Skip to main content
SpringerLink
Log in

Context recognition and ubiquitous computing in smart cities: a systematic mapping

  • Regular Paper
  • Published:
Computing Aims and scope Submit manuscript

Abstract

Smart cities are one of the emerging domains for computational applications. Many of these applications may benefit from the ubiquitous computing paradigm to provide better services. An important aspect of these applications is how to obtain data about their users and understand them. Context-aware approaches has been proven to be successful in understanding these data. These solutions obtain data from one or more sensors and apply context recognition techniques to infer higher level information. Several works in the last decade have presented ubiquitous approaches for context recognition that can be applied in smart cities. Our work presents a systematic mapping that provides an overview of context recognition approaches applied in smart cities domains. Several aspects of these approaches have been analyzed, such as reasoning techniques, sensors usage, context level, and applications. Of the total 3627 papers returned in the search, 93 papers were analyzed after two filtering processes. The analysis of these papers have shown that only few recent works explored situation recognition information and the full potential of the sensing capabilities in smart cities.The main objective of this article is the identification of future open context recognition approaches allowing the development of news solutions and research.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2

Similar content being viewed by others

Notes

  1. This article has about 6470 citations (according to Google Scholar) and the context definition presented in it is used largely by several works in the area.

References

  1. Abidin S, Togneri R, Sohel F (2018) Acoustic scene classification using joint time-frequency image-based feature representations. In: 2018 15th IEEE international conference on advanced video and signal based surveillance (AVSS), IEEE, pp 1–6

  2. Abidin S, Togneri R, Sohel F (2018) Spectrotemporal analysis using local binary pattern variants for acoustic scene classification. IEEE/ACM Trans Audio Speech Lang Process 26(11):2112–2121

    Article  Google Scholar 

  3. Abidin S, Xia X, Togneri R, Sohel F (2018) Local binary pattern with random forest for acoustic scene classification. In: 2018 IEEE international conference on multimedia and expo (ICME), IEEE, pp 1–6

  4. Abowd GD, Dey AK, Brown PJ, Davies N, Smith M, Steggles P (1999) Towards a better understanding of context and context-awareness. In: Gellersen HW (ed) Handheld and ubiquitous computing. Springer, Berlin, pp 304–307

    Chapter  Google Scholar 

  5. Ali M, ElBatt T, Youssef M (2018) Senseio: realistic ubiquitous indoor outdoor detection system using smartphones. IEEE Sens J 18(9):3684–3693

    Article  Google Scholar 

  6. Attard J, Scerri S, Rivera I, Handschuh S (2013) Ontology-based situation recognition for context-aware systems. In: Proceedings of the 9th international conference on semantic systems, ACM, I-SEMANTICS ’13, pp 113–120. https://doi.org/10.1145/2506182.2506197

  7. Balduini M, Bocconi S, Bozzon A, Della Valle E, Huang Y, Oosterman J, Palpanas T, Tsytsarau M (2014) A case study of active, continuous and predictive social media analytics for smart city. In: S4SC@ ISWC, pp 31–46

  8. Battaglino D, Mesaros A, Lepauloux L, Pilati L, Evans N (2015) Acoustic context recognition for mobile devices using a reduced complexity svm. In: 2015 23rd European signal processing conference (EUSIPCO), IEEE, pp 534–538

  9. Battaglino D, Lepauloux L, Evans N (2016) The open-set problem in acoustic scene classification. In: 2016 IEEE international workshop on acoustic signal enhancement (IWAENC), IEEE, pp 1–5

  10. Bazire M, Brézillon P (2005) Understanding context before using it. In: International and interdisciplinary conference on modeling and using context. Springer, pp 29–40

  11. Bhargava P, Gramsky N, Agrawala A (2014) Senseme: a system for continuous, on-device, and multi-dimensional context and activity recognition. In: Proceedings of the 11th international conference on mobile and ubiquitous systems: computing, networking and services, ICST (Institute for Computer Sciences, Social-Informatics and ..., pp 40–49

