Optimization of real-time wireless sensor based big data with deep autoencoder network: a tourism sector application with distributed computing

Internet usage has increased rapidly with the development of information communication technologies. The increase in internet usage led to the growth of data volumes on the internet and the emergence of the big data concept. Therefore, it has become even more important to analyze the data and make it meaningful. In this study, 690 million queries and approximately 5.9 quadrillion data collected daily from different servers were recorded on the Redis servers by using real-time big data analysis method and load balance structure for a company operating in the tourism sector. Here, wireless networks were used as a triggering factor to gather data from visitors of the hotels and the analysis was supported with an optimization approach through the deep autoencoder network. According to the data density gathered from the structure developed with distributed computing and the API software in C# language, server group numbers were increased to list the most affordable hotel in the desired times. Thanks to the developed architecture and software, response times of the servers were significantly reduced. In detail, it was seen that the HAProxy responded 11 times faster than NetScaler as the new architecture responded 1160 times faster than the old one. Also, the HashSet system in the newly developed architecture responded 18 times faster than the List system and as general, the new architecture was found to be 9 times faster than the old architecture.

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[1] Sokhanvar A. Does foreign direct investment accelerate tourism and economic growth within Europe? Tourism Management Perspectives 2019; 29 (1): 86-96. doi: 10.1016/j.tmp.2018.10.005
[2] Ye BH, Fu H, Law R. Use of impact-range performance and asymmetry analyses to improve OTA website quality. Journal of Hospitality and Tourism Management 2016; 26 (1): 9-17. doi: 10.1016/j.jhtm.2015.09.001
[3] Roudi S, Arasli H, Akadiri SS. New insights into an old issue–examining the influence of tourism on economic growth: evidence from selected small island developing states. Current Issues in Tourism 2019; 22 (11): 1280-1300. doi: 10.1080/13683500.2018.1431207
[4] Chung N, Koo C. The use of social media in travel information search. Telematics and Informatics 2015; 32 (2): 215-229. doi: 10.1016/j.tele.2014.08.005
[5] Okazaki S, Andreu L, Campo S. Knowledge sharing among tourists via social media: a comparison between Facebook and TripAdvisor. International Journal of Tourism Research 2017; 19 (1): 107-119. doi: 10.1002/jtr.2090
[6] Yang Y. Understanding tourist attraction cooperation: an application of network analysis to the case of Shanghai, China. Journal of Destination Marketing & Management 2018; 8 (2): 396-411. doi: 10.1016/j.jdmm.2017.08.003
[7] Xiang Z, Gretzel U. Role of social media in online travel information search. Tourism Management 2010; 31(2): 179-188. doi: 10.1016/j.tourman.2009.02.016
[8] Chu SC, Kim Y. Determinants of consumer engagement in electronic word-of-mouth (eWOM) in social networking sites. International Journal of Advertising 2011; 30 (1): 47-75. doi: 10.2501/IJA-30-1-047-075
[9] Fotiadis AK, Stylos N. The effects of online social networking on retail consumer dynamics in the attractions industry: the case of ‘E-da’ theme park, Taiwan. Technological Forecasting and Social Change 2017; 124 (11): 283-294. doi: 10.1016/j.techfore.2016.06.033
[10] See-to EW, Ho KK. Value co-creation and purchase intention in social network sites: the role of electronic word-of-mouth and trust–a theoretical analysis. Computers in Human Behavior 2014; 31 (2): 182-189. doi: 10.1016/j.chb.2013.10.013
[11] Filieri R, Alguezaui S, McLeay F. Why do travelers trust TripAdvisor? Antecedents of trust towards consumergenerated media and its influence on recommendation adoption and word of mouth. Tourism Management 2015; 51 (6): 174-185. doi: 10.1016/j.tourman.2015.05.007
[12] Hudson S. Challenges of tourism marketing in the digital, global economy. In: McCabe S (editor). The Routledge Handbook of Tourism Marketing. 1rd ed. London, England: Routledge and CRC Press, 2014, pp. 475-490.
[13] Pantano E, Di Pietro L. From e-tourism to f-tourism: emerging issues from negative tourists’ online reviews. Journal of Hospitality and Tourism Technology 2013; 4 (3): 211-227. doi: 10.1108/JHTT-02-2013-0005
[14] Xiang Z, Magnini VP, Fesenmaier DR. Information technology and consumer behavior in travel and tourism: insights from travel planning using the internet. Journal of Retailing and Consumer Services 2015; 22 (1): 244-249. doi: 10.1016/j.jretconser.2014.08.005
[15] Aktan E, Koçyiğit M. Sosyal medyanın turizm faaliyetlerindeki rolü üzerine teorik bir inceleme. Dumlupınar Üniversitesi Sosyal Bilimler Dergisi 2016; 1 (1): 62-73 (in Turkish with an abstract in English).
