A Survey on Fusion of Internet of Things and Cloud Computing

doi:10.23940/ijpe.21.11.p5.946954

Abstract

Abstract: The Internet of Things (IoT) is rapidly approaching its capability as the next Digital uprising. It enables smart objects to interact and collaborate, allowing us to share information that helps us to maintain a healthy lifestyle. Cloud computing, on the other hand, delivers on-demand, flexible, and adaptable network access, permitting devices to access computational resources which facilitates the integration of unstructured information from multiple data sources. Cloud and IoT services have profound influences that can have a significant effect on today's ever-growing digital life, as well as the need to address a myriad of challenges for each system, namely scalability, security, and reliability with real-world objects in a more versatile and decentralized manner. Therefore, IoT requires more data storage and computation power. Cloud computing delivers on-demand accessibility of network assets especially for data storage and computing power to share the resources. Several architectures have been undergone with the literature to integrate these two technologies by using current computing paradigms, namely FOG and EDGE computing. This platform can use Cloud resources and services to gather, transmit, analyze, process, and store data created by a variety of sources. With this integration, we can resolve storage and computation issues. Moreover, in many applications, the integration plays a vital role in producing automated services and solutions without any delay.

Key words: cloud computing, edge computing, fog computing, internet of things.

Gayathri D and S.P. Shantharajah. A Survey on Fusion of Internet of Things and Cloud Computing [J]. Int J Performability Eng, 2021, 17(11): 946-954.