  12. Bhattacharya S, Lane ND (2016) From smart to deep: robust activity recognition on smartwatches using deep learning. In: 2016 IEEE international conference on pervasive computing and communication workshops (PerCom Workshops), IEEE, pp 1–6

  13. Bibri SE, Krogstie J (2017) Smart sustainable cities of the future: an extensive interdisciplinary literature review. Sustain Cities Soc 31:183–212

    Article  Google Scholar 

  14. Boukhechba M, Bouzouane A, Gaboury S, Gouin-Vallerand C, Giroux S, Bouchard B (2016) Battery-aware mobile solution for online activity recognition from users’ movements. In: 2016 IEEE international conference on mobile services (MS), IEEE, pp 33–40

  15. Cao L, Wang Y, Zhang B, Jin Q, Vasilakos AV (2018) Gchar: an efficient group-based context—aware human activity recognition on smartphone. J Parall Distrib Comput 118:67–80

    Article  Google Scholar 

  16. Capurso N, Mei B, Song T, Cheng X, Yu J (2018) A survey on key fields of context awareness for mobile devices. J Netw Comput Appl 118:44–60

    Article  Google Scholar 

  17. Celik SC, Incel OD (2018) Semantic place prediction from crowd-sensed mobile phone data. J Ambient Intell Hum Comput 9(6):2109–2124

    Article  Google Scholar 

  18. Cheng N, Chen S, Pathak P, Mohapatra P (2015) Long-term privacy profiling through smartphone sensors. In: 2015 IEEE 12th international conference on mobile ad hoc and sensor systems, IEEE, pp 639–644

  19. Coskun D, Incel OD, Ozgovde A (2015) Phone position/placement detection using accelerometer: impact on activity recognition. In: 2015 IEEE tenth international conference on intelligent sensors. Sensor networks and information processing (ISSNIP), IEEE, pp 1–6

  20. Curiel P, Pretel I, Lago AB (2015) Facing up social activity recognition using smartphone sensors. In: International conference on ubiquitous computing and ambient intelligence. Springer, pp 116–127

  21. Das S, Chatterjee S, Chakraborty S, Mitra B (2018) Groupsense: a lightweight framework or group identification. IEEE Trans Mob Comput 18:2856–2870

    Article  Google Scholar 

  22. Delgado-Contreras JR, Garćıa-Vázquez JP, Brena RF, Galván-Tejada CE, Galván-Tejada JI (2014) Feature selection for place classification through environmental sounds. Proc Comput Sci 37:40–47

    Article  Google Scholar 

  23. Deng Z, Fu X, Wang H (2018) An imu-aided body-shadowing error compensation method for indoor bluetooth positioning. Sensors 18(1):304

    Article  Google Scholar 

  24. Dyba T, Dingsoyr T, Hanssen GK (2007) Applying systematic reviews to diverse study types: an experience report. In: First international symposium on empirical software engineering and measurement (ESEM 2007), IEEE, pp 225–234

  25. Elhoushi M, Georgy J, Korenberg M, Noureldin A (2014) Robust motion mode recognition for portable navigation independent on device usage. In: 2014 IEEE/ION position, location and navigation symposium-PLANS 2014, IEEE, pp 158–163

  26. Exler A, Urschel M, Schankin A, Beigl M (2016) Smartphone-based detection of location changes using wifi data. In: International conference on wireless mobile communication and healthcare. Springer, pp 164–167

  27. Exler A, Braith M, Mincheva K, Schankin A, Beigl M (2018) Smartphone-based estimation of a user being in company or alone based on place, time, and activity. In: International conference on mobile computing, applications, and services. Springer, pp 74–89

  28. Farinella GM, Ravì D, Tomaselli V, Guarnera M, Battiato S (2015) Representing scenes for real-time context classification on mobile devices. Pattern Recognit 48(4):1086–1100

    Article  Google Scholar 

  29. Faye S, Frank R, Engel T (2015) Adaptive activity and context recognition using multimodal sensors in smart devices. In: International conference on mobile computing, applications, and services. Springer, pp 33–50

