research-article

A Simulation-driven Methodology for IoT Data Mining Based on Edge Computing

Authors:

Claudio Savaglio,

Giancarlo FortinoAuthors Info & Claims

ACM Transactions on Internet Technology (TOIT), Volume 21, Issue 2

Article No.: 30, Pages 1 - 22

https://doi.org/10.1145/3402444

Published: 08 March 2021 Publication History

Abstract

With the ever-increasing diffusion of smart devices and Internet of Things (IoT) applications, a completely new set of challenges have been added to the Data Mining domain. Edge Mining and Cloud Mining refer to Data Mining tasks aimed at IoT scenarios and performed according to, respectively, Cloud or Edge computing principles. Given the orthogonality and interdependence among the Data Mining task goals (e.g., accuracy, support, precision), the requirements of IoT applications (mainly bandwidth, energy saving, responsiveness, privacy preserving, and security) and the features of Edge/Cloud deployments (de-centralization, reliability, and ease of management), we propose EdgeMiningSim, a simulation-driven methodology inspired by software engineering principles for enabling IoT Data Mining. Such a methodology drives the domain experts in disclosing actionable knowledge, namely descriptive or predictive models for taking effective actions in the constrained and dynamic IoT scenario. A Smart Monitoring application is instantiated as a case study, aiming to exemplify the EdgeMiningSim approach and to show its benefits in effectively facing all those multifaceted aspects that simultaneously impact on IoT Data Mining.

References

[1]

Charu C. Aggarwal, Naveen Ashish, and Amit Sheth. 2013. The internet of things: A survey from the data-centric perspective. In Managing and Mining Sensor Data. Springer, 383–428.

[2]

Furqan Alam, Rashid Mehmood, Iyad Katib, and Aiiad Albeshri. 2016. Analysis of eight data mining algorithms for smarter Internet of Things (IoT). Proc. Comput. Sci. 98 (2016), 437–442. https://www.sciencedirect.com/science/article/pii/S187705091632213X.

Digital Library

[3]

Luigi Atzori, Antonio Iera, Giacomo Morabito, and Michele Nitti. 2012. The social internet of things (siot)–When social networks meet the internet of things: Concept, architecture and network characterization. Comput. Netw. 56, 16 (2012), 3594–3608.

Digital Library

[4]

Laura Belli, Simone Cirani, Luca Davoli, Gianluigi Ferrari, Lorenzo Melegari, Màrius Montón, and Marco Picone. 2015. A scalable big stream cloud architecture for the internet of things. Int. J. Syst. Service-Orient. Eng. 5, 4 (2015), 26–53.

Digital Library

[5]

Kanishka Bhaduri and Marco Stolpe. 2013. Distributed data mining in sensor networks. In Managing and Mining Sensor Data. Springer, 211–236.

[6]

Md Zakirul Alam Bhuiyan, Jie Wu, Gary M. Weiss, Thaier Hayajneh, Tian Wang, and Guojun Wang. 2020. Event detection through differential pattern mining in cyber-physical systems. IEEE Trans. Big Data 6, 4 (2020), 652--665.

[7]

Shen Bin, Liu Yuan, and Wang Xiaoyi. 2010. Research on data mining models for the internet of things. In Proceedings of the 2010 International Conference on Image Analysis and Signal Processing. IEEE, 127–132.

[8]

Antonio Brogi and Stefano Forti. 2017. QoS-aware deployment of IoT applications through the fog. IEEE IoT J. 4, 5 (2017), 1185–1192.

[9]

James Byrne, Sergej Svorobej, Anna Gourinovitch, Divyaa Manimaran Elango, Paul Liston, Peter J. Byrne, and Theo Lynn. 2017. RECAP simulator: Simulation of cloud/edge/fog computing scenarios. In Proceedings of the 2017 Winter Simulation Conference (WSC’17). IEEE, 4568–4569.

[10]

Rodrigo N. Calheiros, Rajiv Ranjan, Anton Beloglazov, César A. F. De Rose, and Rajkumar Buyya. 2011. CloudSim: A toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Softw.: Pract. Exper. 41, 1 (2011), 23–50.

