Innovative Approaches to Reducing Data Traffic in IoT Networks Using Deep Learning and Compressive Sensing

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Year : 2024 | Volume :02 | Issue : 02 | Page : 45-61
By
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Balajee Sharma,

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Hemant Rajoriya,

  1. Assistant Professor, RKDF University, Bhopal, Madhya Pradesh, India
  2. Assistant Professor, RKDF University, Bhopal, Madhya Pradesh, India

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The exponential growth of Internet of Things (IoT) devices has posed unprecedented challenges in managing the massive data generated by real-time monitoring, automation, and analytics. Existing network infrastructures lack scalability, bandwidth, and suffer from latency problems, further making data transmission less efficient. This study surveys innovative approaches using deep learning and compressive sensing to reduce IoT data traffic. Deep learning is able to upgrade data processing by means of very effective pattern extraction and predictive modeling, while compressive sensing reduces redundancy using sparsity in data. All the three together are scalable, energy-efficient, and latency-aware solutions for the IoT ecosystems. These techniques together are studied across applications such as smart cities, healthcare, and industrial automation, underlining their transformative potential. Difficulties like resource constraints, heterogeneity, and security risks are also presented and future research directions along with technological progress are included.

Keywords: IoT, Data Traffic Reduction, Deep Learning, Compressive Sensing, Smart Cities, Edge Computing, Data Compression, Signal Reconstruction, Machine Learning, Energy Efficiency

[This article belongs to International Journal of Satellite Remote Sensing (ijsrs)]

How to cite this article:
Balajee Sharma, Hemant Rajoriya. Innovative Approaches to Reducing Data Traffic in IoT Networks Using Deep Learning and Compressive Sensing. International Journal of Satellite Remote Sensing. 2024; 02(02):45-61.
How to cite this URL:
Balajee Sharma, Hemant Rajoriya. Innovative Approaches to Reducing Data Traffic in IoT Networks Using Deep Learning and Compressive Sensing. International Journal of Satellite Remote Sensing. 2024; 02(02):45-61. Available from: https://journals.stmjournals.com/ijsrs/article=2024/view=0

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  1. Ouallane, A. A., Bahnasse, A., Bakali, A., & Talea, M. (2022). Overview of road traffic management solutions based on IoT and AI. Procedia Computer Science, 198, 518-523. 10.1016/j.procs.2021.12.279
  2. Al Neyadi, S. Al Shehhi, A. Al Shehhi, N. Al Hashimi, Q. Mohammad, S. Alrabaee, Discovering public wi-fi vulnerabilities using raspberry pi and kali linux, in2020: 12th Annual Undergraduate Research Conference on Applied Computing (URC), IEEE, 2020, pp. 1–4.
  3. Kuzovkova TA, Salutina TY, Sharavova OI. Integral assessment of the state and development potential of info-communication infrastructure for ensuring sustainable digital development. InCEUR Workshop Proceedings. AISI 2021-Proceedings of the International Workshop on Advances in Information Systems, Mathematical Modeling, and IT Applications in Industry 2021 (pp. 76-85).
  4. Azab, A., Khasawneh, M., Alrabaee, S., Choo, K. K. R., & Sarsour, M. (2024). Network traffic classification: Techniques, datasets, and challenges. Digital Communications and Networks, 10(3), 676-692. https://doi.org/10.1016/j.dcan.2022.09.009
  5. Aboubakar, M., Kellil, M., & Roux, P. (2022). A review of IoT network management: Current status and perspectives. Journal of King Saud University-Computer and Information Sciences, 34(7), 4163-4176. https://doi.org/10.1016/j.jksuci.2021.03.006
  6. Shafiq, M., Gu, Z., Cheikhrouhou, O., Alhakami, W., & Hamam, H. (2022). The Rise of “Internet of Things”: Review and Open Research Issues Related to Detection and Prevention of IoT‐Based Security Attacks. Wireless Communications and Mobile Computing, 2022(1), 8669348. https://doi.org/10.1155/2022/8669348
  7. Machidon, A. L., & Pejović, V. (2023). Deep learning for compressive sensing: a ubiquitous systems perspective. Artificial Intelligence Review, 56(4), 3619-3658. https://doi.org/10.1007/s10462-022-10259-5
  8. Noura, H. N., Azar, J., Salman, O., Couturier, R., & Mazouzi, K. (2023). A deep learning scheme for efficient multimedia IoT data compression. Ad Hoc Networks, 138, 102998.