Add to citation manager EndNote|Reference Manager|ProCite|BibTeX|RefWorks

References

1. Hamad, Z.J. and Askar, S, Machine Learning Powered IoT for Smart Applications. International Journal of Science and Business, vol. 5, no. 3, pp. 92-100, 2021.
2. Li, H., Ota, K. and Dong, M.Learning IoT in Edge: Deep Learning for the Internet of Things with Edge Computing. IEEE network, vol. 32, no. 1, pp. 96-101, 2018.
3. Xu Z., Liu W., Huang J., Yang C., Lu J., andTan H,Artificial intelligence for Securing IoT Services in Edge Computing: a Survey.Security and Communication Networks, 2020.
4. Chai N., Mao C., Ren M., Zhang W., Poovendran P., andBalamurugan P,Role of BIC (Big Data, IoT, and Cloud) for Smart Cities.Arabian Journal for Science and Engineering, pp.1-15, 2021.
5. Biswas, A.R. and Giaffreda, R,IoT and Cloud Convergence: Opportunities and Challenges. In 2014 IEEE World Forum on Internet of Things (WF-IoT), IEEE. pp. 375-376, March 2014.
6. Benazzouz Y., Munilla C., Günalp O., Gallissot M., andGürgen, , Sharing User IoT Devices in the Cloud. In 2014 IEEE world forum on internet of things (WF-IoT), IEEE, pp. 373-374, March 2014.
7. Narezal, K. and Kalmani, V.Study of Lightweight ABE for Cloud based IoT. In 2020 Fourth International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud)(I-SMAC), IEEE, pp. 134-137, October 2020.
8. Emeakaroha V.C., Cafferkey N., Healy P., andMorrison J.P.A Cloud-based Iot Data Gathering and Processing Platform. In 2015 3rd International Conference on Future Internet of Things and Cloud, IEEE, pp. 50-57, August 2015.
9. Farris I., Militano L., Nitti M., Atzori L., andIera A,Federated Edge-assisted Mobile Clouds for Service Provisioning in Heterogeneous IoT Environments. In 2015 IEEE 2nd World Forum on Internet of Things (WF-IoT), IEEE, pp. 591-596, December 2015.
10. Park, D. and Cho, J., Cloud-connected Code Executable IoT Device with On-cloud Virtually Memory Controller for Dynamic Instruction Streaming. In 2015 International Conference on Cloud Computing and Big Data (CCBD). IEEE, pp. 29-30, November 2015.
11. Serpanos, D. and Wolf, M, Internet-of-things (IoT) systems: Architectures, Algorithms, Methodologies. Springer, 2017.
12. Baccarelli E., Scarpiniti M., Momenzadeh A., andAhrabi S.S.Learning-in-the-Fog (LiFo): Deep Learning Meets Fog Computing for the Minimum-Energy Distributed Early-Exit of Inference in Delay-Critical IoT Realms. IEEE Access, vol. 9, pp. 25716-25757, 2021.
13. Sarma B., Kumar G., Kumar R., andTuithung T.Fog Computing: An Enhanced Performance Analysis Emulation Framework for IoT with Load Balancing Smart Gateway Architecture. In 2019 International Conference on Communication and Electronics Systems (ICCES), IEEE, pp. 1-5, July 2019.
14. Kaur, H. and Sood, S.K, Energy-efficient IoT-fog-cloud Architectural Paradigm for Real-time Wildfire Prediction and Forecasting. IEEE Systems Journal, vol. 14, no. 2, pp. 2003-2011, 2019.
15. Zhang G., Shen F., Liu Z., Yang Y., Wang K., andZhou M.T,FEMTO: Fair and Energy-minimized Task offloading for Fog-enabled IoT Networks. IEEE Internet of Things Journal, vol. 6, no. 3, pp. 4388-4400, 2018.
16. Al-Khafajiy, M., Baker, T., Waraich, A., Alfandi, O., and Hussien, A. Enabling High Performance Fog Computing through Fog-2-fog Coordination Model. In 2019 IEEE/ACS 16th International Conference on Computer Systems and Applications (AICCSA), IEEE, pp. 1-6, November 2019.
17. Karamoozian A., Hafid A., andAboulhamid E.M,On the Fog-cloud Cooperation: How Fog Computing can Address Latency Concerns of IoT Applications. In 2019 Fourth International Conference on Fog and Mobile Edge Computing (FMEC), IEEE, pp. 166-172, June 2019.
18. Mendki P.Docker Container based Analytics at Iot Edge Video Analytics Usecase. In 2018 3rd International Conference On Internet of Things: Smart Innovation and Usages (IoT-SIU), IEEE, pp. 1-4, February 2018.
19. Jain, R.,Tata, S, Cloud to Edge: Distributed Deployment of Process-aware IoT Applications. In 2017 IEEE International Conference on Edge Computing (EDGE), IEEE, pp. 182-189, June 2017.
20. Jalali F., Lynar T., Smith O.J., Kolluri R.R., Hardgrove C.V., Waywood N., andSuits F.Dynamic Edge Fabric Environment: Seamless and Automatic Switching among Resources at the Edge of Iot Network and Cloud. In 2019 IEEE International Conference on Edge Computing (EDGE), IEEE, pp. 77-86, July 2019.
21. Thangiah L., Ramanathan C., andChodisetty L.S.Distribution Transformer Condition Monitoring based on Edge Intelligence for Industrial IoT. In2019 IEEE 5th World Forum on Internet of Things (WF-IoT), IEEE, pp. 733-736, 2019.
22. Song Y., Yau S.S., Yu R., Zhang X., andXue G.An Approach to QoS-based Task Distribution in Edge Computing Networks for IoT Applications. In2017 IEEE international conference on edge computing (EDGE), IEEE, pp. 32-39, 2017.
23. Singh, S, Optimize Cloud Computations using Edge Computing. In2017 International Conference on Big Data, IoT and Data Science (BID), IEEE, pp. 49-53, 2017.
24. Krishnamurthi R., Kumar A., Gopinathan D., Nayyar A., andQureshi B.An Overview of IoT Sensor Data Processing, Fusion, and Analysis Techniques. Sensors, vol. 20, no. 21, pp. 6076, 2020.