  30. Filios G, Nikoletseas S, Pavlopoulou C, Rapti M, Ziegler S (2015) Hierarchical algorithm for daily activity recognition via smartphone sensors. In: 2015 IEEE 2nd world forum on internet of things (Wf-Iot), IEEE, pp 381–386

  31. Gao H, Groves P (2018) Context detection for advanced self-aware navigation using smartphone sensors. In: Proceedings of the international navigation conference 2017, Royal Institute of Navigation, pp 1–21

  32. Gao H, Groves PD (2016) Context determination for adaptive navigation using multiple sensors on a smartphone. In: Proceedings of the 29th international technical meeting of the satellite division of the institute of navigation (ION GNSS+ 2016), Portland, OR, USA, pp 12–16

  33. Gao H, Groves PD (2018) Environmental context detection for adaptive navigation using gnss measurements from a smartphone. Navig J Inst Navig 65(1):99–116

    Article  Google Scholar 

  34. Gaved M, Luley P, Efremidis S, Georgiou I, Kukulska-Hulme A, Jones A, Scanlon E (2014) Challenges in context-aware mobile language learning: the Maseltov approach. In: International conference on mobile and contextual learning. Springer, pp 351–364

  35. Gordon D, Czerny J, Beigl M (2014) Activity recognition for creatures of habit. Pers Ubiquitous Comput 18(1):205–221

    Article  Google Scholar 

  36. Guinness R (2015) Beyond where to how: a machine learning approach for sensing mobility contexts using smartphone sensors. Sensors 15(5):9962–9985

    Article  Google Scholar 

  37. Gundersen OE (2013) Situational awareness in context. In: International and interdisciplinary conference on modeling and using context. Springer, pp 274–287

  38. Guvensan M, Dusun B, Can B, Turkmen H (2018) A novel segment-based approach for improving classification performance of transport mode detection. Sensors 18(1):87

    Article  Google Scholar 

  39. Haruna K, Akmar Ismail M, Suhendroyono S, Damiasih D, Pierewan AC, Chiroma H, Herawan T (2017) Context-aware recommender system: a review of recent developmental process and future research direction. Appl Sci 7(12):1211

    Article  Google Scholar 

  40. Huai B, Chen E, Zhu H, Xiong H, Bao T, Liu Q, Tian J (2014) Toward personalized context recognition for mobile users: a semisupervised bayesian hmm approach. ACM Trans Knowl Discov Data (TKDD) 9(2):10

    Google Scholar 

  41. Hwang J, Ji Y, Kwak N, Kim EY (2016) Outdoor context awareness device that enables mobile phone users to walk safely through urban intersections. In: ICPRAM, pp 526–533

  42. Hyuga S, Ito M, Iwai M, Sezaki K (2015) Estimate a user’s location using smartphone’s barometer on a subway. In: Proceedings of the 5th international workshop on mobile entity localization and tracking in GPS-less environments, ACM, p 2

  43. Indulska J, Sutton P (2003) Location management in pervasive systems. In: Proceedings of the Australasian information security workshop conference on ACSW frontiers 2003—vol 21, Australian Computer Society, Inc., Darlinghurst, Australia, Australia, ACSW Frontiers’03, pp 143–151

  44. Iwasawa Y, Nagamine K, Yairi IE, Matsuo Y (2015) Toward an automatic road accessibility information collecting and sharing based on human behavior sensing technologies of wheelchair users. Proc Comput Sci 63:74–81

    Article  Google Scholar 

  45. Jänicke M, Sick B, Tomforde S (2018) Self-adaptive multi-sensor activity recognition systems based on gaussian mixture models. Informatics 5(3):38

    Article  Google Scholar 

  46. Kashevnik A, Lashkov I (2018) Decision support system for drivers and passengers: Smartphone-based reference model and evaluation. In: Proceedings of the 23rd conference of open innovations association FRUCT, FRUCT Oy, p 22

  47. Keele S et al (2007) Guidelines for performing systematic literature reviews in software engineering. Technical report, Ver. 2.3 EBSE

  48. Khalifa S, Lan G, Hassan M, Hu W, Seneviratne A (2018) Human context detection from kinetic energy harvesting wearables. In: Examining developments and applications of wearable devices in modern society. IGI Global, pp 107–133