  9. Machidon, A. L., & Pejovic, V. (2021). Deep Learning Techniques for Compressive Sensing-Based Reconstruction and Inference–A Ubiquitous Systems Perspective. arXiv preprint arXiv:2105.13191.
  10. Zhang, M., Zhang, H., Fang, Y., & Yuan, D. (2022). Learning-based data transmissions for future 6G enabled industrial IoT: A data compression perspective. IEEE Network, 36(5), 180-187. https://doi.org/10.1109/MNET.109.2100384
  11. Nasif, A.; Othman, Z.A.; Sani, N.S. The Deep Learning Solutions on Lossless Compression Methods for Alleviating Data Load on IoT Nodes in Smart Cities. Sensors 2021, 21, 4223. https://doi.org/ 10.3390/s21124223
  12. Kumar, S.S.; Ramachandran, P. Review on Compressive Sensing Algorithms for ECG Signal for IoT Based Deep Learning Framework. Appl. Sci. 2022, 12, 8368. https://doi.org/10.3390/ app12168368
  13. Sadeeq, M. M., Abdulkareem, N. M., Zeebaree, S. R., Ahmed, D. M., Sami, A. S., & Zebari, R. R. (2021). IoT and Cloud computing issues, challenges and opportunities: A review. Qubahan Academic Journal, 1(2), 1-7. https://doi.org/10.48161/qaj.v1n2a36
  14. Zikria, Y.B.; Ali, R.; Afzal, M.K.; Kim, S.W. Next-Generation Internet of Things (IoT): Opportunities, Challenges, and Solutions. Sensors 2021, 21, 1174. https://doi.org/10.3390/s21041174
  15. Krishna, R.R.; Priyadarshini, A.; Jha, A.V.; Appasani, B.; Srinivasulu, A.; Bizon, N. State-of-the-Art Review on IoT Threats and Attacks: Taxonomy, Challenges and Solutions. Sustainability 2021, 13, 9463. https:// doi.org/10.3390/su13169463
  16. Zafar, A., Samad, F., Syed, H. J., Ibrahim, A. O., Alohaly, M., & Elsadig, M. (2023). An Advanced Strategy for Addressing Heterogeneity in SDN-IoT Networks for Ensuring QoS. Applied Sciences, 13(13), 7856. https://doi.org/10.3390/app13137856
  17. Shah, Z., Levula, A., Khurshid, K., Ahmed, J., Ullah, I., & Singh, S. (2021). Routing protocols for mobile Internet of things (IoT): A survey on challenges and solutions. Electronics, 10(19), 2320. https://doi.org/10.3390/electronics10192320
  18. Rao, P. M., & Deebak, B. D. (2023). Security and privacy issues in smart cities/industries: technologies, applications, and challenges. Journal of Ambient Intelligence and Humanized Computing, 14(8), 10517-10553. https://doi.org/10.1007/s12652-022-03707-1
  19. Liyakat, K.K.S. (2024). Machine Learning Approach Using Artificial Neural Networks to Detect Malicious Nodes in IoT Networks. In: Udgata, S.K., Sethi, S., Gao, XZ. (eds) Intelligent Systems. ICMIB 2023. Lecture Notes in Networks and Systems, vol 728. Springer, Singapore. https://doi.org/10.1007/978-981-99-3932-9_12 available at: https://link.springer.com/chapter/10.1007/978-981-99-3932-9_12
  20. Nasif A, Othman ZA, Sani NS. The deep learning solutions on lossless compression methods for alleviating data load on IoT nodes in smart cities. Sensors. 2021 Jun 20;21(12):4223.
