ijece-140502292014050229202438CMV Verlagkhoffmann@cmv-verlag.comCMV VerlagNashriyyah -i Muhandisi -i Barq va Muhandisi -i Kampyutar -i Iranijece16823745182022193FarzadH. PanahiFereidounH. PanahiZahraAskarizadeh Ardestani18202219320210.66224/ijece.28543.19.3.193http://ijece.org/fa/Article/28543http://ijece.org/fa/Article/Download/28543http://ijece.org/fa/Article/Download/28543http://ijece.org/fa/Article/Download/28543http://ijece.org/fa/Article/Download/28543http://ijece.org/fa/Article/Download/28543http://ijece.org/fa/Article/Download/28543http://ijece.org/fa/Article/Download/28543[1] H. Kim and S. Han, "An efficient sensor deployment scheme for large-scale wireless sensor networks," IEEE Communications Letters, vol. 19, no. 1, pp. 98-101, Jan. 2015. [2] F. Shahzad, T. R. Sheltami, and E. M. Shakshuki, "Multi-objective optimization for a reliable localization scheme in wireless sensor networks," J. of Communications and Networks, vol. 18, no. 5, pp. 796-805, Oct. 2016. [3] Y. Zhang, X. Zhang, S. Ning, J. Gao, and Y. Liu, "Energy-efficient multilevel heterogeneous routing protocol for wireless sensor networks," IEEE Access, vol. 7, pp. 55873-55884, 2019. [4] R. Morello, S. C. Mukhopadhyay, Z. Liu, D. Slomovitz, and S. R. Samantaray, "Advances on sensing technologies for smart cities and power grids: a review," IEEE Sensors J., vol. 17, no. 23, pp. 7596-7610, Dec. 2017. [5] T. I. Krebesz, G. Kolumban, C. K. Tse, F. C. M. Lau, and H. Dong, "Use of UWB impulse radio technology in in-car communications: power limits and optimization," IEEE Trans. on Vehicular Technology, vol. 66, no. 7, pp. 6037-6049, Jul. 2017. [6] J. L. Burbank, J. Andrusenko, S. Everett, and W. T. M. Kasch, Wireless Networking: Understanding Internetworking Challenges, Wiley-IEEE Press, Jun. 2013. [7] T. de Almeida Oliveira and E. P. Godoy, "Zigbee wireless dynamic sensor networks: feasibility analysis and implementation guide," IEEE Sensors J., vol. 16, no. 11, pp. 4614-4621, Jun. 2016. [8] H. Chen, C. Meng, Z. Shan, Z. Fu, and B. K. Bhargava, "A novel low-rate denial of service attack detection approach in zigbee wireless sensor network by combining hilbert-huang transformation and trust evaluation," IEEE Access, vol. 7, pp. 32853-32866, 2019. [9] S. Sharma, V. Bhatia, and A. Gupta, "Noncoherent IR-UWB receiver using massive antenna arrays for wireless sensor networks," IEEE Sensors Letters, vol. 2, no. 1, Art No.: 1500204, 4 pp., Mar. 2018. [10] Z. Yin, M. Wu, Z. Yang, N. Zhao, and Y. Chen, "A joint multiuser detection scheme for UWB sensor networks using waveform division multiple access," IEEE Access, vol. 5, pp. 11717-11726, 2017. [11] M. Chiani and A. Giorgetti, "Coexistence between UWB and narrow-band wireless communication systems," Proc. of the IEEE, vol. 97, no. 2, pp. 231-254, Feb. 2009. [12] Y. L. Chao and R. A. Scholtz, "Ultra-wideband transmitted reference systems," IEEE Trans. on Veh. Tech., vol. 54, no. 5, pp. 1556-1569, Sept. 2005. [13] F. H. Panahi and A. Falahati, "Spectral efficient impulse radio-ultra-wideband transmission model in presence of pulse attenuation and timing jitter," IET Communications, vol. 6, no. 11, pp. 1544-1554, 2012. [14] Y. P. Nakache and A. F. Molish, "Spectral shaping of UWB signals for time-hopping impulse radio," IEEE J. on Selected Areas in Com., vol. 24, no. 4, pp. 738-744, Apr. 2006. [15] S. Frattasi and F. Della Rosa, Mobile Positioning and Tracking: from Conventional to Cooperative Techniques, John Wiley & Sons Ltd, Jul. 2017. [16] K. Kouassi, L. Clavier, I. Doumbia, and P. Rolland, "Optimal PWR codes for TH-PPM UWB multiple-access interference mitigation," IEEE Communication Letters, vol. 17, no. 1, pp. 103-106, Jan. 2013. [17] M. Bashar and Q. Nidal, "Enhancing the bitrate and power spectral density of PPM TH-IR UWB signals using a sub-slot technique," Int. J. of Advanced Computer Science and Applications, vol. 11, no. 2, pp. 341-346, 2020. [18] S. V. Mir-Moghtadaei, "A new UHF/ultra wideband‐radio frequency identification system to solve coexistence issues of ultra wideband‐radio frequency identification and other in‐band narrowband systems," Trans. on Emerging Telecom. Tech., vol. 32, no. 1, Article No.: e4147, Jan. 2020. [19] X. Wu, Z. Tian, T. N. Davidson, and G. B. Giannakis, "Optimal waveform design for UWB radios," IEEE Trans. on Signal Proc., vol. 54, no. 6, pp. 2009-2021, Jun. 2006. [20] P. Walk, P. Jung, and J. Timmermann, "Lowdin transform on FCC optimized UWB pulses," in Proc. IEEE Wireless Communication and Networking Conf., 6 pp., Sydney, NSW, Australia, 18-21 Apr. 2010. [21] Y. Wang, X. Dong, and I. J. Fair, "Spectrum shaping and NBI suppression in UWB communications," IEEE Trans. on Wireless Com., vol. 6, no. 5, pp. 1944-1952, May 2007. [22] M. E. Khedr, A. El-Helw, and M. H. Afifi, "Adaptive mitigation of narrowband interference in impulse radio UWB systems using time-hopping sequence design," J. of Communications and Networks, vol. 17, no. 6, pp. 622-633, Dec. 2015. [23] S. R. Acdudodla, S. Vijayakumaran, and T. E. Wong, "Timing acquisition in ultra-wideband communication systems," IEEE Trans. Veh. Technol., vol. 54, no. 5, pp. 1570-1583, Sept. 2005. [24] J. D. Choi and W. E. Stark, "Performance of ultra-wideband communications with suboptimal receivers in multipath channels," IEEE J. Sel. Areas Commun., vol. 20, no. 9, pp. 1754-1766, Dec. 2002. [25] J. Romme and K. Witrisal, "Transmitted-reference UWB systems using weighted autocorrelation receivers," IEEE Trans. Microw. Theory Tech., vol. 54, no. 4, pp. 1754-1761, Apr. 2006. [26] Y. L. Chao and R. A. Scholtz, "Optimal and suboptimal receivers for ultrawideband transmitted reference systems," in Proc. IEEE Global Commun. Conf., vol. 2, pp. 759-763, San Francisco, CA, USA, 1-5 Dec. 2003. [27] S. Gezici, F. Tufvesson, and A. F. Molisch, "On the performance of transmitted-reference impulse radio," in Proc. IEEE Global Commun. Conf., pp. 2874-2879, Dallas, TX, USA, 29 Nov.