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International Journal of Computer Networks and Applications (IJCNA)

International Journal of Computer Networks and Applications (IJCNA)

Published By EverScience Publications

ISSN : 2395-0455

Strategies for Achieving Energy Efficiency and Data Security Through Data Aggregation in IoT Healthcare Applications: A Comprehensive Study

Author NameAuthor Details

Ganesh Srinivasa Shetty, Raghu N

Ganesh Srinivasa Shetty[1]

Raghu N[2]

[1]Faculty of Engineering and Technology, JAIN (Deemed to be University), Bengaluru, India.

[2]Department of Electrical Engineering, Faculty of Engineering and Technology, JAIN (Deemed to be University), Bengaluru, India.

Cite this Article

DOI: 10.22247/ijcna/2024/224440

Pages : 127-139

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Abstract

The healthcare sector has been completely transformed using the internet of technology (IoT) for patient monitoring, diagnosis, and treatment. The Data aggregation (DA) plays a crucial role in achieving both energy efficiency and data security goals in IoT based healthcare systems. The sensitive nature of health data, coupled with the interconnectedness of IoT devices raises significant data sensitivity and privacy concerns. The primary intention of this review is a thorough analysis of several protocols for data aggregation that have been developed to deal with problems such as energy consumption and data security in IoT networks. Since the invention of blockchain technology, many studies have investigated its potential application in the IoT to resolve security issues. Many systems aim to improve network lifetime by scheduling duty cycles; however, they struggle to manage redundant data and have deprived throughput. Cluster-based data aggregation algorithms remove redundancy and conserve energy. This overview highlights interesting future research topics in the areas of energy efficiency in IoTs, security and privacy of user data maintenance problems, and integration of machine learning and blockchain algorithms.

Index Terms

Internet of Health Things (IoHT)