  21. K. S. Liyakat. (2023).Detecting Malicious Nodes in IoT Networks Using Machine Learning and Artificial Neural Networks, 2023 International Conference on Emerging Smart Computing and Informatics (ESCI), Pune, India, 2023, pp. 1-5, doi: 10.1109/ESCI56872.2023.10099544.
  22. Kasat, N. Shaikh, V. K. Rayabharapu, M. Nayak. (2023). Implementation and Recognition of Waste Management System with Mobility Solution in Smart Cities using Internet of Things, 2023 Second International Conference on Augmented Intelligence and Sustainable Systems (ICAISS), Trichy, India, 2023, pp. 1661-1665, doi: 10.1109/ICAISS58487.2023.10250690
  23. Moon A, Kim J, Zhang J, Son SW. Evaluating fidelity of lossy compression on spatiotemporal data from an IoT enabled smart farm. Computers and electronics in agriculture. 2018 Nov 1;154:304-13.
  24. Al-Qurabat AK, Mohammed ZA, Hussein ZJ. Data traffic management based on compression and MDL techniques for smart agriculture in IoT. Wireless Personal Communications. 2021 Oct;120(3):2227-58.
  25. Kazi, K. (2024b). Modelling and Simulation of Electric Vehicle for Performance Analysis: BEV and HEV Electrical Vehicle Implementation Using Simulink for E-Mobility Ecosystems. In L. D., N. Nagpal, N. Kassarwani, V. Varthanan G., & P. Siano (Eds.), E-Mobility in Electrical Energy Systems for Sustainability (pp. 295-320). IGI Global. https://doi.org/10.4018/979-8-3693-2611-4.ch014 Available at: https://www.igi-global.com/gateway/chapter/full-text-pdf/341172
  26. Kazi, K. S. (2024a). Computer-Aided Diagnosis in Ophthalmology: A Technical Review of Deep Learning Applications. In M. Garcia & R. de Almeida (Eds.), Transformative Approaches to Patient Literacy and Healthcare Innovation (pp. 112-135). IGI Global. https://doi.org/10.4018/979-8-3693-3661-8.ch006 Available at: https://www.igi-global.com/chapter/computer-aided-diagnosis-in-ophthalmology/342823
  27. Prashant K Magadum (2024). Machine Learning for Predicting Wind Turbine Output Power in Wind Energy Conversion Systems, 15th International Conference on Advances in Computing, Control, ad Telecommunication Technologies, ACT 2024, 2024, 1, pp. 2074-2080. Grenze ID: 01.GIJET.10.1.4_1
  28. Neeraja, R. G. Kumar, M. S. Kumar, K. K. S. Liyakat and M. S. Vani. (2024). DL-Based Somnolence Detection for Improved Driver Safety and Alertness Monitoring. 2024 IEEE International Conference on Computing, Power, and Communication Technologies, IC2PCT 2024, Greater Noida, India, 2024, pp. 589-594, doi: 10.1109/IC2PCT60090.2024.10486714. Available at: https://ieeexplore.ieee.org/document/10486714
  29. Das L, Garg D, Srinivasan B. NeuralCompression: A machine learning approach to compress high frequency measurements in smart grid. Applied Energy. 2020 Jan 1;257:113966.
  30. Moon A, Kim J, Zhang J, Liu H, Son SW. Understanding the impact of lossy compressions on IoT smart farm analytics. In2017 IEEE International Conference on Big Data (Big Data) 2017 Dec 11 (pp. 4602-4611). IEEE.