-3 Dec. 2004. [28] M. Casu and G. Durisi, "Implementation aspects of a transmitted-reference UWB receiver," Wireless Commun. Mobile Comput., vol. 5, no. 5, pp. 537-549, Aug. 2005. [29] R. Hoctor and H. Tomlinson, "Delay-hopped transmitted-reference RF communications," in Proc. IEEE Conf. Ultra Wideband Syst. Technol., pp. 265-269, Baltimore, MD, USA, 21-23 May 2002. [30] M. Ho, V. S. Somayazulu, J. Foerster, and S. Roy, "A differential detector for an ultra-wideband communications system," in Proc. 55th IEEE Veh. Technol. Conf., Birmingham, AK, USA, vol. 4, pp. 1896-1900, May 2002. [31] I. Guvenc, Z. Sahinoglu, and P. V. Orlik, "TOA estimation for IR-UWB systems with different transceiver types," IEEE Trans. Microw. Theory Tech., vol. 54, no. 4, pp. 1876-1886, Apr. 2006. [32] W. Gifford and M. Win, "On transmitted-reference UWB communications," in Proc. Asilomar Conf. Signals, Syst. Comput., vol. 2, pp. 1526-1531, Pacific Grove, CA, USA, 7-10 Nov. 2004. [33] Y. Jin and K. S. Kwak, "A transmitted reference pulse cluster averaging UWB receiver," IEEE Systems J., vol. 11, no. 2, pp. 1107-1115, Jun. 2017. [34] H. Matti, et al., "Ultra-wideband radar-based indoor activity monitoring for elderly care," Sensors, vol. 21, no. 9, Article No.: 3158, 2 May 2021. [35] J. Fusselman, M. Gilliam, Y. Shrestha, Y. Zhang, and K. Kelly, "Ultra-compact ultra-wideband radar for high-speed target tracking," Radar Sensor Technology, vol. 11742, Article No.: 117420F, 2021. [36] M. K. Simon and M. S. Alouini, Digital Communication over Fading Channels a Unified Approach to Performance Analysis, John Wiley & Sons, Inc., 2000.JavadSalimi SartakhtiSalmanGoli18202218319210.66224/ijece.28884.19.3.183http://ijece.org/fa/Article/28884http://ijece.org/fa/Article/Download/28884http://ijece.org/fa/Article/Download/28884http://ijece.org/fa/Article/Download/28884http://ijece.org/fa/Article/Download/28884http://ijece.org/fa/Article/Download/28884http://ijece.org/fa/Article/Download/28884http://ijece.org/fa/Article/Download/28884[1] D. Bhattacharya and M. Mitra, Analytics on Big Fast Data Using Real Time Stream Data Processing Prchitecture, EMC Corporation, 2013. [2] B. E. Boser, I. M. Guyon, and V. N. Vapnik, "A training algorithm for optimal margin classifiers," in Proc. of the 5th Annual Workshop on Computational Learning Theory, pp. 144-152, Pittsburgh, PA, USA, 27-29 Jul. 1992. [3] R. T. Rockafellar, "Lagrange multipliers and optimality," SIAM Review, vol. 35, no. 2, pp. 183-238, 1993. [4] J. A. Suykens and J. Vandewalle, "Least squares support vector machine classifiers," Neural Processing Letters, vol. 9, no. 3, pp. 293-300, Jun. 1999. [5] J. A. K. Suykens, T. Van. Gestel, J. De Brabanter, B. De Moor, and J, Vandewalle, Least Squares Support Vector Machines, World Scientific, 2002. [6] R. Khemchandani and S. Chandra, "Twin support vector machines for pattern classification," IEEE Trans. on Pattern Analysis and Machine Intelligence, vol. 29, no. 5, pp. 905-910, May 2007. [7] M. A. Kumar and M. Gopal, "Least squares twin support vector machines for pattern classification," Expert Systems with Applications, vol. 36, no. 4, pp. 7535-7543, May 2009. [8] J. S. Sartakhti, H. Afrabandpey, and N. Ghadiri, "Fuzzy least squares twin support vector machines," Engineering Applications of Artificial Intelligence, vol. 85, pp. 402-409, Oct. 2019. [9] V. Losing, B. Hammer, and H. Wersing, "Incremental on-line learning: a review and comparison of state of the art algorithms," Neurocomputing, vol. 275, pp. 1261-1274, Jan. 2018. [10] R. KhemchandaniJayadeva, and S. Chandra, "Incremental twin support vector machines," in Modeling, Computation and Optimization, in S. K. Neogy, A. K. Das, and R. B. Bapat (eds.), pp. 263-272, World Scientific, 2009. [11] P. Mitra, C. A. Murthy and S. K. Pal, "Data condensation in large databases by incremental learning with support vector machines," in Proc.of 15th Int. Conf. on Pattern Recognition. ICPR'00, vol.2, pp. 708-711, , Barcelona, Spain, 3-7 Sept. 2000, [12] Y. Hao and H. Zhang, "A fast incremental learning algorithm based on twin support vector machine," in Proc. IEEE 7th Int. Symp. on Computational Intelligence and Design, , vol. 2, pp. 92-95, Hangzhou, China, 13-14 Dec. 2014. [13] F. Alamdar, S. Ghane, and A. Amiri, "On-line twin independent support vector machines," Neurocomputing, vol. 186, no. C, pp. 8-21, Apr. 2016. [14] G. Fung and O. L. Mangasarian, "Incremental support vector machine classification," in Proc. of the SIAM Int. Conf. on Data Mining, pp. 247-260, Arlimgton, VA, USA, 11-13 Apr. 2002. [15] A. Tveit, M. L. Hetland, and H. Engum, "Incremental and decremental proximal support vector classification using decay coefficients," in Proc. of the Int. Conf. on Data Warehousing and Knowledge Discovery, pp. 422-429, Prague, Czech Republic, 3-5 Sept. 2003. [16] A. R. Mello, M. R. Stemmer, and A. L. Koerich, "Incremental and decremental fuzzy bounded twin support vector machine," Information Sciences, vol. 526, pp. 20-38, Jul. 2020. [17] J. Xu, C. Xu, B. Zou, Y. Y. Tang, J. Peng, and X. You, "New incremental learning algorithm with support vector machines," IEEE Trans. on Systems, Man, and Cybernetics, Systems, vol. 49, no. 11, pp. 2230-2241, Nov. 2018. [18] I. A. Lawal, "Incremental SVM learning," Studies in Big Data Learning from Data Streams in Evolving Environments, pp. 279-296, 2019. [19] W. Xie, S. Uhlmann, S. Kiranyaz, and M. Gabbouj, "Incremental learning with support vector data description," in Proc. IEEE 22nd Int. Conf. on Pattern Recognition, pp. 3904-3909, Stockholm, Sweden, 24-28 Aug. 2014. [20] Z. Zhu, X. Zhu, Y. F. Guo, and X. Xue, "Transfer incremental learning for pattern classification," in Proc. of the 19th ACM International Conf. on Information and Knowledge Management, pp. 1709-1712, Toronto, Canada, 26-30 Oct. 2010. [21] G. Cauwenberghs and T. Poggio, "Incremental and decremental support vector machine learning," in Proc. of the 13th Int Conf. on Neural Information Processing Systems, pp. 388-394, Denver, CO, USA, 1-1 Jan. 2000. [22] H. Duan, X. Shao, W. Hou, G. He, and Q. Zeng, "An incremental learning algorithm for Lagrangian support vector machines," Pattern Recognition Letters, vol. 30, no. 15, pp. 1384-1391, 1 Nov. 2009. [23] H. Galmeanu, L. M. Sasu, and R. Andonie, "Incremental and decremental SVM for regression," International J. of Computers Communications & Control, vol. 11, no. 6, pp. 755-775, Dec. 2016. [24] H. Galmeanu and R. Andonie, "A multi-class incremental and decremental SVM approach using adaptive directed acyclic graphs," in Proc. IEEE Int Conf. on Adaptive and Intelligent Systems, pp. 114-119, Klagenfurt, Austria, 24-26 Sept. 2009. [25] J. Wang, D. Yang, W. Jiang, and J. Zhou, "Semisupervised incremental support vector machine learning based on neighborhood kernel estimation," IEEE Trans. on Systems, Man, and Cybernetics: Systems, vol. 47, no. 10, pp. 2677-2687, Oct. 2017. [26] M. S. Chen, T. Y. Ho, and D. Y. Huang, "Online transductive support vector machines for classification," in Proc. IEEE Int. Conf. on Information Security and Intelligent Control, pp. 258-261, Yunlin, Taiwan, 14-16 Aug. 2012. [27] P. Rai, H. Daume, and S. Venkatasubramanian, "Streamed learning: one-pass SVMs," in Proc. 21st Int. Joint Conf. on Artificial Intelligence, pp. 1211-1216, Pasadena, CA, USA, 11-17 Jul. 2009. [28] N. A. Syed, S. Huan, L. Kah, and K. Sung, "Incremental learning with support vector machines," in ¬ Proc. IEEE Int. Conf. on Data Mining, San Jose, CA, USA, 29 Nov.