intellectual Evolutionary Energy-Preserve Rate Clustering Technique.” Innovations in Computer Science and Engineering. ICICSE 2022. Lecture Notes in Networks and Systems, Springer, Singapore, 565, (2022): 27–41, DOI:10.1007/978-981-19-7455-7_10.
90. Arodh Lal Karn et al., “Evaluation and Language Training of Multinational Enterprises Employees by Deep Learning in Cloud Manufacturing Resources, Innovations in Computer Science and Engineering.” ICICSE 2022. Lecture Notes in Networks and Systems, Springer, Singapore, 565, (2022): 369–380, DOI:10.1007/978-981-19-7455-7_28.
91. Arodh Lal Karn et al., “Design of Concurrent Engineering Systems for Global Product Development Using Artificial Intelligence, Innovations in Computer Science and Engineering.” ICICSE 2022. Lecture Notes in Networks and Systems, Springer, Singapore, 565, (2022): 425–434, DOI:10.1007/978-981-19-7455-7_32.
92. Abolfazl Mehbodniya et al., “Certain Investigations of MEMS for Optimised Sensor Coverage, Innovations in Computer Science and Engineering.” ICICSE 2022. Lecture Notes in Networks and Systems, Springer, Singapore, 565, (2022):479–489. DOI:10.1007/978-981-19-7455-7_35.
93. Abolfazl Mehbodniya et al., “Medical Images Analysis for Segmentation and Classification Using DNN.” Innovations in Computer Science and Engineering. ICICSE 2022. Lecture Notes in Networks and Systems, Springer, Singapore, 565, (2022):525–534. DOI:10.1007/978-981-19-7455-7_39.
94. Gladson Maria Britto James et al., “Deep Convolutional Neural Networks-Based Market Strategy for Early-Stage Product Development.” Innovations in Computer Science and Engineering. ICICSE 2022. Lecture Notes in Networks and Systems, Springer, Singapore, 565, (2022): 597–606, DOI:10.1007/978-981-19-7455-7_46.
95. Julian L. Webber et al., “Glioma Segmentation in MR Images Using 2D Double U-Net: An Empirical Investigation.” Innovations in Computer Science and Engineering. ICICSE 2022. Lecture Notes in Networks and Systems, Springer, Singapore, 565, (2022), 645–654. DOI:10.1007/978-981-19-7455-7_50.
96. K. Bhavana Raj et al., “Equipment Planning for an Automated Production Line Using a Cloud System.” Innovations in Computer Science and Engineering. ICICSE 2022. Lecture Notes in Networks and Systems, Springer, Singapore. 565, (2022): 707–717, DOI:10.1007/978-981-19-7455-7_57.
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