Veri Füzyonu Veri Kaynakları, Mimariler, Zorluklar ve Çözüm Yaklaşımları

Yıl 2022, Cilt: 34 Sayı: 2, 899 - 922, 30.09.2022

Berna Çengiz Resul Daş

Öz

Nesnelerin İnterneti, yeni teknoloji ve cihazları kullanarak insan müdahelesi olmadan nesnelerin iletişimini ve karar verme yetilerini sağlayan insanların problemlemlerine çözüm sağlayan sistemlerdir. Nesnelerin İnterneti çözümleri insanların konfor, güvenilirlik, hareketlilik, sağlık ve refah seviyesinin yükseltilmesinde etkin rol almaktadır. Karmaşık ve zorlu uygula- malar içeren IoT çözümlerinden elde edilen büyük veriler, birbirinden farklı ve çok kaynaklı heterojen veri kümelerini içere- bilir. Sensör teknolojilerini kullanan teknolojik çözümlerinde veri ve sensör füzyonu işlemleri büyük önem taşımaktadır. Veri boyutunun azaltılması, veri trafik yoğunluğunu optimize edilmesi ve ham verilerden yararlı bilgileri çıkarılması gibi işlem- lerin gerçekleştirebilmesi kritik ve çok önemlidir. Bu bağlamda, veri birleştirme sürecinde, sorunlu verilerin düzeltilmesi, veri güvenilirliğinin arttırılması ve veri bütünlüğünün korunması hedeflenmektedir. Akıllı sistemler kullanılarak gerçekleştirilen IoT çözümlerinde en çok yaşanan veri füzyonu zorluklarının tespit edilmesi ve olası çözüm yaklaşımlarının sunulması çalış- mamızın özgün yönünü ön plana çıkartmaktadır. Bu makale çalışmasında, güncel literatür çalışmaları detaylıca incelenmiş, akıllı şehirlerde uygulanan IoT çözümlerindeki veri tipleri, füzyon seviyeleri, kullanılan yöntem ve elde edilen sonuçlar tablo halinde sunulmuştur.

Anahtar Kelimeler

Nesnelerin İnterneti, Büyük Veri, Akıllı Şehirler, Veri Füzyonu, Enerji Tüketim

Kaynakça

• [1] M. M. Gaber, A. Aneiba, S. Basurra, et al., “Internet of things and data mining: From applications to tech-465 niques and systems,” Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, vol. 9, no. 3,466 May 1, 2019, ISSN: 1942-4787. DOI: 10.1002/widm.1292.467
• [2] B. P. L. Lau, S. H. Marakkalage, Y. Zhou, et al., “A survey of data fusion in smart city applications,” In-468 formation Fusion, vol. 52, pp. 357–374, Dec. 1, 2019, ISSN: 1566-2535. DOI: 10 . 1016 / j . inffus .469 2019.05.004. [Online]. Available: https://www.sciencedirect.com/science/article/pii/470 S1566253519300326 (visited on 06/07/2022).471
• [3] F. Alam, R. Mehmood, I. Katib, N. N. Albogami, and A. Albeshri, “Data fusion and IoT for smart ubiquitous472 environments: A survey,” IEEE Access, vol. 5, pp. 9533–9554, 2017, ISSN: 2169-3536. DOI: 10.1109/473 ACCESS.2017.2697839. [Online]. Available: http://ieeexplore.ieee.org/document/7911293/474 (visited on 06/07/2022).475
• [4] J. Liu, T. Li, P. Xie, S. Du, F. Teng, and X. Yang, “Urban big data fusion based on deep learning: An476 overview,” Information Fusion, vol. 53, pp. 123–133, Jan. 1, 2020, ISSN: 1566-2535. DOI: 10.1016/j.477 inffus.2019.06.016. [Online]. Available: https://www.sciencedirect.com/science/article/478 pii/S1566253519301393 (visited on 05/18/2022).479
• [5] R. Kumar, R. Mishra, H. P. Gupta, and T. Dutta, “Smart sensing for agriculture: Applications, advancements,480 and challenges,” IEEE Consumer Electronics Magazine, vol. 10, no. 4, pp. 51–56, Jul. 1, 2021, ISSN: 2162-481 2248, 2162-2256. DOI: 10 . 1109 / MCE . 2021 . 3049623. [Online]. Available: https : / / ieeexplore .482 ieee.org/document/9316711/ (visited on 06/13/2022).483
• [6] A. Shamsuzzoha, J. Nieminen, S. Piya, and K. Rutledge, “Smart city for sustainable environment: A com-484 parison of participatory strategies from helsinki, singapore and london,” Cities, vol. 114, p. 103 194, Jul. 1,485 2021, ISSN: 0264-2751. DOI: 10.1016/j.cities.2021.103194. [Online]. Available: https://www.486 sciencedirect.com/science/article/pii/S0264275121000925 (visited on 06/16/2022).487
• [7] S. B. Atitallah, M. Driss, W. Boulila, and H. B. Ghézala, “Leveraging deep learning and IoT big data an-488 alytics to support the smart cities development: Review and future directions,” Computer Science Review,489 vol. 38, p. 100 303, Nov. 1, 2020, ISSN: 1574-0137. DOI: 10.1016/j.cosrev.2020.100303. [Online].490 Available: https://www.sciencedirect.com/science/article/pii/S1574013720304032 (visited491 on 05/26/2022).492
• [8] A. S. Syed, D. Sierra-Sosa, A. Kumar, and A. Elmaghraby, “IoT in smart cities: A survey of technologies,493 practices and challenges,” Smart Cities, vol. 4, no. 2, pp. 429–475, Jun. 2021, Number: 2 Publisher: Multidis-494 ciplinary Digital Publishing Institute, ISSN: 2624-6511. DOI: 10.3390/smartcities4020024. [Online].495 Available: https://www.mdpi.com/2624-6511/4/2/24 (visited on 06/28/2022).496
