کشف دانش و کاربرد آن در اینترنت اشیاء

نوع مقاله: مقاله پژوهشی

نویسندگان

1 . استادیار، گروه علوم کامپیوتر، دانشکده علوم ریاضی و کامپیوتر، دانشگاه خوارزمی، تهران، ایران

2 کارشناس ارشد، مهندسی کامپیوتر (نرم‌افزار)، موسسه آموزش و ترویج کشاورزی، سازمان تحقیقات، آموزش و ترویج کشاورزی، تهران، ایران،

3 استادیار، گروه علم اطلاعات و دانش شناسی، دانشگاه علامه طباطبائی

چکیده

اینترنت اشیاء، به طور چشمگیری زندگی ما را در آینده‌ای نزدیک تغییر خواهد داد و بسیاری از ناممکن‌ها را ممکن خواهد ساخت. حجم عظیم داده‌ی تولید شده یا گرفته شده توسط تجهیزات اینترنت اشیاء ، حاوی اطلاعات ارزشمند و قابل استفاده‌ است. با رواج دستگاه‌های توسعه یافته فناوری بی‌سیم مانند بلوتوث، شناسایی با فرکانس رادیویی (RFID)، Wi-Fi، و خدمات داده برروی تلفن و همچنین سنسور و محرک و نودهای تعبیه شده در وسایل، شبکه های حسگر بی سیم، اینترنت اشیاء مراحل ابتدایی خود را پشت سر گذاشته و در آستانه تبدیل اینترنت ایستای کنونی، به اینترنت کاملاً یکپارچه در آینده است. کشف دانش از طریق داده کاوی و متن کاوی نیز بدون شک نقش زیادی در زمینه هوشمندسازی سیستم ها و در نتیجه ارائه خدمات و محیط مناسب برای ارائه خدمات خواهد داشت. همچنین از روش های داده کاوی برای خوشه بندی تجهیزات در شبکه های حسگر بی سیم و تعیین سرخوشه استفاده بسیاری می‌شود. در این مقاله به معرفی اینترنت اشیاء، معماری، کشف دانش ، نقش وکاربرد داده کاوی و متن کاوی در این حوزه پرداخته شده است.

کلیدواژه‌ها


عنوان مقاله [English]

Knowledge Discovery and its Application in the Internet of Things

نویسندگان [English]

  • keivan borna 1
  • farhad fathi 2
  • Esmat Momeni 3
1 Faculty of Mathematics and Computer Science, Kharazmi University
چکیده [English]

The internet of things will change our life in future significantly and will make the impossible, possible. A large volume of big data which is produced or taken by Internet of Things (IOT) contains valuable and useful information. By the prevalence of the wireless apparatuses technology such as Bluetooth, detection of radio frequency (RFID), Wi-Fi and data services on telephone, sensor, actuators and nodes embedded in the equipment, Wireless Sensor Networks (WSN) the internet of things has already passed its primary stages and is in the threshold of changing the current static internet into a fully integrated internet. Data mining; too, with no doubt plays a large role in smartness of the system and subsequently, provides suitable services and environment in offering services. Also, data mining techniques are used to cluster nodes and determine cluster head, in wireless sensor networks. This paper introduces the internet of things, architecture and its applications.
 
 

کلیدواژه‌ها [English]

  • Internet of Things
  • Knowledge Discovery
  • Data mining
  • Big Data
  • Wireless sensor networks (WSN)

رمضانی، هادی، علیپور حافظی، مهدی و مؤمنی، عصمت. (1393). نقشه‌های علمی: فنون و روش‌ها. ترویج علم، 5(6)، 53- 84.

غفارزادگان، مریم. (1392). کشف ساختار درونی مطالعات خلاقیت به روش متن‌کاوی. پایان‌نامه کارشناسی ارشد علم اطلاعات و دانش‌شناسی، دانشگاه علامه طباطبائی، تهران.

References

Abbasi, A. A., & Younis, M. (2007). A survey on clustering algorithms for wireless sensor networks. Computer communications30(14-15), 2826-2841.‏

Ashton, K. (2009). That ‘internet of things’ thing. RFID journal22(7), 97-114.‏

Atzori, L., Iera, A., & Morabito, G. (2010). The internet of things: A survey. Computer networks54(15), 2787-2805.‏

Baraniuk, R. G. (2011). More is less: signal processing and the data deluge. Science331(6018), 717-719.‏

Bélissent, J. (2010). Getting clever about smart cities: New opportunities require new business models. Cambridge, Massachusetts, USA.