-2 Dec. 2001. [29] F. Orabona, C. Castellini, B. Caputo, L. Jie, and G. Sandini, "On-line independent support vector machines," Pattern Recognition, vol. 43, no. 4, pp. 1402-1412, Apr. 2010. [30] L. Ralaivola and F. d'Alche-Buc, "Incremental support vector machine learning: a local approach," in Proc. Int. Conf. on Artificial Neural Networks, pp. 322-330, Vienna, Austria, 21-25 Aug. 2001. [31] M. N. Kapp, R. Sabourin, and P. Maupin, "Adaptive incremental learning with an ensemble of support vector machines," in Proc. IEEE 20th Int. Conf. on Pattern Recognition, pp. 4048-4051, Istanbul, Turkey, 23-26 Aug. 2010. [32] C. Domeniconi and D. Gunopulos, "Incremental support vector machine construction," in Proc. IEEE Int. Conf. on Data Mining, pp. 589-592, San Jose, CA, USA, 29 Nov.-2 Dec. 2001. [33] A. R. de Mello, M. R. Stemmer, and A. L. Koerich, Incremental and Decremental Fuzzy Bounded Twin Support Vector Machine, arXiv preprint arXiv:1907.09613, 2019. [34] "Activities of Daily Living (ADLs) Recognition Using Binary Sensors Data Set," ed, 2013. [35] F. Ordonez, P. De Toledo, and A. Sanchis, "Activity recognition using hybrid generative/discriminative models on home environments using binary sensors," Sensors, vol. 13, no. 5, pp. 5460-5477, 2013.SahandHashemiSeyyed AmirAsghariMohammad RezaBinesh Marvasti18202217018210.66224/ijece.28958.19.3.170http://ijece.org/fa/Article/28958http://ijece.org/fa/Article/Download/28958http://ijece.org/fa/Article/Download/28958http://ijece.org/fa/Article/Download/28958http://ijece.org/fa/Article/Download/28958http://ijece.org/fa/Article/Download/28958http://ijece.org/fa/Article/Download/28958http://ijece.org/fa/Article/Download/28958[1] J. Luo, Y. Chen, M. Wu, and Y. Yang, "A survey of routing protocols for underwater wireless sensor networks," IEEE Commun. Surv. Tutorials, vol. 23, no. 1, pp. 137-160, Jan. 2021. [2] R. Piyare, A. L. Murphy, C. Kiraly, P. Tosato, and D. Brunelli, "Ultra low power wake-up radios: a hardware and networking survey," IEEE Communications Surveys and Tutorials, vol. 19, no. 4, pp. 2117-2157, Fourthquarter 2017. [3] D. S. Deif and Y. Gadallah, "An ant colony optimization approach for the deployment of reliable wireless sensor networks," IEEE Access, vol. 5, pp. 10744-10756, 2017. [4] A. B. Noel, A. Abdaoui, T. Elfouly, M. H. Ahmed, A. Badawy, and M. S. Shehata, "Structural health monitoring using wireless sensor networks: a comprehensive survey," IEEE Commun. Surv. Tutorials, vol. 19, no. 3, pp. 1403-1423, Third quarter 2017. [5] N. Jan, et al., "A balanced energy-consuming and hole-alleviating algorithm for wireless sensor networks," IEEE Access, vol. 5, pp. 6134-6150, 2017. [6] M. Aazam, S. Zeadally, and E. F. Flushing, "Task offloading in edge computing for machine learning-based smart healthcare," Comput. Networks, vol. 191, Article ID: 108019, 11 pp., May 2021. [7] Y. Li, X. Zhang, and Q. Rong, "Optimization of hospital computer network and helicobacter pylori ulcer nursing analysis," Microprocess. Microsyst., vol. 81, Article ID:. 103771, Mar. 2021. [8] T. M. Behera and S. K. Mohapatra, "A novel scheme for mitigation of energy hole problem in wireless sensor network for military application," Int. J. Commun. Syst., vol. 34, no. 11, Article ID: e4886, 25 Jul. 2021. [9] B. R. Al-Kaseem, Z. K. Taha, S. W. Abdulmajeed, and H. S. Al-Raweshidy, "Optimized energy-efficient path planning strategy in WSN with multiple mobile sinks," IEEE Access, vol. 9, pp. 82833-82847, 2021. [10] M. A. Khan, et al., "Network lifetime maximization via energy hole alleviation in wireless sensor networks," in Advances on Broad-Band Wireless Computing, Communication and Applications, (eds.) M. A. Khan, et al., pp. 279-290, Springer, 2017. [11] G. Shi, K. Liu, and J. Zeng, "Cooperative depth rotation to avoid energy hole for 3D underwater sensor networks," in Proc. IEEE 24th Int. Conf. Comput. Support. Coop. Work Des. CSCWD’21, pp. 825-830, Dalian, China, 5-7 May 2021. [12] G. S. Binu and B. Shajimohan, "A novel heuristic based energy efficient routing strategy in wireless sensor network," Peer-to-Peer Netw. Appl. vol. 13, no. 6, pp. 1853-1871, Jun. 2020. [13] M. Abu Alsheikh, D. T. Hoang, D. Niyato, H. P. Tan, and S. Lin, "Markov decision processes with applications in wireless sensor networks: a survey," IEEE Commun. Surv. Tutorials, vol. 17, no. 3, pp. 1239-1267, Third Quarter 2015. [14] T. M. Hansen, E. K. P. Chong, S. Suryanarayanan, A. A. Maciejewski, and H. J. Siegel, "A partially observable markov decision process approach to residential home energy management," IEEE Trans. Smart Grid, vol. 9, no. 2, pp. 1271-1281, Mar. 2018. [15] H. Nigam, A. Karmakar, and A. K. Saini, "Wireless sensor network based structural health monitoring for multistory building," in Proc. 4th Int. Conf. Comput. Commun. Signal Process, ICCCSP’20, 5 pp., Chennai, India, 28,-29 Sept. 2020. [16] S. Alamandala, R. L. N. Sai Prasad, and P. Rathish Kumar, "Cost-effective load measurement system for health monitoring using long-period grating as an edge filter," Opt. Fiber Technol., vol. 59, Article ID: 102328, Oct. 2020. [17] S. Dey, R. Bhattacharyya, S. E. Sarma, and N. C. Karmakar, "A novel 'smart skin' sensor for chipless RFID-based structural health monitoring applications," IEEE Internet Things J., vol. 8, no. 5, pp. 3955-3971, Mar. 2021. [18] X. Liu, J. Cao, and P. Guo, "SenetSHM: towards practical structural health monitoring using intelligent sensor networks," in Proc. IEEE Int. Confs. on Big Data and Cloud Computing, BDCloud'16, Social Computing and Networking, SocialCom'16 and Sustainable Computing