• [9] R. Frank, Understanding Smart Sensors. Artech House, 2013, 390 pp., Google-Books-ID: v4G9jKBCghMC,497 ISBN: 978-1-60807-507-2.498
• [10] C. Gomez, S. Chessa, A. Fleury, G. Roussos, and D. Preuveneers, “Internet of things for enabling smart499 environments: A technology-centric perspective,” Journal of Ambient Intelligence and Smart Environments,500 vol. 11, no. 1, pp. 23–43, Jan. 1, 2019, Publisher: IOS Press, ISSN: 1876-1364. DOI: 10 . 3233 / AIS -501 180509. [Online]. Available: https://content.iospress.com/articles/journal-of-ambient-502 intelligence-and-smart-environments/ais180509 (visited on 06/07/2022).503
• [11] G. Muhammad, F. Alshehri, F. Karray, A. E. Saddik, M. Alsulaiman, and T. H. Falk, “A comprehensive504 survey on multimodal medical signals fusion for smart healthcare systems,” Information Fusion, vol. 76,505 pp. 355–375, Dec. 1, 2021, ISSN: 1566-2535. DOI: 10 . 1016 / j . inffus . 2021 . 06 . 007. [Online].506 Available: https://www.sciencedirect.com/science/article/pii/S1566253521001330 (visited507 on 06/01/2022).508
• [12] R. M. Abdelmoneem, E. Shaaban, and A. Benslimane, “A survey on multi-sensor fusion techniques in IoT509 for healthcare,” in 2018 13th International Conference on Computer Engineering and Systems (ICCES),510 Dec. 2018, pp. 157–162. DOI: 10.1109/ICCES.2018.8639188.511
• [13] M. Gochoo, S. B. U. D. Tahir, A. Jalal, and K. Kim, “Monitoring real-time personal locomotion behaviors512 over smart indoor-outdoor environments via body-worn sensors,” IEEE Access, vol. 9, pp. 70 556–70 570,513 2021, Conference Name: IEEE Access, ISSN: 2169-3536. DOI: 10.1109/ACCESS.2021.3078513.514
• [14] R. Gravina, P. Alinia, H. Ghasemzadeh, and G. Fortino, “Multi-sensor fusion in body sensor networks: State-515 of-the-art and research challenges,” Information Fusion, vol. 35, pp. 68–80, May 1, 2017, ISSN: 1566-2535.516 DOI: 10.1016/j.inffus.2016.09.005. [Online]. Available: https://www.sciencedirect.com/517 science/article/pii/S156625351630077X (visited on 06/03/2022).518
• [15] Z. Qin, Y. Zhang, S. Meng, Z. Qin, and K.-K. R. Choo, “Imaging and fusing time series for wearable sensor-519 based human activity recognition,” Information Fusion, vol. 53, pp. 80–87, Jan. 1, 2020, ISSN: 1566-2535.520 DOI: 10.1016/j.inffus.2019.06.014. [Online]. Available: https://www.sciencedirect.com/521 science/article/pii/S1566253519302180 (visited on 06/03/2022).522
• [16] S. Qiu, L. Liu, H. Zhao, Z. Wang, and Y. Jiang, “MEMS inertial sensors based gait analysis for rehabilitation523 assessment via multi-sensor fusion,” Micromachines, vol. 9, no. 9, p. 442, Sep. 2018, Number: 9 Publisher:524 Multidisciplinary Digital Publishing Institute, ISSN: 2072-666X. DOI: 10 . 3390 / mi9090442. [Online].525 Available: https://www.mdpi.com/2072-666X/9/9/442 (visited on 06/03/2022).526
• [17] T. Diethe, N. Twomey, M. Kull, P. Flach, and I. Craddock, “Probabilistic sensor fusion for ambient assisted527 living,” arXiv, arXiv:1702.01209, Feb. 3, 2017, type: article. DOI: 10.48550/arXiv.1702.01209. arXiv:528 1702 . 01209[cs , stat]. [Online]. Available: http : / / arxiv . org / abs / 1702 . 01209 (visited on529 06/03/2022).530
• [18] H. Lindskog, “Smart communities initiatives,” Jan. 1, 2004.531
• [19] S. Consoli, D. Reforgiato Recupero, M. Mongiovi, V. Presutti, G. Cataldi, and W. Patatu, “An urban fault532 reporting and management platform for smart cities,” in Proceedings of the 24th International Conference on533 World Wide Web, ser. WWW ’15 Companion, New York, NY, USA: Association for Computing Machinery,534 May 18, 2015, pp. 535–540, ISBN: 978-1-4503-3473-0. DOI: 10 . 1145 / 2740908 . 2743910. [Online].535 Available: https://doi.org/10.1145/2740908.2743910 (visited on 06/06/2022).536
• [20] Z. Alazawi, O. Alani, M. B. Abdljabar, S. Altowaijri, and R. Mehmood, “A smart disaster management537 system for future cities,” in Proceedings of the 2014 ACM international workshop on Wireless and mobile538 technologies for smart cities, ser. WiMobCity ’14, New York, NY, USA: Association for Computing Ma-539 chinery, Aug. 11, 2014, pp. 1–10, ISBN: 978-1-4503-3036-7. DOI: 10.1145/2633661.2633670. [Online].540 Available: https://doi.org/10.1145/2633661.2633670 (visited on 06/06/2022).541