Digital Library

[11]

Longbing Cao and Chengqi Zhang. 2007. The evolution of KDD: Towards domain-driven data mining. Int. J. Pattern Recogn. Artif. Intell. 21, 4 (2007), 677–692.

[12]

Roberto Casadei, Giancarlo Fortino, Danilo Pianini, Wilma Russo, Claudio Savaglio, and Mirko Viroli. 2019. A development approach for collective opportunistic Edge-of-Things services. Inf. Sci. 498 (2019), 154–169.

Digital Library

[13]

Roberto Casadei, Giancarlo Fortino, Danilo Pianini, Wilma Russo, Claudio Savaglio, and Mirko Viroli. 2019. Modelling and simulation of opportunistic IoT services with aggregate computing. Fut. Gener. Comput. Syst. 91 (2019), 252–262.

[14]

Feng Chen, Pan Deng, Jiafu Wan, Daqiang Zhang, Athanasios V. Vasilakos, and Xiaohui Rong. 2015. Data mining for the internet of things: Literature review and challenges. Int. J. Distrib. Sens. Netw. 11, 8 (2015), 431047.

Digital Library

[15]

Ying Cheng, Ken Chen, Hemeng Sun, Yongping Zhang, and Fei Tao. 2018. Data and knowledge mining with big data towards smart production. J. Industr. Inf. Integr. 9 (2018), 1–13.

[16]

Marcos Dias de Assuncao, Alexandre da Silva Veith, and Rajkumar Buyya. 2018. Distributed data stream processing and edge computing: A survey on resource elasticity and future directions. J. Netw. Comput. Appl. 103 (2018), 1–17.

Digital Library

[17]

Inderjit S. Dhillon and Dharmendra S. Modha. 2002. A data-clustering algorithm on distributed memory multiprocessors. In Large-scale Parallel Data Mining. Springer, 245–260.

Digital Library

[18]

Sofia Dimitriadou and Helen Karatza. 2010. Job scheduling in a distributed system using backfilling with inaccurate runtime computations. In Proceedings of the 2010 International Conference on Complex, Intelligent and Software Intensive Systems. IEEE, 329–336.

Digital Library

[19]

Usama Fayyad, Gregory Piatetsky-Shapiro, and Padhraic Smyth. 1996. From data mining to knowledge discovery in databases. AI Mag. 17, 3 (1996), 37–37.

Digital Library

[20]

Giancarlo Fortino, Raffaele Gravina, Wilma Russo, and Claudio Savaglio. 2017. Modeling and simulating internet-of-things systems: A hybrid agent-oriented approach. Comput. Sci. Eng. 19, 5 (2017), 68–76.

[21]

Giancarlo Fortino, Anna Rovella, Wilma Russo, and Claudio Savaglio. 2016. Towards cyberphysical digital libraries: Integrating IoT smart objects into digital libraries. In Management of Cyber Physical Objects in the Future Internet of Things. Springer, 135–156.

[22]

Giancarlo Fortino and Wilma Russo. 2012. ELDAMeth: An agent-oriented methodology for simulation-based prototyping of distributed agent systems. Inf. Softw. Technol. 54, 6 (2012), 608–624.

Digital Library

[23]

Giancarlo Fortino, Wilma Russo, Claudio Savaglio, Weiming Shen, and Mengchu Zhou. 2017. Agent-oriented cooperative smart objects: From IoT system design to implementation. IEEE Trans. Syst. Man Cybernet.: Syst. 48, 11 (2017), 1939–1956.

[24]

Elena I. Gaura, James Brusey, Michael Allen, Ross Wilkins, Dan Goldsmith, and Ramona Rednic. 2013. Edge mining the internet of things. IEEE Sens. J. 13, 10 (2013), 3816–3825.

[25]

Jie Han and Michael Orshansky. 2013. Approximate computing: An emerging paradigm for energy-efficient design. In Proceedings of the 2013 18th IEEE European Test Symposium (ETS’13). IEEE, 1–6.