and Communications, SustainCom'16, pp. 416-423, Atlanta, GA, USA, 8-10 Oct. 2016. [19] E. Zaraket, N. M. Murad, S. S. Yazdani, L. Rajaoarisoa, and B. Ravelo, "An overview on low energy wake-up radio technology: active and passive circuits associated with MAC and routing protocols," J. Netw. Comput. Appl., vol. 190, Article ID: 103140, Sept. 2021. [20] M. Magno, V. Jelicic, B. Srbinovski, V. Bilas, E. Popovici, and L. Benini, "Design, implementation, and performance evaluation of a flexible low-latency nanowatt wake-up radio receiver," IEEE Trans. Ind. Informatics, vol. 12, no. 2, pp. 633-644, Apr. 2016. [21] J. Oller, I. Demirkol, J. Casademont, J. Paradells, G. U. Gamm, and L. Reindl, "Has time come to switch from duty-cycled MAC protocols to wake-up radio for wireless sensor networks?," IEEE/ACM Trans. Netw., vol. 24, no. 2, pp. 674-687, Apr. 2016. [22] A. Fumtchum, F. Hutu, P. Tsafack, G. Villemaud, and E. Tanyi, "High efficiency rectifier for a quasi-passive wakeup radio," in Proc. Int. Symp. Signals, Circuits Syst., ISSCS'19, 4 pp., Iasi, Romania, 11-12 Jul. 2019. [23] S. B. Amsalu, W. K. Zegeye, D. Hailemariam, and Y. Astatke, "Design and performance evaluation of an energy efficient routing protocol for wireless sensor networks," in Proc. 50th Annu. Conf. Inf. Syst. Sci, CISS’16, pp. 48-53, Princeton, NJ, USA, 16-18 Mar. 2016. [24] S. J. Marinkovic and E. M. Popovici, "Nano-power wireless wake-up receiver with serial peripheral interface," IEEE J. Sel. Areas Commun., vol. 29, no. 8, pp. 1641-1647, Sep. 2011. [25] M. Del Prete, D. Masotti, A. Costanzo, M. Magno, and L. Benini, "A dual-band wake-up radio for ultra-low power wireless sensor networks," in Proc. IEEE Top. Conf. Wirel. Sensors Sens. Networks, WiSNet’16, pp. 81-84, Austin, TX, USA, 24-27 Jan. 2016. [26] G. Wittenburg, N. Dziengel, S. Adler, Z. Kasmi, M. Ziegert, and J. Schiller, "Cooperative event detection in wireless sensor networks," IEEE Commun. Mag., vol. 50, no. 12, pp. 124-131, Dec. 2012. [27] X. Li, H. Wang, Y. Yu, and C. Qian, "An IoT data communication framework for authenticity and integrity," in Proc. of the 2nd Int. Conf. on Internet-of-Things Design and Implementation, IoTDI'17, pp. 159-170, Pittsburgh, PA, USA, 18-21 Apr 2017. [28] F. Al-Quayed, A. Soudani, and S. Al-Ahmadi, "Lightweight feature extraction method for efficient acoustic-based animal recognition in wireless acoustic sensor networks," EURASIP J. Wirel. Commun. Netw., vol. 2020, Article ID: 256, 21 pp., 14 Dec. 2020. [29] C. Titouna, M. Aliouat, and M. Gueroui, "FDS: fault detection scheme for wireless sensor networks," Wirel. Pers. Commun., vol. 86, no. 2, pp. 549-562, Aug. 2015. [30] L. Gu, et al., "Lightweight detection and classification for wireless sensor networks in realistic environments," in Proc. of the 3rd Int. Conf. on Embedded Networked Sensor Systems, SenSys'05, pp. 205-217, San Diego, CA, USA, 2-4 Nov. 2005. [31] L. Feng, C. XiaoDong, W. Youying, S. Huazhong, and Z. Haijing, "Research on wireless ad hoc network technology for building monitoring," J. Phys. Conf. Ser., vol. 1684, no. 1, Article ID: 012048, Nov. 2020. [32] J. Chen, K. H. Low, Y. Yao, and P. Jaillet, "Gaussian process decentralized data fusion and active sensing for spatiotemporal traffic modeling and prediction in mobility-on-demand systems," IEEE Trans. Autom. Sci. Eng., vol. 12, no. 3, pp. 901-921, Jul. 2015. [33] H. Baali, H. Djelouat, A. Amira, and F. Bensaali, "Empowering technology enabled care using IoT and smart devices: a review," IEEE Sens. J., vol. 18, no. 5, pp. 1790-1809, Mar. 2018. [34] W. Dong, C. Chen, X. Liu, and J. Bu, "Providing OS support for wireless sensor networks: challenges and approaches," IEEE Commun. Surv. Tutorials, vol. 12, no. 4, pp. 519-530, Fourth Quarter 2010. [35] M. Amjad, M. Sharif, M. K. Afzal, and S. W. Kim, "TinyOS-new trends, comparative views, and supported sensing applications: a review," IEEE Sens. J., vol. 16, no. 9, pp. 2865-2889, May 2016. [36] B. Li and W. Dong, "Edgeprog: edge-centric programming for IoT applications," in Proc.-Int. Conf. Distrib. Comput. Syst., pp. 212-222, Wuyishan, China, 11-13 Nov. 2020. [37] L. Gu and J. A. Stankovic, "t-kernel: providing reliable OS support to wireless sensor networks," in Proc. 4th Int. Conf. Embed. Networked Sens. Syst.-SenSys'06, pp. 1-14, Boulder, CO, USA, 31 Oct.-3 Nov. 2006. [38] A. Dunkels, N. Finne, J. Eriksson, and T. Voigt, "Run-time dynamic linking for reprogramming wireless sensor networks," in Proc. 4th Int. Conf. Embed. Networked Sens. Syst.-SenSys'06, pp. 15-28, Boulder, CO, USA, 31 Oct.-3 Nov. 2006.. [39] I. Khan, F. Belqasmi, R. Glitho, N. Crespi, M. Morrow, and P. Polakos, "Wireless sensor network virtualization: a survey," IEEE Commun. Surv. Tutorials, vol. 18, no. 1, pp. 553-576, Jan. 2016. [40] D. Gay, et al., "The nesC language: a holistic approach to networked embedded systems," ACM Sigplan Not., vol. 38, no. 5, pp. 1-11, May 2003. [41] Q. Cao, T. Abdelzaher, J. Stankovic, and T. He, "The LiteOS operating system: towards unix-like abstractions for wireless sensor networks," in Proc. Int. Conf. on Information Processing in Sensor Networks, pp. 233-244, St. Louis, MO, USA, 22-24 Apr. 2008. [42] P. Levis, "Experiences from a decade of TinyOS development," in Proc. 10th of the 10th USENIX Conf. on Operating Systems Design and Implementation. OSDI'12, pp. 207-220, Hollywood ,CA USA, 8-10 Oct. 2012. [43] E. Baccelli, O. Hahm, M. Gunes, M. Wahlisch, and T. Schmidt, "RIOT OS: towards an OS for the Internet of Things," in Proc. IEEE Conf. on Computer Communications Workshops, pp. 79-80, Turin, Italy, 14-19 Apr 2014. [44] M. Z. A. Bhuiyan, G. Wang, J. Cao, and J. Wu, "Deploying wireless sensor networks with fault-tolerance for structural health monitoring," IEEE Trans. Comput., vol. 64, no. 2, pp. 382-395, Feb. 2015. [45] Y. Liu, T. Voigt, N. Wirstrom, and J. Hoglund, "EcoVibe: on-demand sensing for railway bridge structural health monitoring," IEEE Internet Things J., vol. 6, no. 1, pp. 1068-1078, Feb. 2019. [46] M. A. Maisto, G. Leone, A. Brancaccio, and R. Solimene, "Efficient planar near-field measurements for radiation pattern evaluation by a warping strategy," IEEE Access, vol. 9, pp. 62255-62265, 2021. [47] X. Chen, G. Wang, and K. C. Ho, "Semidefinite relaxation method for unified near-field and far-field localization