  31. Veena, M. Sridevi, K. K. S. Liyakat, B. Saha, S. R. Reddy and N. Shirisha,(2023). HEECCNB: An Efficient IoT-Cloud Architecture for Secure Patient Data Transmission and Accurate Disease Prediction in Healthcare Systems, 2023 Seventh International Conference on Image Information Processing (ICIIP), Solan, India, 2023, pp. 407-410, doi: 10.1109/ICIIP61524.2023.10537627. Available at: https://ieeexplore.ieee.org/document/10537627
  32. Rajendra Prasad, Santoshachandra Rao Karanam (2024). AI in public-private partnership for IT infrastructure development, Journal of High Technology Management Research, Volume 35, Issue 1, May 2024, 100496. https://doi.org/10.1016/j.hitech.2024.100496
  33. Putra KT, Chen HC, Prayitno, Ogiela MR, Chou CL, Weng CE, Shae ZY. Federated compressed learning edge computing framework with ensuring data privacy for PM2. 5 prediction in smart city sensing applications. Sensors. 2021 Jul 4;21(13):4586.
  34. Kutubuddin Kazi, (2024a). Machine Learning (ML)-Based Braille Lippi Characters and Numbers Detection and Announcement System for Blind Children in Learning, In Gamze Sart (Eds.), Social Reflections of Human-Computer Interaction in Education, Management, and Economics, IGI Global. https://doi.org/10.4018/979-8-3693-3033-3.ch002
  35. Kazi, K. S. (2024). Artificial Intelligence (AI)-Driven IoT (AIIoT)-Based Agriculture Automation. In S. Satapathy & K. Muduli (Eds.), Advanced Computational Methods for Agri-Business Sustainability (pp. 72-94). IGI Global. https://doi.org/10.4018/979-8-3693-3583-3.ch005
  36. Konnur, R. G. (2024). Vehicle Health Monitoring System (VHMS) by Employing IoT and Sensors, 15th International Conference on Advances in Computing, Control, ad Telecommunication Technologies, ACT 2024, 2024,2, pp.- 5367-5374.

37.   Liyakat, K.K.S, (2024m).A Novel Approach on ML based Palmistry, 15th International Conference on Advances in Computing, Control, ad Telecommunication Technologies,  ACT 2024, 2024, 2, pp.- 5186-5193.

38.   Liyakat, K.K.S, (2024e). IoT based Boiler Health Monitoring for Sugar Industries, 15th International Conference on Advances in Computing, Control, ad Telecommunication Technologies, ACT 2024, 2024, 2, pp. 5178 -5185.

39.   Liyakat, K.K.S., (2024). Explainable AI in healthcare, Explainable Artificial Intelligence in Healthcare Systems, 2024, pp. 271–284.

  1. Kazi, K. S. (2024). Machine Learning-Based Pomegranate Disease Detection and Treatment. In M. Zia Ul Haq & I. Ali (Eds.), Revolutionizing Pest Management for Sustainable Agriculture (pp. 469-498). IGI Global. https://doi.org/10.4018/979-8-3693-3061-6.ch019
  2. Ananthi J, Sengottaiyan N, Anbukaruppusamy S, Upreti K, Dubey AK. Forest fire prediction using IoT and deep learning. International Journal of Advanced Technology and Engineering Exploration. 2022 Feb 1;9(87):246-56.
  3. Lin C, Han G, Qi X, Du J, Xu T, Martínez-García M. Energy-optimal data collection for unmanned aerial vehicle-aided industrial wireless sensor network-based agricultural monitoring system: A clustering compressed sampling approach. IEEE Transactions on Industrial Informatics. 2020 Sep 30;17(6):4411-20.
  4. Vedavalli P, Ch D. A deep learning based data recovery approach for missing and erroneous data of iot nodes. Sensors. 2022 Dec 24;23(1):170.