  49. Koster A, Koch F, Kim YB (2014) Serendipitous recommendation based on big context. In: Ibero-American conference on artificial intelligence. Springer, pp 319–330

  50. Lane ND, Georgiev P (2015) Can deep learning revolutionize mobile sensing? In: Proceedings of the 16th international workshop on mobile computing systems and applications. ACM, pp 117–122

  51. Lee DG (2017) A multi-level behavior network-based dangerous situation recognition method in cloud computing environments. J Supercomput 73(7):3291–3306

    Article  Google Scholar 

  52. Lee SW, Lee CY, Kwak DH, Ha JW, Kim J, Zhang BT (2017) Dual-memory neural networks for modeling cognitive activities of humans via wearable sensors. Neural Netw 92:17–28

    Article  Google Scholar 

  53. Lee YS, Cho SB (2014) Activity recognition with android phone using mixture-of-experts co-trained with labeled and unlabeled data. Neurocomputing 126:106–115

    Article  Google Scholar 

  54. Li M, Gao Z, Zang X, Wang X (2018) Environmental noise classification using convolution neural networks. In: Proceedings of the 2018 international conference on electronics and electrical engineering technology. ACM, pp 182–185

  55. Li S, Qin Z, Song H, Si C, Sun B, Yang X, Zhang R (2017) A lightweight and aggregated system for indoor/outdoor detection using smart devices. Fut Gener Comput Syst 107:988–997

    Article  Google Scholar 

  56. Li X, Wei D, Lai Q, Xu Y, Yuan H (2017) Smartphone-based integrated pdr/gps/bluetooth pedestrian location. Adv Space Res 59(3):877–887

    Article  Google Scholar 

  57. Li Z, Chen W, Li C, Li M, Li XY, Liu Y (2014) Flight: clock calibration and context recognition using fluorescent lighting. IEEE Trans Mobile Comput 13(7):1495–1508

    Article  Google Scholar 

  58. Liono J, Abdallah ZS, Qin A, Salim FD (2018) Inferring transportation mode and human activity from mobile sensing in daily life. In: Proceedings of the 15th EAI international conference on mobile and ubiquitous systems: computing, networking and services. ACM, pp 342–351

  59. Liu Q, Zhou Z, Shakya SR, Uduthalapally P, Qiao M, Sung AH (2018) Smartphone sensor-based activity recognition by using machine learning and deep learning algorithms. Int J Mach Learn Comput 8(2):121

    Article  Google Scholar 

  60. Lorintiu O, Vassilev A (2016) Transportation mode recognition based on smartphone embedded sensors for carbon footprint estimation. In: 2016 IEEE 19th international conference on intelligent transportation systems (ITSC). IEEE, pp 1976–1981

  61. Machado GM, Maran V, Dornelles LP, Gasparini I, Thom LH, de Oliveira JPM (2018) A systematic mapping on adaptive recommender approaches for ubiquitous environments. Computing 100(2):183–209

    Article  MathSciNet  Google Scholar 

  62. Machado RS, Almeida RB, Pernas AM, Yamin AC (2019) State of the art in hybrid strategies for context reasoning: a systematic literature review. Inf Softw Technol 111:122–130. https://doi.org/10.1016/j.infsof.2019.01.010

    Article  Google Scholar 

  63. Magara MB, Ojo S, Ngwira S, Zuva T (2016) Mplist: context aware music playlist. In: 2016 IEEE international conference on emerging technologies and innovative business practices for the transformation of societies (EmergiTech). IEEE, pp 309–316

  64. Magno M, Cavigelli L, Andri R, Benini L (2015) Ultra-low power context recognition fusing sensor data from an energy-neutral smart watch. In: International internet of things summit. Springer, pp 331–343

  65. Martinelli A, Gao H, Groves PD, Morosi S (2018) Probabilistic context-aware step length estimation for pedestrian dead reckoning. IEEE Sens J 18(4):1600–1611

