{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T11:38:51Z","timestamp":1743075531825,"version":"3.40.3"},"publisher-location":"Cham","reference-count":84,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030474102"},{"type":"electronic","value":"9783030474119"}],"license":[{"start":{"date-parts":[[2020,6,27]],"date-time":"2020-06-27T00:00:00Z","timestamp":1593216000000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2020,6,27]],"date-time":"2020-06-27T00:00:00Z","timestamp":1593216000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021]]},"DOI":"10.1007\/978-3-030-47411-9_28","type":"book-chapter","created":{"date-parts":[[2020,6,26]],"date-time":"2020-06-26T18:58:46Z","timestamp":1593197926000},"page":"511-536","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["A Proposed Context-Awareness Taxonomy for Multi-data Fusion in Smart Environments: Types, Properties, and Challenges"],"prefix":"10.1007","author":[{"given":"Doaa Mohey","family":"El-Din","sequence":"first","affiliation":[]},{"given":"Aboul Ella","family":"Hassanein","sequence":"additional","affiliation":[]},{"given":"Ehab E.","family":"Hassanien","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2020,6,27]]},"reference":[{"key":"28_CR1","doi-asserted-by":"crossref","unstructured":"Thapliyal, R., Patel, R.K., Yadav, A.K., Singh, A.: Internet of Things for smart environment and integrated ecosystem. In: International Conference on Advanced Research in Engineering Science and Management At: Dehradun, Uttarakhand (2018)","DOI":"10.14419\/ijet.v7i3.12.17841"},{"key":"28_CR2","unstructured":"Bhayani, M., Patel, M., Bhatt, C.: Internet of Things (IoT): in a way. In: Proceedings of the International Congress on Information and Communication Technology, Advances in Intelligent Systems and Computing (2016)"},{"key":"28_CR3","doi-asserted-by":"crossref","unstructured":"Bongartz, S., Jin, Y., Patern\u00f2, F., Rett, J., Santoro, C., Spano, L.D.: Adaptive User Interfaces for Smart Environments with the Support of Model-Based Languages. Springer, Berlin (2012)","DOI":"10.1007\/978-3-642-34898-3_3"},{"key":"28_CR4","unstructured":"Ayed, S.B., Trichili, H., Alimi, A.M.: Data fusion architectures: a survey and comparison. In: 15th International Conference on Intelligent Systems Design and Applications (ISDA) (2015)"},{"key":"28_CR5","unstructured":"Chao, W., Jishuang, Q., Zhi, L.: Data fusion, the core technology for future on-board data processing system. Pecora 15\/Land Satellite Information IV\/ISPRS Commission I\/FIEOS 2002 Conference Proceedings (2002)"},{"key":"28_CR6","unstructured":"Kalyan, L.O.: Veeramachaneni, Fusion, Decision-Level, Hindawi Publishing Corporation The Scientific World Journal Volume 2013, Article ID 704504, 19 pages"},{"key":"28_CR7","doi-asserted-by":"crossref","unstructured":"Lahat, D., Adal, T., Jutten, C.: Multimodal data fusion: an overview of methods, challenges and prospects. In: Proceedings OF THE IEEE (2015)","DOI":"10.1109\/JPROC.2015.2460697"},{"issue":"1","key":"28_CR8","doi-asserted-by":"publisher","first-page":"116","DOI":"10.1016\/j.cviu.2006.10.019","volume":"108","author":"A Jaimes","year":"2007","unstructured":"Jaimes, A., Sebe, N.: Multimodal human computer interaction: a survey. Comput. Vis. Image Underst. 108(1), 116\u2013134 (2007)","journal-title":"Comput. Vis. Image Underst."