Abstract
Fog computing is a novel distributed computing paradigm that provides cloud-like services at the edge of the network. It emerges as an efficient paradigm to process the enormous amount of Internet of Things (IoT) data and can address the limitations of cloud-centric IoT models in terms of large end-to-end delays, and huge network bandwidth consumption. Recently, fog computing and IoT have been employed in several domains, including transportation, education, healthcare, and manufacturing industry. To imitate different complex application scenarios for these domains, a notable number of fog computing-based simulators has already been developed. Among them, iFogSim has attained significant attention because of its simplified interface and low complexity. In this article, we present a tutorial on how to use iFogSim toolkit to simulate four real-time case studies for (1) smart car parking, (2) smart waste management system, (3) smart coal mining industry, and (4) sensing as a service. This article is expected to assist the researchers in understanding and implementing various aspects of fog computing using the iFogSim toolkit.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
R. Mahmud, K. Ramamohanarao, and R. Buyya, “Edge Affinity-based Management of Applications in Fog Computing Environments,” in Proceedings - 12th IEEE/ACM International Conference on Utility and Cloud Computing, UCC 2019, 2019, pp. 61–70.
S. Yi, Z. Hao, Z. Qin, and Q. Li, “Fog computing: Platform and applications,” in Proceedings - 3rd Workshop on Hot Topics in Web Systems and Technologies, HotWeb 2015, 2016, pp. 73–78.
I. Stojmenovic and S. Wen, “The Fog computing paradigm: Scenarios and security issues,” in 2014 Federated Conference on Computer Science and Information Systems, FedCSIS 2014, 2014, pp. 1–8.
M. Afrin, M. R. Mahmud, and M. A. Razzaque, “Real time detection of speed breakers and warning system for on-road drivers,” in Proceedings - IEEE International WIE Conference on Electrical and Computer Engineering, WIECON-ECE 2015, 2015, pp. 495–498.
R. Mahmud and R. Buyya, “Modeling and Simulation of Fog and Edge Computing Environments Using iFogSim Toolkit,” in Fog and Edge Computing, 2019, pp. 433–465.
T. Qayyum, A. W. Malik, M. A. K. Khattak, O. Khalid, and S. U. Khan, “FogNetSim++: A Toolkit for Modeling and Simulation of Distributed Fog Environment,” IEEE Access, vol. 6, pp. 63570–63583, 2018.
C. Sonmez, A. Ozgovde, and C. Ersoy, “EdgeCloudSim: An environment for performance evaluation of edge computing systems,” Trans. Emerg. Telecommun. Technol., vol. 29, no. 11, Nov. 2018.
H. Gupta and R. Buyya, “iFogSim: A toolkit for modeling and simulation of resource management techniques in the Internet of Things , Edge,” no. October 2016, pp. 1275–1296, 2017.
K. S. Awaisi et al., “Towards a Fog Enabled Efficient Car Parking Architecture,” IEEE Access, vol. 7, no. 1, pp. 159100–159111, 2019.
D. Hoornweg and P. Bhada-Tata, “What a waste: a global review of solid waste management,” 2012.
M. Aazam, S. Zeadally, and K. A. Harras, “Deploying Fog Computing in Industrial Internet of Things and Industry 4.0,” IEEE Trans. Ind. Informatics, vol. 14, no. 10, pp. 4674–4682, 2018.
M. Aazam, M. St-Hilaire, C. H. Lung, and I. Lambadaris, “Cloud-based smart waste management for smart cities,” in IEEE International Workshop on Computer Aided Modeling and Design of Communication Links and Networks, CAMAD, 2016, pp. 188–193.
R. Mahmud, A. N. Toosi, K. Ramamohanarao, and R. Buyya, “Context-aware Placement of Industry 4.0 Applications in Fog Computing Environments,” IEEE Transactions on Industrial Informatics, vol. 16, no. 11, pp. 7004–7013, 2020.
“IBM”,https://www.ibm.com/blogs/internet-of-things/mining-industry-benefits/, Accessed on August 14, 2020 .
M. Afrin, J. Jin, and A. Rahman, “Energy-delay co-optimization of resource allocation for robotic services in cloudlet infrastructure,” in International Conference on Service-Oriented Computing, 2018, pp. 295–303.
M. Afrin, J. Jin, A. Rahman, Y. Tian, and A. Kulkarni, “Multi-objective resource allocation for Edge Cloud based robotic workflow in smart factory,” Future Generation Computer Systems, vol. 97, pp. 119–130, 2019.
R. Mahmud, S. N. Srirama, K. Ramamohanarao, and R. Buyya, “Quality of Experience (QoE)-aware placement of applications in Fog computing environments,” Journal of Parallel and Distributed Computing, vol. 132, pp. 190–203, 2019.
A. N. Toosi, R. Mahmud, Q. Chi, and R. Buyya, “Management and Orchestration of Network Slices in 5G, Fog, Edge and Clouds,” in Fog and Edge Computing, 2019, pp. 79–102.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this chapter
Cite this chapter
Awaisi, K.S., Abbas, A., Khan, S.U., Mahmud, R., Buyya, R. (2021). Simulating Fog Computing Applications Using iFogSim Toolkit. In: Mukherjee, A., De, D., Ghosh, S.K., Buyya, R. (eds) Mobile Edge Computing. Springer, Cham. https://doi.org/10.1007/978-3-030-69893-5_22
Download citation
DOI: https://doi.org/10.1007/978-3-030-69893-5_22
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-69892-8
Online ISBN: 978-3-030-69893-5
eBook Packages: Computer ScienceComputer Science (R0)