25. Yoshida E., Yokotani T., Ishibashi K., andMukai H,Proposals on the Root Data Domain Gateway and System Operations for IoT Data Interoperability. In2021 International Conference on Information Networking (ICOIN). IEEE, pp. 408-412, 2021.
26. Dave, M., Doshi, J., and Arolkar, H, MQTT-CoAP Interconnector: IoT Interoperability Solution for Application Layer Protocols. In 2020 Fourth International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud)(I-SMAC), IEEE, pp. 122-127, 2020.
27. Delsing, J., Eliasson, J., van Deventer, J., Derhamy, H., and Varga, P, Enabling IoT Automation using Local Clouds. In2016 IEEE 3rd World Forum on Internet of Things (WF-IoT), IEEE, pp. 502-507, 2016.
28. Kertesz, A, Interoperating Cloud Services for Enhanced Data Management. In2014 IEEE Fourth International Conference on Big Data and Cloud Computing, IEEE, pp. 269-270, 2014.
29. Žarko I.P., Mueller S., Płociennik M., Rajtar T., Jacoby M., Pardi M., Insolvibile G., Glykantzis V., Antonić A., Kušek M., andSoursos S,The SymbIoTe Solution for Semantic and Syntactic Interoperability of Cloud-based IoT Platforms. In2019 Global IoT Summit (GIoTS), IEEE, pp. 1-6, 2019.
30. Truong, H.L, Towards a Resource Slice Interoperability Hub for Iot. In2018 IEEE International Conference on Cloud Engineering (IC2E), IEEE, pp. 310-316, 2018.
31. Belsa A.,Sarabia-Jacome, D., Palau, C.E., and Esteve, M, Flow-based Programming Interoperability Solution for IoT Platform Applications. In2018 IEEE International Conference on Cloud Engineering (IC2E), IEEE, pp. 304-309, 2018.
32. Mir, M. and Ravindran, D, LETISA: Latency Optimal Edge Computing Technique for IoT based Smart Applications. International Journal of Scientific Research in Computer Science, Engineering and Information Technology, vol. 2, no. 4, pp. 688-694, 2017.
33. Ma K., Bagula A., Nyirenda C., andAjayi O.An Iot-based Fog Computing Model. Sensors, vol. 19, no. 12, pp. 2783, 2019.
34. Shukla S., Hassan M.F., Khan M.K., Jung L.T., andAwang A,An Analytical Model to Minimize the Latency in Healthcare Internet-of-things in Fog Computing Environment. PLoS One, vol. 14, no. 11, pp. e0224934, 2019.
35. Paul A., Pinjari H., Hong W.H., Seo H.C., andRho S.Fog Computing-based IoT for Health Monitoring System.Journal of Sensors, 2018
36. Mondragón-Ruiz, G., Tenorio-Trigoso, A., Castillo-Cara, M., Caminero, B., and Carrión, C. An Experimental Study of Fog and Cloud Computing in CEP-based Real-Time IoT Applications. Journal of Cloud Computing, vol. 10, no. 1, pp. 1-17, 2021.
37. Tran Q.M., Nguyen P.H., Tsuchiya T., andToulouse M,Designed Features for Improving Openness, Scalability and Programmability in the Fog Computing-based IoT Systems. SN Computer Science, vol. 1, pp. 1-12, 2020.
38. Hou X., Ren Z., Cheng W., Chen C., andZhang H.Fog based Computation offloading for Swarm of Drones. In ICC 2019-2019 IEEE International Conference on Communications (ICC), IEEE, pp. 1-7, May 2019.
39. Aguzzi S., Bradshaw D., Canning M., Cansfield M., Carter P., Cattaneo G., Gusmeroli S., Micheletti G., Rotondi D., andStevens R.Definition of a Research and Innovation Policy Leveraging Cloud Computing and IoT Combination. Final Report, European Commission, SMART, vol. 37, pp. 2013, 2013.
40. Alessio B.,De Donato, W., Persico, V., and Pescapé, A, On the Integration of Cloud Computing and Internet of Things.Proc. Future Internet of Things and Cloud (FiCloud), pp. 23-30, 2014.
41. Suciu G., Vulpe A., Halunga S., Fratu O., Todoran G., andSuciu V.Smart Cities built on Resilient Cloud Computing and Secure Internet of Things. In2013 19th international conference on control systems and computer science, IEEE, pp. 513-518, 2013.
42. Peng S.L., Pal S., andHuang L.Principles of Internet of Things (IoT) ecosystem: Insight Paradigm. Springer International Publishing, 2020.
43. Rao B.P., Saluia P., Sharma N., Mittal A., andSharma S.V,Cloud Computing for Internet of things & Sensing based Applications. In2012 Sixth International Conference on Sensing Technology (ICST), pp. 374-380. IEEE, Dec 2012.
44. S. M. Babu, A. J.Lakshmi and B. T. Rao, A Study on Cloud-based Internet of Things: CloudIoT,2015 Global Conference on Communication Technologies (GCCT), pp. 60-65, 2015
45. S. Chen, S. Member, H. Xu, D. Liu, S. Member, B. Hu,H. Wang, A Vision of IoT: Applications, Challenges, and Opportunities with China Perspective, IEEE INTERNET THINGS, vol. 1, no. 4, 2014, pp. 349-359.
46. Botta A.,De Donato, W., Persico, V., Pescapé, A.: Integration of Cloud Computing and Internet of Things: a Survey. Future Gener. Comput. Syst, vol. 56, 684-700, 2016.
47. S. Tyagi, A. Agarwal,P. Maheshwari, A Conceptual Framework for IoT-based Healthcare System using Cloud Computing, in2016 6th International Conference-Cloud System and Big Data Engineering (Confluence). IEEE, pp. 503-507, 2016.
48. Mansaf Alam, Kashish Ara Shakil, Samiya Khan, Internet of Things (IoT) Concepts and Applications, S.M.A.R.T. Environments,Springer Nature Switzerland AG, 2020.