},{"issue":"2","key":"28_CR9","doi-asserted-by":"publisher","first-page":"245","DOI":"10.1134\/S1064230717020125","volume":"56","author":"AM Kashevnika","year":"2017","unstructured":"Kashevnika, A.M., Ponomareva, A.V., Smirnov, A.V.: A multi-model context-aware tourism recommendation service: approach and architecture. J. Comput. Syst. Sci. Int. 56(2), 245\u2013258 (2017). (ISSN 1064-2307)","journal-title":"J. Comput. Syst. Sci. Int."},{"key":"28_CR10","doi-asserted-by":"crossref","unstructured":"Lahat, D., Adali, T., Jutten, C.: Multimodal data fusion: an overview of methods, challenges, and prospects. Proc. IEEE 103(9) (2015)","DOI":"10.1109\/JPROC.2015.2460697"},{"key":"28_CR11","doi-asserted-by":"crossref","unstructured":"Hall, D.L., Llinas, J.: An introduction to multi-sensor data fusion. Proc. IEEE 85(1) (1997)","DOI":"10.1109\/5.554205"},{"issue":"12","key":"28_CR12","doi-asserted-by":"publisher","first-page":"659","DOI":"10.1177\/0037549704050997","volume":"80","author":"MA Hofmann","year":"2004","unstructured":"Hofmann, M.A.: Challenges of model interoperation in military simulations. Simulation 80(12), 659\u2013667 (2004)","journal-title":"Simulation"},{"key":"28_CR13","doi-asserted-by":"crossref","unstructured":"El-Sappagh, S., Ali, F., Elmasri, S., Kim, K., Ali, A., Kwa, K.-S.: Mobile Health Technologies for Diabetes Mellitus: Current State and Future Challenges, pp. 2169\u20133536 (2018)","DOI":"10.1109\/ACCESS.2018.2881001"},{"key":"28_CR14","unstructured":"\u017dontar, R., Heri\u010dko, M., Rozman, I.: Taxonomy of context-aware systems. Elektrotehni\u0161ki Vestnik 79(1\u20132), 41\u201346 (2012). (English Edition)"},{"issue":"1","key":"28_CR15","doi-asserted-by":"publisher","first-page":"103","DOI":"10.1016\/j.jnca.2012.04.007","volume":"36","author":"C Emmanouilidis","year":"2013","unstructured":"Emmanouilidis, C., Koutsiamanis, R.-A., Tasidou, A.: Mobile guides: taxonomy of architectures, context awareness, technologies and applications. J. Netw. Comput. Appl. 36(1), 103\u2013125 (2013)","journal-title":"J. Netw. Comput. Appl."},{"key":"28_CR16","unstructured":"Almasri, M., Elleithy, K.: Data fusion in WSNs: architecture, taxonomy, evaluation of techniques, and challenges. Int. J. Sci. Eng. Res. 6(4) (2015)"},{"key":"28_CR17","doi-asserted-by":"crossref","unstructured":"Biancolillo, A., Boqu\u00e9, R., Cocchi, M., Marini, F.: Data fusion strategies in food analysis (Chap.\u00a010). In: Data Fusion Methodology and Applications, vol. 31, pp. 271\u2013310 (2019)","DOI":"10.1016\/B978-0-444-63984-4.00010-7"},{"key":"28_CR18","unstructured":"Ferrin, G., Snidaro, L., Foresti, G.L.: Contexts, co-texts and situations in fusion domain. In: 14th International Conference on Information Fusion Chicago, Illinois, USA (2011)"},{"key":"28_CR19","doi-asserted-by":"crossref","unstructured":"den Berg, N., Schumann, M., Kraft, K., Hoffmann, W.: Telemedicine and telecare for older patients\u2014a systematic review. Maturitas 73(2) (2012)","DOI":"10.1016\/j.maturitas.2012.06.010"},{"key":"28_CR20","doi-asserted-by":"crossref","unstructured":"Ka\u0144toch, E.: Recognition of sedentary behavior by machine learning analysis of wearable sensors during activities of daily living for telemedical assessment of cardiovascular risk. Sensors (2018)","DOI":"10.3390\/s18103219"},{"issue":"4","key":"28_CR21","doi-asserted-by":"publisher","first-page":"2611","DOI":"10.1007\/s11277-014-1784-1","volume":"79","author":"S-K Kang","year":"2014","unstructured":"Kang, S.-K., Chung, K., Lee, J.-H.: Real-time tracking and recognition systems for interactive telemedicine health services. Wireless Pers. Commun. 