    Article  Google Scholar 

  66. Matsuyama S, Yamabe T, Nakayama Y, Okuwaki Y, Kiyohara R (2016) A method for recognizing driver’s location context with a vehicle information device. In: 2016 IEEE 30th international conference on advanced information networking and applications (AINA). IEEE, pp 704–710

  67. Mongia A, Gunturi VM, Naik V (2018) Detecting activities at metro stations using smartphone sensors. In: 2018 10th international conference on communication systems & networks (COMSNETS), IEEE, pp 57–65

  68. Morandi C, Rolando A, Di Vita S (2016) From smart city to smart region: digital services for an internet of places. Springer, Berlin

    Book  Google Scholar 

  69. Nguyen M, Le H, Yan WQ, Dawda A (2018) A vision aid for the visually impaired using commodity dual-rear-camera smartphones. In: 2018 25th international conference on mechatronics and machine vision in practice (M2VIP). IEEE, pp 1–6

  70. Nguyen T, Gupta S, Venkatesh S, Phung D (2015) Continuous discovery of co-location contexts from bluetooth data. Pervasive Mobile Comput 16:286–304

    Article  Google Scholar 

  71. Otebolaku A, Lee GM (2018) A framework for exploiting internet of things for context-aware trust-based personalized services. Mobile Inform Syst

  72. Otebolaku AM, Andrade MT (2016) User context recognition using smartphone sensors and classification models. J Netw Comput Appl 66:33–51

    Article  Google Scholar 

  73. Parviainen J, Bojja J, Collin J, Leppänen J, Eronen A (2014) Adaptive activity and environment recognition for mobile phones. Sensors 14(11):20753–20778

    Article  Google Scholar 

  74. Perera C, Zaslavsky A, Christen P, Georgakopoulos D (2014) Context aware computing for the internet of things: a survey. IEEE Commun Surv Tutor 16(1):414–454. https://doi.org/10.1109/SURV.2013.042313.00197

    Article  Google Scholar 

  75. Pernas AM, Diaz A, Motz R, de Oliveira JPM (2012) Enriching adaptation in e-learning systems through a situation-aware ontology network. Interact Technol Smart Educ 9(2):60–73. https://doi.org/10.1108/17415651211242215

    Article  Google Scholar 

  76. Petersen K, Vakkalanka S, Kuzniarz L (2015) Guidelines for conducting systematic mapping studies in software engineering: an update. Inf Softw Technol 64:1–18

    Article  Google Scholar 

  77. Pipelidis G, Fraaz F, Prehofer C (2018) Extracting semantics of indoor places based on context recognition. In: 2018 IEEE international conference on pervasive computing and communications workshops (PerCom Workshops). IEEE, pp 464–467

  78. Radu V, Katsikouli P, Sarkar R, Marina MK (2014) A semi-supervised learning approach for robust indoor-outdoor detection with smartphones. In: Proceedings of the 12th ACM conference on embedded network sensor systems. ACM, pp 280–294

  79. Radu V, Tong C, Bhattacharya S, Lane ND, Mascolo C, Marina MK, Kawsar F (2018) Multimodal deep learning for activity and context recognition. Proc ACM Interact Mobile Wearable Ubiquitous Technol 1(4):157

    Google Scholar 

  80. Ramakrishnan AK, Preuveneers D, Berbers Y (2014) A Bayesian framework for life-long learning in context-aware mobile applications. In: Context in computing. Springer, pp 127–141

  81. Razzaque MA, Milojevic-Jevric M, Palade A, Clarke S (2016) Middleware for internet of things: a survey. IEEE Intern Things J 3(1):70–95

    Article  Google Scholar 

  82. Roma G, Herrera P, Nogueira W (2018) Environmental sound recognition using short-time feature aggregation. J Intell Inf Syst 51(3):457–475

    Article  Google Scholar 

  83. Ruotsalainen L, Kirkko-Jaakkola M, Rantanen J, Mäkelä M (2018) Error modelling for multi-sensor measurements in infrastructure-free indoor navigation. Sensors 18(2):590