[1]	D. Sakthivel and B. Radha. Adaptive Model to Detect Anomaly and Real-Time Attacks in Cloud Environment Using Data Mining Algorithm [J]. Int J Performability Eng, 2021, 17(10): 889-899.
[2]	Kun Li, Liwei Jia, and Xiaoming Shi. IPSOMC: An Improved Particle Swarm Optimization and Membrane Computing based Algorithm for Cloud Computing [J]. Int J Performability Eng, 2021, 17(1): 135-142.
[3]	Xiaoxiang Fan. Cloud Computing Task Scheduling based on Improved Bird Swarm Algorithm [J]. Int J Performability Eng, 2021, 17(1): 85-94.
[4]	Priti Kumari and Parmeet Kaur. Connected Data Set-based Virtual Machine Replication in Cloud Computing [J]. Int J Performability Eng, 2020, 16(9): 1351-1361.
[5]	Shujun Pei, Qinggen Zhang, and Xuehui Cheng. Workflow Scheduling using Graph Segmentation and Reinforcement Learning [J]. Int J Performability Eng, 2020, 16(8): 1262-1270.
[6]	Yuan Liang, Hongfang Cheng, and Wangshun Chen. Building Energy Consumption Data Index Method in Cloud Computing Environment [J]. Int J Performability Eng, 2020, 16(5): 747-756.
[7]	Xuan Chen, Zhengjiang Song, Hongfeng Zheng, and Zhiping Wan. Task Scheduling based on Fruit Fly Optimization Algorithm in Mobile Cloud Computing [J]. Int J Performability Eng, 2020, 16(4): 618-628.
[8]	Yong Cui, Liang Zhu, Zengyu Cai, and Ying Hu. An Adaptive Traffic-Aware Migration Algorithm Selection Framework in Live Migration of Multiple Virtual Machines [J]. Int J Performability Eng, 2020, 16(2): 314-324.
[9]	Yuxia Li. ACO-SOS-based Task Scheduling in Cloud Computing [J]. Int J Performability Eng, 2019, 15(9): 2534-2543.
[10]	Yan Li and Yao Yao. Scheduling Algorithm for a Task under Cloud Computing [J]. Int J Performability Eng, 2019, 15(8): 2081-2090.
[11]	Guangqian Kong, Xun Duan, and Yun Wu. Cloud-OM Patching: A Novel Video Stream Scheduling Scheme based on Hybrid Cloud-Overlay Architecture [J]. Int J Performability Eng, 2019, 15(8): 2208-2216.
[12]	Yuxia Li. Cloud Computing Resource Load Forecasting based on Bat Algorithm Optimized SVM [J]. Int J Performability Eng, 2019, 15(7): 1955-1964.
[13]	Ge Jiao, Lang Li, and Yi Zou. Improved Security for Android System based on Multi-Chaotic Maps using a Novel Image Encryption Algorithm [J]. Int J Performability Eng, 2019, 15(6): 1692-1701.
[14]	Zhili Zhang, Chunping Liu, and Xiaoming Ma. Intelligent Distance Measurement of Robot Obstacle Avoidance in Cloud Computing Environment [J]. Int J Performability Eng, 2019, 15(3): 959-968.
[15]	Shuaiqiu Xiang and Zhenjia Zhu. Dynamic Access Control of Encrypted Data in Cloud Computing Environment [J]. Int J Performability Eng, 2019, 15(3): 969-976.