79(4), 2611\u20132626 (2014)","journal-title":"Wireless Pers. Commun."},{"key":"28_CR22","doi-asserted-by":"crossref","unstructured":"Gite, S., Agrawal, H.: On context awareness for multisensor data fusion in IoT. In: Proceedings of the Second International Conference on Computer and Communication Technologies, pp. 85\u201393 (2015)","DOI":"10.1007\/978-81-322-2526-3_10"},{"key":"28_CR23","unstructured":"Deshmukh, M., Bhosale, U.: Image fusion and image quality assessment of fused images. Int. J. Image Process. (IJIP) 4(5) (2010)"},{"key":"28_CR24","unstructured":"Moravec, J., \u0160\u00e1ra, R.: Robust maximum-likelihood on-line LiDAR-to-camera calibration monitoring and refinement. In: Kukelov\u00e1, Z., Skovierov\u0103, J.: (eds.) 23rd Computer Vision Winter Workshop, \u010cesk\u00fd Krumlov, Czech Republic (2018)"},{"key":"28_CR25","doi-asserted-by":"crossref","unstructured":"De Silva, V., Roche, J., Kondoz, A.: Robust fusion of LiDAR and wide-angle camera data for autonomous mobile robots. Sensors (2018)","DOI":"10.3390\/s18082730"},{"key":"28_CR26","doi-asserted-by":"crossref","unstructured":"Ghassemian, H.: A review of remote sensing image fusion methods. Inf. Fusion 32(part A) (2016)","DOI":"10.1016\/j.inffus.2016.03.003"},{"key":"28_CR27","doi-asserted-by":"crossref","unstructured":"Palsson, F., Sveinsson, J.R., Ulfarsson, M.O., Benediktsson, J.A.: Model-based fusion of multi- and hyperspectral images using PCA and wavelets. IEEE Trans. Geosci. Remote Sens. 53(5) (2015)","DOI":"10.1109\/TGRS.2014.2363477"},{"key":"28_CR28","doi-asserted-by":"crossref","unstructured":"Kim, Y.M., Theobalt, C., Diebel, J., Kosecka, J., Miscusik, B.: Sebastian, multi-view image and ToF sensor fusion for dense 3D reconstruction. In: IEEE 12th International Conference on Computer Vision Workshops, ICCV (2009)","DOI":"10.1109\/ICCVW.2009.5457430"},{"key":"28_CR29","doi-asserted-by":"crossref","unstructured":"Choia, J., Radau, P., Xubc, R., Wright, G.A.: X-ray and magnetic resonance imaging fusion for cardiac resynchronization therapy. Med. Image Anal. 31 (2016)","DOI":"10.1016\/j.media.2016.03.004"},{"key":"28_CR30","unstructured":"Krout, D.W., Okopal, G., Hanusa, E.: Video data and sonar data: real world data fusion example. In: 14th International Conference on Information Fusion (2011)"},{"key":"28_CR31","unstructured":"Snidaro, L., Foresti, G.L., Niu, R., Varshney, P.K.: Sensor fusion for video surveillance. Electr. Eng. Comput. Sci. 108 (2004)"},{"key":"28_CR32","doi-asserted-by":"crossref","unstructured":"Heracleous, P., Badin, P., Bailly, G., Hagita, N.: Exploiting multimodal data fusion in robust speech recognition. In: IEEE International Conference on Multimedia and Expo (2010)","DOI":"10.1109\/ICME.2010.5583086"},{"key":"28_CR33","unstructured":"Boujelbene, S.Z., Mezghani, D.B.A., Ellouze, N.: General machine learning classifiers and data fusion schemes for efficient speaker recognition. Int. J. Comput. Sci. Emer. Technol. 2(2) (2011)"},{"key":"28_CR34","doi-asserted-by":"crossref","unstructured":"Gu, Y., Li, X., Chen, S., Zhang, J., Marsic, I.: Speech intention classification with multimodal deep learning. Adv. Artif. Intell. (2017)","DOI":"10.1007\/978-3-319-57351-9_30"},{"key":"28_CR35","doi-asserted-by":"crossref","unstructured":"Zahavy, T., Mannor, S., Magnani, A., Krishnan, A.: Is a picture worth a thousand words? A deep multi-modal fusion architecture for product classification in E-commerce. Under Review as a Conference Paper at ICLR 