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Edge Computing to Solve Security Issues for Infectious Disease Intelligence Prevention

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Published:29 November 2021Publication History
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Abstract

Nowadays, with the rapid development of intelligent technology, it is urgent to effectively prevent infectious diseases and ensure people's privacy. The present work constructs the intelligent prevention system of infectious diseases based on edge computing by using the edge computing algorithm, and further deploys and optimizes the privacy information security defense strategy of users in the system, controls the cost, constructs the optimal conditions of the system security defense, and finally analyzes the performance of the model. The results show that the system delay decreases with the increase of power in the downlink. In the analysis of the security performance of personal privacy information, it is found that six different nodes can maintain the optimal strategy when the cost is minimized in the finite time domain and infinite time domain. In comparison with other classical algorithms in the communication field, when the intelligent prevention system of infectious diseases constructed adopts the best defense strategy, it can effectively reduce the consumption of computing resources of edge network equipment, and the prediction accuracy is obviously better than that of other algorithms, reaching 83%. Hence, the results demonstrate that the model constructed can ensure the safety performance and forecast accuracy, and achieve the best defense strategy at low cost, which provides experimental reference for the prevention and detection of infectious diseases in the later period.

REFERENCES

  1. [1] Hossain M. S., Muhammad G., and Guizani N.. 2020. Explainable AI and mass surveillance system-based healthcare framework to combat COVID-I9 like pandemics. IEEE Network 34, 4 (2020), 126132.Google ScholarGoogle ScholarCross RefCross Ref
  2. [2] Zhang W., Yu C., Wang X., and Liu F.. 2019. Predicting CircRNA-disease associations through linear neighborhood label propagation method. IEEE Access 7 (2019), 8347483483.Google ScholarGoogle ScholarCross RefCross Ref
  3. [3] Calderón-Gómez H., Mendoza-Pittí L., Vargas-Lombardo M., Gómez-Pulido J. M., Castillo-Sequera J. L., Sanz-Moreno J., and Sención G.. 2020. Telemonitoring system for infectious disease prediction in elderly people based on a novel microservice architecture. IEEE Access 8 (2020), 118340118354.Google ScholarGoogle ScholarCross RefCross Ref
  4. [4] Feng Z., George S., Harkes J., Pillai P., Klatzky R., and Satyanarayanan M.. 2018. Edge-based discovery of training data for machine learning. In 2018 IEEE/ACM Symposium on Edge Computing (SEC’18) 145158.Google ScholarGoogle ScholarCross RefCross Ref
  5. [5] Vijayakumar V., Malathi D., Subramaniyaswamy V., Saravanan P., and Logesh R.. 2019. Fog computing-based intelligent healthcare system for the detection and prevention of mosquito-borne diseases. Computers in Human Behavior 100, 275285.Google ScholarGoogle ScholarDigital LibraryDigital Library
  6. [6] Zhang W., Yang W., Lu X., Huang F., and Luo F.. 2018. The bi-direction similarity integration method for predicting microbe-disease associations. IEEE Access 6 (2018), 3805238061.Google ScholarGoogle ScholarCross RefCross Ref
  7. [7] Tang W., Zhang K., Zhang D., Ren J., Zhang Y., and Shen X. S.. 2019. Fog-enabled smart health: Toward cooperative and secure healthcare service provision. IEEE Communications Magazine 57, 5 (2019), 4248.Google ScholarGoogle ScholarCross RefCross Ref
  8. [8] Hao Y., Usama M., Yang J., Hossain M. S., and Ghoneim A.. 2019. Recurrent convolutional neural network based multimodal disease risk prediction. Future Generation Computer Systems 92 (2019), 7683.Google ScholarGoogle ScholarCross RefCross Ref
  9. [9] Tran T. X., Hajisami A., Pandey P. et al. 2017. Collaborative mobile edge computing in 5G networks: New paradigms, scenarios, and challenges. IEEE Communications Magazine 55, 4 (2017), 5461. Google ScholarGoogle ScholarDigital LibraryDigital Library
  10. [10] Premsankar G., Di Francesco M., and Taleb T.. 2018. Edge computing for the Internet of Things: A case study. IEEE Internet of Things Journal 5, 2 (2018), 12751284.Google ScholarGoogle ScholarCross RefCross Ref