    Article  Google Scholar 

  84. Sadiq FI, Selamat A, Ibrahim R (2018) A systematic literature review on activity recognition with context-awareness techniques for mitigation of disasters. Int J Dig Enterp Technol 1(1–2):177–217

    Article  Google Scholar 

  85. Sankaran K, Zhu M, Guo XF, Ananda AL, Chan MC, Peh LS (2014) Using mobile phone barometer for low-power transportation context detection. In: Proceedings of the 12th ACM conference on embedded network sensor systems. ACM, pp 191–205

  86. Shi J, Ren M, Wang P, Meng J (2018) Research on pf-slam indoor pedestrian localization algorithm based on feature point map. Micromachines 9(6):267

    Article  Google Scholar 

  87. Shoaib M, Bosch S, Incel OD, Scholten H, Havinga PJ (2015) A survey of online activity recognition using mobile phones. Sensors 15(1):2059–2085

    Article  Google Scholar 

  88. Singla K, Bose J (2018) System for user context determination in a network of iot devices. In: International conference on smart homes and health telematics. Springer, pp 317–323

  89. Souabni R, Saadi IB, Salah NB, Ghezala HB et al (2016) Approach based on fuzzy ontology for situation identification in situation-aware ubiquitous learning environment. In: 2016 IEEE international conference on fuzzy systems (FUZZ-IEEE). IEEE, pp 1805–1812

  90. Sujadevi V, Ashok A, Krishnamoorthy S, Prabaharan P, Shankar P, Bharataraju M, Keerti S, Khyati D (2017) ‘mobaware’-harnessing context awareness, sensors and cloud for spontaneous personal safety emergency help requests. In: International conference on ubiquitous communications and network computing. Springer, pp 1–12

  91. Theodorou T, Mporas I, Fakotakis N (2015) Automatic sound recognition of urban environment events. In: International conference on speech and computer. Springer, pp 129–136

  92. Török A, Nagy A, Kálomista I (2015) Trekie-ubiquitous indoor localization with trajectory reconstruction based on knowledge inferred from environment. In: International conference on mobile web and information systems. Springer, pp 15–26

  93. Tregel T, Gilbert A, Konrad R, Schäfer P, Göbel S (2018) Examining approaches for mobility detection through smartphone sensors. In: Joint international conference on serious games. Springer, pp 217–228

  94. Unger M, Shapira B, Rokach L, Livne A (2018) Inferring contextual preferences using deep encoder–decoder learners. New Rev Hypermed Multimed 24(3):262–290

    Article  Google Scholar 

  95. Vahdat-Nejad H, Ramazani A, Mohammadi T, Mansoor W (2016) A survey on context-aware vehicular network applications. Vehicul Commun 3:43–57

    Article  Google Scholar 

  96. Vaizman Y, Weibel N, Lanckriet G (2018) Context recognition in-the-wild: unified model for multi-modal sensors and multi-label classification. Proc ACM Interact Mobile Wearable Ubiquitous Technol 1(4):168

    Google Scholar 

  97. Van Erum K, Schöning J (2017) Subwayapps: using smartphone barometers for positioning in underground transportation environments. In: Progress in location-based services 2016. Springer, pp 69–85

  98. Villegas NM, Müller HA (2010) Managing dynamic context to optimize smart interactions and services. The smart internet: current research and future applications. Springer, Berlin, pp 289–318

    Chapter  Google Scholar 

  99. Wang EK, Liu H, Wang G, Ye Y, Wu TY, Chen CM (2015) Context recognition for adaptive hearing-aids. In: 2015 IEEE 13th international conference on industrial informatics (INDIN). IEEE, pp 1102–1107

  100. Wang L, Cheng W, Pan L, Gu T, Wu T, Tao X, Lu J (2018) Spiderwalk: circumstance-aware transportation activity detection using a novel contact vibration sensor. Proc ACM Interact Mobile Wearable Ubiquitous Technol 2(1):42

    Google Scholar 

  101. Wang W, Chang Q, Li Q, Shi Z, Chen W (2016) Indoor-outdoor detection using a smart phone sensor. Sensors 16(10):1563