2017","DOI":"10.1609\/aaai.v32i1.11419"},{"key":"28_CR36","doi-asserted-by":"crossref","unstructured":"Gallo, I., Calefati, A., Nawaz, S., Janjua, M.K.: Image and encoded text fusion for multi-modal classification. Published in the Digital Image Computing: Techniques and Applications (DICTA), Australia (2018)","DOI":"10.1109\/DICTA.2018.8615789"},{"key":"28_CR37","unstructured":"Viswanathan, P., Venkata Krishna, P.: Text fusion watermarking in medical image with semi-reversible for secure transfer and authentication"},{"key":"28_CR38","doi-asserted-by":"crossref","unstructured":"Huang, F., Zhang, X., Zhao, Z., Xu, J., Li, Z.: Image-text sentiment analysis via deep multimodal attentive fusion. Knowl.-Based Syst. (2019)","DOI":"10.1016\/j.knosys.2019.01.019"},{"key":"28_CR39","unstructured":"Blasch, E., Nagy, J., Aved, A., Pottenger, W.M., et al.: Context aided video-to-text information fusion. In: 17th International Conference on Information Fusion (FUSION) (2014)"},{"key":"28_CR40","unstructured":"Video-to-Text Information Fusion Evaluation for Level 5 User Refinement,18th International Conference on Information Fusion Washington, DC, 6\u20139 July 2015"},{"key":"28_CR41","unstructured":"Jain, S., Gonzalez, J.E.: Inter-BMV: Interpolation with Block Motion Vectors for Fast Semantic Segmentation on Video, arXiv:1810.04047v1"},{"key":"28_CR42","doi-asserted-by":"crossref","unstructured":"Gidel, S., Blanc, C., Chateau, T., Checchin, P., Trassoudaine, L.: Non-parametric laser and video data fusion: application to pedestrian detection in urban environment. In: 12th International Conference on Information Fusion Seattle, WA, USA, 6\u20139 July 2009","DOI":"10.1109\/IVS.2008.4621166"},{"key":"28_CR43","doi-asserted-by":"crossref","unstructured":"Katsaggelos, A.K., Bahaadini, S., Molina, R.: Audiovisual fusion: challenges and new approaches. Proc. IEEE 103(9) (2015)","DOI":"10.1109\/JPROC.2015.2459017"},{"key":"28_CR44","doi-asserted-by":"crossref","unstructured":"Datcu, D., Rothkrantz, L.J.M.: Semantic audio-visual data fusion for automatic emotion recognition, recognition. Emot. Recognit. 411\u2013435 (2015)","DOI":"10.1002\/9781118910566.ch16"},{"key":"28_CR45","doi-asserted-by":"crossref","unstructured":"O\u2019Conaire, C., O\u2019Connor, N.E., Smeaton, A.: Thermo-visual feature fusion for object tracking using multiple spatiogram trackers. Mach. Vis. Appl. 19(5\u20136), 483\u2013494 (2008)","DOI":"10.1007\/s00138-007-0078-y"},{"key":"28_CR46","doi-asserted-by":"crossref","unstructured":"Kumar, P., Gauba, H., Roy, P.P., Dogra, D.P.: Coupled HMM-based multi-sensor data fusion for sign language recognition. Pattern Recogn. Lett. 86 (2017)","DOI":"10.1016\/j.patrec.2016.12.004"},{"issue":"1","key":"28_CR47","doi-asserted-by":"publisher","first-page":"114","DOI":"10.1109\/TSMCC.2008.2001716","volume":"39","author":"C Chen","year":"2009","unstructured":"Chen, C., Liang, J., Zhao, H., Tian, J.: Factorial HMM and parallel HMM for gait recognition. IEEE Trans. Syst. Man Cybern. Part C (Appl. Rev.) 39(1), 114\u2013123 (2009)","journal-title":"IEEE Trans. Syst. Man Cybern. Part C (Appl. Rev.)"},{"key":"28_CR48","doi-asserted-by":"crossref","unstructured":"Cetin, O., Ostendorf, M. and Bernard, G.D.: Multi-rate coupled hidden markov models and their application to machining tool-wear classification. IEEE Trans. Signal Process. 