Secured Data Aggregation

Energy Efficiency

Blockchain Technology

Network Lifetime

Throughput

Reference

  1. 1.
    H. F. Nweke, Y. W. Teh, G. Mujtaba, and M. A. Al-garadi, “Data fusion and multiple classifier systems for human activity detection and health monitoring: Review and open research directions,” Information Fusion, vol. 46, pp. 147–170, Mar. 2019, doi: 10.1016/j.inffus.2018.06.002.
  2. 2.
    A. I. Taloba et al., “A blockchain-based hybrid platform for multimedia data processing in IoT-Healthcare,” Alexandria Engineering Journal, vol. 65, pp. 263–274, Feb. 2023, doi: 10.1016/j.aej.2022.09.031.
  3. 3.
    S. Selvaraj and S. Sundaravaradhan, “Challenges and opportunities in IoT healthcare systems: a systematic review,” SN Appl. Sci., vol. 2, no. 1, p. 139, Jan. 2020, doi: 10.1007/s42452-019-1925-y.
  4. 4.
    X. Zhou, W. Liang, K. I.-K. Wang, H. Wang, L. T. Yang, and Q. Jin, “Deep-Learning-Enhanced Human Activity Recognition for Internet of Healthcare Things,” IEEE Internet Things J., vol. 7, no. 7, pp. 6429–6438, Jul. 2020, doi: 10.1109/JIOT.2020.2985082.
  5. 5.
    [H. Elayan, M. Aloqaily, and M. Guizani, “Digital Twin for Intelligent Context-Aware IoT Healthcare Systems,” IEEE Internet Things J., vol. 8, no. 23, pp. 16749–16757, Dec. 2021, doi: 10.1109/JIOT.2021.3051158.
  6. 6.
    A. Ahmed, S. Abdullah, M. Bukhsh, I. Ahmad, and Z. Mushtaq, “An Energy-Efficient Data Aggregation Mechanism for IoT Secured by Blockchain,” IEEE Access, vol. 10, pp. 11404–11419, 2022, doi: 10.1109/ACCESS.2022.3146295.
  7. 7.
    P. A. Apostolopoulos, E. E. Tsiropoulou, and S. Papavassiliou, “Cognitive Data Offloading in Mobile Edge Computing for Internet of Things,” IEEE Access, vol. 8, pp. 55736–55749, 2020, doi: 10.1109/ACCESS.2020.2981837.
  8. 8.
    A. Rehman, T. Saba, K. Haseeb, T. Alam, and J. Lloret, “Sustainability Model for the Internet of Health Things (IoHT) Using Reinforcement Learning with Mobile Edge Secured Services,” Sustainability, vol. 14, no. 19, p. 12185, Sep. 2022, doi: 10.3390/su141912185.
  9. 9.
    S. S. Rani, J. A. Alzubi, S. K. Lakshmanaprabu, D. Gupta, and R. Manikandan, “RETRACTED ARTICLE: Optimal users based secure data transmission on the internet of healthcare things (IoHT) with lightweight block ciphers,” Multimed Tools Appl, vol. 79, no. 47–48, pp. 35405–35424, Dec. 2020, doi: 10.1007/s11042-019-07760-5.
  10. 10.
    M. A. Rahman, M. S. Hossain, A. J. Showail, N. A. Alrajeh, and M. F. Alhamid, “A secure, private, and explainable IoHT framework to support sustainable health monitoring in a smart city,” Sustainable Cities and Society, vol. 72, p. 103083, Sep. 2021, doi: 10.1016/j.scs.2021.103083.
  11. 11.
    M. Mamdouh, A. I. Awad, A. A. M. Khalaf, and H. F. A. Hamed, “Authentication and Identity Management of IoHT Devices: Achievements, Challenges, and Future Directions,” Computers & Security, vol. 111, p. 102491, Dec. 2021, doi: 10.1016/j.cose.2021.102491.
  12. 12.
    M. A. Rahman, M. S. Hossain, M. S. Islam, N. A. Alrajeh, and G. Muhammad, “Secure and Provenance Enhanced Internet of Health Things Framework: A Blockchain Managed Federated Learning Approach,” IEEE Access, vol. 8, pp. 205071–205087, 2020, doi: 10.1109/ACCESS.2020.3037474.
  13. 13.
    J. J. Kang, M. Dibaei, G. Luo, W. Yang, P. Haskell-Dowland, and X. Zheng, “An Energy-Efficient and Secure Data Inference Framework for Internet of Health Things: A Pilot Study,” Sensors, vol. 21, no. 1, p. 312, Jan. 2021, doi: 10.3390/s21010312.
  14. 14.