  11. [11] Wang D., Bai B., Lei K., Zhao W., Yang Y., and Han Z.. 2019. Enhancing information security via physical layer approaches in heterogeneous IoT with multiple access mobile edge computing in smart city. IEEE Access 7 (2019), 5450854521.Google ScholarGoogle ScholarCross RefCross Ref
  12. [12] Khan M. K., Shuja J., Jararweh Y., Yu G., Guizani M., Verikoukis C., and Ahmad R. W.. 2020. IEEE Access special section editorial: Mobile edge computing and mobile cloud computing: Addressing heterogeneity and energy issues of compute and network resources. IEEE Access 8 (2020), 163769163774.Google ScholarGoogle ScholarCross RefCross Ref
  13. [13] Song Q., Zhao M. R., Zhou X. H., Xue Y., and Zheng Y. J.. 2017. Predicting gastrointestinal infection morbidity based on environmental pollutants: Deep learning versus traditional models. Ecological Indicators 82 (2017), 7681.Google ScholarGoogle ScholarCross RefCross Ref
  14. [14] Lakhani P. and Sundaram B.. 2017. Deep learning at chest radiography: Automated classification of pulmonary tuberculosis by using convolutional neural networks. Radiology 284, 2 (2017), 574582.Google ScholarGoogle ScholarCross RefCross Ref
  15. [15] Verma P. and Sood S. K.. 2018. Cloud-centric IoT based disease diagnosis healthcare framework. Journal of Parallel and Distributed Computing 116 (2018), 2738.Google ScholarGoogle ScholarCross RefCross Ref
  16. [16] Akyildiz I. F., Ghovanloo M., Guler U., Ozkaya-Ahmadov T., Sarioglu A. F., and Unluturk B. D.. 2020. PANACEA: An internet of bio-nanothings application for early detection and mitigation of infectious diseases. IEEE Access 8 (2020), 140512140523.Google ScholarGoogle ScholarCross RefCross Ref
  17. [17] Kanwal M., Malik A. W., Rahman A. U., Mahmood I., and Shahzad M.. 2019. Sustainable vehicle-assisted edge computing for big data migration in smart cities. IEEE Internet of Things Journal 7, 3 (2019), 18571871.Google ScholarGoogle ScholarCross RefCross Ref
  18. [18] Yan C., Duan G., Wu F., Pan Y., and Wang J.. 2019. BRWMDA: Predicting microbe-disease associations based on similarities and bi-random walk on disease and microbe networks. IEEE/ACM Transactions on Computational Biology and Bioinformatics 17, 5 (2019), 1595--1604.Google ScholarGoogle Scholar
  19. [19] Al Hossain F., Lover A. A., Corey G. A., Reich N. G., and Rahman T.. 2020. FluSense: A contactless syndromic surveillance platform for influenza-like illness in hospital waiting areas. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 4, 1 (2020), 128. Google ScholarGoogle ScholarDigital LibraryDigital Library
  20. [20] Biswas S., Mitra P., and Rao K. S.. 2019. Relation prediction of co-morbid diseases using knowledge graph completion. IEEE/ACM Transactions on Computational Biology and Bioinformatics 18, 2 (2019), 708--717.Google ScholarGoogle Scholar
  21. [21] Nguyen C. T., Saputra Y. M., Van Huynh N., Nguyen N. T., Khoa T. V., Tuan B. M., and Chatzinotas S.. 2020. A comprehensive survey of enabling and emerging technologies for social distancing—Part II: Emerging technologies and open issues. IEEE Access 8 (2020), 154209154236.Google ScholarGoogle ScholarCross RefCross Ref
  22. [22] Cartelle Gestal M., Dedloff M. R., and Torres-Sangiao E.. 2019. Computational health engineering applied to model infectious diseases and antimicrobial resistance spread. Applied Sciences 9, 12 (2019), 2486.Google ScholarGoogle ScholarCross RefCross Ref
  23. [23] Ni J., Zhang K., Lin X., and Shen X. S.. 2017. Securing fog computing for internet of things applications: Challenges and solutions. IEEE Communications Surveys & Tutorials 20, 1 (2017), 601628.Google ScholarGoogle ScholarCross RefCross Ref
  24. [24] Ghulam A., Lei X., Guo M., and Bian C.. 2020. Disease-pathway association prediction based on random walks with restart and pagerank. IEEE Access 8 (2020), 7202172038.Google ScholarGoogle ScholarCross RefCross Ref
  25. [25] Yu X., Zhang J., Sun S., Zhou X., Zeng T., and Chen L.. 2017. Individual-specific edge-network analysis for disease prediction. Nucleic acIDs research 45, 20 (2017), e17—e170.Google ScholarGoogle ScholarCross RefCross Ref
  26. [26] Edoh T.. 2018. Risk prevention of spreading emerging infectious diseases using a hybrid crowdsensing paradigm, optical sensors, and smartphone. Journal of Medical Systems 42, 5 (2018), 91. Google ScholarGoogle ScholarDigital LibraryDigital Library