55(6) (2007)","DOI":"10.1109\/TSP.2007.893972"},{"key":"28_CR49","unstructured":"Eyigoz, E., Gildea, D., Oflazer, K.: Multi-rate HMMs for word alignment. In: Proceedings of the Eighth Workshop on Statistical Machine Translation, Bulgaria, pp. 494\u2013502 (2013)"},{"key":"28_CR50","doi-asserted-by":"crossref","unstructured":"Zajdel, W., Krijnders, J.D., Andringa, T., Gavrila, D.M.: CASSANDRA: audio-video sensor fusion for aggression detection. In: IEEE International Conference Advanced Video and Signal Based Surveillance (AVSS), London, UK (2007)","DOI":"10.1109\/AVSS.2007.4425310"},{"key":"28_CR51","doi-asserted-by":"crossref","unstructured":"Kampman, O., Barezi, E.J., Bertero, D., Fung, P.: Investigating audio, video, and text fusion methods for end-to-end automatic personality prediction. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Short Papers), pp. 606\u2013611 (2018)","DOI":"10.18653\/v1\/P18-2096"},{"key":"28_CR52","doi-asserted-by":"crossref","unstructured":"Ji, C.B., Duan, G., Ma, H.Y., Zhang, L., Xu, H.Y.: Modeling of image, video and text fusion quality data packet system for aerospace complex products based on business intelligence (2019)","DOI":"10.1016\/j.jvcir.2018.12.053"},{"key":"28_CR53","doi-asserted-by":"crossref","unstructured":"Xiong, Y., Wang, D., Zhang, Y., Feng, S., Wang, G.: Multimodal data fusion in text-image heterogeneous graph for social media recommendation. In: International Conference on Web-Age Information Management WAIM, Web-Age Information Management (2014)","DOI":"10.1007\/978-3-319-08010-9_12"},{"key":"28_CR54","doi-asserted-by":"crossref","unstructured":"Naphade, M., Kristjansson, T., Frey, B., Huang, T.S.: Probabilistic multimedia objects (multijects): a novel approach to 9 video indexing and retrieval in multimedia systems. In: Proceedings of IEEE International Conference on Image Processing, vol. 3, pp. 536\u2013540, Chicago, USA (1998)","DOI":"10.1109\/ICIP.1998.999041"},{"key":"28_CR55","unstructured":"Ellis, D.: Prediction-driven computational auditory scene analysis. Ph.D. thesis, MIT Department of Electrical Engineering and Computer Science, Cambridge, Mass, USA (1996)"},{"key":"28_CR56","doi-asserted-by":"crossref","unstructured":"Adams, W.H., Iyengar, G., Lin, C.-Y., Naphade, M.R., Neti, C., Nock, H.J., Smith, J.R.: Semantic indexing of multimedia content using visual, audio, and text cues. EURASIP J. Appl. Signal Process. (2003)","DOI":"10.1155\/S1110865703211173"},{"key":"28_CR57","doi-asserted-by":"crossref","unstructured":"Wu, Z., Cai, L., Meng, H.: Multi-level fusion of audio and visual features for speaker identification. In: International Conference on Biometrics ICB 2006: Advances in Biometrics (2006)","DOI":"10.1007\/11608288_66"},{"key":"28_CR58","doi-asserted-by":"crossref","unstructured":"Yurur, O., Labrador, M., Moreno, W.: Adaptive and energy efficient context representation framework in mobile sensing. IEEE Trans. Mob. Comput. 13(8) (2014)","DOI":"10.1109\/TMC.2013.47"},{"key":"28_CR59","doi-asserted-by":"crossref","unstructured":"De Paola, A., Gaglio, S., Re, G.L., Ortolani, M.: Multi-sensor fusion through adaptive Bayesian networks. Congress of the Italian Association for Artificial Intelligence AI*IA 2011: AI*IA 2011: Artificial Intelligence Around Man and Beyond (2011)","DOI":"10.1007\/978-3-642-23954-0_33"},{"key":"28_CR60","unstructured":"Hossain, M.A., Atrey, P.K., El Saddik, A.: Learning multi-sensor confidence using a reward-and-punishment mechanism, integrate machine-learning algorithms in the data fusion process. IEEE Trans. Instrum. Meas. 58(5), 1525\u20131534 (2009)"},{"key":"28_CR61","doi-asserted-by":"crossref","unstructured":"Gite, S., Agrawal, H.: On