A Survey on Data Perception in Cognitive Internet of Things

• Autorzy: Bhajantri Lokesh B. , Baluragi Prashant

Identyfikatory

DOI

10.26636/jtit.2019.131419

• Języki publikacji: EN

Abstrakty

EN

A Cognitive Internet of Things (CIoT) is a brand of Internet of Things (IoT) with cognitive and agreeable mechanisms, which are incorporated to advance performance and accomplish insights into real world environments. CIoT can perceive present system’s conditions, analyze the apparent information, make smart choices, and increase the network performance. In this survey paper, we present classifications of data perception techniques used in CIoT. This paper also compares the data perception works against energy consumption, network life-time, resource allocation, and throughput, as well as quality of data and delay. In addition, simulation tools for IoT and their performance are discussed. Finally, we provide the model of cognitive agent-based data perception in CIoT for future research and development, which ensures the network performance in terms of reliability, energy efficient, accuracy, scalable, fault tolerant, and quality of data.

Słowa kluczowe

EN

Internet of things; Cognitive IT; data perception; cognitive agent

• Wydawca: Instytut Łączności - Państwowy Instytut Badawczy
• Czasopismo: Journal of Telecommunications and Information Technology
• Rocznik: 2019
• Tom: nr 3
• Strony: 75--86
• Opis fizyczny: Bibliogr. 41 poz., rys., tab.

Twórcy

autor

Bhajantri Lokesh B.

• Department of Information Science and Engineering, Basaveshwar Engineering College, Bagalkot, India

autor

Baluragi Prashant

• Department of Master of Computer Application, KLE Institute of Technology, Hubli, India