• [21] P. Xu, F. Davoine, J.-B. Bordes, H. Zhao, and T. Denœux, “Multimodal information fusion for urban scene542 understanding,” Machine Vision and Applications, vol. 27, no. 3, pp. 331–349, Apr. 1, 2016, ISSN: 1432-543 1769. DOI: 10.1007/s00138-014-0649-7. [Online]. Available: https://doi.org/10.1007/s00138-544 014-0649-7 (visited on 06/07/2022).545
• [22] L. Yu, S. Qin, M. Zhang, C. Shen, T. Jiang, and X. Guan, “A review of deep reinforcement learning for546 smart building energy management,” IEEE Internet of Things Journal, vol. 8, no. 15, pp. 12 046–12 063,547 Aug. 2021, Conference Name: IEEE Internet of Things Journal, ISSN: 2327-4662. DOI: 10.1109/JIOT.548 2021.3078462.549
• [23] R. Eini, L. Linkous, N. Zohrabi, and S. Abdelwahed, “Smart building management system: Performance550 specifications and design requirements,” Journal of Building Engineering, vol. 39, p. 102 222, Jul. 1, 2021,551 ISSN: 2352-7102. DOI: 10 . 1016 / j . jobe . 2021 . 102222. [Online]. Available: https : / / www .552 sciencedirect.com/science/article/pii/S2352710221000784 (visited on 06/07/2022).553
• [24] X. Gao, P. Pishdad-Bozorgi, D. R. Shelden, and S. Tang, “Internet of things enabled data acquisition frame-554 work for smart building applications,” Journal of Construction Engineering and Management, vol. 147,555 no. 2, p. 04 020 169, Feb. 1, 2021, Publisher: American Society of Civil Engineers, ISSN: 1943-7862. DOI:556 10.1061/(ASCE)CO.1943- 7862.0001983. [Online]. Available: https://ascelibrary.org/doi/557 full/10.1061/%28ASCE%29CO.1943-7862.0001983 (visited on 06/07/2022).558
• [25] M. K. M. Shapi, N. A. Ramli, and L. J. Awalin, “Energy consumption prediction by using machine learning559 for smart building: Case study in malaysia,” Developments in the Built Environment, vol. 5, p. 100 037,560 Mar. 1, 2021, ISSN: 2666-1659. DOI: 10 . 1016 / j . dibe . 2020 . 100037. [Online]. Available: https :561 //www.sciencedirect.com/science/article/pii/S266616592030034X (visited on 06/07/2022).562
• [26] Y. Hajjaji, W. Boulila, I. R. Farah, I. Romdhani, and A. Hussain, “Big data and IoT-based applications in563 smart environments: A systematic review,” Computer Science Review, vol. 39, p. 100 318, Feb. 1, 2021,564 ISSN: 1574-0137. DOI: 10 . 1016 / j . cosrev . 2020 . 100318. [Online]. Available: https : / / www .565 sciencedirect.com/science/article/pii/S1574013720304184 (visited on 06/08/2022).566
• [27] M. Wu, C. Wu, W. Huang, et al., “An improved high spatial and temporal data fusion approach for combining567 landsat and MODIS data to generate daily synthetic landsat imagery,” Information Fusion, vol. 31, pp. 14–568 25, Sep. 1, 2016, ISSN: 1566-2535. DOI: 10.1016/j.inffus.2015.12.005. [Online]. Available: https:569 //www.sciencedirect.com/science/article/pii/S1566253515001177 (visited on 06/08/2022).570
• [28] Y. Zeng, W. Huang, M. Liu, H. Zhang, and B. Zou, “Fusion of satellite images in urban area: Assessing the571 quality of resulting images,” in 2010 18th International Conference on Geoinformatics, ISSN: 2161-0258,572 Jun. 2010, pp. 1–4. DOI: 10.1109/GEOINFORMATICS.2010.5568105.573
• [29] M. Marchiori, “The smart cheap city: Efficient waste management on a budget,” in 2017 IEEE 19th574 International Conference on High Performance Computing and Communications; IEEE 15th Interna-575 tional Conference on Smart City; IEEE 3rd International Conference on Data Science and Systems576 (HPCC/SmartCity/DSS), Dec. 2017, pp. 192–199. DOI: 10.1109/HPCC-SmartCity-DSS.2017.25.577
• [30] A. A. Khan, A. A. Sajib, F. Shetu, S. Bari, M. S. R. Zishan, and K. Shikder, “Smart waste management578 system for bangladesh,” in 2021 2nd International Conference on Robotics, Electrical and Signal Processing579 Techniques (ICREST), Jan. 2021, pp. 659–663. DOI: 10.1109/ICREST51555.2021.9331159.580
• [31] Y. A. Fatimah, K. Govindan, R. Murniningsih, and A. Setiawan, “Industry 4.0 based sustainable circular581 economy approach for smart waste management system to achieve sustainable development goals: A case582 study of indonesia,” Journal of Cleaner Production, vol. 269, p. 122 263, Oct. 1, 2020, ISSN: 0959-6526.583 DOI: 10.1016/j.jclepro.2020.122263. [Online]. Available: https://www.sciencedirect.com/584 science/article/pii/S0959652620323106 (visited on 06/08/2022).585
• [32] K. Pardini, J. J. P. C. Rodrigues, O. Diallo, A. K. Das, V. H. C. de Albuquerque, and S. A. Kozlov, “A smart586 waste management solution geared towards citizens,” Sensors, vol. 20, no. 8, p. 2380, Jan. 2020, Number:587 8 Publisher: Multidisciplinary Digital Publishing Institute, ISSN: 1424-8220. DOI: 10.3390/s20082380.588 [Online]. Available: https://www.mdpi.com/1424-8220/20/8/2380 (visited on 06/08/2022).589