context awareness for multi-sensor data fusion in IoT. In: Proceedings of the Second International Conference on Computer and Communication Technologies (2016)","DOI":"10.1007\/978-81-322-2526-3_10"},{"key":"28_CR62","unstructured":"Malandrakis, N., Iosif, E., Prokopi, V., Potamianos, A., Narayanan, S.: DeepPurple: lexical, string and affective feature fusion for sentence-level semantic similarity estimation. In: Second Joint Conference on Lexical and Computational Semantics (*SEM), Volume 1: Proceedings of the Main Conference, and the Shared Task. ACM (2013)"},{"key":"28_CR63","doi-asserted-by":"crossref","unstructured":"Barzilay, R., McKeown, K.R.: Sentence fusion for multidocument news summarization. Comput. Linguist. 31(3) (2005)","DOI":"10.1162\/089120105774321091"},{"key":"28_CR64","unstructured":"Durkan, C., Storkey, A., Edwards, H.: The context-aware learner. In: ICLR 2018"},{"key":"28_CR65","unstructured":"Weimer Ariandy, D., Benggolo, Y., Freitag, M.: Context-aware deep convolutional neural networks for industrial inspection. In: Australasian Conference on Artificial Intelligence, Canberra, Australia, Volume: Deep Learning and its Applications in Vision and Robotics (Workshop) (2015)"},{"key":"28_CR66","doi-asserted-by":"crossref","unstructured":"Brenon, A., Portet, F., Vacher, M.: Context feature learning through deep learning for adaptive context-aware decision making in the home. In: The 14th International Conference on Intelligent Environments, Rome, Italy (2018)","DOI":"10.1109\/IE.2018.00013"},{"key":"28_CR67","doi-asserted-by":"crossref","unstructured":"Kantorov, V., Oquab, M., Cho, M., Laptev, I.: ContextLocNet: context-aware deep network models for weakly supervised localization. ECCV 2016, Oct 2016, Amsterdam, Netherlands. Springer, pp. 350\u2013365 (2016)","DOI":"10.1007\/978-3-319-46454-1_22"},{"key":"28_CR68","unstructured":"Savopol, F., Armenakis, C.: Merging of heterogeneous data for emergency mapping: data integration and data fusion? In: Symposium of Geospatial Theory, Processing and Applications (2002)"},{"key":"28_CR69","doi-asserted-by":"crossref","unstructured":"Dong, X.L., Naumann, F.: Data fusion: resolving data conflicts for integration. J. Proc. VLDB 2(2) (2009)","DOI":"10.14778\/1687553.1687620"},{"key":"28_CR70","doi-asserted-by":"crossref","unstructured":"Zhu, Y., Song, E., Zhou, J., You, Z.: Optimal dimensionality reduction of sensor data in multisensor estimation fusion. IEEE Trans. Signal Process. 53(5) (2005)","DOI":"10.1109\/TSP.2005.845429"},{"key":"28_CR71","doi-asserted-by":"crossref","unstructured":"Nesa, N., Ghosh, T., Banerjee, I.: Outlier detection in sensed data using statistical learning models for IoT. In: 2018 IEEE Wireless Communications and Networking Conference (WCNC) (2018)","DOI":"10.1109\/WCNC.2018.8376988"},{"key":"28_CR72","doi-asserted-by":"crossref","unstructured":"Chandola, V., Banerjee, A., Kumar, V.: Outlier detection: a survey. ACM Comput. Surv. 41(3), Article 15 (2009)","DOI":"10.1145\/1541880.1541882"},{"key":"28_CR73","unstructured":"Aggarwal, C.C.: Outlier Analysis, 2nd edn. Springer, Berlin (2016)"},{"key":"28_CR74","unstructured":"Tonjes, R., Ali, M.I., Barnaghi, P., Ganea, S., et al.: Real Time IoT Stream Processing and Large-scale Data Analytics for Smart City Applications (2014)"},{"key":"28_CR75","doi-asserted-by":"crossref","unstructured":"Bonino, D., Rizzo, F., Pastrone, C., Soto, J.A.C., Ahlsen, M., Axling, M.: Block-based realtime big-data processing for smart cities. According