[16] Zhang Y, Shu S, Ji Z, Wang Y. A study of the commercial application of big data of the international hotel group in China: based on the case study of marriott international. In: IEEE 2015 First International Conference on Big Data Computing Service and Applications; New York, NY, USA; 2015. pp. 412-417. doi:10.1109/BigDataService.2015.59
[17] Qin H, Ye X. Big data analysis based on the hotel online reviews. In: Proceedings of the 2018 3rd International Conference on Automation, Mechanical Control and Computational Engineering; Dalian, China; 2018. pp.1-20. doi: 10.2991/amcce-18.2018.36
[18] Gandomi A, Haider M. Beyond the hype: big data concepts, methods, and analytics. International Journal of Information Management 2015; 35 (2): 137-144. doi: 10.1016/j.ijinfomgt.2014.10.007
[19] Chen CP, Zhang CY. Data-intensive applications, challenges, techniques and technologies: a survey on big data. Information Sciences 2014; 275 (22): 314-347. doi: 10.1016/j.ins.2014.01.015
[20] Rodríguez-Mazahua L, Rodríguez-Enríquez CA, Sánchez-Cervantes JL, Cervantes J, García-Alcaraz JL et al. A general perspective of big data: applications, tools, challenges and trends. The Journal of Supercomputing 2016; 72 (8): 3073-3113. doi: 10.1007/s11227-015-1501-1
[21] Hashem IAT, Yaqoob I, Anuar NB, Mokhtar S, Gani A et al. The rise of “big data” on cloud computing: review and open research issues. Information Systems 2015; 47 (1): 98-115. doi: 10.1016/j.is.2014.07.006
[22] Chen M, Mao S, Liu Y. Big data: a survey. Mobile Networks and Applications 2014; 19 (2): 171-209. doi: 10.1007/s11036-013-0489-0
[23] Wang Y, Kung L, Byrd TA. Big data analytics: understanding its capabilities and potential benefits for healthcare organizations. Technological Forecasting and Social Change 2018; 126 (1): 3-13. doi: 10.1016/j.techfore.2015.12.019
[24] Rathore MMU, Paul A, Ahmad A, Chen BW, Huang B et al. Real-time big data analytical architecture for remote sensing application. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2015; 8 (10): 4610-4621. doi: 10.1109/JSTARS.2015.2424683
[25] Leclerc B, Cale J. Big Data. New York, NY, USA: Routledge and CRC Press, 2020.
[26] Waga D, Rabah K. Environmental conditions’ big data management and cloud computing analytics for sustainable agriculture. World Journal of Computer Application and Technology 2014; 2 (3): 73-81. doi: 10.13189/wjcat.2014.020303
[27] Cooper J, Noon M, Jones C, Kahn E, Arbuckle P. Big data in life cycle assessment. Journal of Industrial Ecology 2013; 17 (6): 796-799. doi: 10.1111/jiec.12069
[28] Elhoseny M, Abdelaziz A, Salama AS, Riad AM, Muhammad K et al. A hybrid model of internet of things and cloud computing to manage big data in health services applications. Future Generation Computer Systems 2018; 86 (9): 1383-1394. doi: 10.1016/j.future.2018.03.005
[29] Attaran M, Stark J, Stotler D. Opportunities and challenges for big data analytics in US higher education: a conceptual model for implementation. Industry and Higher Education 2018; 32 (3): 169-182. doi: 10.1177/0950422218770937
[30] Evans MR, Oliver D, Yang K, Zhou X, Ali RY et al. Enabling spatial big data via CyberGIS: challenges and opportunities. In: Wang S, Goodchild M (editors). CyberGIS for Geospatial Discovery and Innovation. Dordrecht, Netherlands: Springer, 2019, pp. 143-170.
[31] Mehta N, Pandit A, Kulkarni M. Elements of healthcare big data analytics. In: Kulkarni AJ, Siarry P, Singh PK, Abraham A, Zhang M et al. (editors). Big Data Analytics in Healthcare. Studies in Big Data, Vol. 66. Cham, Switzerland: Springer Nature Switzerland AG, 2020, pp. 23-43.
