Indian Journal of Computer Science

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Comparative Study of Data Analysis Methods in a Fog


Affiliations
1 P. G. Department of Computer Science and Technology, Degree College of Physical Education, Amravati, India
2 Professor, P. G. Department of Computer Science and Technology, Degree College of Physical Education, Amravati. Maharashtra, India

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Fog computing is distributed computing consisting of Cloud layer, Fog Layer, and IoT devices/ sensors. Fog computing extends cloud based services closer to end devices in order to provide quick response to real time IoT applications. IoT devices with different sensors generate a huge amount of data which are processed at Fog layer to find insight for business, to reduce workload at Cloud, to reduce use of network bandwidth, to respond in time effective manner or reduce latency and response location awareness. For analysing data effectively at insight for business, Fog layer is a need for intelligent computing systems like data mining and data analytics. In a Fog environment the data analytics can be performed at Fog layer or at Cloud Layer and Fog Layer. The Fog nodes in fog layer are limited in computing resources, so the data analytics must be distributed among the fog nodes to work in a distributed way. Machine learning based data analytics model with various Data analytic methods works better in such situations. In this paper we are comparing various data analytics methods used in Fog computing.

Keywords

Cloud, Data Analytics, Fog Computing

Manuscript Received : August 21, 2022 ; Revised : September 8, 2022 ; Accepted : September 10, 2022. Date of Publication : October 5, 2022.

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  • M. Antonini, M. Vecchio, and F. Antonelli, Fog computing architectures: A reference for practitioners, IEEE Internet of Things Mag., vol. 2, no. 3, pp. 1925, 2019, doi: 10.1109/IOTM.0001.1900029.

  • R. Mahmud, R. Kotagiri, and R. Buyya, Fog computing: A taxonomy, survey and future directions, In Di Martino, B., Li, KC., Yang, L., Esposito, A. (Eds.) Internet of Everything. Springer, Singapore. doi: 10.1007/978-981-10-5861-5_5.

  • M. Mukherjee, L. Shu and D. Wang, "Survey of Fog computing: Fundamental, network applications, and research challenges," IEEE Commun. Surveys Tut., vol. 20, no. 3, pp .18261857, 2018, doi : 10.1109/COMST.2018.2814571.

  • C. S. R. Prabhu, T. Jan, M. Prasad, and V. Varadarajan, Fog Analytics - A Survey, Malaysian J. Comput. Sci., pp. 140151, 2020, doi: 10.22452/mjcs.sp2020no1.10.

  • M. K. Pandit, R. Naaz and M. A. Chishti, "Distributed IoT Analytics across Edge, Fog and Cloud," in 2018 4th Int. Conf. Res. Comput. Intell. Commun. Netw., 2018, pp. 2732, doi: 10.1109/ICRCICN.2018.8718738.

  • R. Jaiswal, A. Chakravorty and C. Rong, "Distributed Fog Computing Architecture for real-time anomaly detection in Smart Meter Data," in 2020 IEEE 6th Int. Conf. Big Data Comput. Service Appl. (BigDataService), 2020, pp. 18, doi: 10.1109/BigDataService49289.2020.00009.

  • M. Taneja, N. Jalodia and A. Davy, "Distributed decomposed data analytics in fog enabled IoT deployments," IEEE Access, vol. 7, pp. 4096940981, 2019, doi: 10.1109/ACCESS.2019.2907808.

  • L. Zhao, Privacy-preserving distributed analytics in Fog-enabled IoT systems sensors, Sensors 2020, vol. 20, no. 21, 6153, 2020, doi: 10.3390/s20216153.

  • P. -H. Tsai, H. -J. Hong, A. -C. Cheng, and C. -H. Hsu, "Distributed analytics in fog computing platforms using tensorflow and kubernetes," in 2017 19th Asia-Pacific Netw. Operations Manage. Symp., 2017, pp. 145150, doi: 10.1109/APNOMS.2017.8094194.

  • J. He, J. Wei, K. Chen, Z. Tang, Y. Zhou and Y. Zhang, "Multitier fog computing with large-scale IoT data analytics for smart cities," IEEE Internet Things J., vol. 5, no .2, pp. 677686, 2018, doi : 10.1109/JIOT.2017.2724845.

  • M. Ahmed, R. Mumtaz, S. M. H. Zaidi, M. Hafeez, S. A. R. Zaidi, and M. Ahmad, Distributed fog computing for Internet of Things (IoT) based ambient data processing and analysis, Electronics 2020, vol. 9, no. 11, 1756, doi: 10.3390/electronics9111756.

