Abstract
This paper proposes a novel model and device based on distributed knowledge reasoning, for predicting the environmental risk degree for an abnormal event (e.g., fire) in the Internet of Things (IoT) environment, which includes: (i) determining the predictive environmental information of the current node based on its historical environmental information and the predefined time-series prediction algorithm; (ii) determining the probability distribution function (PDF) of the environmental information obtained at each time corresponding to the historical environmental information; (iii) determining the deviated environment information based on the current environmental information and the probability distribution function mentioned above; and (iv) determining the environmental risk degree of the current node, based on the current environmental information, the predictive environmental information and the deviated environmental information. The wireless node of in the proposed model has the ability of perceiving and reasoning for the occurrence of an abnormal event. According to the current environmental information, the predictive environmental information and the deviated environmental information, the environmental risk degree of the current node could be determined jointly.
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References
Almaraashi, M., John, R., Hopgood, A., Ahmadi, S.: Learning of interval and general type-2 fuzzy logic systems using simulated annealing: theory and practice. Inf. Sci. 360, 21–42 (2016)
Beaulieu, N.C.: An infinite series for the computation of the complementary probability distribution function of a sum of independent random variables and its application to the sum of Rayleigh random variables. IEEE Trans. Commun. 38(9), 1463–1474 (1990)
Cheng, J., Xu, R., Tang, X., Sheng, V.: An abnormal network flow feature sequence prediction approach for DDoS attacks detection in big data environment. CMC: Comput. Mater. Continua 55(1), 095–119 (2018)
Coupland, S., John, R.: Geometric type-1 and type-2 fuzzy logic systems. IEEE Trans. Fuzzy Syst. 15(1), 3–15 (2007)
Duan, Y., Lv, Y., Wang, F.Y.: Travel time prediction with LSTM neural network. In: IEEE International Conference on Intelligent Transportation Systems (2016)
Guo, L., Sun, Y., Li, J., Ren, Q., Ren, M.: A framework of fire monitoring system based on sensor networks. In: Wang, X., Zheng, R., Jing, T., Xing, K. (eds.) WASA 2012. LNCS, vol. 7405, pp. 398–410. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-31869-6_35
Hwang, T., Nam, Y., So, J., Na, M., Choi, C.: An energy-efficient data reporting scheme based on spectrum sensing in wireless sensor networks. Wireless Pers. Commun. 93(4), 1–19 (2017)
Jiang, W., Sha, E.H.M., Zhuge, Q., Dong, H., Chen, X.: Optimal functional unit assignment and voltage selection for pipelined MPSoC with guaranteed probability on time performance. ACM Sigplan Not. 52(4), 41–50 (2017)
Junior, F.R.L., Osiro, L., Carpinetti, L.C.R.: A fuzzy inference and categorization approach for supplier selection using compensatory and non-compensatory decision rules. Appl. Soft Comput. 13(10), 4133–4147 (2013)
Karnik, N.N., Mendel, J.M., Liang, Q.: Type-2 fuzzy logic systems. IEEE Trans. Fuzzy Syst. 7(6), 643–658 (1999)
Komijani, H., Parsaei, M.R., Khajeh, E., Golkar, M.J., Zarrabi, H.: EEG classification using recurrent adaptive neuro-fuzzy network based on time-series prediction. Neural Comput. Appl. 1, 1–12 (2017)
Kristan, M., Leonardis, A.: Online discriminative kernel density estimator with Gaussian kernels. IEEE Trans. Cybern. 44(3), 355–365 (2017)
Kucian, K., Loenneker, T., Dietrich, T., Dosch, M., Martin, E., Von Aster, M.: Impaired neural networks for approximate calculation in dyscalculic children: a functional MRI study. Behav. Brain Funct. 2(1), 1–17 (2006)
