An analytical model to minimize the latency in healthcare internet-of-things in fog computing environment
Autoři:
Saurabh Shukla aff001; Mohd Fadzil Hassan aff001; Muhammad Khalid Khan aff002; Low Tang Jung aff001; Azlan Awang aff003
Působiště autorů:
Centre for Research in Data Science (CeRDaS), Computer and Information Science Department, Universiti Teknologi PETRONAS(UTP), Seri Iskandar, Perak Darul Ridzuan, Malaysia
aff001; College of Computing and Information Sciences, PAF-KIET, Karachi, Pakistan
aff002; Electrical and Electronic Engineering Department, Universiti Teknologi PETRONAS(UTP), Seri Iskandar, Perak Darul Ridzuan, Malaysia
aff003
Vyšlo v časopise:
PLoS ONE 14(11)
Kategorie:
Research Article
doi:
https://doi.org/10.1371/journal.pone.0224934
Souhrn
Fog computing (FC) is an evolving computing technology that operates in a distributed environment. FC aims to bring cloud computing features close to edge devices. The approach is expected to fulfill the minimum latency requirement for healthcare Internet-of-Things (IoT) devices. Healthcare IoT devices generate various volumes of healthcare data. This large volume of data results in high data traffic that causes network congestion and high latency. An increase in round-trip time delay owing to large data transmission and large hop counts between IoTs and cloud servers render healthcare data meaningless and inadequate for end-users. Time-sensitive healthcare applications require real-time data. Traditional cloud servers cannot fulfill the minimum latency demands of healthcare IoT devices and end-users. Therefore, communication latency, computation latency, and network latency must be reduced for IoT data transmission. FC affords the storage, processing, and analysis of data from cloud computing to a network edge to reduce high latency. A novel solution for the abovementioned problem is proposed herein. It includes an analytical model and a hybrid fuzzy-based reinforcement learning algorithm in an FC environment. The aim is to reduce high latency among healthcare IoTs, end-users, and cloud servers. The proposed intelligent FC analytical model and algorithm use a fuzzy inference system combined with reinforcement learning and neural network evolution strategies for data packet allocation and selection in an IoT–FC environment. The approach is tested on simulators iFogSim (Net-Beans) and Spyder (Python). The obtained results indicated the better performance of the proposed approach compared with existing methods.
Klíčová slova:
Algorithms – Cloud computing – Clouds – Computer networks – Electrocardiography – Evolutionary algorithms – Machine learning – Machine learning algorithms
Zdroje
1. Hammi B, Khatoun R, Zeadally S, Fayad A, Khoukhi L. IoT technologies for smart cities. IET Networks. 2017;7(1):1–13.
2. Wortmann F, Flüchter K. Internet of things. Business & Information Systems Engineering. 2015;57(3):221–4.
3. Mukherjee M, Shu L, Wang D. Survey of fog computing: Fundamental, network applications, and research challenges. IEEE Communications Surveys & Tutorials. 2018;20(3):1826–57.
4. Naha RK, Garg S, Georgakopoulos D, Jayaraman PP, Gao L, Xiang Y, et al. Fog Computing: Survey of trends, architectures, requirements, and research directions. IEEE access. 2018;6:47980–8009.
5. Nandyala CS, Kim H-K. From cloud to fog and IoT-based real-time U-healthcare monitoring for smart homes and hospitals. International Journal of Smart Home. 2016;10(2):187–96.
6. Hassan MM, Lin K, Yue X, Wan J. A multimedia healthcare data sharing approach through cloud-based body area network. Future Generation Computer Systems. 2017;66:48–58.
7. Meng X, Wang W, Zhang Z. Delay-constrained hybrid computation offloading with cloud and fog computing. IEEE Access. 2017;5:21355–67.