• [33] H. P. Breivold, “Internet-of-things and cloud computing for smart industry: A systematic mapping study,” in590 2017 5th International Conference on Enterprise Systems (ES), ISSN: 2572-6609, Sep. 2017, pp. 299–304.591 DOI: 10.1109/ES.2017.56.592
• [34] A. Diez-Olivan, J. Del Ser, D. Galar, and B. Sierra, “Data fusion and machine learning for industrial prog-593 nosis: Trends and perspectives towards industry 4.0,” Information Fusion, vol. 50, pp. 92–111, Oct. 1, 2019,594 ISSN: 1566-2535. DOI: 10 . 1016 / j . inffus . 2018 . 10 . 005. [Online]. Available: https : / / www .595 sciencedirect.com/science/article/pii/S1566253518304706 (visited on 06/08/2022).596
• [35] J. Leng, D. Wang, W. Shen, X. Li, Q. Liu, and X. Chen, “Digital twins-based smart manufacturing sys-597 tem design in industry 4.0: A review,” Journal of Manufacturing Systems, vol. 60, pp. 119–137, Jul. 1,598 2021, ISSN: 0278-6125. DOI: 10.1016/j.jmsy.2021.05.011. [Online]. Available: https://www.599 sciencedirect.com/science/article/pii/S0278612521001151 (visited on 06/08/2022).600
• [36] Ö. Gültekin, E. Cinar, K. Özkan, and A. Yazıcı, “Multisensory data fusion-based deep learning approach for601 fault diagnosis of an industrial autonomous transfer vehicle,” Expert Systems with Applications, vol. 200,602 p. 117 055, Aug. 15, 2022, ISSN: 0957-4174. DOI: 10.1016/j.eswa.2022.117055. [Online]. Avail-603 able: https://www.sciencedirect.com/science/article/pii/S0957417422004699 (visited on604 06/08/2022).605
• [37] V. U. Ihekoronye, C. I. Nwakanma, G. O. Anyanwu, D.-S. Kim, and J.-M. Lee, “Benefits, challenges and606 practical concerns of IoT for smart manufacturing,” in 2021 International Conference on Information and607 Communication Technology Convergence (ICTC), ISSN: 2162-1233, Oct. 2021, pp. 827–830. DOI: 10 .608 1109/ICTC52510.2021.9620771.609
• [38] P. Wang and M. Luo, “A digital twin-based big data virtual and real fusion learning reference framework610 supported by industrial internet towards smart manufacturing,” Journal of Manufacturing Systems, vol. 58,611 pp. 16–32, Jan. 1, 2021, ISSN: 0278-6125. DOI: 10 . 1016 / j . jmsy . 2020 . 11 . 012. [Online]. Avail-612 able: https://www.sciencedirect.com/science/article/pii/S0278612520301990 (visited on613 06/08/2022).614
• [39] V. K. Quy, N. V. Hau, D. V. Anh, et al., “IoT-enabled smart agriculture: Architecture, applications, and615 challenges,” Applied Sciences, vol. 12, no. 7, p. 3396, Jan. 2022, Number: 7 Publisher: Multidisciplinary616 Digital Publishing Institute, ISSN: 2076-3417. DOI: 10.3390/app12073396. [Online]. Available: https:617 //www.mdpi.com/2076-3417/12/7/3396 (visited on 06/09/2022).618
• [40] A. Vangala, A. K. Das, N. Kumar, and M. Alazab, “Smart secure sensing for IoT-based agriculture:619 Blockchain perspective,” IEEE Sensors Journal, vol. 21, no. 16, pp. 17 591–17 607, Aug. 2021, Confer-620 ence Name: IEEE Sensors Journal, ISSN: 1558-1748. DOI: 10.1109/JSEN.2020.3012294.621
• [41] M. Ayaz, A. Uddin, Z. Sharif, A. Mansour, and H. Aggoune, “Internet-of-things (IoT)-based smart agricul-622 ture: Toward making the fields talk,” IEEE Access, vol. PP, pp. 1–1, Aug. 1, 2019. DOI: 10.1109/ACCESS.623 2019.2932609.624
• [42] K. M. Nahiduzzaman, M. Holland, S. Sikder, P. Shaw, K. Hewage, and R. Sadiq, “Urban transformation625 toward a smart city: An e-commerce–induced path-dependent analysis,” Journal of Urban Planning and626 Development, vol. 147, p. 04 020 060, Mar. 1, 2021. DOI: 10.1061/(ASCE)UP.1943-5444.0000648.627
• [43] W. Wenji, “Recognition of rural e-commerce smart assistant system based on smart voice technology,” In-628 ternational Journal of Speech Technology, Sep. 3, 2021, ISSN: 1572-8110. DOI: 10.1007/s10772-021-629 09887 - z. [Online]. Available: https : / / doi . org / 10 . 1007 / s10772 - 021 - 09887 - z (visited on630 06/14/2022).631
• [44] D. Zhang, L. G. Pee, and L. Cui, “Artificial intelligence in e-commerce fulfillment: A case study of resource632 orchestration at alibaba’s smart warehouse,” International Journal of Information Management, vol. 57,633 p. 102 304, Apr. 1, 2021, ISSN: 0268-4012. DOI: 10 . 1016 / j . ijinfomgt . 2020 . 102304. [Online].634 Available: https://www.sciencedirect.com/science/article/pii/S0268401220315036 (visited635 on 06/14/2022).636