‏Bellazzi, R., & Zupan, B. (2008). Predictive data mining in clinical medicine: current issues and guidelines. International journal of medical informatics77(2), 81-97.‏

Berkovich, S., & Liao, D. (2012, July). On clusterization of big data streams. In Proceedings of the 3rd International Conference on Computing for Geospatial Research and Applications (p. 26). ACM.

‏Bin, S., Yuan, L., & Xiaoyi, W. (2010, April). Research on data mining models for the internet of things. In 2010 International Conference on Image Analysis and Signal Processing (pp. 127-132). IEEE.

‏Blum, C., & Roli, A. (2003). Metaheuristics in combinatorial optimization: Overview and conceptual comparison. ACM computing surveys (CSUR)35(3), 268-308.‏

Chadwick, A., & May, C. (2003). Interaction between States and Citizens in the Age of the Internet:“e‐Government” in the United States, Britain, and the European Union. Governance16(2), 271-300.‏

Chen, H., Chung, W., Qin, Y., Chau, M., Xu, J. J., Wang, G., ... & Atabakhsh, H. (2003, May). Crime data mining: an overview and case studies. In Proceedings of the 2003 annual national conference on Digital government research (pp. 1-5). Digital Government Society of North America.

‏Chen, H., Chung, W., Xu, J. J., Wang, G., Qin, Y., & Chau, M. (2004). Crime data mining: a general framework and some examples. computer.‏

Chen, L., & Ren, G. (2012). The research of data mining technology of privacy preserving in sharing platform of internet of things. In Internet of Things (pp. 481-485). Springer, Berlin, Heidelberg.‏

Chen, M. (2013). Towards smart city: M2M communications with software agent intelligence. Multimedia Tools and Applications67(1), 167-178.‏

Chen, M., Gonzalez, S., Leung, V., Zhang, Q., & Li, M. (2010). A 2G-RFID-based e-healthcare system. IEEE Wireless Communications17(1), 37-43.‏

Chen, M., Ma, Y., Wang, J., Mau, D. O., & Song, E. (2013, October). Enabling comfortable sports therapy for patient: a novel lightweight durable and portable ECG monitoring system. In 2013 IEEE 15th International Conference on e-Health Networking, Applications and Services (Healthcom 2013) (pp. 271-273). IEEE.‏

Chen, M., Mao, S., & Liu, Y. (2014). Big data: A survey. Mobile networks and applications, 19(2), 171-209.

‏Choi, W., Shah, P., & Das, S. K. (2004, August). A framework for energy-saving data gathering using two-phase clustering in wireless sensor networks. In The First Annual International Conference on Mobile and Ubiquitous Systems: Networking and Services, 2004. MOBIQUITOUS 2004. (pp. 203-212). IEEE.‏

Da Xu, L., He, W., & Li, S. (2014). Internet of things in industries: A survey. IEEE Transactions on industrial informatics10(4), 2233-2243.‏

Demirkol, I., Ersoy, C., & Alagoz, F. (2006). MAC protocols for wireless sensor networks: a survey. IEEE Communications Magazine44(4), 115-121.‏

Ding, C., & He, X. (2004, July). K-means clustering via principal component analysis. In Proceedings of the twenty-first international conference on Machine learning (p. 29). ACM.‏

Domingo, M. C. (2012). An overview of the Internet of Things for people with disabilities. Journal of Network and Computer Applications35(2), 584-596.‏

Du, X. F., Leung, S. C., Zhang, J. L., & Lai, K. K. (2013). Demand forecasting of perishable farm products using support vector machine. International journal of systems Science44(3), 556-567.‏

Elgendy, N., & Elragal, A. (2014, July). Big data analytics: a literature review paper. In Industrial Conference on Data Mining(pp. 214-227). Springer, Cham.‏

Galushka, M., Patterson, D., & Rooney, N. (2006). Temporal data mining for smart homes. In Designing Smart Homes (pp. 85-108). Springer, Berlin, Heidelberg.

‏Gubbi, J., Buyya, R., Marusic, S., & Palaniswami, M. (2013). Internet of Things (IoT): A vision, architectural elements, and future directions. Future generation computer systems29(7), 1645-1660.‏

Guha, S., Meyerson, A., Mishra, N., Motwani, R., & O'Callaghan, L. (2003). Clustering data streams: Theory and practice. IEEE transactions on knowledge and data engineering15(3), 515-528.‏

Hammouda, K. M., & Kamel, M. S. (2004). Efficient phrase-based document indexing for web document clustering. IEEE Transactions on knowledge and data engineering16(10), 1279-1296.‏

Han, J., Cheng, H., Xin, D., & Yan, X. (2007). Frequent pattern mining: current status and future directions. Data mining and knowledge discovery15(1), 55-86.