8. Cirani S, Ferrari G, Iotti N, Picone M, editors. The IoT hub: A fog node for seamless management of heterogeneous connected smart objects. 2015 12th Annual IEEE International Conference on Sensing, Communication, and Networking-Workshops (SECON Workshops); 2015: IEEE.
9. Gia TN, Jiang M, Rahmani A-M, Westerlund T, Liljeberg P, Tenhunen H, editors. Fog computing in healthcare internet of things: A case study on ecg feature extraction. 2015 IEEE International Conference on Computer and Information Technology; Ubiquitous Computing and Communications; Dependable, Autonomic and Secure Computing; Pervasive Intelligence and Computing; 2015: IEEE.
10. Shi Y, Ding G, Wang H, Roman HE, Lu S, editors. The fog computing service for healthcare. 2015 2nd International Symposium on Future Information and Communication Technologies for Ubiquitous HealthCare (Ubi-HealthTech); 2015: IEEE.
11. Rahmani AM, Gia TN, Negash B, Anzanpour A, Azimi I, Jiang M, et al. Exploiting smart e-Health gateways at the edge of healthcare Internet-of-Things: A fog computing approach. Future Generation Computer Systems. 2018;78:641–58.
12. Hung S-C, Liau D, Lien S-Y, Chen K-C, editors. Low latency communication for Internet of Things. 2015 IEEE/CIC International Conference on Communications in China (ICCC); 2015: IEEE.
13. Lee G, Saad W, Bennis M. An online optimization framework for distributed fog network formation with minimal latency. IEEE Transactions on Wireless Communications. 2019.
14. Gupta H, Vahid Dastjerdi A, Ghosh SK, Buyya R. iFogSim: A toolkit for modeling and simulation of resource management techniques in the Internet of Things, Edge and Fog computing environments. Software: Practice and Experience. 2017;47(9):1275–96.
15. Skorin-Kapov L, Matijasevic M. Analysis of QoS requirements for e-health services and mapping to evolved packet system QoS classes. International journal of telemedicine and applications. 2010;2010:9.
16. Gállego JR, Hernández-Solana Á, Canales M, Lafuente J, Valdovinos A, Fernández-Navajas J. Performance analysis of multiplexed medical data transmission for mobile emergency care over the UMTS channel. IEEE transactions on information technology in biomedicine. 2005;9(1):13–22. 15787003
17. Bonomi F, Milito R, Zhu J, Addepalli S, editors. Fog computing and its role in the internet of things. Proceedings of the first edition of the MCC workshop on Mobile cloud computing; 2012: ACM.
18. Liu Y, Fieldsend JE, Min G. A framework of fog computing: Architecture, challenges, and optimization. IEEE Access. 2017;5:25445–54.
19. Deng R, Lu R, Lai C, Luan TH, Liang H. Optimal workload allocation in fog-cloud computing toward balanced delay and power consumption. IEEE Internet of Things Journal. 2016;3(6):1171–81.
20. Nishtala R, Carpenter P, Petrucci V, Martorell X, editors. Hipster: Hybrid task manager for latency-critical cloud workloads. 2017 IEEE International Symposium on High Performance Computer Architecture (HPCA); 2017: IEEE.
21. Conti S, Faraci G, Nicolosi R, Rizzo SA, Schembra G. Battery management in a green fog-computing node: a reinforcement-learning approach. IEEE Access. 2017;5:21126–38.
22. Linthicum D. cisco 2018 [cited 2018 29 March]. https://blogs.cisco.com/perspectives/iot-from-cloud-to-fog.com.
23. Alam MGR, Tun YK, Hong CS, editors. Multi-agent and reinforcement learning based code offloading in mobile fog. 2016 International Conference on Information Networking (ICOIN); 2016: IEEE.
24. Baccarelli E, Naranjo PGV, Scarpiniti M, Shojafar M, Abawajy JH. Fog of everything: Energy-efficient networked computing architectures, research challenges, and a case study. IEEE access. 2017;5:9882–910.