to Eurostat, IEEE 2016","DOI":"10.1109\/ISC2.2016.7580768"},{"key":"28_CR76","doi-asserted-by":"crossref","unstructured":"Cho, K., Hwang, I., Kang, S., Kim, B., Lee, J., Lee, S., Park, S., Song, J., Rhee, Y.: HiCon: a hierarchical context monitoring and composition framework for next-generation context-aware services. IEEE Netw. 22(4) (2008)","DOI":"10.1109\/MNET.2008.4579769"},{"key":"28_CR77","doi-asserted-by":"crossref","unstructured":"Padovitz, A., Loke, S.W., Zaslavsky, A., Burg, B., Bartolini, C.: An approach to data fusion for context awareness. In: International and Interdisciplinary Conference on Modeling and Using Context, Modeling and Using Context (2005)","DOI":"10.1007\/11508373_27"},{"key":"28_CR78","unstructured":"Roy, N., Das, S.K., Julien, C.: Resolving and mediating ambiguous contexts in pervasive environments. In: User-Driven Healthcare: Concepts, Methodologies, Tools, and Applications, IGI Global disseminator of knowledge (2013)"},{"key":"28_CR79","doi-asserted-by":"crossref","unstructured":"Roy, N., Das, S.K., Julien, C..: Resource-optimized quality-assured ambiguous context mediation framework in pervasive environment. IEEE Trans. Mob. Comput. 11(2) (2012)","DOI":"10.1109\/TMC.2011.20"},{"key":"28_CR80","unstructured":"De Paola, A., La Cascia, M., Lo Re, G., Ortolani, M.: User detection through multi-sensor fusion in an AmI scenario. In: 2012 15th International Conference on Information Fusion (FUSION) (2012)"},{"key":"28_CR81","doi-asserted-by":"crossref","unstructured":"Roy, N., Pallapa, G.V., Das, S.K.: A middleware framework for ambiguous context mediation in smart healthcare application, user activity recognition. In: Third IEEE International Conference on Wireless and Mobile Computing, Networking and Communications, WiMob 2007, White Plains, New York, USA, 8\u201310 Oct 2007","DOI":"10.1109\/WIMOB.2007.4390866"},{"key":"28_CR82","unstructured":"Nwe, M.S., Tun, H.M.: Implementation of multi-sensor data fusion algorithm. Int. J. Sens. Sens. Netw. (2017)"},{"key":"28_CR83","doi-asserted-by":"crossref","unstructured":"Rahmati, A., Zhong, L.: Context-based network estimation for energy-efficient ubiquitous. IEEE Trans. Mob. Comput. 10(1) (2011)","DOI":"10.1109\/TMC.2010.139"},{"key":"28_CR84","doi-asserted-by":"crossref","unstructured":"Klein, L., Mihaylova, L., El Faouzi, N.E: Sensor and data fusion: taxonomy challenges and applications. In: Pal, S.K., Petrosino, A., Maddalena, L. (eds.) Handbook on Soft Computing for Video Surveillance. Taylor & Francis. Sensor and Data Fusion: Taxonomy Challenges and applications. Chapman & Hall\/CRC (2013)","DOI":"10.1201\/b11631-7"}],"container-title":["Studies in Systems, Decision and Control","Recent Advances in Intelligent Systems and Smart Applications"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-47411-9_28","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,8,8]],"date-time":"2024-08-08T14:55:42Z","timestamp":1723128942000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/978-3-030-47411-9_28"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,6,27]]},"ISBN":["9783030474102","9783030474119"],"references-count":84,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-47411-9_28","relation":{},"ISSN":["2198-4182","2198-4190"],"issn-type":[{"type":"print","value":"2198-4182"},{"type":"electronic","value":"2198-4190"}],"subject":[],"published":{"date-parts":[[2020,6,27]]},"assertion":[{"value":"27 June 2020","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}}]}}