    E. M. Abou-Nassar, A. M. Iliyasu, P. M. El-Kafrawy, O.-Y. Song, A. K. Bashir, and A. A. A. El-Latif, “DITrust Chain: Towards Blockchain-Based Trust Models for Sustainable Healthcare IoT Systems,” IEEE Access, vol. 8, pp. 111223–111238, 2020, doi: 10.1109/ACCESS.2020.2999468.
  15. 15.
    K. Tange, M. De Donno, X. Fafoutis, and N. Dragoni, “A Systematic Survey of Industrial Internet of Things Security: Requirements and Fog Computing Opportunities,” IEEE Commun. Surv. Tutorials, vol. 22, no. 4, pp. 2489–2520, 2020, doi: 10.1109/COMST.2020.3011208.
  16. 16.
    P. Singh, A. Nayyar, A. Kaur, and U. Ghosh, “Blockchain and Fog Based Architecture for Internet of Everything in Smart Cities,” Future Internet, vol. 12, no. 4, p. 61, Mar. 2020, doi: 10.3390/fi12040061.
  17. 17.
    A. K. Idrees and A. K. M. Al-Qurabat, “Energy-Efficient Data Transmission and Aggregation Protocol in Periodic Sensor Networks Based Fog Computing,” J Netw Syst Manage, vol. 29, no. 1, p. 4, Jan. 2021, doi: 10.1007/s10922-020-09567-4.
  18. 18.
    L. Feng, P. Kortoçi, and Y. Liu, “A multi-tier data reduction mechanism for IoT sensors,” in Proceedings of the Seventh International Conference on the Internet of Things, Linz Austria: ACM, Oct. 2017, pp. 1–8. doi: 10.1145/3131542.3131557.
  19. 19.
    Junkuo Cao, Mingcai Lin, and Xiaojin Ma, "A Survey of Big Data for IoT in Cloud Computing," IAENG International Journal of Computer Science, vol. 47, no.3, pp585-592, 2020
  20. 20.
    A. K. M. Al-Qurabat and A. K. Idrees, “Two level data aggregation protocol for prolonging lifetime of periodic sensor networks,” Wireless Netw, vol. 25, no. 6, pp. 3623–3641, Aug. 2019, doi: 10.1007/s11276-019-01957-0.
  21. 21.
    A. M. Hussein, A. K. Idrees, and R. Couturier, “Distributed energy?efficient data reduction approach based on prediction and compression to reduce data transmission in IoT networks,” Int J Communication, vol. 35, no. 15, Oct. 2022, doi: 10.1002/dac.5282.
  22. 22.
    M. El-hajj, A. Fadlallah, M. Chamoun, and A. Serhrouchni, “A Survey of Internet of Things (IoT) Authentication Schemes,” Sensors, vol. 19, no. 5, p. 1141, Mar. 2019, doi: 10.3390/s19051141.
  23. 23.
    J. Liu, H. Tang, R. Sun, X. Du, and M. Guizani, “Lightweight and Privacy-Preserving Medical Services Access for Healthcare Cloud,” IEEE Access, vol. 7, pp. 106951–106961, 2019, doi: 10.1109/ACCESS.2019.2931917.
  24. 24.
    M. R. Naqvi, M. Aslam, M. W. Iqbal, S. Khuram Shahzad, M. Malik, and M. U. Tahir, “Study of Block Chain and its Impact on Internet of Health Things (IoHT):Challenges and Opportunities,” in 2020 International Congress on Human-Computer Interaction, Optimization and Robotic Applications (HORA), Ankara, Turkey: IEEE, Jun. 2020, pp. 1–6. doi: 10.1109/HORA49412.2020.9152846.
  25. 25.
    A. Abdallah and X. S. Shen, “A Lightweight Lattice-Based Homomorphic Privacy-Preserving Data Aggregation Scheme for Smart Grid,” IEEE Trans. Smart Grid, vol. 9, no. 1, pp. 396–405, Jan. 2018, doi: 10.1109/TSG.2016.2553647.
  26. 26.
    F. Leukam Lako, P. Lajoie-Mazenc, and M. Laurent, “Privacy-Preserving Publication of Time-Series Data in Smart Grid,” Security and Communication Networks, vol. 2021, pp. 1–21, Mar. 2021, doi: 10.1155/2021/6643566.
  27. 27.
    D. Mercier, A. Lucieri, M. Munir, A. Dengel, and S. Ahmed, “PPML-TSA: A modular privacy-preserving time series classification framework,” Software Impacts, vol. 12, p. 100286, May 2022, doi: 10.1016/j.simpa.2022.100286.
  28. 28.
    L. Wu, W. Zhang, and W. Zhao, “Privacy Preserving Data Aggregation for Smart Grid with User Anonymity and Designated Recipients,” Symmetry, vol. 14, no. 5, p. 847, Apr. 2022, doi: 10.3390/sym14050847.
  29. 29.