Le-Khac, N.A., Aouad, L., Kechadi, M.T.: Toward a distributed knowledge discovery system for grid systems (2017)
Li, C., Gao, J., Yi, J., Zhang, G.: Analysis and design of functionally weighted single-input-rule-modules connected fuzzy inference systems. IEEE Trans. Fuzzy Syst. 26(1), 56–71 (2018)
Liu, W., Luo, X., Liu, Y., Liu, J.: Localization algorithm of indoor Wi-Fi access points based on signal strength relative relationship and region division. CMC: Comput. Mater. Continua 55(1), 071–093 (2018)
Lombardo, L., Corbellini, S., Parvis, M., Elsayed, A., Angelini, E., Grassini, S.: Wireless sensor network for distributed environmental monitoring. IEEE Trans. Instrum. Meas. 67(5), 1214–1222 (2018)
Love, D.J.: On the probability of error of antenna-subset selection with space-time block codes. IEEE Trans. Commun. 53(11), 1799–1803 (2005)
Mendez, G.M., Cavazos, A., Soto, R., Leduc, L.: Entry temperature prediction of a hot strip mill by a hybrid learning type-2 FLS. J. Intell. Fuzzy Syst. Appl. Eng. Technol. 17(6), 583–596 (2006)
Nguyen, T., Nahavandi, S.: Modified AHP for gene selection and cancer classification using type-2 fuzzy logic. IEEE Trans. Fuzzy Syst. 24(2), 273–287 (2016)
Piparo, G.B.L., Kisielewicz, T., Mazzetti, C., Rousseau, A.: Selection procedures for surge protective devices according to the probability of damage. Electr. Power Syst. Res. 146, 321–330 (2017)
Razzaque, M.A., Milojevic-Jevric, M., Palade, A., Clarke, S.: Middleware for Internet of Things: a survey. IEEE Internet Things J. 3(1), 70–95 (2017)
Saxena, M., Gupta, P., Jain, B.N.: Experimental analysis of RSSI-based location estimation in wireless sensor networks. In: 2008 International Conference on Communication Systems Software and Middleware and Workshops, COMSWARE, pp. 503–510 (2008)
Scott, D.W.: Kernel Density Estimation. Wiley, Hoboken (2018)
Selvin, S., Vinayakumar, R., Gopalakrishnan, E.A., Menon, V.K., Soman, K.P.: Stock price prediction using LSTM, RNN and CNN-sliding window model. In: International Conference on Advances in Computing (2017)
Shukla, D., Dawson, D.M., Paul, F.W.: Multiple neural-network-based adaptive controller using orthonormal activation function neural networks. IEEE Trans. Neural Netw. 10(6), 1494–501 (1999)
Starczewski, J.T.: What differs interval type-2 FLS from type-1 FLS? Pamm 1(1), 514–515 (2004)
Sun, J., Ling, B.: Software module clustering algorithm using probability selection. Wuhan Univ. J. Natural Sci. 23(2), 93–102 (2018)
Turkdoganaydinol, F.I., Yetilmezsoy, K.: A fuzzy-logic-based model to predict biogas and methane production rates in a pilot-scale mesophilic UASB reactor treating molasses wastewater. J. Hazard. Mater. 182(1), 460–471 (2010)
Volkov, V.V., Erokhin, V.I., Krasnikov, A.S., Razumov, A.V., Khvostov, M.N.: Minimum-Euclidean-norm matrix correction for a pair of dual linear programming problems. Comput. Math. Math. Phys. 57(11), 1757–1770 (2017)
Zhao, H.D., Wang, H.Z., Liu, G.N., Li, C., Zhao, M.H.: The application of Internet of Things (IoT) technology in the safety monitoring system for hoisting machines. Appl. Mech. Mater. 209–211, 2142–2145 (2012)
Acknowledgement
This work is funded by China Postdoctoral Science Foundation (No.2018M641436), and the Joint Advanced Research Foundation of China Electronics Technology Group Corporation (CETC) (No. 6141B08010102).
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Wang, Y., Liu, Y., Xie, H. (2019). A Novel Distributed Knowledge Reasoning Model. In: Sun, X., Pan, Z., Bertino, E. (eds) Artificial Intelligence and Security. ICAIS 2019. Lecture Notes in Computer Science(), vol 11632. Springer, Cham. https://doi.org/10.1007/978-3-030-24274-9_21
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