Bibliografia

• [1] R. Minerva and A. B. Chebudie, “Towards a definition of the Internet of Things (IoT)”, Technical Report, IEEE Internet Initiative, May 2015 [Online]. Available: https://iot.ieee.org/images/files/pdf/ IEEE IoT Towards Definition Internet of Things Revision1 27MAY15.pdf
• [2] J. Gubbi, R. Buyya, S. Marusic, and M. Palaniswami, “Internet of Things (IoT): A vision, architectural elements, and future directions”, J. of Future Gener. Comp. Syst., vol. 29, no. 7, pp. 1645–1660, 2013 (doi: 10.1016/j.future.2013.01.010).
• [3] N. C. Luong et al., “Data collection and wireless communication in Internet of Things (IoTs) using economic analysis and pricing models: a survey”, IEEE Commun. Surv. & Tutor., vol. 18, no. 4, pp. 2546–2590, 2016 (doi: 10.1109/COMST.2016.2582841).
• [4] W. Li et. al., “Performance comparison of cognitive radio sensor networks for industrial IoT with different deployment patterns”, IEEE Systems J., vol. 11, no. 3, pp. 1456–1466, 2017 (doi: 10.1109/JSYST.2015.1500518).
• [5] G. J. Hong, S. Lee, J. Lim, W. Yoon, and S. Han, “RF spectrum sensing receiver system with improved frequency channel selectivity for Cognitive IoT Sensor Network applications”, in Proc. of IEEE MTT-S Int. Microw. Symp. IMS 2016, San Francisco, CA, USA, 2016 (doi: 10.1109/MWSYM.2016.7540311).
• [6] Z. Baloch, F. K. Shaikh, and M. A. Unar, “A context aware data fusion approach for health-IoT”, Int. J. of Inform. Technol., vol. 10, no. 3, pp. 241–245, 2018 (doi:10.1007/s41870-018-0116-1).
• [7] J. Zhu, Y. Song, D. Jiang, and H. Song, “Multi-armed bandit channel access scheme with cognitive radio technology in wireless sensor networks for the Internet of Things”, J. on IEEE Access, vol. 4, pp. 4609–4617, 2016 (doi: 10.1109/ACCESS.2016.2600633).
• [8] W. Yang and X. Zhao, “Robust resource allocation for orthogonal frequency division multiplexing based cooperative cognitive radio networks with imperfect channel state information”, J. of IET Commun., vol. 11, no. 2, pp. 273–281, 2016 (doi: 10.1049/iet-com.2016.0742).
• [9] P. Bhardwaj, A. Panwar, and O. Ozdemir, “Enhanced dynamic spectrum access in multiband cognitive radio networks via optimized resource allocation”, IEEE Trans. on Wirel. Commun., vol. 15, no. 12, pp. 8083–8106, 2016 (doi: 10.1109/TWC.2016.2612627).
• [10] Z. Chen, H. Wang, Y. Liu, F. Bu, and Z. Wei, “A context aware routing protocol on Internet of Things based on sea computing model”, J. of Computers, vol. 7, no. 1, pp. 96–105, 2012 (doi: 10.4304/jcp.7.1.96-106).
• [11] M. Z. Naser and V. K. R. Kodur, “Cognitive Infrastructure – a modern concept for resilient performance under extreme event”, Autom. in Construction, vol. 90, pp. 253–264, 2018 (doi: 10.1016/j.autcon.2018.03.004).
• [12] S. Feng, P. Setoodeh, and S. Haykin, “Smart home: cognitive interactive people-centric Internet of Things”, IEEE Commun. Mag., vol. 55, no. 2, pp. 34–39, 2017 (doi: 10.1109/MCOM.2017.1600682CM).
• [13] W. Qihui et al., “Cognitive Internet of Things: A new paradigm beyond connection”, IEEE J. on Internet of Things, vol. 1, no. 2, pp. 129–143, 2014 (doi: 10.1109/JIOT.2014.2311513).
• [14] A. Dohr, R. Modre-Opsrian, M. Drobics, D. Hayn, and G. Schreier, “The Internet of Things for ambient assisted living”, in Proc. of 7th Int. Conf. on Inform. Technol.: New Generation, Las Vegas, NV, USA, 2010, pp. 804–809 (doi: 10.1109/ITNG.2010.104).
• [15] M. Zhang et al., “A novel architecture for cognitive Internet of Things”, Int. J. of Secur. and its Appl., vol. 9, no. 9, pp. 235–252, 2015 (doi: 10.1425/9ijsia.2015.9.9.21).
• [16] P. Kasnesis, C. Z. Patrikakis, D. Kogias, L. Toumanidis, and I. S. Venieris, “Cognitive Friendship and Goal Management for the Social IoT”, Computers and Elec. Engin., vol. 58, pp. 412–428, 2017 (doi: 10.1016/j.compeleceng.2016.09.024).
• [17] K. Zhou, J. Zeng, Y. Liu, and F. Zou, “Deep sentiment hashing for text retrieval in social CIoT”, J. of Future Gener. Comp. Syst., vol. 28, pp. 362–371, 2018 (doi: 10.1016/j.future.2018.03.047).
• [18] D. Zhan et al., “A high-performance virtual machine file system monitor in cloud-assisted cognitive IoT”, J. of Future Gener. Comp. Syst., vol. 88, pp. 209–219, 2018 (doi: 10.1016/j.future.2018.05.055).
• [19] A. M. Alberti et al., “Cognitive radio in the context of Internet of Things using a novel future internet architecture called NovaGenesis”, J. of Computers and Elec. Engin., vol. 57, pp. 147–161, 2017 (doi: 10.1016/j.comeleceng.2016.07.008).
• [20] K. Lin, D. Wang, F. Xia, and H. Ge, “Device clustering algorithm based on multimodal data correlation in Cognitive Internet of Things”, IEEE Internet of Things J., vol 5, no. 4, pp. 2263–2271, 2017 (doi: 10.1109/JIOT.2017.2728705).
• [21] A. Somov, C. Dupont, and R. Giaffreda, “Supporting smart city mobility with Cognitive Internet of Things”, in Proc. Future Network & Mobile Summit, Lisboa, Portugal, 2013 (ISBN: 978-1-905824-37-3).
• [22] F. Tao, Y. Zuo, L. D. Xu, and L. Zhang, “IoT-based intelligent perception and access of manufacturing resource toward cloud manufacturing”, IEEE Trans. on Indust. Informat., vol. 10, no. 2, pp. 1547–1557, 2014 (doi: 10.1109/TII.2014.2306397).