[26]

Devki Nandan Jha, Khaled Alwasel, Areeb Alshoshan, Xianghua Huang, Ranesh Kumar Naha, Sudheer Kumar Battula, Saurabh Garg, Deepak Puthal, Philip James, Albert Zomaya, et al. 2020. IoTSim-Edge: A simulation framework for modeling the behavior of Internet of Things and edge computing environments. Softw.: Pract. Exper. 50, 6 (2020), 844--867.

[27]

Jiong Jin, Jayavardhana Gubbi, Slaven Marusic, and Marimuthu Palaniswami. 2014. An information framework for creating a smart city through internet of things. IEEE IoT J. 1, 2 (2014), 112–121.

[28]

Cheonshik Kim, San-Yep Nam, Duk-Je Park, Injung Park, and Taek-Young Hyun. 2006. Product control system using RFID tag information and data mining. In Proceedings of the International Conference on Ubiquitous Convergence Technology. Springer, 100–109.

Digital Library

[29]

Karthik Kumar and Yung-Hsiang Lu. 2010. Cloud computing for mobile users: Can offloading computation save energy? Computer 43, 4 (2010), 51–56.

Digital Library

[30]

Xiaolei Ma, Yao-Jan Wu, Yinhai Wang, Feng Chen, and Jianfeng Liu. 2013. Mining smart card data for transit riders’ travel patterns. Transport. Res. C: Emerg. Technol. 36 (2013), 1–12.

[31]

Óscar Marbán, Gonzalo Mariscal, and Javier Segovia. 2009. A data mining & knowledge discovery process model. In Data Mining and Knowledge Discovery in Real Life Applications. IntechOpen.

[32]

Gonzalo Mariscal, Oscar Marban, and Covadonga Fernandez. 2010. A survey of data mining and knowledge discovery process models and methodologies. Knowledge Eng. Rev. 25, 2 (2010), 137–166.

Digital Library

[33]

Andras Markus and Attila Kertesz. 2020. A survey and taxonomy of simulation environments modelling fog computing. Simul. Model. Pract. Theory 101 (2020), 102042.

[34]

Charafeddine Mechalikh, Hajer Taktak, and Faouzi Moussa. 2019. PureEdgeSim: A simulation toolkit for performance evaluation of cloud, fog, and pure edge computing environments. In Proceedings of the 2019 International Conference on High Performance Computing & Simulation. 700–707.

[35]

Boris Milovic and Milan Milovic. 2012. Prediction and decision making in health care using data mining. Arab. J. Bus. Manage. Rev. (Kuwait Chap.) 1, 12 (2012), 126.

[36]

Decebal Constantin Mocanu, Elena Mocanu, Phuong H. Nguyen, Madeleine Gibescu, and Antonio Liotta. 2016. Big IoT data mining for real-time energy disaggregation in buildings. In Proceedings of the 2016 IEEE International Conference on Systems, Man, and Cybernetics (SMC’16). IEEE, 003765–003769.

[37]

Ruxandra-Stefania Petre et al. 2012. Data mining in cloud computing. Database Syst. J. 3, 3 (2012), 67–71.

[38]

Gregory J. Pottie and William J. Kaiser. 2000. Wireless integrated network sensors. Commun. ACM 43, 5 (2000), 51–58.

Digital Library

[39]

Claudio Savaglio, Giuseppe Campisano, Giuseppe Di Fatta, and Giancarlo Fortino. 2019. IoT services deployment over edge vs cloud systems: A simulation-based analysis. In Proceedings of the IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS’19). IEEE, 554–559.

[40]

Claudio Savaglio, Pietro Gerace, Giuseppe Di Fatta, and Giancarlo Fortino. 2019. Data mining at the IoT edge. In Proceedings of the 2019 28th International Conference on Computer Communication and Networks (ICCCN’19). IEEE, 1–6.