Heer, J., & Chi, E. H. (2001, April). Identification of web user traffic composition using multi-modal clustering and information scent. In Proc. of the Workshop on Web Mining, SIAM Conference on Data Mining (pp. 51-58).‏

Helbig, N., Gil-García, J. R., & Ferro, E. (2009). Understanding the complexity of electronic government: Implications from the digital divide literature. Government Information Quarterly26(1), 89-97.‏

Hsieh, N. C., & Hung, L. P. (2010). A data driven ensemble classifier for credit scoring analysis. Expert systems with Applications37(1), 534-545.‏

Peng, Y., Zhang, Y., Tang, Y., & Li, S. (2011). An incident information management framework based on data integration, data mining, and multi-criteria decision making. Decision Support Systems51(2), 316-327.

Jia, X., Feng, Q., Fan, T., & Lei, Q. (2012, April). RFID technology and its applications in Internet of Things (IoT). In 2012 2nd international conference on consumer electronics, communications and networks (CECNet) (pp. 1282-1285). IEEE.‏

Juels, A. (2006). RFID security and privacy: A research survey. IEEE journal on selected areas in communications24(2), 381-394.‏

Kambal, E., Osman, I., Taha, M., Mohammed, N., & Mohammed, S. (2013, August). Credit scoring using data mining techniques with particular reference to Sudanese banks. In 2013 INTERNATIONAL CONFERENCE ON COMPUTING, ELECTRICAL AND ELECTRONIC ENGINEERING (ICCEEE)(pp. 378-383). IEEE.‏

Kincade, K. (1998). Data mining: digging for healthcare gold. Insurance & Technology23(2), 2-7.‏

Koh, H. C., & Tan, G. (2011). Data mining applications in healthcare. Journal of healthcare information management19(2), 65.‏

Lakshman, T. V., & Madhow, U. (1997). The performance of TCP/IP for networks with high bandwidth-delay products and random loss. IEEE/ACM transactions on networking5(3), 336-350.‏

Lee, H., Kim, S. G., Park, H. W., & Kang, P. (2014). Pre-launch new product demand forecasting using the Bass model: A statistical and machine learning-based approach. Technological Forecasting and Social Change86, 49-64.‏

Lee, Y. H., Kim, H. J., Roh, B. H., Yoo, S. W., & Oh, Y. C. (2005, December). Tree-based classification algorithm for heterogeneous unique item ID schemes. In International Conference on Embedded and Ubiquitous Computing (pp. 1078-1087). Springer, Berlin, Heidelberg.‏

Liu, C. H., Yang, B., & Liu, T. (2014). Efficient naming, addressing and profile services in Internet-of-Things sensory environments. Ad Hoc Networks18, 85-101.‏

Liu, J., Pan, J., Wang, Y., Lin, D., Shen, D., Yang, H., ... & Cao, X. (2013). Component analysis of Chinese medicine and advances in fuming-washing therapy for knee osteoarthritis via unsupervised data mining methods. Journal of Traditional Chinese Medicine33(5), 686-691.

‏Liu, J., Wan, J., He, S., & Zhang, Y. (2014). E-healthcare supported by big data. ZTE Communications12(3), 46-52.‏

Liu, J., Wang, Q., Wan, J., Xiong, J., & Zeng, B. (2013). Towards Key Issues of Disaster Aid based on Wireless Body Area Networks. KSII Transactions on Internet & Information Systems7(5).‏

Liu, Q., Wan, J., & Zhou, K. (2014). Cloud manufacturing service system for industrial-cluster-oriented application.  Journal of Internet Technology15(3), 373-380.

Lu, C. J., & Wang, Y. W. (2010). Combining independent component analysis and growing hierarchical self-organizing maps with support vector regression in product demand forecasting. International Journal of Production Economics128(2), 603-613.‏

Maaß, D., Spruit, M., & de Waal, P. (2014). Improving short-term demand forecasting for short-lifecycle consumer products with data mining techniques. Decision Analytics1(1), 4.‏

Moed, H. F., Glänzel, W., & Schmoch, U. (Ed.) (2004). Handbook of quantitative science and technology research. The use of publication and patent statistics in studies of S&T systems. Dordrecht (the Netherlands): Kluwer Academic Publishers.