• [45] X.-F. Shao, W. Liu, Y. Li, H. R. Chaudhry, and X.-G. Yue, “Multistage implementation framework for smart637 supply chain management under industry 4.0,” Technological Forecasting and Social Change, vol. 162,638 p. 120 354, Jan. 1, 2021, ISSN: 0040-1625. DOI: 10.1016/j.techfore.2020.120354. [Online]. Avail-639 able: https://www.sciencedirect.com/science/article/pii/S004016252031180X (visited on640 06/14/2022).641
• [46] T. Dzhuguryan and A. Deja, “Sustainable waste management for a city multifloor manufacturing cluster: A642 framework for designing a smart supply chain,” Sustainability, vol. 13, no. 3, p. 1540, Jan. 2021, Number: 3643 Publisher: Multidisciplinary Digital Publishing Institute, ISSN: 2071-1050. DOI: 10.3390/su13031540.644 [Online]. Available: https://www.mdpi.com/2071-1050/13/3/1540 (visited on 06/14/2022).645
• [47] B. K. Dey, S. Bhuniya, and B. Sarkar, “Involvement of controllable lead time and variable demand for a646 smart manufacturing system under a supply chain management,” Expert Systems with Applications, vol. 184,647 p. 115 464, Dec. 1, 2021, ISSN: 0957-4174. DOI: 10 . 1016 / j . eswa . 2021 . 115464. [Online]. Avail-648 able: https://www.sciencedirect.com/science/article/pii/S0957417421008769 (visited on649 06/14/2022).650
• [48] S. Gupta, V. A. Drave, S. Bag, and Z. Luo, “Leveraging smart supply chain and information system agility651 for supply chain flexibility,” Information Systems Frontiers, vol. 21, no. 3, pp. 547–564, Jun. 1, 2019, ISSN:652 1572-9419. DOI: 10.1007/s10796-019-09901-5. [Online]. Available: https://doi.org/10.1007/653 s10796-019-09901-5 (visited on 06/14/2022).654
• [49] S. S. Bhattacharyya, D. Maitra, and S. Deb, “Study of adoption and absorption of emerging technologies655 for smart supply chain management: A dynamic capabilities perspective,” International Journal of Applied656 Logistics (IJAL), vol. 11, no. 2, pp. 14–54, Jul. 1, 2021, Publisher: IGI Global, ISSN: 1947-9573. DOI:657 10.4018/IJAL.2021070102.
• [50] W. Wang, N. Kumar, J. Chen, et al., “Realizing the potential of internet of things for smart tourism with 5g662 and AI,” vol. 34, no. 6, Oct. 23, 2020, Accepted: 2021-03-09T03:08:27Z, ISSN: 0890-8044. DOI: 10.1109/663 MNET.011.2000250. [Online]. Available: https://repository.um.edu.mo/handle/10692/31690664 (visited on 06/14/2022).665
• [51] P. Lee, W. C. Hunter, and N. Chung, “Smart tourism city: Developments and transformations,” Sustainability,666 vol. 12, no. 10, p. 3958, Jan. 2020, Number: 10 Publisher: Multidisciplinary Digital Publishing Institute,667 ISSN: 2071-1050. DOI: 10.3390/su12103958. [Online]. Available: https://www.mdpi.com/2071-668 1050/12/10/3958 (visited on 06/14/2022).669
• [52] S. Hasan, C. M. Schneider, S. V. Ukkusuri, and M. C. González, “Spatiotemporal patterns of urban human670 mobility,” Journal of Statistical Physics, vol. 151, no. 1, pp. 304–318, Apr. 1, 2013, ISSN: 1572-9613. DOI:671 10.1007/s10955- 012- 0645- 0. [Online]. Available: https://doi.org/10.1007/s10955- 012-672 0645-0 (visited on 06/14/2022).673
• [53] L. Rosa, F. Silva, and C. Analide, “Mobile networks and internet of things: Contributions to smart hu-674 man mobility,” in Distributed Computing and Artificial Intelligence, 17th International Conference, Y.675 Dong, E. Herrera-Viedma, K. Matsui, S. Omatsu, A. González Briones, and S. Rodríguez González, Eds.,676 ser. Akıllı Sistemlerdeki Gelişmeler ve Bilgi İşlem, Cham: Springer International Publishing, 2021, pp. 168–677 178, ISBN: 978-3-030-53036-5. DOI: 10.1007/978-3-030-53036-5_18.678
• [54] L. Rosa, H. Faria, R. Tabrizi, S. Gonçalves, F. Silva, and C. Analide, “Sentiment analysis based on smart hu-679 man mobility: A comparative study of ML models,” in Bio-inspired Systems and Applications: from Robotics680 to Ambient Intelligence, J. M. Ferrández Vicente, J. R. Álvarez-Sánchez, F. de la Paz López, and H. Adeli,681 Eds., ser. Bilgisayar Bilimleri kitap serisindeki Ders Notlarının, Cham: Springer International Publishing,682 2022, pp. 55–64, ISBN: 978-3-031-06527-9. DOI: 10.1007/978-3-031-06527-9_6.683
• [55] M. W. Traunmueller, N. Johnson, A. Malik, and C. E. Kontokosta, “Digital footprints: Using WiFi probe684 and locational data to analyze human mobility trajectories in cities,” Computers, Environment and Ur-685 ban Systems, vol. 72, pp. 4–12, Nov. 1, 2018, ISSN: 0198-9715. DOI: 10 . 1016 / j . compenvurbsys .686 2018.07.006. [Online]. Available: https://www.sciencedirect.com/science/article/pii/687 S0198971517305914 (visited on 06/14/2022).688