[32] Bibri SE. The IoT for smart sustainable cities of the future: An analytical framework for sensor-based big data applications for environmental sustainability. Sustainable Cities and Society 2018; 38 (3): 230-253. doi: 10.1016/j.scs.2017.12.034
[33] Dezfouli MB, Shahraki MHN, Zamani H. A novel tour planning model using big data. In: 2018 International Conference on Artificial Intelligence and Data Processing; Malatya, Turkey; 2018. pp. 1-6.
[34] Miah SJ, Vu HQ, Gammack J, McGrath M. A big data analytics method for tourist behaviour analysis. Information & Management 2017; 54 (6): 771-785. doi: 10.1016/j.im.2016.11.011
[35] Salas-Olmedo MH, Moya-Gómez B, García-Palomares JC, Gutiérrez J. Tourists’ digital footprint in cities: comparing big data sources. Tourism Management 2018; 66 (3): 13-25. doi: 10.1016/j.tourman.2017.11.001
[36] Del Vecchio P, Secundo G, Passiante G. Analyzing big data through the lens of customer knowledge management: evidence from a set of regional tourism experiences. Kybernetes 2018; 47 (7): 1348-1362. doi: 10.1108/K-07-2017- 0273
[37] Fuchs M, Höpken W, Lexhagen M. Big data analytics for knowledge generation in tourism destinations–a case from Sweden. Journal of Destination Marketing & Management 2014; 3 (4): 198-209. doi: 10.1016/j.jdmm.2014.08.002
[38] Sun Y, Song H, Jara AJ, Bie R. Internet of things and big data analytics for smart and connected communities. IEEE Access 2016; 4 (1): 766-773. doi:10.1109/ACCESS.2016.2529723
[39] Nguyen TT, Camacho D, Jung JE. Identifying and ranking cultural heritage resources on geotagged social media for smart cultural tourism services. Personal and Ubiquitous Computing 2017; 21 (2): 267-279. doi: 10.1007/s00779- 016-0992-y
[40] Boulaalam O, Aghoutane B, El Ouadghiri D, Moumen A, Malinine MLC. Proposal of a big data system based on the recommendation and profiling techniques for an intelligent management of moroccan tourism. Procedia Computer Science 2018; 134 (11): 346-351. doi: 10.1016/j.procs.2018.07.200
[41] Casado R, Younas M. Emerging trends and technologies in big data processing. Concurrency and Computation: Practice and Experience 2015; 27 (8): 2078-2091. doi: 10.1002/cpe.3398
[42] Terzi DS, Demirezen U, Sagiroglu S. Evaluations of big data processing. Services Transactions on Big Data 2016; 3 (1): 44-53. doi: 10.29268/stbd.2016.3.1.4
[43] Huang X, Zhang L, Ding Y. The Baidu index: uses in predicting tourism flows–a case study of the forbidden city. Tourism Management 2017; 58 (1): 301-306. doi: 10.1016/j.tourman.2016.03.015
[44] Zhang YF, Tian YC, Kelly W, Fidge C. Scalable and efficient data distribution for distributed computing of all-to-all comparison problems. Future Generation Computer Systems 2017; 67 (2): 152-162. doi: 10.1016/j.future.2016.08.020
[45] Chou TCK, Abraham JA. Load balancing in distributed systems. IEEE Transactions on Software Engineering 1982; SE-8 (4): 401-412. doi: 10.1109/TSE.1982.235574
[46] Jiang Y. A survey of task allocation and load balancing in distributed systems. IEEE Transactions on Parallel and Distributed Systems 2016; 27 (2): 585-599. doi: 10.1109/TPDS.2015.2407900
[47] Attiya G, Hamam Y. Task allocation for maximizing reliability of distributed systems: a simulated annealing approach. Journal of parallel and Distributed Computing 2006; 66 (10): 1259-1266. doi: 10.1016/j.jpdc.2006.06.006
[48] Jiang Y, Jiang J. Contextual resource negotiation-based task allocation and load balancing in complex software systems. IEEE Transactions on Parallel and Distributed Systems 2008; 20 (5): 641-653. doi: 10.1109/TPDS.2008.133
[49] Liu J, Jin X, Wang Y. Agent-based load balancing on homogeneous minigrids: Macroscopic modeling and characterization. IEEE Transactions on Parallel and Distributed Systems 2005; 16 (7): 586-598. doi: 10.1109/TPDS.2005.76
[50] Rudolph L, Slivkin-Allalouf M, Upfal E. A simple load balancing scheme for task allocation in parallel machines. In: Proceedings of the third annual ACM Symposium on Parallel algorithms and architectures; New York, NY, USA; 1991. pp. 237-245. doi: 10.1145/113379.113401
[51] Chen X, Zhou L. Design and implementation of an intelligent system for tourist routes recommendation based on hadoop. In: 2015 6th IEEE International Conference on Software Engineering and Service Science; Beijing, China; 2015. pp. 774-778. doi: 10.1109/ICSESS.2015.7339171
[52] Jiang SJ, Wu CT, Tseng KH. Utilization of cloud computing app for homestay operation-design and analysis. The International Journal of Organizational Innovation 2018; 11 (2): 35-47.