  5. Liyakat, K. K. (2025). Heart Health Monitoring Using IoT and Machine Learning Methods. In A. Shaik (Ed.), AI-Powered Advances in Pharmacology (pp. 257-282). IGI Global. https://doi.org/10.4018/979-8-3693-3212-2.ch010
  6. Kazi, K. S. (2025f). AI-Powered-IoT (AIIoT)-Based Decision-Making System for BP Patient’s Healthcare Monitoring: KSK Approach for BP Patient Healthcare Monitoring. In S. Aouadni & I. Aouadni (Eds.), Recent Theories and Applications for Multi-Criteria Decision-Making (pp. 205-238). IGI Global. https://doi.org/10.4018/979-8-3693-6502-1.ch008
  7. Kazi, K. S. (2025c). AI-Driven-IoT (AIIoT)-Based Decision Making in Drones for Climate Change: KSK Approach. In S. Aouadni & I. Aouadni (Eds.), Recent Theories and Applications for Multi-Criteria Decision-Making (pp. 311-340). IGI Global. https://doi.org/10.4018/979-8-3693-6502-1.ch011
  8. Haupt J, Bajwa WU, Rabbat M, Nowak R. Compressed sensing for networked data. IEEE Signal Processing Magazine. 2008 Mar 21;25(2):92-101.
  9. Kazi K(2025e). Machine Learning-Driven-Internet of Medical Things (ML-IoMT) based Healthcare Monitoring System. In Ben Othman Soufiene, Chinmay Chakraborty, (Eds), Responsible AI for Digital Health and Medical Analytics, IGI Global.
  10. Ng PC, She J, Ran R. A compressive sensing approach to detect the proximity between smartphones and BLE beacons. IEEE Internet of Things Journal. 2019 May 3;6(4):7162-74.
  11. Sarma S. Essence: Machine Learning Approaches to Scalable and Energy Efficient Sense-making for Internet-of-Things (IoT). University of California, Irvine; 2015.
  12. Zheng H, Guo W, Xiong N. A kernel-based compressive sensing approach for mobile data gathering in wireless sensor network systems. IEEE Transactions on Systems, Man, and Cybernetics: Systems. 2017 Aug 21;48(12):2315-27.
  13. Kazi K(2025i). Red Deer Algorithm based Polycystic Ovarian Syndrome by using Random Forest Classifier, In Altaf Mulani, Korhan Cengiz, Suman Tripathi, (Eds), AI, Machine Learning, and IoT for Communication and Medical Applications, IGI Global.
  14. KKS Liyakat (2024f). Malicious node detection in IoT networks using artificial neural networks, Intelligent Networks: Techniques, and Applications, 2024, pp.182- 197, CRC Press.
  15. Pandey C, Angryk RA, Aydin B. Solar flare forecasting with deep neural networks using compressed full-disk HMI magnetograms. In2021 IEEE International Conference on Big Data (Big Data) 2021 Dec 15 (pp. 1725-1730). IEEE.
  16. Liyakat K K S,(2025h). Advancing Towards Sustainable Energy with Hydrogen Solutions: Adaptation and Challenges, In Fahri Özsungur, Mortaza Chaychi Semsari, Hülya Küçük Bayraktar, (Eds), Geopolitical Landscapes of Renewable Energy and Urban Growth, IGI Global.
  17. Liyakat K K S,(2025i). Machine Learning for Brand Protection: A Review of a Proactive Defense Mechanism, In Muhammad Khan, Mirza Amin Ul Haq, (Eds), Avoiding Ad Fraud and Supporting Brand Safety: Programmatic Advertising Solutions, IGI Global
  18. Liyakat K K S,(2025j). Braille-Lippi Numbers and Characters Detection and Announcement System for Blind Children Using KSK Approach: AI-Driven-Decision Making, In Sivaram Ponnusamy, Antari Antari, Gwanggil Jeon, Mansour Assaf, Bhisham Sharma, (Eds), Revolutionizing Education with Remote Experimentation and Learning Analytics, IGI Global.
  19. Liyakat K K S,(2025k). AI-Driven-IoT (AIIoT) Based Jawar Leaf Disease Detection: KSK Approach for Jawar Disease Detection, In Uzair Bhatti, Muhammad Aamir, Yonis Gulzar, Sibghat Ullah Bazai, (Eds), Modern Intelligent Techniques for Image Processing, IGI Global.