• [56] Z. Chen, M. K. Masood, and Y. C. Soh, “A fusion framework for occupancy estimation in office build-689 ings based on environmental sensor data,” Energy and Buildings, vol. 133, pp. 790–798, Dec. 1, 2016,690 ISSN: 0378-7788. DOI: 10 . 1016 / j . enbuild . 2016 . 10 . 030. [Online]. Available: https : / / www .691 sciencedirect.com/science/article/pii/S0378778816312543 (visited on 03/31/2021).692
• [57] J. Yan, J. Liu, and F.-M. Tseng, “An evaluation system based on the self-organizing system framework693 of smart cities: A case study of smart transportation systems in china,” Technological Forecasting and694 Social Change, vol. 153, p. 119 371, Apr. 1, 2020, ISSN: 0040-1625. DOI: 10 . 1016 / j . techfore .695 2018.07.009. [Online]. Available: https://www.sciencedirect.com/science/article/pii/696 S0040162518301021 (visited on 06/14/2022).697
• [58] L. Guevara and F. Auat Cheein, “The role of 5g technologies: Challenges in smart cities and intelligent698 transportation systems,” Sustainability, vol. 12, no. 16, p. 6469, Jan. 2020, Number: 16 Publisher: Multidis-699 ciplinary Digital Publishing Institute, ISSN: 2071-1050. DOI: 10.3390/su12166469. [Online]. Available:700 https://www.mdpi.com/2071-1050/12/16/6469 (visited on 06/14/2022).701
• [59] F. Zantalis, G. Koulouras, S. Karabetsos, and D. Kandris, “A review of machine learning and IoT in smart702 transportation,” Future Internet, vol. 11, no. 4, p. 94, Apr. 2019, Number: 4 Publisher: Multidisciplinary703 Digital Publishing Institute, ISSN: 1999-5903. DOI: 10.3390/fi11040094. [Online]. Available: https:704 //www.mdpi.com/1999-5903/11/4/94 (visited on 06/14/2022).705
• [60] J. Yang, Y. Han, Y. Wang, B. Jiang, Z. Lv, and H. Song, “Optimization of real-time traffic network assignment706 based on IoT data using DBN and clustering model in smart city,” Future Generation Computer Systems,707 vol. 108, pp. 976–986, Jul. 1, 2020, ISSN: 0167-739X. DOI: 10.1016/j.future.2017.12.012. [Online].708 Available: https://www.sciencedirect.com/science/article/pii/S0167739X17310609 (visited709 on 06/14/2022).710
• [61] A. Al-Dweik, R. Muresan, M. Mayhew, and M. Lieberman, IoT-based multifunctional Scalable real-time711 Enhanced Road Side Unit for Intelligent Transportation Systems. Apr. 1, 2017, 1 p., Pages: 6. DOI: 10.712 1109/CCECE.2017.7946618.713
• [62] A. Selim, P. Yousef, and M. Hagag, “Smart infrastructure by (PPPs) within the concept of smart cities to714 achieve sustainable development,” pp. 182–198, Jan. 1, 2018.715
• [63] M. Gündüz and R. Das, A comparison of cyber-security oriented testbeds for IoT-based smart grids. Mar. 1,716 2018, 1 p., Pages: 6. DOI: 10.1109/ISDFS.2018.8355329.717
• [64] M. Z. Gunduz and R. Das, “Cyber-security on smart grid: Threats and potential solutions,” Computer718 Networks, vol. 169, p. 107 094, Mar. 14, 2020, ISSN: 1389-1286. DOI: 10 . 1016 / j . comnet . 2019 .719 107094. [Online]. Available: https : / / www . sciencedirect . com / science / article / pii /720 S1389128619311235 (visited on 06/16/2022).721
• [65] A. A. Khan, V. Kumar, M. Ahmad, and S. Rana, “LAKAF: Lightweight authentication and key agreement722 framework for smart grid network,” Journal of Systems Architecture, vol. 116, p. 102 053, Jun. 1, 2021,723 ISSN: 1383-7621. DOI: 10 . 1016 / j.sysarc.2021.102053. [Online]. Available: https ://www.724 sciencedirect.com/science/article/pii/S1383762121000461 (visited on 06/16/2022).725
• [66] A. U. Rehman, Z. Wadud, R. M. Elavarasan, et al., “An optimal power usage scheduling in smart grid726 integrated with renewable energy sources for energy management,” IEEE Access, vol. 9, pp. 84 619–84 638,727 2021, Conference Name: IEEE Access, ISSN: 2169-3536. DOI: 10.1109/ACCESS.2021.3087321.728
• [67] T. Ekwevugbe, N. Brown, and D. Fan, “A design model for building occupancy detection using sensor729 fusion,” in 2012 6th IEEE International Conference on Digital Ecosystems and Technologies (DEST), ISSN:730 2150-4946, Jun. 2012, pp. 1–6. DOI: 10.1109/DEST.2012.6227924.731
• [68] G. Apostolou, S. Krinidis, D. Ioannidis, et al., “GreenSoul￿ a novel platform for the reduction of energy732 consumption in communal and shared spaces,” in 2016 4th International Symposium on Environmental733 Friendly Energies and Applications (EFEA), Sep. 2016, pp. 1–6. DOI: 10.1109/EFEA.2016.7748783.734