[53] Sharma S, Singh S, Sharma M. Performance analysis of load balancing algorithms. World Academy of Science, Engineering and Technology 2008; 38 (3): 269-272.
[54] Andrews GR, Dobkin DP, Downey PJ. Distributed allocation with pools of servers. In: Proceedings of the first ACM SIGACT-SIGOPS Symposium on Principles of Distributed Computing; Ottawa, ON, Canada; 1982. pp. 73-83. doi: 10.1145/800220.806684
[55] Kumar A, Kalra M. Load balancing in cloud data center using modified active monitoring load balancer. In: International Conference on Advances in Computing, Communication, & Automation; Dehradun, India; 2016. pp. 1-5. doi: 10.1109/ICACCA.2016.7578903
[56] Aslam S, Shah MA. Load balancing algorithms in cloud computing: a survey of modern techniques. In: National Software Engineering Conference; Rawalpindi, Pakistan; 2015. pp. 30-35. doi: 10.1109/NSEC.2015.7396341
[57] Cybenko G. Dynamic load balancing for distributed memory multiprocessors. Journal of Parallel and Distributed Computing 1989; 7 (2): 279-301. doi:10.1016/0743-7315(89)90021-X
[58] Cardellini V, Colajanni M, Yu PS. Dynamic load balancing on web-server systems. IEEE Internet Computing 1999; 3 (3): 28-39. doi: 10.1109/4236.769420
[59] Kim C, Kameda H. An algorithm for optimal static load balancing in distributed computer systems. Transactions on Computers 1992; 41 (3): 381-384. doi:10.1109/12.12745
[60] Tantawi AN, Towsley D. Optimal static load balancing in distributed computer systems. Journal of the ACM 1985; 32 (2): 445-465. doi:10.1145/3149.3156
[61] Zhang J, Ji Y, Song M, Zhao Y, Yu X et al. Dynamic traffic grooming in sliceable bandwidth-variable transponder-enabled elastic optical networks. Journal of Lightwave Technology 2015; 33 (1): 183-191. doi: 10.1109/JLT.2014.2383444
[62] Cattell R. Scalable SQL and NoSQL data stores. Acm Sigmod Record 2011; 39 (4): 12-27. doi: 10.1145/1978915.1978919
[63] Hao R, Guangmin F, Qiang W, Lingxu G, Jian C et al. Research on unified storage technology of multidimensional historical data of substation based on the combination of redis and SSDB. In: 2018 China International Conference on Electricity Distribution; Tianjin, China: 2018. pp. 1251-1255. doi: 10.1109/CICED.2018.8592231
[64] Li S, Jiang H, Shi M. Redis-based web server cluster session maintaining technology. In: 13th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery; Guilin, China; 2017; pp. 3065- 3069. doi: 10.1109/FSKD.2017.8393274
[65] Carlson JL. Redis in Action. Shelter Island, NY, USA: Manning Publications Co., 2013.
[66] Qin J, Ma L, Liu Q. DFTHR: a distributed framework for trajectory similarity query based on HBase and Redis. Information 2019; 10 (2): 77. doi: 10.3390/info10020077
[67] Leea T, Kim Y, Hwang E. Abnormal payment transaction detection scheme based on scalable architecture and redis cluster. In: International Conference on Platform Technology and Service; Jeju, South Korea; 2018. pp. 1-6. doi: 10.1109/PlatCon.2018.8472732
[68] Franciscus N, Ren X, Stantic B. Precomputing architecture for flexible and efficient big data analytics. Vietnam Journal of Computer Science 2018; 5 (2): 133-142. doi: 10.1007/s40595-018-0109-9
[69] Callaway Jr, Edgar H. Wireless Sensor Networks: Architectures and Protocols. Boca Raton, FL, USA: CRC Press, 2003.
[70] Khan S, Pathan ASK, Alrajeh NA. Wireless Sensor Networks: Current Status and Future Trends. Boca Raton, FL, USA: CRC Press, 2016.
[71] Raghavendra CS, Sivalingam KM, Znati T. Wireless Sensor Networks. Rijeka, Croatia: InTech, 2006.