by AOA," Signal Processing, vol. 181pp. 107916-Apr. 2021. [48] N. T. Hanh, H. T. T. Binh, N. Van Son, and M. Kim, "Minimal relay node placement for ensuring network connectivity in mobile wireless sensor networks," IEEE 19th Int. Symp. Netw. Comput. Appl. NCA’20, 8 pp., Cambridge, MA, USA, 24-27Nov. 2020. [49] W. Doghri, A. Saddoud, and L. C. Fourati, "Cyber-physical systems for structural health monitoring: sensing technologies and intelligent computing," J. Supercomput., pp. 1-44, Jun. 2021. [50] S. Kim, et al., "Health monitoring of civil infrastructures using wireless sensor networks," in Proc. of the 6th Int. Conf. on Information Processing in Sensor Networks, IPSN'07, pp. 254-263, Cambridge, MA, USA, 25-27 Apr. 2007. [51] Y. Tselishchev and A. Boulis, "Wireless sensor network tesbed for structural health monitoring of bridges," in Proc. of the IEEE 36th Conf. on Local Computer Networks, pp. 1040-1043, Bonn, Germany, 4-7 Oct. 2011. [52] D. Phanish, et al., "A wireless sensor network for monitoring the structural health of a football stadium," in Proc. of the IEEE 2nd World Forum on Internet of Things, WF-IoT’15, pp. 471-477, Milan, Italy, 14-16 Dec. 2015. [53] M. K. Wittmann, et al., "Predictive decision making driven by multiple time-linked reward representations in the anterior cingulate cortex," Nat. Commun., vol. 6, Article ID: 12327, 2016. [54] S. Misra, S. D. Hong, G. Xue, and J. Tang, "Constrained relay node placement in wireless sensor networks: formulation and approximations," IEEE/ACM Trans. Netw., vol. 18, no. 2, pp. 434-447, Apr. 2010. [55] J. Ranieri, A. Chebira, and M. Vetterli, "Near-optimal sensor placement for linear inverse problems," IEEE Trans. Signal Process., vol. 62, no. 5, pp. 1135-1146, 1 Mar. 2014. [56] W. Zhang, Q. Yin, H. Chen, F. Gao, and N. Ansari, "Distributed angle estimation for localization in wireless sensor networks," IEEE Trans. Wirel. Commun., vol. 12, no. 2, pp. 527-537, Feb. 2013. [57] J. Yang and Z. Peng, "Beetle-swarm evolution competitive algorithm for bridge sensor optimal placement in SHM," IEEE Sens. J., vol. 20, no. 15, pp. 8244- 8255, 1 Aug. 2019. [58] M. Mezzavilla, S. Goyal, S. Panwar, S. Rangan, and M. Zorzi, "An MDP model for optimal handover decisions in mmWave cellular networks," in Proc. European Conf. on Networks and Communications, EuCNC’16, pp. 100-105, Athens, Greece, 27-30 Jun. 2016. [59] M. Abdulkarem, K. Samsudin, F. Z. Rokhani, and M. F. A. Rasid, "Wireless sensor network for structural health monitoring: a contemporary review of technologies, challenges, and future direction" Structural Health Monitoring, vol. 19, no. 3, pp. 693-735, Jul. 2019. [60] A. Biason and M. Zorzi, "Battery-powered devices in WPCNs," IEEE Trans. Commun., vol. 65, no. 1, pp. 216-229, Jan. 2017. [61] M. Frei, C. Deb, R. Stadler, Z. Nagy, and A. Schlueter, "Wireless sensor network for estimating building performance," Autom. Constr., vol. 111, Article ID: 103043, Mar. 2020. [62] A. Arulmurugan and A. Amuthan, "Markov modulated bernoulli prediction process-based cluster head selection mechanism for improving resilience in wireless sensor networks," Int. J. Commun. Syst., vol. 34, no. 8, Article ID: e4771, May 2021. [63] L. Muduli, P. K. Jana, and D. P. Mishra, "Wireless sensor network based fire monitoring in underground coal mines: a fuzzy logic approach," Process Saf. Environ. Prot., vol. 113, pp. 435-447, Jan. 2018. [64] D. Catenazzo, B. Orflynn, and M. Walsh, "On the use of wireless sensor networks in preventative maintenance for industry 4.0," in Proc. Int. Conf. Sens. Technol. ICST’19, pp. 256-262, Limerick, Ireland, 4-6 Jan. 2019. [65] M. E. Haque, et al., "Comparative study of IoT-based topology maintenance protocol in a wireless sensor network for structural health monitoring," Remote Sens., vol. 12, no. 15, Article ID: 2358-, Jul. 2020.rezasarabi miyanajisamjabbehdarinassermodiri18202220321210.66224/ijece.29021.19.3.203http://ijece.org/fa/Article/29021http://ijece.org/fa/Article/Download/29021http://ijece.org/fa/Article/Download/29021http://ijece.org/fa/Article/Download/29021http://ijece.org/fa/Article/Download/29021http://ijece.org/fa/Article/Download/29021http://ijece.org/fa/Article/Download/29021http://ijece.org/fa/Article/Download/29021[1] M. Ammar, G. Russello, and B. Crispo, "Internet of Things: a survey on the security of IoT frameworks," J. Inf. Secur. Appl., vol. 38, pp. 8-27, Feb. 2018. [2] K. Sha, W. Wei, T. Andrew Yang, Z. Wang, and W. Shi, "On security challenges and open issues in Internet of Things," Futur. Gener. Comput. Syst., vol. 83, pp. 326-337, Jun. 2018. [3] Y. H. Chuang, N. W. Lo, C. Y. Yang, and S. W. Tang, "A lightweight continuous authentication protocol for the Internet of Things," Sensors, vol. 8, no. 4, Article No. 4, 26 pp., 2018. [4] M. Dammak, O. Rafik, M. Boudia, M. A. Messous, S. M. Senouci, and C. Gransart, "Token-based lightweight authentication to secure IoT networks," in Proc. 16th IEEE Annu. Consum. Commun. Netw. Conf., 4 pp., Las Vegas, NV, USA, 11-14 Jan. 2019. [5] Z. Xu, C. Xu, W. Liang, J. Xu, and H. Chen, "A lightweight mutual authentication and key agreement scheme for medical Internet of Things," IEEE Access, vol. 7, pp. pp. 53922-53931, 2019. [6] L. Harn, "Group authentication," IEEE Trans. Comput., vol. 62, no. 9, pp. 1893-1898, Sept. 2013. [7] A. Gupta, "A lightweight mutually authenticated key-agreement scheme for wireless body area networks in Internet of things environment," in Proc. of the 24th Annual Int. Conf. on Mobile Computing and Networking, pp. 804-806, New Delhi, India, 29 Oct.