• [69] Y.-L. Hsu, P.-H. Chou, H.-C. Chang, et al., “Design and implementation of a smart home system using mul-735 tisensor data fusion technology,” Sensors, vol. 17, no. 7, p. 1631, Jul. 2017, Number: 7 Publisher: Multidis-736 ciplinary Digital Publishing Institute, ISSN: 1424-8220. DOI: 10.3390/s17071631. [Online]. Available:737 https://www.mdpi.com/1424-8220/17/7/1631 (visited on 04/13/2022).738
• [70] S. Arvidsson, M. Gullstrand, B. Sirmacek, and M. Riveiro, “Sensor fusion and convolutional neural networks739 for indoor occupancy prediction using multiple low-cost low-resolution heat sensor data,” Sensors, vol. 21,740 no. 4, p. 1036, Jan. 2021, Number: 4 Publisher: Multidisciplinary Digital Publishing Institute, ISSN: 1424-741 8220. DOI: 10.3390/s21041036. [Online]. Available: https://www.mdpi.com/1424- 8220/21/4/742 1036 (visited on 04/13/2022).743
• [71] A. K. Das, P. H. Pathak, J. Jee, C.-N. Chuah, and P. Mohapatra, “Non-intrusive multi-modal estimation of744 building occupancy,” in Proceedings of the 15th ACM Conference on Embedded Network Sensor Systems,745 Delft Netherlands: ACM, Nov. 6, 2017, pp. 1–14, ISBN: 978-1-4503-5459-2. DOI: 10.1145/3131672.746 3131680. [Online]. Available: https://dl.acm.org/doi/10.1145/3131672.3131680 (visited on747 04/20/2022).748
• [72] Z. Wang, T. Hong, and M. A. Piette, “Data fusion in predicting internal heat gains for office buildings through749 a deep learning approach,” Applied Energy, vol. 240, pp. 386–398, Apr. 15, 2019, ISSN: 0306-2619. DOI:750 10 . 1016 / j . apenergy . 2019 . 02 . 066. [Online]. Available: https : / / www . sciencedirect . com /751 science/article/pii/S0306261919303630 (visited on 04/20/2022).752
• [73] V. Barthelmes, V. Fabi, S. Corgnati, and V. Serra, “Human factor and energy efficiency in buildings: Moti-753 vating end-users behavioural change,” in Proceedings of the 20th Congress of the International Ergonomics754 Association (IEA 2018), S. Bagnara, R. Tartaglia, S. Albolino, T. Alexander, and Y. Fujita, Eds., vol. 825, Se-755 ries Title: Advances in Intelligent Systems and Computing, Cham: Springer International Publishing, 2019,756 pp. 514–525, DOI: 10.1007/978-3-319-96068-5_58.757
• [74] F. Fiebig, S. Kochanneck, I. Mauser, and H. Schmeck, “Detecting occupancy in smart buildings by data760 fusion from low-cost sensors: Poster description,” in Proceedings of the Eighth International Conference on761 Future Energy Systems, Shatin Hong Kong: ACM, May 16, 2017, pp. 259–261, ISBN: 978-1-4503-5036-762 5. DOI: 10.1145/3077839.3081675. [Online]. Available: https://dl.acm.org/doi/10.1145/763 3077839.3081675 (visited on 05/12/2022).764
• [75] W. Wang, J. Chen, and T. Hong, “Occupancy prediction through machine learning and data fusion of en-765 vironmental sensing and wi-fi sensing in buildings,” Automation in Construction, vol. 94, pp. 233–243,766 Oct. 1, 2018, ISSN: 0926-5805. DOI: 10.1016/j.autcon.2018.07.007. [Online]. Available: https:767 //www.sciencedirect.com/science/article/pii/S0926580518302656 (visited on 05/15/2022).768
• [76] W. Wang, T. Hong, N. Xu, X. Xu, J. Chen, and X. Shan, “Cross-source sensing data fusion for building769 occupancy prediction with adaptive lasso feature filtering,” Building and Environment, vol. 162, p. 106 280,770 Sep. 1, 2019, ISSN: 0360-1323. DOI: 10.1016/j.buildenv.2019.106280. [Online]. Available: https:771 //www.sciencedirect.com/science/article/pii/S0360132319304901 (visited on 05/15/2022).772
• [77] X. Jing, S. Li, J. Cheng, and J. Guo, “Multidimensional situational information fusion method for energy sav-773 ing on campus,” Journal of Intelligent & Fuzzy Systems, vol. 38, no. 4, pp. 4793–4807, Apr. 30, 2020, ISSN:774 10641246, 18758967. DOI: 10.3233/JIFS- 191513. [Online]. Available: https://www.medra.org/775 servlet/aliasResolver?alias=iospress&doi=10.3233/JIFS-191513 (visited on 05/17/2022).776
• [78] S. H. Marakkalage, S. Sarica, B. P. L. Lau, et al., “Understanding the lifestyle of older population: Mobile777 crowdsensing approach,” IEEE Transactions on Computational Social Systems, vol. 6, no. 1, pp. 82–95,778 2019, Conference Name: IEEE Transactions on Computational Social Systems, ISSN: 2329-924X. DOI:779 10.1109/TCSS.2018.2883691.780
• [79] R. Luo, C.-C. Yih, and K. L. Su, “Multisensor fusion and integration: Approaches, applications, and future781 research directions,” IEEE Sensors Journal, vol. 2, no. 2, pp. 107–119, Apr. 2002, Conference Name: IEEE782 Sensors Journal, ISSN: 1558-1748. DOI: 10.1109/JSEN.2002.1000251.783