[72] Rehmani MH, Pathan ASK. Emerging Communication Technologies Based on Wireless Sensor Networks: Current Research and Future Applications. Boca Raton, FL, USA: CRC Press, 2016.
[73] Randhawa S, Jain, S. Data aggregation in wireless sensor networks: previous research, current status and future directions. Wireless Personal Communications 2017; 97 (3): 3355-3425. doi: 10.1007/s11277-017-4674-5
[74] Elayan H, Shubair RM, Kiourti A. Wireless sensors for medical applications: current status and future challenges. In: 11th European Conference on Antennas and Propagation; Paris, France: 2017. pp. 2478-2482. doi: 10.23919/EuCAP.2017.7928405
[75] Zhang X, Dong L, Peng H, Chen H, Zhao S et al. Collusion-aware privacy-preserving range query in tiered wireless sensor networks. Sensors 2014; 14 (12): 23905-23932. doi: 10.3390/s141223905
[76] Kemmerling M, Schliepkorte H, Duisburg F. An orientation and information system for blind people based on RF-speech-beacons. In: TIDE 3th Congress; Helsinki, Finland; 1998. pp.1-20.
[77] Kose U, Vasant P. Better campus life for visually impaired University students: intelligent social walking system with beacon and assistive technologies. Wireless Networks 2018; 24 (8): 1-15. doi: 10.1007/s11276-018-1868-z
[78] Antevski K, Redondi AE, Pitic RA. Hybrid BLE and Wi-Fi localization system for the creation of study groups in smart libraries. In: 9th IFIP Wireless and Mobile Networking Conference (WMNC); Colmar, France; 2016. pp. 41-48. doi: 10.1109/WMNC.2016.7543928
[79] Suda Y, Arai T, Yoshizawa T, Fujita Y, Zempo K et al. Shopping baskets for on-line beacon sensor network in retail store. In: 16th IEEE Annual Consumer Communications & Networking Conference (CCNC); Las Vegas, NV, USA; 2019. pp. 1-2. doi: 10.1109/CCNC.2019.8651857
[80] Al-Kaisy A, Miyake GT, Staszcuk J, Scharf D. Motorists’ voluntary yielding of right of way at uncontrolled midblock crosswalks with rectangular rapid flashing beacons. Journal of Transportation Safety & Security 2018; 10 (4): 303– 317. doi: 10.1080/19439962.2016.1267827
[81] Molapo NA, Malekian R, Nair L. Real-time livestock tracking system with integration of sensors and beacon navigation. Wireless Personal Communications 2019; 104 (2): 853-879. doi: 10.1007/s11277-018-6055-0 [82] Sun W, Shao S, Zhao R, Yan R, Zhang X et al. A sparse auto-encoder-based deep neural network approach for induction motor faults classification. Measurement 2016; 89 (13): 171-178. doi: 10.1016/j.measurement.2016.04.007
[83] Su Y, Li J, Plaza A, Marinoni A, Gamba P et al. DAEN: Deep autoencoder networks for hyperspectral unmixing. IEEE Transactions on Geoscience and Remote Sensing 2019; 57 (7): 4309-4321. doi: 10.1109/TGRS.2018.2890633
[84] Xiong Y, Zuo R. Recognition of geochemical anomalies using a deep autoencoder network. Computers & Geosciences 2016; 86 (1): 75-82. doi:10.1016/j.cageo.2015.10.006 [85] Mohammadi M, Al-Fuqaha A, Sorour S, Guizani M. Deep learning for IoT big data and streaming analytics: a survey. IEEE Communications Surveys & Tutorials 2018; 20 (4): 2923-2960. doi: 10.1109/COMST.2018.2844341
[86] Hosseini MP, Lu S, Kamaraj K, Slowikowski A, Venkatesh HC. Deep Learning Architectures. In: Deep Learning: Concepts and Architectures. Cham, Switzerland: Springer, 2020, pp. 1-24.
[87] Goodfellow I, Bengio Y, Courville A. Deep Learning. London, England: MIT Press, 2016.
[88] Buduma N, Locascio N. Fundamentals of Deep Learning: Designing Next-Generation Machine Intelligence Algorithms. Sebastopol, CA, USA: O’Reilly Media Inc., 2017.
[89] Zhang Q, Yang LT, Chen Z, Li P. A survey on deep learning for big data. Information Fusion 2018; 42 (1): 146-157. doi: 10.1016/j.inffus.2017.10.006
[90] Sohangir S, Wang D, Pomeranets A, Khoshgoftaar TM. Big data: deep learning for financial sentiment analysis. Journal of Big Data 2018; 5 (1): 1-25. doi: 10.1186/s40537-017-0111-6