-2 Nov. 2018. [8] M. Hamada, S. Kumari, and A. Kumar, "Secure anonymous mutual authentication for star two-tier wireless body area networks," Comput. Methods Programs Biomed., vol. 135, pp. 37-50, Oct. 2016. [9] C. Chen, B. Xiang, T. Wu, and K. Wang, "An anonymous mutual authenticated key agreement scheme for wearable sensors in wireless body area networks," Appl. Sci., vol. 8, no. 7, Article No.: 1074, 2018. [10] P. Punithavathi, S. Geetha, M. Karuppiah, S. K. H. Islam, M. M. Hassan, and K. R. Choo, "A lightweight machine learning-based authentication framework for smart IoT devices," Inf. Sci., vol. 84, pp. 255-268, May 2019. [11] K. Fan, Y. Gong, C. Liang, H. Li, and Y. Yang, "Lightweight and ultralightweight RFID mutual authentication protocol with cache in the reader for IoT in 5G," Secur. Commun. Networks, vol. 9, pp. 3095-3104, 2016. [12] K. Fan, P. Song, and Y. Yang, "ULMAP: ultralightweight NFC mutual authentication protocol with pseudonyms in the tag for IoT in 5G," Mob. Inf. Syst., vol. 2017, Article No.: 2349149, 7 pp., 2017. [13] M. Durairaj and K. Muthuramalingam, "A new authentication scheme with elliptical curve cryptography for Internet of Things (IoT) environments," Int. J. Eng. Technol., vol. 7, no. 2.26, pp. 119-124, 2018. [14] G. Sharma and S. Kalra, "A lightweight user authentication scheme for cloud-IoT based healthcare services," Iranian J. of Science and Technology-Trans. of Electrical Engineering, vol. 43, pp. 619-636, 2019. [15] A. Xiang and J. Zheng, "A situation-aware scheme for efficient device authentication in smart grid-enabled home area networks," Electronics, vol. 9, no. 6, Article No.: 989, 2020. [16] P. Kumar and L. Chouhan, "A privacy and session key based authentication scheme for medical IoT networks," Comput. Commun., vol. 166, pp. 154-164, 15 Jan. 2021. [17] M. N. Aman, K. C. Chua, and B. Sikdar, "Mutual authentication in IoT systems using physical unclonable functions," IEEE Internet Things J., vol. 4, no. 5, pp. 1327-1340, Oct. 2017. [18] M. T. Hammi, B. Hammi, P. Bellot, and A. Serhrouchni, "Bubbles of trust: a decentralized blockchain-based authentication system for IoT," Comput. Secur., vol. 78, pp. 126-142, Sept. 2018. [19] L. Zhou, X. Li, K. H. Yeh, C. Su, and W. Chiu, "Lightweight IoT-based authentication scheme in cloud computing circumstance," Futur. Gener. Comput. Syst., vol. 91, pp. 244-251, Feb. 2019. [20] Y. Chen, W. Xu, L. Peng, and H. Zhang, "Light-weight and privacy-preserving authentication protocol for mobile payments in the context of IoT," IEEE Access, vol. 7, pp. 15210-15221, 2019. [21] D. Dolev and A. Yao, "On the security of public key protocols," IEEE Trans. Inf. Theory, vol. 29, no. 2, pp. 198-208, Mar. 1983. [22] A. Armando, D. Basin, Y. Boichut, Y. Chevalier, and L. Compagna, "The AVISPA Tool for the Automated Validation," pp. 281-285. [23] R. Amin and G. P. Biswas, "A secure light weight scheme for user authentication and key agreement in multi-gateway based wireless sensor networks," Ad Hoc Networks, vol. 36, pt. 1, pp. 58-80, Jan. 2016.SepidehJamshidinejadFatemehAhmadi-AbkenariPeimanBayat18202215316910.66224/ijece.29052.19.3.153http://ijece.org/fa/Article/29052http://ijece.org/fa/Article/Download/29052http://ijece.org/fa/Article/Download/29052http://ijece.org/fa/Article/Download/29052http://ijece.org/fa/Article/Download/29052http://ijece.org/fa/Article/Download/29052http://ijece.org/fa/Article/Download/29052http://ijece.org/fa/Article/Download/29052[1] B. Sabeti, P. Hosseini, G. Ghassem-Sani, and S. A. Mirroshandel, LexiPers: An Ontology Based Sentiment Lexicon for Persian. arXiv preprint arXiv:1911.05263, 2019. [2] E. S. Tellez, et al., "A simple approach to multilingual polarity classification in Twitter," Pattern Recognition Letters, vol. 94, pp. 68-74, 15 Jul. 2017. [3] R. Dehkharghani, "Building phrase polarity lexicons for sentiment analysis," Int. J. Interact. Multim. Artif. Intell., vol. 5, no. 3, pp. 98-105, 2018. [4] S. Al-Azani and E. S. M. El-Alfy, "Hybrid deep learning for sentiment polarity determination of arabic microblogs," in Proc. Int. Conf. on Neural Information Processing, pp. 491-500, Guangzhou, China, 14-18 Nov. 2017. [5] K. Dashtipour, M. Gogate, J. Li, F. Jiang, B. Kong, and A. Hussain, "A hybrid Persian sentiment analysis framework: integrating dependency grammar based rules and deep neural networks," Neurocomputing, vol. 380, pp. 1-10, 7 Mar. 2020. [6] Y. Chandra and A. Jana, "Sentiment analysis using machine learning and deep learning," in Proc. IEEE 7th Int. Conf. on Computing for Sustainable Global Development, 4 pp., New Delhi, India, 12-14 Mar. 2020. [7] S. Chen, C. Peng, L. Cai, and L. Guo, "A deep neural network model for target-based sentiment analysis," in Proc. IEEE Int Joint Conf. on Neural Networks, 7 pp., Rio de Janeiro, Brazil, 8-13Jul. 2018. [8] M. El-Masri, N. Altrabsheh, H. Mansour, and A. Ramsay, "A web-based tool for Arabic sentiment analysis," Procedia Computer Science, vol. 117, pp. 38-45, 2017. [9] M. Zhang, "E-commerce comment sentiment classification based on deep learning," in Proc. IEEE 5th Int. Conf. on Cloud Computing and Big Data Analytics, pp. 184-187, Chengdu, China, 10-13 Apr. 2020. [10] B. Liu, "Sentiment analysis and opinion mining," Synthesis Lectures on Human Language Technologies, vol. 5, no. 1, pp. 1-167, 2012. [11] E. Asgarian, A. Saeedi, B. Stiri, and H. Ghaemi, NLPTools [Online]. Available: https://wtlab.um.ac.ir, 2016. [12] M. Hu and B. Liu, "Mining opinion features in customer reviews," AAAI, vol. 4, no. 4, pp. 755-760, Jul. 2004. [13] A. Hassan and A. Mahmood, "Deep learning approach for sentiment analysis of short texts," in Proc. IEEE 3rd Int. Conf. on Control, Automation and Roboticspp. 705-710, Nagoya, Japan, 24-26 Apr. 2017. [14] https://github.com/ICTRC/Parsivar [15] N. Jindal and B. Liu, "Opinion spam and analysis," in Proc. of the Int. Conf. on Web Search and Data Mining, pp. 219-230, Palo Alto, CA, USA 11-12 Feb. 2008. [16] F. H. Li, M. Huang, Y. Yang, and X. Zhu, "Learning to identify review spam," in Proc. 22nd Int. Joint Conf. on Artificial Intelligence, pp. 2488-2493, Barcelona, Spain, 16–22 Jul. 2011. [17] M. E. Basiri, N. Safarian, and H. K. Farsani, "A supervised framework for review spam detection in the Persian language," in Proc. IEEE 5th Int. Conf. on Web Research, pp. 203-207, Tehran, Iran, 24-25 Apr. 2019. [18] S. Jamshidi-Nejad, F. Ahmadi-Abkenari, and P. Bayat, "A combination of frequent pattern mining and graph traversal approaches for aspect elicitation in customer reviews," IEEE Access, vol. 8, pp. 151908-151925, 2020. [19] M. Khalash and M. Imany, "Persian Language Processing Tool," http://www.sobhe.ir/hazm, 2013. [20] A. Mohammadi, M. R. Pajoohan, M. Montazeri, and M. Nematbakhsh, "Identifying explicit features of Persian comments," J. of Computing and Security, vol. 6, no. 1, pp. 1-11, Winter/ Spring 2019. [21] G. Jain, M. Sharma, and B. Agarwal, "Spam detection in social media using convolutional and long short term memory neural network," Annals of Mathematics and Artificial Intelligence, vol. 85, pp. 21-44, 2019. [22] H. Nguyen and K. Shirai, "A joint