25. Wu J, Dong M, Ota K, Li J, Guan Z. FCSS: Fog computing based content-aware filtering for security services in information centric social networks. IEEE Transactions on Emerging Topics in computing. 2017.
26. Dinh N-T, Kim Y. An Efficient Availability Guaranteed Deployment Scheme for IoT Service Chains over Fog-Core Cloud Networks. Sensors. 2018;18(11):3970.
27. Li G, Wu J, Li J, Wang K, Ye T. Service popularity-based smart resources partitioning for fog computing-enabled industrial Internet of Things. IEEE Transactions on Industrial Informatics. 2018;14(10):4702–11.
28. Kao Y-H, Krishnamachari B, Ra M-R, Bai F. Hermes: Latency optimal task assignment for resource-constrained mobile computing. IEEE Transactions on Mobile Computing. 2017;16(11):3056–69.
29. Naas MI, Parvedy PR, Boukhobza J, Lemarchand L, editors. iFogStor: an IoT data placement strategy for fog infrastructure. 2017 IEEE 1st International Conference on Fog and Edge Computing (ICFEC); 2017: IEEE.
30. Pan J, McElhannon J. Future edge cloud and edge computing for internet of things applications. IEEE Internet of Things Journal. 2017;5(1):439–49.
31. Cao H, Cai J. Distributed multiuser computation offloading for cloudlet-based mobile cloud computing: A game-theoretic machine learning approach. IEEE Transactions on Vehicular Technology. 2017;67(1):752–64.
32. Brogi A, Forti S. QoS-aware deployment of IoT applications through the fog. IEEE Internet of Things Journal. 2017;4(5):1185–92.
33. Mahmud R, Koch FL, Buyya R, editors. Cloud-fog interoperability in IoT-enabled healthcare solutions. Proceedings of the 19th International Conference on Distributed Computing and Networking; 2018: ACM.
34. Rafique H, Shah MA, Islam SU, Maqsood T, Khan S, Maple C. A Novel Bio-Inspired Hybrid Algorithm (NBIHA) for Efficient Resource Management in Fog Computing. IEEE Access. 2019;7:115760–73.
35. Ahsan MM, Ali I, Imran M, Idris MYI, Khan S, Khan A. A Fog-centric Secure Cloud Storage Scheme. IEEE Transactions on Sustainable Computing. 2019.
36. Waqar A, Raza A, Abbas H, Khan MK. A framework for preservation of cloud users’ data privacy using dynamic reconstruction of metadata. Journal of Network and Computer Applications. 2013;36(1):235–48.
37. Soleymani SA, Abdullah AH, Zareei M, Anisi MH, Vargas-Rosales C, Khan MK, et al. A secure trust model based on fuzzy logic in vehicular ad hoc networks with fog computing. IEEE Access. 2017;5:15619–29.
38. Andras Janosi WS, Matthias Pfisterer, Robert Detrano. UCI Machine Learning Repository 2018 [cited 2018 25 February]. https://archive.ics.uci.edu/ml/datasets/heart+Disease.
39. Aha D, Kibler D. Instance-based prediction of heart-disease presence with the Cleveland database. University of California. 1988;3(1):3.2.
40. Blake CL, Merz CJ. UCI repository of machine learning databases, 1998. 1998.
41. Detrano R, Janosi A, Steinbrunn W, Pfisterer M, Schmid J-J, Sandhu S, et al. International application of a new probability algorithm for the diagnosis of coronary artery disease. The American journal of cardiology. 1989;64(5):304–10. doi: 10.1016/0002-9149(89)90524-9 2756873
42. Gennari JH, Langley P, Fisher D. Models of incremental concept formation. Artificial intelligence. 1989;40(1–3):11–61.
43. Le TP, Vien NA, Chung T. A deep hierarchical reinforcement learning algorithm in partially observable Markov decision processes. IEEE Access. 2018;6:49089–102.