• [23] B. Deen, K. Koldewyn, N. Kanwisher, and R. Saxe, “Functional organization of social perception and cognition in the superior temporal sulcus”, Cerebral Cortex, vol. 25, no. 11, pp. 4596–4609, 2015 (doi: 10.1093/cercor/bhv111).
• [24] C. Chai, X. Shi, Y. D. Wong, M. J. Er, and E. T. Meng, “Fuzzy logicbased observation and evaluation of pedestrians behavioral patterns by age and gender”, Transportat. Res. Part F: Traffic Psychol. and Behav., vol. 40, pp. 104–118, 2016 (doi: 10.1016/j.trf.2016.04.004).
• [25] G. Benincasa, G. D’Aniello, C. De Maio, V. Loia, and F. Orciuoli, “Towards perception-oriented situation awareness systems”, in Proc. of the 7th IEEE Int. Conf. Intelligent Syst. IS’2014, Warsaw, Poland, 2014, pp. 813–824 (doi: 10.007/978-3-319-11313-5 71).
• [26] Z. Cui, S. S. Ge, Z. Cao, J. Yang, and H. Ren, “Analysis of different sparsity methods in constrained RBM for sparse representation in cognitive robotic perception”, J. of Intell. and Robotic Syst., vol. 80, no. 1, pp. 121–132, 2015 (doi: 10.1007/s10846-015-0213-3).
• [27] I. O. Pappas, P. E. Kourouthanassis, M. N. Giannakos, and V. Chrissikopoulos, “Explaining online shopping behavior with fsQCA: the role of cognitive and affective perceptions”, J. of Business Res., vol. 69, no. 2, pp. 794–803, 2016 (doi: 10.1016/j.jbusres.2015.07.010).
• [28] S. R. Gray et al., “Are coastal managers detecting the problem? Assessing stakeholder perception of climate vulnerability using fuzzy cognitive mapping”, Ocean & Coastal Manag., vol. 94, pp. 74–89, 2014 (doi: 10.1016/j.ocecoaman.2013.11.008).
• [29] A. Guirguis, M. Karmoose, K. Habak, M. El-Nainay, and M. Youssef, “Cooperation-based multi-hop routing protocol for cognitive radio networks”, J. of Netw. and Comp. Appl., vol. 110, pp. 27–42, 2018 (doi: 10.1016/j.jnca.2018.03.005).
• [30] H. D. S. Ara´ujo et al., “A proposal for iot dynamic routes selection based on contextual information”, Sensors, vol. 18, no. 2, pp. 353–344, 2018 (doi: 10.3390/s18020353).
• [31] M. Nitti, M. Murroni, M. Fadda, and L. Atzori, “Exploiting social Internet of Things features in cognitive radio”, IEEE Access, vol. 4, pp. 9204–9212, 2016 (doi: 10.1109/ACCESS.2016.2645979).
• [32] Y. Vishwanath, T. S. Murali, and M. V. Vijayakumar, “Implementation of perception classification based on BDI model using Bayesian classifier”, in Proc. of Int. J. of Comp. Sci. and Inform. Technol. (IJCSIT), vol. 5, no. 6, pp. 8161–8165, 2014 [Online]. Available: http://ijcsit.com/docs/Volume%205/vol5issue06/ ijcsit20140506282.pdf
• [33] D. Anchisi and M. Zanon, “A Bayesian perspective on sensory and cognitive integration in pain perception and placebo analgesia”, PLoS One, vol. 10, no. 2, pp. 1–12, 2015 (doi: 10.1371/journal.pone.0117270 ).
• [34] S. A. Gray et al., “Using fuzzy cognitive mapping as a participatory approach to analyze change, preferred states, and perceived resilience of social-ecological systems”, Ecology and Society, vol. 20, no. 2, pp. 1–10, 2015 (doi: 10.5751/ES-07396-200211).
• [35] L. Wenxiang et al., “Performance comparison of cognitive radio sensor networks for industrial IoT with different deployment patterns”, IEEE Systems J., vol. 11, no. 3, pp. 1456–1466, 2017 (doi: 10.1109/JSYST.2015.2500518).
• [36] Y. Liu, “Research on the Brain-inspired Cross-media Neural Cognitive Computing Framework”, 2018, arXiv:1805.01385 [Online]. Available: https://arxiv.org/ftp/arxiv/papers/1805/1805.01385.pdf
• [37] X. Zhang, L. Yao, S. Zhang, S. S., Kanhere, Q. Z., Sheng, Y. Liu, “Internet of Things meets brain-computer interface: a unified deep learning framework for enabling human-thing cognitive interactivity”, J. of IEEE Internet of Things, vol. 6, no. 2, pp. 2084–2092, 2018 (doi: 10.1109/JIOT.2018.2877786).
• [38] G. Jamnal and X. D. Liu, “A cognitive-IoE approach to ambient intelligent smart home”, in Proc. of the 2nd Int. Conf. on Internet of Things, Big Data and Secur. IoTBDS 2017, Porto, Portugal, 2017, vol. 2, pp. 302–308 (doi: 10.5220/0006304103020308).
• [39] L. Huimin, L. Hu, G. Liu, and J. Zhou, “Cognitive Model-based evolution mechanism of the information status in Internet of Things”, Inform. Technol. J., vol. 13, no. 15, pp. 2431–2436, 2014 (doi: 10.3923/itj.2014.2431.2436).
• [40] Y. Zhang, P. W. S. Zhang, Y. Wang, and N. Li, “A spectrum sensing method based on signal feature and clustering algorithm in cognitive wireless multimedia sensor networks”, Adv. in Multimed., vol. 2017, pp. 1–10, 2017 (doi: 10.1155/2017/2895680).
• [41] M. Belk, E. Papatheocharous, P. Germanakos, and G. Samaras, “Investigating the relation between users cognitive style and web navigation behavior with K-means clustering”, in Advances in Conceptual Modeling ER 2012 Workshops CMS, ECDM-NoCoDA, MoDIC, MORE-BI, RIGiM, SeCoGIS, WISM, Florence, Italy, October 15-18, 2012. Proceedings, S. Castano, P. Vassiliadis, L V. Lakshmanan, and M. L. Lee, Eds. LNCS, vol. 7518. Springer, 2012, pp. 337–346 (doi: 10.1007/978-3-642-33999-8 40).

Uwagi

Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2019).