Musaddiq, A., Zikria, Y. B., Hahm, O., Yu, H., Bashir, A. K., & Kim, S. W. (2018). A survey on resource management in IoT operating systems. IEEE Access6, 8459-8482.‏

Nath, S. V. (2006, December). Crime pattern detection using data mining. In 2006 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology Workshops (pp. 41-44). IEEE.‏

Ng, R. T., & Han, J. (2002). CLARANS: A method for clustering objects for spatial data mining. IEEE Transactions on Knowledge & Data Engineering, (5), 1003-1016.‏

Padhy, N., Mishra, D., & Panigrahi, R. (2012). The survey of data mining applications and feature scope. arXiv preprint arXiv:1211.5723.‏

Pan, G., Qi, G., Zhang, W., Li, S., Wu, Z., & Yang, L. T. (2013). Trace analysis and mining for smart cities: issues, methods, and applications. IEEE Communications Magazine51(6), 120-126.‏

Paulraj, D., Swamynathan, S., & Madhaiyan, M. (2012). Process model-based atomic service discovery and composition of composite semantic web services using web ontology language for services (OWL-S). Enterprise Information Systems6(4), 445-471.‏

Peng, Y., Zhang, Y., Tang, Y., & Li, S. (2011). An incident information management framework based on data

Rashidi, P., Cook, D. J., Holder, L. B., & Schmitter-Edgecombe, M. (2010). Discovering activities to recognize and track in a smart environment. IEEE transactions on knowledge and data engineering23(4), 527-539.‏

Sethi, P., & Sarangi, S. R. (2017). Internet of things: architectures, protocols, and applications. Journal of Electrical and Computer Engineering2017.

Silver, M., Sakata, T., Su, H. C., Herman, C., Dolins, S. B., & O Shea, M. J. (2001). Case study: how to apply data mining techniques in a healthcare data warehouse. Journal of healthcare information management15(2), 155-164.‏

Sundmaeker, H., Guillemin, P., Friess, P., & Woelfflé, S. (2010). Vision and challenges for realising the Internet of Things. Cluster of European Research Projects on the Internet of Things, European Commision3(3), 34-36.‏

Taylor, P. (2006). From patient data medical knowledge: the principles & practice of health informatics. Massachusetts: Black Well.

 Thornton, D., Mueller, R. M., Schoutsen, P., & Van Hillegersberg, J. (2013). Predicting healthcare fraud in medicaid: a multidimensional data model and analysis techniques for fraud detection. Procedia technology, 9, 1252-1264.‏

Tsai, C. W., Lai, C. F., Chiang, M. C., & Yang, L. T. (2013). Data mining for internet of things: A survey. IEEE Communications Surveys & Tutorials16(1), 77-97.‏

Turban, E., Mclean, E., & Wetherbe, J. (1999). Information technology for management: making connections for strategic advantage. New York: Willey & sons.

Wan, J., Li, D., Zou, C., & Zhou, K. (2012, October). M2M communications for smart city: An event-based architecture. In 2012 IEEE 12th International Conference on Computer and Information Technology (pp. 895-900). IEEE.‏

Wang, G., Chen, H., & Atabakhsh, H. (2004). Automaticially detecting deceptive criminal identities.‏

Welbourne, E., Battle, L., Cole, G., Gould, K., Rector, K., Raymer, S., ... & Borriello, G. (2009). Building the internet of things using RFID: the RFID ecosystem experience. IEEE Internet computing13(3), 48-55.‏

Xie, J. L. (2004). Mobility Management in Next Generation All-IP Based Wireless Systems (Doctoral dissertation, Georgia Institute of Technology).‏

Yin, H., Sun, Y., Cui, B., Hu, Z., & Chen, L. (2013, August). LCARS: a location-content-aware recommender system. In Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining (pp. 221-229). ACM.‏

Younis, O., & Fahmy, S. (2004, March). Distributed clustering in ad-hoc sensor networks: A hybrid, energy-efficient approach. In IEEE INFOCOM 2004 (Vol. 1). IEEE.‏

Yun, M., & Yuxin, B. (2010, June). Research on the architecture and key technology of Internet of Things (IoT) applied on smart grid. In 2010 International Conference on Advances in Energy Engineering (pp. 69-72). IEEE.

‏Zhang, T., Ramakrishnan, R., & Livny, M. (1996, June). BIRCH: an efficient data clustering method for very large databases. In ACM Sigmod Record (Vol. 25, No. 2, pp. 103-114). ACM.‏

Zhao, Q., & Bhowmick, S. S. (2003). Sequential pattern mining: A survey. ITechnical Report CAIS Nayang Technological University Singapore1, 26.‏

Zorzi, M., Gluhak, A., Lange, S., & Bassi, A. (2010). From today's intranet of things to a future internet of things: a wireless-and mobility-related view. IEEE Wireless communications17(6), 44-51.‏