    M. Yang, T. Zhu, B. Liu, Y. Xiang, and W. Zhou, “Machine Learning Differential Privacy with Multifunctional Aggregation in a Fog Computing Architecture,” IEEE Access, vol. 6, pp. 17119–17129, 2018, doi: 10.1109/ACCESS.2018.2817523.
  30. 30.
    Manjula C Belavagi, and Balachandra Muniyal, "Intrusion Detection Using Rule Based Approach in RPL Networks," IAENG International Journal of Computer Science, vol. 50, no.3, pp988-999, 2023.
  31. 31.
    Li Wuke, Yin Guangluan, and Chen Xiaoxiao, "Application of Deep Extreme Learning Machine in Network Intrusion Detection Systems," IAENG International Journal of Computer Science, vol. 47, no.2, pp136-143, 2020
  32. 32.
    A. Kumar, R. Saha, M. Alazab, and G. Kumar, “A Lightweight Signcryption Method for Perception Layer in Internet-of-Things,” Journal of Information Security and Applications, vol. 55, p. 102662, Dec. 2020, doi: 10.1016/j.jisa.2020.102662.
  33. 33.
    J. Al-Jaroodi, N. Mohamed, and E. Abukhousa, “Health 4.0: On the Way to Realizing the Healthcare of the Future,” IEEE Access, vol. 8, pp. 211189–211210, 2020, doi: 10.1109/ACCESS.2020.3038858.
  34. 34.
    X. Zhu and Y. Badr, “Identity Management Systems for the Internet of Things: A Survey Towards Blockchain Solutions,” Sensors, vol. 18, no. 12, p. 4215, Dec. 2018, doi: 10.3390/s18124215.
  35. 35.
    M. Kumar, M. Sethi, S. Rani, D. K. Sah, S. A. AlQahtani, and M. S. Al-Rakhami, “Secure Data Aggregation Based on End-to-End Homomorphic Encryption in IoT-Based Wireless Sensor Networks,” Sensors, vol. 23, no. 13, p. 6181, Jul. 2023, doi: 10.3390/s23136181.
  36. 36.
    W. Ding, X. Jing, Z. Yan, and L. T. Yang, “A survey on data fusion in internet of things: Towards secure and privacy-preserving fusion,” Information Fusion, vol. 51, pp. 129–144, Nov. 2019, doi: 10.1016/j.inffus.2018.12.001.
  37. 37.
    T.-Y. Wu, T. Wang, Y.-Q. Lee, W. Zheng, S. Kumari, and S. Kumar, “Improved Authenticated Key Agreement Scheme for Fog-Driven IoT Healthcare System,” Security and Communication Networks, vol. 2021, pp. 1–16, Jan. 2021, doi: 10.1155/2021/6658041.
  38. 38.
    X. Jia, D. He, N. Kumar, and K.-K. R. Choo, “Authenticated key agreement scheme for fog-driven IoT healthcare system,” Wireless Netw, vol. 25, no. 8, pp. 4737–4750, Nov. 2019, doi: 10.1007/s11276-018-1759-3.
  39. 39.
    V. Tudor, V. Gulisano, M. Almgren, and M. Papatriantafilou, “BES: Differentially private event aggregation for large-scale IoT-based systems,” Future Generation Computer Systems, vol. 108, pp. 1241–1257, Jul. 2020, doi: 10.1016/j.future.2018.07.026.
  40. 40.
    H. Huang, T. Gong, N. Ye, R. Wang, and Y. Dou, “Private and Secured Medical Data Transmission and Analysis for Wireless Sensing Healthcare System,” IEEE Trans. Ind. Inf., vol. 13, no. 3, pp. 1227–1237, Jun. 2017, doi: 10.1109/TII.2017.2687618.
  41. 41.
    Alla Levina, Vladimir Varyukhin, Dmitry Kaplun, Anna Zamansky, and Dirk van der Linden, "A Case Study Exploring Side-Channel Attacks On Pet Wearables," IAENG International Journal of Computer Science, vol. 48, no.4, pp878-883, 2021.
  42. 42.
    Aqeel A. Yaseen, Kalyani Patel, Ali A. Yassin, Abdulla J. Aldarwish, and Haitham A. Hussein, "Secure Electronic Healthcare Record Using Robust Authentication Scheme," IAENG International Journal of Computer Science, vol. 50, no.2, pp468-476, 2023
  43. 43.
    B. Wang, F. Wu, Y. Long, L. Rimanic, C. Zhang, and B. Li, “DataLens: Scalable Privacy Preserving Training via Gradient Compression and Aggregation,” in Proceedings of the 2021 ACM SIGSAC Conference on Computer and Communications Security, Nov. 2021, pp. 2146–2168. doi: 10.1145/3460120.3484579.