  27. [27] Yang X., Lu R., Shao J., Tang X., and Yang H.. 2018. An efficient and privacy-preserving disease risk prediction scheme for e-healthcare. IEEE Internet of Things Journal 6, 2 (2018), 32843297.Google ScholarGoogle ScholarCross RefCross Ref
  28. [28] Mehrabi M., You D., Latzko V., Salah H., Reisslein M., and Fitzek F. H.. 2019. Device-enhanced MEC: Multi-access edge computing (MEC) aided by end device computation and caching: A survey. IEEE Access 7 (2019), 166079166108.Google ScholarGoogle ScholarCross RefCross Ref
  29. [29] Luo P., Tian L. P., Ruan J., and Wu F. X.. 2017. Disease gene prediction by integrating PPI networks, clinical RNA-Seq data and OMIM data. IEEE/ACM Transactions on Computational Biology and Bioinformatics 16, 1 (2017), 222232. Google ScholarGoogle ScholarDigital LibraryDigital Library
  30. [30] Guizani N., Elghariani A., Kobes J., and Ghafoor A.. 2019. Effects of social network structure on epidemic disease spread dynamics with application to ad hoc networks. IEEE Network 33, 3 (2019), 139145.Google ScholarGoogle ScholarCross RefCross Ref
  31. [31] Zhao Q., Yang Y., Ren G., Ge E., and Fan C.. 2019. Integrating bipartite network projection and KATZ measure to identify novel CircRNA-disease associations. IEEE Transactions on Nanobioscience 18, 4 (2019), 578584.Google ScholarGoogle ScholarCross RefCross Ref
  32. [32] Balas M. J. and Frost S. A.. 2019. Direct adaptive control of non-minimum phase linear infinite-dimensional systems in Hilbert space using a zero dynamics estimator. In 2019 IEEE 58th Conference on Decision and Control (CDC’19). IEEE, 30723079.Google ScholarGoogle ScholarDigital LibraryDigital Library
  33. [33] Parkison S. A., Ghaffari M., Gan L., Zhang R., Ushani A. K., and Eustice R. M.. 2019. Boosting shape registration algorithms via reproducing kernel Hilbert space regularizers. IEEE Robotics and Automation Letters 4, 4 (2019), 45634570.Google ScholarGoogle ScholarCross RefCross Ref
  34. [34] Holladay R., Salzman O., and Srinivasa S.. 2019. Minimizing task-space Frechet error via efficient incremental graph search. IEEE Robotics and Automation Letters 4, 2 (2019), 19992006.Google ScholarGoogle ScholarCross RefCross Ref
  35. [35] Xu P., Wu Q., Rao Y., Kou Z., Fang G., Liu W., and Han H.. 2020. Predicting the influence of MicroRNAs on drug therapeutic effects by random walking. IEEE Access 8 (2020), 117347117353.9Google ScholarGoogle ScholarCross RefCross Ref
  36. [36] Firouzi F., Farahani B., Ibrahim M., and Chakrabarty K.. 2018. Keynote paper: From EDA to IoT eHealth: Promises, challenges, and solutions. IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems 37, 12 (2018), 29652978.Google ScholarGoogle ScholarDigital LibraryDigital Library
  37. [37] Peng L., Zhou D., Liu W., Zhou L., Wang L., Zhao B., and Yang J.. 2020. Prioritizing human microbe-disease associations utilizing a node-information-based link propagation method. IEEE Access 8 (2020), 3134131349.Google ScholarGoogle ScholarCross RefCross Ref
  38. [38] Ameer S., Shah M. A., Khan A., Song H., Maple C., Islam S. U., and Asghar M. N.. 2019. Comparative analysis of machine learning techniques for predicting air quality in smart cities. IEEE Access 7 (2019), 128325128338.Google ScholarGoogle ScholarCross RefCross Ref
  39. [39] Alam M. M., Malik H., Khan M. I., Pardy T., Kuusik A., and Le Moullec Y.. 2018. A survey on the roles of communication technologies in IoT-based personalized healthcare applications. IEEE Access 6 (2018), 3661136631.Google ScholarGoogle ScholarCross RefCross Ref

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    • Published in

      cover image ACM Transactions on Internet Technology
      ACM Transactions on Internet Technology  Volume 22, Issue 3
      August 2022
      631 pages
      ISSN:1533-5399
      EISSN:1557-6051
      DOI:10.1145/3498359
      • Editor:
      • Ling Liu
      Issue’s Table of Contents

      Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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      Association for Computing Machinery

      New York, NY, United States

      Publication History

      • Published: 29 November 2021
      • Revised: 1 July 2021
      • Accepted: 1 July 2021
      • Received: 1 November 2020
      Published in toit Volume 22, Issue 3

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