  20. Liyakat K K S,(2025l). AI-Powered IoT (AI IoT) for Decision-Making in Smart Agriculture: KSK Approach for Smart Agriculture, In, Shalin Hai-Jew, (Eds), Enhancing Automated Decision-Making Through AI, IGI Global.
  21. Razzaque MA, Dobson S. Energy-efficient sensing in wireless sensor networks using compressed sensing. Sensors. 2014 Feb 12;14(2):2822-59.
  22. Liyakat K K S,(2025q). Machine Learning-Powered-IoT (MLIoT) for Retail Apparel Industry, In Theodore Tarnanidis, Evridiki Papachristou, Michail Karypidis, Vijaya Kittu Manda, (Eds), Sustainable Practices in the Fashion and Retail Industry, IGI Global.
  23. Liyakat K K S, (2025r), KK Approach to Increase Resilience in Internet of Things: A T-Cell Security Concept, In Dina Darwish, Kali Charan, (Eds), Analyzing Privacy and Security Difficulties in Social Media: New Challenges and Solutions, IGI Global.
  24. Halli U M, “Nanotechnology in IoT Security”, Journal of Nanoscience, Nanoengineering & Applications, 2022, Vol 12, issue 3, pp. 11 – 16.
  25. Wale Anjali D., Rokade Dipali, et al, “Smart Agriculture System using IoT”, International Journal of Innovative Research In Technology, 2019, Vol 5, Issue 10, pp.493 – 497.
  26. M Sunil Kumar, D Ganesh, Anil V Turukmane, Umamaheswararao Batta, ,”Deep Convolution Neural Network based solution for detecting plant Diseases”, Journal of Pharmaceutical Negative Results, 2022, Vol 13, Special Issue- I, pp. 464-471.
  27. Alsubai, S., Dutta, A. K., & Sait, A. R. W. (2024). Hybrid deep learning-based traffic congestion control in IoT environment using enhanced arithmetic optimization technique. Alexandria Engineering Journal, 105, 331-340. https://doi.org/10.1016/j.aej.2024.06.072
  28. Lydia, E.L.; Jovith, A.A.; Devaraj, A.F.S.; Seo, C.; Joshi, G.P. Green Energy Efficient Routing with Deep Learning Based Anomaly Detection for Internet of Things (IoT) Communications. Mathematics 2021, 9, 500. https://doi.org/10.3390/ math9050500
  29. Ferrag, M.A.; Shu, L.; Djallel, H.; Choo, K.-K.R. Deep Learning-Based Intrusion Detection for Distributed Denial of Service Attack in Agriculture 4.0. Electronics 2021, 10, 1257. https://doi.org/ 10.3390/electronics10111257
  30. Sarhan, M., Layeghy, S., Moustafa, N., Gallagher, M., & Portmann, M. (2022). Feature extraction for machine learning-based intrusion detection in IoT networks. Digital Communications and Networks. https://doi.org/10.1016/j.dcan.2022.08.012
  31. Muthanna, M. S. A., Muthanna, A., Rafiq, A., Hammoudeh, M., Alkanhel, R., Lynch, S., & Abd El-Latif, A. A. (2022). Deep reinforcement learning based transmission policy enforcement and multi-hop routing in QoS aware LoRa IoT networks. Computer Communications, 183, 33-50. https://doi.org/10.1016/j.comcom.2021.11.010
  32. Damadam, S.; Zourbakhsh, M.; Javidan, R.; Faroughi, A. An Intelligent IoT Based Traffic Light Management System: Deep Reinforcement Learning. Smart Cities 2022, 5, 1293–1311. https://doi.org/ 10.3390/smartcities5040066
  33. Selva, E. (2021). Digitization, compression and reconstruction of a large-scale radio traffic for the Internet of Things (Doctoral dissertation, CentraleSupélec).