  44. 44.
    X. Liu et al., “Secure Data Aggregation Aided by Privacy Preserving in Internet of Things,” Wireless Communications and Mobile Computing, vol. 2022, pp. 1–14, Mar. 2022, doi: 10.1155/2022/4858722.
  45. 45.
    J. Wang, L. Wu, S. Zeadally, M. K. Khan, and D. He, “Privacy-preserving Data Aggregation against Malicious Data Mining Attack for IoT-enabled Smart Grid,” ACM Trans. Sen. Netw., vol. 17, no. 3, pp. 1–25, Aug. 2021, doi: 10.1145/3440249.
  46. 46.
    K. T. Kadhim, A. M. Alsahlany, S. M. Wadi, and H. T. Kadhum, “An Overview of Patient’s Health Status Monitoring System Based on Internet of Things (IoT),” Wireless Pers Commun, vol. 114, no. 3, pp. 2235–2262, Oct. 2020, doi: 10.1007/s11277-020-07474-0.
  47. 47.
    A. Seyfollahi and A. Ghaffari, “Reliable data dissemination for the Internet of Things using Harris hawks optimization,” Peer-to-Peer Netw. Appl., vol. 13, no. 6, pp. 1886–1902, Nov. 2020, doi: 10.1007/s12083-020-00933-2.
  48. 48.
    S. N. Sajedi, M. Maadani, and M. Nesari Moghadam, “F-LEACH: a fuzzy-based data aggregation scheme for healthcare IoT systems,” J Supercomput, vol. 78, no. 1, pp. 1030–1047, Jan. 2022, doi: 10.1007/s11227-021-03890-6.
  49. 49.
    M. Manicka Raja and S. Manoj Kumar, “Aggregated PSO for Secure Data Transmission in WSN Using Fog Server,” Intelligent Automation & Soft Computing, vol. 34, no. 2, pp. 1017–1032, 2022, doi: 10.32604/iasc.2022.025665.
  50. 50.
    S. Siamala Devi, K. Venkatachalam, Y. Nam, and M. Abouhawwash, “Discrete GWO Optimized Data Aggregation for Reducing Transmission Rate in IoT,” Computer Systems Science and Engineering, vol. 44, no. 3, pp. 1869–1880, 2023, doi: 10.32604/csse.2023.025505.
  51. 51.
    K. A. Darabkh, A. B. Amareen, M. Al-Akhras, and W. K. Kassab, “An innovative cluster-based power-aware protocol for Internet of Things sensors utilizing mobile sink and particle swarm optimization,” Neural Comput & Applic, vol. 35, no. 26, pp. 19365–19408, Sep. 2023, doi: 10.1007/s00521-023-08752-1.
  52. 52.
    D. D. Datiri and M. Li, “Effects of Particle Swarm Optimisation on a Hybrid Load Balancing Approach for Resource Optimisation in Internet of Things,” Sensors, vol. 23, no. 4, p. 2329, Feb. 2023, doi: 10.3390/s23042329.
  53. 53.
    Y. Meshcheryakov, A. Melman, O. Evsutin, V. Morozov, and Y. Koucheryavy, “On Performance of PBFT Blockchain Consensus Algorithm for IoT-Applications with Constrained Devices,” IEEE Access, vol. 9, pp. 80559–80570, 2021, doi: 10.1109/ACCESS.2021.3085405.
  54. 54.
    S. B. ElMamy, H. Mrabet, H. Gharbi, A. Jemai, and D. Trentesaux, “A Survey on the Usage of Blockchain Technology for Cyber-Threats in the Context of Industry 4.0,” Sustainability, vol. 12, no. 21, p. 9179, Nov. 2020, doi: 10.3390/su12219179.
  55. 55.
    A. Ahmed, S. Abdullah, M. Bukhsh, I. Ahmad, and Z. Mushtaq, “An Energy-Efficient Data Aggregation Mechanism for IoT Secured by Blockchain,” IEEE Access, vol. 10, pp. 11404–11419, 2022, doi: 10.1109/ACCESS.2022.3146295.
  56. 56.
    J. Lockl, V. Schlatt, A. Schweizer, N. Urbach, and N. Harth, “Toward Trust in Internet of Things Ecosystems: Design Principles for Blockchain-Based IoT Applications,” IEEE Trans. Eng. Manage., vol. 67, no. 4, pp. 1256–1270, Nov. 2020, doi: 10.1109/TEM.2020.2978014.
  57. 57.
    G. Ravi, M. Swamy Das, and K. Karmakonda, “Reliable cluster-based data aggregation scheme for IoT network using hybrid deep learning techniques,” Measurement: Sensors, vol. 27, pp. 1-12, Jun. 2023, doi: 10.1016/j.measen.2023.100744.
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