  • D. Jha, A. Rauniyar, H. D. Johansen, D. Johansen, M. A. Riegler, P. Halvorsen, and U. Bagchi, "Video analytics in elite soccer: A distributed computing perspective," presented at the 2022 IEEE 12th Sensor Array Multichannel Signal Process. Workshop (SAM), 2022, pp. 221225, doi: 10.1109/SAM53842.2022.9827827.

  • H. Cao and M. Wachowicz, An edge-fog-cloud architecture of streaming analytics for internet of things applications, Sensors 2019, vol. 19, no. 16, 3594, 2019, doi: 10.3390/s19163594.

  • J. Clemente, M. Valero, J. Mohammadpour, X. Li, and W. Song, "Fog computing middleware for distributed cooperative data analytics," 2017 IEEE Fog World Congr., 2017, pp. 16, doi: 10.1109/FWC.2017.8368520.

  • G. Li, P. Zhao, X. Lu, J. Liu, and Y. Shen, "Data analytics for Fog computing by distributed online learning with asynchronous update," in ICC 2019 - 2019 IEEE Int. Conf. Commun., 2019, pp. 16, doi: 10.1109/ICC.2019.8761303.

  • H. -J. Hong, P. -H. Tsai, A. -C. Cheng, M. Y. S. Uddin, N. Venkatasubramanian, and C. -H. Hsu, "Supporting Internet of Things analytics in a Fog computing platform," in 2017 IEEE Int. Conf. Cloud Comput. Technol. Sci. (CloudCom), 2017, pp. 138145, doi: 10.1109/CloudCom.2017.45.

  • F. Mehdipour, B. Javadi, and A. Mahanti, "FOGEngine: Towards Big Data Analytics in the Fog," in 2016 IEEE 14th Int. Conf. Dependable, Autonomic Secure Comput., in 14th Int. Conf. Pervasive Intell. Comput., 2nd Int. Conf. Big Data Intell. Compu. Cyber Sci. Technol. Congr. (DASC/PiCom/DataCom/CyberSciTech), 2016, pp. 640646, doi: 10.1109/DASC-PICom-DataCom-CyberSciTec.2016.116.

  • B. B. Ma, S. Fong, and R. Millham, "Data stream mining in fog computing environment with feature selection using ensemble of swarm search algorithms," in 2018 Conf. Inf. Commun. Technol. Soc., 2018, pp. 16, doi: 10.1109/ICTAS.2018.8368770.

  • L. Valerio, A. Passarella, and M. Conti, "Optimising cost vs accuracy of decentralised analytics in Fog computing environments," in IEEE Trans. Netw. Sci. Eng., vol. 9, no. 4, pp. 19862002, 1 July-Aug. 2022, doi: 10.1109/TNSE.2021.3101986.

  • D. Borthakur, H. Dubey, N. Constant, L. Mahler, and K. Mankodiya, "Smart fog: Fog computing framework for unsupervised clustering analytics in wearable Internet of Things," in 2017 IEEE Global Conf. Signal Inf. Process. (GlobalSIP), 2017, pp. 472476, doi: 10.1109/GlobalSIP.2017.8308687.


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  • Comparative Study of Data Analysis Methods in a Fog

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Authors

S. N. Khandare
P. G. Department of Computer Science and Technology, Degree College of Physical Education, Amravati, India
S. P. Deshpande
Professor, P. G. Department of Computer Science and Technology, Degree College of Physical Education, Amravati. Maharashtra, India

Abstract


Fog computing is distributed computing consisting of Cloud layer, Fog Layer, and IoT devices/ sensors. Fog computing extends cloud based services closer to end devices in order to provide quick response to real time IoT applications. IoT devices with different sensors generate a huge amount of data which are processed at Fog layer to find insight for business, to reduce workload at Cloud, to reduce use of network bandwidth, to respond in time effective manner or reduce latency and response location awareness. For analysing data effectively at insight for business, Fog layer is a need for intelligent computing systems like data mining and data analytics. In a Fog environment the data analytics can be performed at Fog layer or at Cloud Layer and Fog Layer. The Fog nodes in fog layer are limited in computing resources, so the data analytics must be distributed among the fog nodes to work in a distributed way. Machine learning based data analytics model with various Data analytic methods works better in such situations. In this paper we are comparing various data analytics methods used in Fog computing.

Keywords


Cloud, Data Analytics, Fog Computing

Manuscript Received : August 21, 2022 ; Revised : September 8, 2022 ; Accepted : September 10, 2022. Date of Publication : October 5, 2022.


References





DOI: https://doi.org/10.17010/ijcs%2F2022%2Fv7%2Fi5%2F172580