[41]

Claudio Savaglio, Clara Isabel Valero, Andreu Belsa, Carlos Palau, and Giancarlo Fortino. 2020. Interoperability in cloud IoT platforms. In Springer Handbook of Internet of Things (unpublished).

[42]

Shabnam Shadroo and Amir Masoud Rahmani. 2018. Systematic survey of big data and data mining in internet of things. Comput. Netw. 139 (2018), 19–47.

Digital Library

[43]

Weisong Shi, Jie Cao, Quan Zhang, Youhuizi Li, and Lanyu Xu. 2016. Edge computing: Vision and challenges. IEEE IoT J. 3, 5 (2016), 637–646.

[44]

A. Shobanadevi and G. Maragatham. 2017. Data mining techniques for IoT and big data—A survey. In Proceedings of the 2017 International Conference on Intelligent Sustainable Systems (ICISS’17). IEEE, 607–610.

[45]

Cagatay Sonmez, Atay Ozgovde, and Cem Ersoy. 2018. Edgecloudsim: An environment for performance evaluation of edge computing systems. Trans. Emerg. Telecommun. Technol. 29, 11 (2018), e3493.

Digital Library

[46]

Nagender Kumar Suryadevara, Subhas C. Mukhopadhyay, Ruili Wang, and R. K. Rayudu. 2013. Forecasting the behavior of an elderly using wireless sensors data in a smart home. Eng. Appl. Artif. Intell. 26, 10 (2013), 2641–2652.

Digital Library

[47]

Shikhar Suryavansh, Chandan Bothra, Mung Chiang, Chunyi Peng, and Saurabh Bagchi. 2019. Tango of edge and cloud execution for reliability. In Proceedings of the 4th Workshop on Middleware for Edge Clouds & Cloudlets. 10–15.

Digital Library

[48]

Chun-Wei Tsai, Chin-Feng Lai, Ming-Chao Chiang, and Laurence T. Yang. 2013. Data mining for internet of things: A survey. IEEE Commun. Surv. Tutor. 16, 1 (2013), 77–97.

[49]

M. Ammad Uddin, Ali Mansour, Denis Le Jeune, and El Hadi M. Aggoune. 2017. Agriculture internet of things: AG-IoT. In Proceedings of the 2017 27th International Telecommunication Networks and Applications Conference (ITNAC’17). IEEE, 1–6.

[50]

Hajo Wiemer, Lucas Drowatzky, and Steffen Ihlenfeldt. 2019. Data mining methodology for engineering applications (DMME)—A holistic extension to the CRISP-DM model. Appl. Sci. 9, 12 (2019), 2407.

[51]

Peter Wlodarczak, Mustafa Ally, and Jeffrey Soar. 2017. Data mining in IoT: Data analysis for a new paradigm on the internet. In Proceedings of the International Conference on Web Intelligence. ACM, 1100–1103.

Digital Library

[52]

Xuyun Zhang, Julian Jang-Jaccard, Lianyong Qi, Md Z. A. Bhuiyan, and Chang Liu. 2018. Privacy issues in big data mining infrastructure, platforms, and applications. Secur. Commun. Netw. 2018, Article 6238607 (2018), 3 pages. https://doi.org/10.1155/2018/6238607

Digital Library

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Index Terms

A Simulation-driven Methodology for IoT Data Mining Based on Edge Computing
1. Computer systems organization
  1. Embedded and cyber-physical systems
2. Information systems
  1. Information systems applications
    1. Computing platforms
    2. Data mining

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Published In

cover image ACM Transactions on Internet Technology

ACM Transactions on Internet Technology Volume 21, Issue 2

June 2021

599 pages

ISSN:1533-5399

EISSN:1557-6051

DOI:10.1145/3453144

Editor:
Ling Liu
Georgia Institute of Technology, USA

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Copyright © 2021 Association for Computing Machinery.

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Publication History

Published: 08 March 2021

Accepted: 01 May 2020

Revised: 01 April 2020

Received: 01 March 2020

Published in TOIT Volume 21, Issue 2

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Italian MIUR, PRIN 2017 Project “Fluidware”

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