model of term extraction and polarity classification for aspect-based sentiment analysis," in Proc. IEEE 10th In. Conf. on Knowledge and Systems Engineering, pp. 323-328, Ho Chi Minh City, Vietnam, 1-3 Nov. 2018. [23] C. Wu, F. Wu, S. Wu, Z. Yuan, and Y. Huang, "A hybrid unsupervised method for aspect term and opinion target extraction," Knowledge-Based Systems, vol. 148, pp. 66-73, 2018. [24] R. Dehkharghani, Y. Saygin, B. Yanikoglu, and K. Oflazer, "SentiTurkNet: a Turkish polarity lexicon for sentiment analysis," Language Resources and Evaluation, vol. 50, no. 3, pp. 667-685, Sept. 2016. [25] T. Hofmann, "Probabilistic latent semantic indexing," in Proc. of the 22nd Annual International ACM SIGIR Conf. on Research and Development in Information Retrieval, pp. 50-57, Berkele, CA, USA, 15-19 Aug. 1999. [26] D. M. Blei, A. Y. Ng, and M. I. Jordan, "Latent dirichlet allocation," The J. of Machine Learning Research, vol. 3, pp. 993-1022, 2003. [27] T. Griffiths and M. Steyvers, "Prediction and semantic association," Advances in Neural Information Processing Systems, pp. 11-18, 2002. [28] M. Steyvers and T. Griffiths, Probabilistic Topic Models: Handbook of Latent Semantic Analysis, pp. 439-460, Psychology Press, 2007. [29] M. Hu and B. Liu, "Mining and summarizing customer reviews," in Proc. of the 10th ACM SIGKDD Int. Conf. on Knowledge Discovery and Data Mining, pp. 168-177, Seattle, WA, USA, 22-25 Aug. 2004. [30] S. Blair-Goldensohn, et al., "Building a sentiment summarizer for local service reviews," in Proc. of the WWW2008 Workshop: NLP in the Information Explosion Era, pp. 14-23, Beijing, China, 22-22 Apr. 2008. [31] L. W. Ku, Y. T. Liang, and H. H. Chen, "Opinion extraction, summarization and tracking in news and blog corpora," in Proc. AAAI Spring Symp.: Computational Approaches to Analyzing Weblogs, pp. 100-107, Mar. 2006. [32] A. Bagheri, "Integrating word status for joint detection of sentiment and aspect in reviews," J. of Information Science, vol. 45, no. 6, pp. 736-755, 2019. [33] M. Shams and A. Baraani-Dastjerdi, "Enriched LDA (ELDA): combination of latent Dirichlet allocation with word co-occurrence analysis for aspect extraction," Expert Systems with Applications, vol. 80, pp. 136-146, 1 Sept. 2017. [34] A. Bagheri, M. Saraee, and F. de Jong, "Sentiment classification in Persian: introducing a mutual information-based method for feature selection," in Proc. 21st Iranian Conf. on Electrical Engineering, 6 pp., Mashhad, Iran, 14-16 May 2013.m.koohzadiN.Moghadam18202221322010.66224/ijece.29062.19.3.213http://ijece.org/fa/Article/29062http://ijece.org/fa/Article/Download/29062http://ijece.org/fa/Article/Download/29062http://ijece.org/fa/Article/Download/29062http://ijece.org/fa/Article/Download/29062http://ijece.org/fa/Article/Download/29062http://ijece.org/fa/Article/Download/29062http://ijece.org/fa/Article/Download/29062[1] A. Karpathy, et al., "Large-scale video classification with convolutional neural networks," in Proc. IEEE Conf. on Computer Vision and Pattern Recognition, CVPR'14, pp. 1725-1732, Columbus, OH, USA, 23-28 Jun. 2014. [2] D. Tran, L. Bourdev, R. Fergus, L. Torresani, and M. Paluri, "Learning spatiotemporal features with 3D convolutional networks," in Proc. of the IEEE Int. Conf. on Computer Vision, pp. 4489-4497, Santiago, Chile, 7-13 Dec. 2015. [3] L. Wang, et al., Temporal Segment Networks: Towards Good Practices for Deep Action Recognition, Springer, 2016. [4] L. Wang, et al., "Temporal segment networks for action recognition in videos," IEEE Trans. on Pattern Analysis and Machine Intelligence, vol. 41, no. 11, pp. 2740- 2755, Nov. 2018. [5] A. Diba, V. Sharma, and L. Van Gool, Deep Temporal Linear Encoding Networks, 2017. [6] Z. Lan, et al., Deep local video feature for action recognition, 2017. [7] W. Du, Y. Wang, and Y. Qiao, "Recurrent spatial-temporal attention network for action recognition in videos," IEEE Trans. on Image Processing, vol. 27, no. 3, pp. 1347-1360, Mar. 2017. [8] Q. Liu, X. Che, and M. Bie, "R-STAN: residual spatial-temporal attention network for action recognition," IEEE Access, vol. 7, pp. 82246-82255, 2019. [9] J. Li, X. Liu, M. Zhang, and D. Wang, "Spatio-temporal deformable 3D ConvNets with attention for action recognition," Pattern Recognition, vol. 98, Article ID: 107037, Feb. 2020. [10] Y. Quan, Y. Chen, R. Xu, and H. Ji, "Attention with structure regularization for action recognition," Computer Vision and Image Understanding, vol. 187, Article ID: 102794, Oct. 2019. [11] J. Zhang, H. Hu, and X. Lu, "Moving foreground-aware visual attention and key volume mining for human action recognition," ACM Trans. on Multimedia Computing, Communications, and Applications, vol. 15, no. 3, Article ID:. 74, 16 pp., Aug. 2019. [12] H. Sang, Z. Zhao, and D. He, "Two-level attention model based video action recognition network," IEEE Access, vol. 7, pp. 118388-118401, 2019. [13] S. Sharma, R. Kiros, and R. Salakhutdinov, Action Recognition Using Visual Attention, arXiv preprint arXiv:1511.04119, 2015. [14] Y. Peng, Y. Zhao, and J. Zhang, "Two-stream collaborative learning with spatial-temporal attention for video classification," IEEE Trans. on Circuits and Systems for Video Technology, vol. 29, no. 3, pp. 773-786, Mar. 2018. [15] D. Li, et al., "Unified spatio-temporal attention networks for action recognition in videos," IEEE Trans. on Multimedia, vol. 21, no. 2, pp. 416-428, Feb. 2018. [16] H. Zhang, et al., "End-to-end temporal attention extraction and human action recognition," Machine Vision and Applications, vol. 29, no. 7, pp. 1127-1142, Oct. 2018. [17] H. Ge, et al., "An attention mechanism based convolutional LSTM network for video action recognition," Multimedia Tools and Applications, vol. 78, pp. 20533-20556, Mar. 2019. [18] M. Koohzadi and N. M. Charkari, "A context based deep temporal embedding network in action recognition," Neural Processing Letters, no. 1, 34 pp., 2020. [19] M. Abadi, et al., "Tensorflow: a system for large-scale machine learning," in Proc. of the 12th USENIX Conf. on Operating Systems Design and Implementation, pp. 265-283, Savannah, GA, USA, 2-4 Nov. 2016. [20] Z. Zhang, Z. Lvm C. Gan, and Q. Zhu, "Human action recognition using convolutional LSTM and fully-connected LSTM with different attentions," Neurocomputing, vol. 410, pp. 304-316, 14 Oct. 