  34. Kuldeep, G., & Zhang, Q. (2021). A novel efficient secure and error-robust scheme for Internet of Things using compressive sensing. IEEE Access, 9, 40903-40914. Digital Object Identifier 10.1109/ACCESS.2021.3064700
  35. Salim A, Ismail A, Osamy W, M. Khedr A (2021) Compressive sensing based secure data aggregation scheme for IoT based WSN applications. PLoS ONE 16(12): e0260634. https:// doi.org/10.1371/journal.pone.0260634
  36. Joseph Azar, Gaby Bou Tayeh, Abdallah Makhoul, Raphael Couturier. Efficient Lossy Compression for IoT Using SZ and Reconstruction with 1D U-Net. Mobile Networks and Applications, 2022, 27 (3), pp.984 – 996. ff10.1007/s11036-022-01918-6
  37. Vivekanand, V. (2023). A Theoretical Framework for Sparse Recovery Algorithm Design and Evaluation in Compressed Sensing Perspective (Doctoral dissertation, Indian Institute of Space Science and Technology).
  38. Zhou, J.; Yang, J. Compressive Sensing in Image/Video Compression: Sampling, Coding, Reconstruction, and Codec Optimization. Information 2024, 15, 75. https://doi.org/10.3390/info15020075
  39. de Oliveira, M. A., da Rocha, A. M., Puntel, F. E., & Cavalheiro, G. G. H. (2023). Time Series Compression for IoT: A Systematic Literature Review. Wireless Communications and Mobile Computing, 2023(1), 5025255. https://doi.org/10.1155/2023/5025255
  40. Kahdim, A. N., & Manaa, M. E. (2022). Design an efficient internet of things data compression for healthcare applications. Bulletin of Electrical Engineering and Informatics, 11(3), 1678-1686. DOI: 10.11591/eei.v11i3.3758
  41. Dantas, P. V., Sabino da Silva Jr, W., Cordeiro, L. C., & Carvalho, C. B. (2024). A comprehensive review of model compression techniques in machine learning. Applied Intelligence, 1-41. https://doi.org/10.1007/s10489-024-05747-w
  42. Xie, Z., Liu, L., & Chen, Z. (2024). Image compressed sensing: From deep learning to adaptive learning. Knowledge-Based Systems, 293, 111659. https://doi.org/10.1016/j.knosys.2024.111659
  43. Janu, D., Singh, K., & Kumar, S. (2022). Machine learning for cooperative spectrum sensing and sharing: A survey. Transactions on Emerging Telecommunications Technologies, 33(1), e4352. https://doi.org/10.1002/ett.4352
  44. Heidari, A., Navimipour, N. J., Dag, H., Talebi, S., & Unal, M. (2024). A novel blockchain-based deepfake detection method using federated and deep learning models. Cognitive Computation, 1-19. https://doi.org/10.1007/s12559-024-10255-7
  45. Zeng, C.; Xia, S.; Wang, Z.; Wan, X. Multi-Channel Representation Learning Enhanced Unfolding Multi-Scale Compressed Sensing Network for High Quality Image Reconstruction. Entropy 2023, 25, 1579. https://doi.org/10.3390/ e25121579
  46. Cheng, Z., Chen, B., Liu, G., Zhang, H., Lu, R., Wang, Z., & Yuan, X. (2021). Memory-efficient network for large-scale video compressive sensing. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 16246-16255).
  47. Humayun, Mamoona & Afsar, Sadia & Almufareh, Maram & Jhanjhi, Noor & AlSuwailem, Mashayel. (2022). Smart Traffic Management System for Metropolitan Cities of Kingdom Using Cutting Edge Technologies. Journal of Advanced Transportation. 2022. 1-13. 10.1155/2022/4687319.
  48. Chen Z, Li R, Li Y, Feng Y, Hou X, Qian X. Deep Motion Regularizer for Video Snapshot Compressive Imaging. IEEE Transactions on Computational Imaging. 2024 Oct 14.
Regular Issue Subscription Review Article
Volume 02
Issue 02
Received 12/11/2024
Accepted 13/11/2024
Published 24/11/2024
296