• [80] Z. Gao, W. Cheng, X. Qiu, and L. Meng, “A missing sensor data estimation algorithm based on temporal and784 spatial correlation,” International Journal of Distributed Sensor Networks, vol. 11, no. 10, p. 435 391, Oct. 1,785 2015, Publisher: SAGE Publications, ISSN: 1550-1329. DOI: 10.1155/2015/435391. [Online]. Available:786 https://journals.sagepub.com/doi/abs/10.1155/2015/435391 (visited on 05/31/2022).787
• [81] I. Mary and L. Arockiam, “Imputing the missing data in IoT based on the spatial and temporal correlation,”788 2017 IEEE International Conference on Current Trends in Advanced Computing (ICCTAC), 2017. DOI:789 10.1109/ICCTAC.2017.8249990.790
• [82] Y. Li and L. E. Parker, “Nearest neighbor imputation using spatial–temporal correlations in wireless sensor791 networks,” Information Fusion, Special Issue: Resource Constrained Networks, vol. 15, pp. 64–79, Jan. 1,792 2014, ISSN: 1566-2535. DOI: 10.1016/j.inffus.2012.08.007. [Online]. Available: https://www.793 sciencedirect.com/science/article/pii/S1566253512000711 (visited on 05/31/2022).794
• [83] P. Li, E. A. Stuart, and D. B. Allison, “Multiple imputation: A flexible tool for handling missing data,” JAMA,795 vol. 314, no. 18, pp. 1966–1967, Nov. 10, 2015, ISSN: 1538-3598. DOI: 10.1001/jama.2015.15281.796
• [84] N. Vijayakumar and B. Plale. “Prediction of missing events in sensor data streams using kalman797 filters.” (2007), [Online]. Available: https : / / www . semanticscholar . org / paper /798 Prediction - of - Missing - Events - in - Sensor - Data - Streams - Vijayakumar - Plale /799 57c2a42693e615dc5cf4ae27eb2d3cce933732c2 (visited on 05/31/2022).800
• [85] Faculy of Computer Science, Østfold University College, Halden 1783, Norway, A. Shahraki, and Ø. Hau-801 gen, “An outlier detection method to improve gathered datasets for network behavior analysis in IoT,” Jour-802 nal of Communications, pp. 455–462, 2019, ISSN: 23744367. DOI: 10 . 12720 / jcm . 14 . 6 . 455 - 462.803 [Online]. Available: http : / / www . jocm . us / index . php ? m = content&c = index&a = show&catid =804 221&id=1372 (visited on 05/31/2022).805
• [86] M. Hasan, M. M. Islam, M. I. I. Zarif, and M. M. A. Hashem, “Attack and anomaly detection in IoT sensors806 in IoT sites using machine learning approaches,” Internet of Things, vol. 7, p. 100 059, Sep. 1, 2019, ISSN:807 2542-6605. DOI: 10.1016/j.iot.2019.100059. [Online]. Available: https://www.sciencedirect.808 com/science/article/pii/S2542660519300241 (visited on 05/31/2022).809
• [87] A. Gaddam, T. Wilkin, M. Angelova, and J. Gaddam, “Detecting sensor faults, anomalies and outliers in the810 internet of things: A survey on the challenges and solutions,” Electronics, vol. 9, no. 3, p. 511, Mar. 2020,811 Number: 3 Publisher: Multidisciplinary Digital Publishing Institute, ISSN: 2079-9292. DOI: 10 . 3390 /812 electronics9030511. [Online]. Available: https://www.mdpi.com/2079-9292/9/3/511 (visited on813 05/31/2022).814
• [88] P. Smets, “Analyzing the combination of conflicting belief functions,” Information Fusion, vol. 8, no. 4,815 pp. 387–412, Oct. 1, 2007, ISSN: 1566-2535. DOI: 10.1016/j.inffus.2006.04.003. [Online]. Avail-816 able: https://www.sciencedirect.com/science/article/pii/S1566253506000467 (visited on817 05/31/2022).818
• [89] Z. Zhang, T. Liu, D. Chen, and W. Zhang, “Novel algorithm for identifying and fusing conflicting data in819 wireless sensor networks,” Sensors, vol. 14, no. 6, pp. 9562–9581, Jun. 2014, Number: 6 Publisher: Multidis-820 ciplinary Digital Publishing Institute, ISSN: 1424-8220. DOI: 10.3390/s140609562. [Online]. Available:821 https://www.mdpi.com/1424-8220/14/6/9562 (visited on 07/08/2022).822
• [90] S. K. Sowe, T. Kimata, M. Dong, and K. Zettsu, “Managing heterogeneous sensor data on a big data plat-823 form: IoT services for data-intensive science,” in 2014 IEEE 38th International Computer Software and824 Applications Conference Workshops, Jul. 2014, pp. 295–300. DOI: 10.1109/COMPSACW.2014.52.825
• [91] R. Krishnamurthi, A. Kumar, D. Gopinathan, A. Nayyar, and B. Qureshi, “An overview of IoT sensor data826 processing, fusion, and analysis techniques,” Sensors, vol. 20, no. 21, p. 6076, Jan. 2020, Number: 21 Pub-827 lisher: Multidisciplinary Digital Publishing Institute, ISSN: 1424-8220. DOI: 10.3390/s20216076. [On-828 line]. Available: https://www.mdpi.com/1424-8220/20/21/6076 (visited on 05/31/2022).829