2020. [21] J. Carreira, A. Zisserman, and Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset, arXiv preprint arXiv:1705.07750, 2017. [22] A. Diba, et al., Spatio-temporal channel correlation networks for action classification, 2018. [23] J. Zhu, W. Zou, Z. Zhu, and L. Li, "End-to-end video-level representation learning for action recognition," in Proc. 24th Int. Conf on Pattern Recognition, pp. 645-650, Beijing, China, 20-24 Aug. 2018. [24] Z. Li, K. Gavrilyuk, E.Gavves, M. Jain, C G. Snoekab, "VideoLSTM convolves, attends and flows for action recognition," Computer Vision and Image Understanding, vol. 166, pp. 41-50, 20-24 Jan. 2018. [25] T. Yu, et al., "Joint spatial-temporal attention for action recognition," Pattern Recognition Letters, vol. 112, pp. 226-233, Jul. 2018. [26] Z. Qiu, T. Yao, C. W. Ngo, X. Tian, and T. Mei, "Learning spatio-temporal representation with local and global diffusion," in Proc. of the IEEE Conf. on Computer Vision and Pattern Recognition, pp. 12056-12065, Long Beach, CA, USA, 15-20 Jun. 2019. [27] C. Feichtenhofer, H. Fan, J. Malik, and K. He, "Slowfast networks for video recognition," in Proc. of the IEEE/CVF Int. Conf. on Computer Vision, pp. 6202-6211, Seoul, South Korea, 27 Oct.-2 Nov. 2019. [28] N. Crasto, P. Weinzaepfel, K. Alahari, and C. Schmid, "MARS: motion-augmented RGB stream for action recognition," in Proc. of the IEEE Conf. on Computer Vision and Pattern Recognition, pp. 7874-7883, Long Beach, CA, USA, 15-20 Jun. 2019. [29] C. Y. Ma, M. H. Chen, Z. Kirab, and G.n AlRegib, "TS-LSTM and temporal-inception: exploiting spatiotemporal dynamics for activity recognition," Signal Processing: Image Communication, vol. 1, pp. 76-87, 2019. [30] B. Pang, K. Zha, H. Cao, C. Shi, and C. Lu, "Deep RNN framework for visual sequential applications," in Proc. of the IEEE Conf. on Computer Vision and Pattern Recognition, pp. 423-432, Long Beach, CA, USA, 15-20 Jun. 2019. [31] A. Piergiovanni, A. Angelova, A. Toshev, and M. S. Ryoo, "Evolving space-time neural architectures for videos," in Proc. of the IEEE In. Conf. on Computer Vision, pp. 1793-1802, Long Beach, CA, USA, 15-20 Jun. 2019. [32] C. Zhuang, A. Andonian, and D. Yamins, Unsupervised Learning from Video with Deep Neural Embeddings, arXiv preprint arXiv:1905.11954, 2019. [33] N. Sayed, B. Brattoli, and B. Ommer, Cross and Learn: Cross-Modal Self-Supervision, arXiv preprint arXiv:1811.03879, 2018. [34] L. Meng, et al., "Interpretable spatio-temporal attention for video action recognition," in Proc. of the IEEE/CVF Int. Conf. on Computer Vision Workshops, , pp. 1513-1522, Seoul, South Korea, 27-28 Oct. 2019. [35] C. Dai, X. Liu, and J. Lai, "Human action recognition using two-stream attention based LSTM networks," Applied Soft Computing, vol. 86, Article ID: 105820, Jan. 2019. [36] L. Wang, et al., "Temporal segment networks: towards good practices for deep action recognition," in Proc. 14th European Conf., pp. 20-36, Amsterdam, The Netherlands, 11-14 October, 2016.A.mirzaeiyans.aliakbary18202222122710.66224/ijece.29072.19.3.221http://ijece.org/fa/Article/29072http://ijece.org/fa/Article/Download/29072http://ijece.org/fa/Article/Download/29072http://ijece.org/fa/Article/Download/29072http://ijece.org/fa/Article/Download/29072http://ijece.org/fa/Article/Download/29072http://ijece.org/fa/Article/Download/29072http://ijece.org/fa/Article/Download/29072[1] S. Lv, et al., "Yet another approach to understanding news event evolution," World Wide Web, vol. 23, no. 4, pp. 2449-2470, May 2020. [2] O. N. N. Fernando and C. W. Chang, "Twittener: an aggregated news platform," in Proc. IEEE Int. Conf. on Cyberworlds, pp. 378-381, Kyoto, Japan, 2-4 Oct. 2019. [3] Q. He, Topical Analysis of Text Streams, Ph.D. Dissertation, Nanyang Technological University, Singapore, 2009. [4] L. Hu, B. Zhang, L. Hou, and J. Li, "Adaptive online event detection in news streams," Knowledge-Based Systems, vol. 138, pp. 105-112, 15 Dec. 2017. [5] T. Kala, Event Detection from Text Data, Bacholor Thesis,Department of Cybernetics Faculty of Electrical Engineering, Czech Technical University in Prague, May 2017. [6] F. Atefeh and W. Khreich, "A survey of techniques for event detection in twitter," Computational Intelligence, vol. 31, no. 1, pp. 132-164, Feb. 2015. [7] D. M. Blei, A. Y. Ng, and M. I. Jordan, "Latent dirichlet allocation," J. of Machine Learning Research, vol. 3, pp. 993-1022, Mar. 2003. [8] Q. He, K. Chang, and E. P. Lim, "Analyzing feature trajectories for event detection," in Proc. of the 30th Annual Int. ACM SIGIR Conf. on Research and Development in Information Retrieval, pp. 207-214, Amsterdam, The Netherlands, 22-27 Jul. 2007. [9] Y. Sumikawa and A. Jatowt, "System for category-driven retrieval of historical events," in Proc. of the 18th ACM/IEEE on Joint Conf. on Digital Libraries, pp. 413-414, Fort Worth Texas USA, 3-7 Jun. 2018. [10] D. Metzler, C. Cai, and E. Hovy, "Structured event retrieval over microblog archives," in Proc. of the Conf. of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pp. 646-655, Montreal, Canada, 3-8 Jun. 2012. [11] I. Moutidis and H. T. P. Williams, "Utilizing complex networks for event detection in heterogeneous high-volume news streams," Complex Networks and Their Applications VIII: Proc. of the 8th Int. Conf. on Complex Networks and Their Applications, vol. 1, pp. 659-672, Lisbon, Portugal, 10-12 Dec. 2019. [12] H. Schutze, C. D. Manning, and P. Raghavan, Introduction to Information Retrieval, vol. 39, Cambridge University Press Cambridge, 2008. [13] T. Nicholls and J. Bright, "Understanding news story chains using information retrieval and network clustering techniques," Communication Methods and Measures, Routledge, vol. 13, no. 1, pp. 43-59, 2019. [14] V. D. Blondel, J. L. Guillaume, R. Lambiotte, and E. Lefebvre, "Fast unfolding of communities in large networks," J. of Statistical Mechanics: Theory and Experiment, vol. 2008, no. 10, Article No.: P10008, Oct. 2008. [15] M. J. Kusner, Y. Sun, N. I. Kolkin, and K. Q. Weinberger, "From word embeddings to document distances," in Proc. of the 32nd Int. Conf. on Machine Learning, vol. 37, pp. 957-966, Lille, France, 6-11 Jul. 2015. [16] R. Rehurek and P. Sojka, "Software framework for topic modelling with large corpora," in Proc. of LREC Workshop New Challenges for NLP Frameworks, pp. 46-50, Valletta, Malta, 22-22 May 2010.