44. Mai L, Dao N-N, Park M. Real-Time Task Assignment Approach Leveraging Reinforcement Learning with Evolution Strategies for Long-Term Latency Minimization in Fog Computing. Sensors. 2018;18(9):2830.
45. Aazam M, Huh E-N, editors. Fog computing and smart gateway based communication for cloud of things. 2014 International Conference on Future Internet of Things and Cloud; 2014: IEEE.
46. Baek J-y, Kaddoum G, Garg S, Kaur K, Gravel V. Managing Fog Networks using Reinforcement Learning Based Load Balancing Algorithm. arXiv preprint arXiv:190110023. 2019.
47. Sundharakumar K, Dhivya S, Mohanavalli S, Chander RV. Cloud based fuzzy healthcare system. Procedia computer science. 2015;50:143–8.
48. Kaur P, Khurmi DSS, Josan DGS. Fuzzy based analysis of proposed model for physical health standard based on association rule mining techniques. International Journal of Computer Science and Communication Engineering. 2012;1(2).
49. Kraemer FA, Braten AE, Tamkittikhun N, Palma D. Fog computing in healthcare–a review and discussion. IEEE Access. 2017;5:9206–22.
50. Osanaiye O, Chen S, Yan Z, Lu R, Choo K-KR, Dlodlo M. From cloud to fog computing: A review and a conceptual live VM migration framework. IEEE Access. 2017;5:8284–300.
51. Yousefpour A, Ishigaki G, Jue JP, editors. Fog computing: Towards minimizing delay in the internet of things. 2017 IEEE International Conference on Edge Computing (EDGE); 2017: IEEE.
52. Rolim CO, Koch FL, Westphall CB, Werner J, Fracalossi A, Salvador GS, editors. A cloud computing solution for patient’s data collection in health care institutions. 2010 Second International Conference on eHealth, Telemedicine, and Social Medicine; 2010: IEEE.
53. Shahid N, Rappon T, Berta W. Applications of artificial neural networks in health care organizational decision-making: A scoping review. PloS one. 2019;14(2):e0212356. doi: 10.1371/journal.pone.0212356 30779785
54. Wang M, Lu S, Zhu D, Lin J, Wang Z, editors. A High-Speed and Low-Complexity Architecture for Softmax Function in Deep Learning. 2018 IEEE Asia Pacific Conference on Circuits and Systems (APCCAS); 2018: IEEE.
55. Michalewicz Z. Evolution strategies and other methods. Genetic algorithms+ data structures = evolution programs: Springer; 1994. p. 167–84.
56. Verma S, Yadav AK, Motwani D, Raw R, Singh HK, editors. An efficient data replication and load balancing technique for fog computing environment. 2016 3rd International Conference on Computing for Sustainable Global Development (INDIACom); 2016: IEEE.
57. L’heureux A, Grolinger K, Elyamany HF, Capretz MA. Machine learning with big data: Challenges and approaches. IEEE Access. 2017;5:7776–97.
58. Fu Xin Y H, Chenxi Liu, Wang Jianwei, Wang Yinhai. A hybrid neural network for large-scale expressway network OD prediction based on toll data. PloS one. 2019:15.
59. Saqib E, Awan MB, Sohel Ferdous, Sanfilippo Frank M., Chow Benjamin J., Dwivedi Grish. Feature selection and transformation by machine learning reduce variable numbers and improve prediction for heart failure readmission or death. PloS one. 2019:13.
60. Sebban M, Nock R, Lallich S. Stopping criterion for boosting-based data reduction techniques: from binary to multiclass problem. Journal of machine learning research. 2002;3(Dec):863–85.
61. Ehteram M, Singh VP, Ferdowsi A, Mousavi SF, Farzin S, Karami H, et al. An improved model based on the support vector machine and cuckoo algorithm for simulating reference evapotranspiration. PloS one. 2019;14(5):e0217499. doi: 10.1371/journal.pone.0217499 31150443
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