    Article  Google Scholar 

  102. Wang Z, Wu D, Gravina R, Fortino G, Jiang Y, Tang K (2017) Kernel fusion based extreme learning machine for cross-location activity recognition. Inf Fus 37:1–9

    Article  Google Scholar 

  103. Xiao J, Joseph SL, Zhang X, Li B, Li X, Zhang J (2015) An assistive navigation framework for the visually impaired. IEEE Trans Hum Mach Syst 45(5):635–640

    Article  Google Scholar 

  104. Xie T, Zheng Q, Zhang W (2017) Recognizing physical contexts of mobile video learners via smartphone sensors. Knowl Based Syst 136:75–84

    Article  Google Scholar 

  105. Xu W, Chen R, Chu T, Kuang L, Yang Y, Li X, Liu J, Chen Y (2014) A context detection approach using gps module and emerging sensors in smartphone platform. In: 2014 ubiquitous positioning indoor navigation and location based service (UPINLBS). IEEE, pp 156–163

  106. Yağanoğlu M, Köse C (2018) Real-time detection of important sounds with a wearable vibration based device for hearing-impaired people. Electronics 7(4):50

    Article  Google Scholar 

  107. Yan N, Chen J, Yu T (2018) A feature set for the similar activity recognition using smartphone. In: 2018 10th international conference on wireless communications and signal processing (WCSP). IEEE, pp 1–6

  108. Yang K, Wang J, Bao L, Ding M, Wang J, Wang Y (2016) Towards future situation-awareness: A conceptual middleware framework for opportunistic situation identification. In: Proceedings of the 12th ACM symposium on QoS and security for wireless and mobile networks. ACM, New York, NY, USA, Q2SWinet’16, pp 95–101. https://doi.org/10.1145/2988272.2990291

  109. Yang K, Gong X, Liu Y, Li Z, Xing T, Chen X, Fang D (2018) cdeeparch: a compact deep neural network architecture for mobile sensing. In: 2018 15th annual IEEE international conference on sensing, communication, and networking (SECON), IEEE, pp 1–9

  110. Yao Y, Su X, Tong H (2018) Hierarchical model. In: Mobile data mining. Springer, pp 25–30

  111. Ye H, Dong K, Gu T (2018) Himeter: telling you the height rather than the altitude. Sensors 18(6):1712

    Article  Google Scholar 

  112. Yürür Ö, Liu CH, Sheng Z, Leung VC, Moreno W, Leung KK (2016) Context-awareness for mobile sensing: a survey and future directions. IEEE Commun Surv Tutor 18(1):68–93

    Article  Google Scholar 

  113. Zavala L, Murukannaiah PK, Poosamani N, Finin T, Joshi A, Rhee I, Singh MP (2015) Platys: from position to place-oriented mobile computing. AI Mag 36(2):50–62

    Google Scholar 

  114. Zhang D, Huang H, Lai CF, Liang X, Zou Q, Guo M (2013) Survey on context-awareness in ubiquitous media. Multimed Tools Appl 67(1):179–211

    Article  Google Scholar 

  115. Zhang Z, Xu S, Cao S, Zhang S (2018) Deep convolutional neural network with mixup for environmental sound classification. In: Chinese conference on pattern recognition and computer vision (PRCV). Springer, pp 356–367

  116. Zhou X, Yu W, Sullivan WC (2016) Making pervasive sensing possible: effective travel mode sensing based on smartphones. Comput Environ Urban Syst 58:52–59

    Article  Google Scholar 

  117. Zhu Q, Zhu M, Li M, Fu M, Huang Z, Gan Q, Zhou Z (2016) Identifying transportation modes from raw gps data. In: International conference of pioneering computer scientists, engineers and educators. Springer, pp 395–409

  118. Zou X, Gonzales M, Saeedi S (2016) A context-aware recommendation system using smartphone sensors. In: 2016 IEEE 7th annual information technology, electronics and mobile communication conference (IEMCON). IEEE, pp 1–6

Download references

Acknowledgements

The authors wish to thank CNPq (Universal 423518/2018-6), IFRS (Federal Institute of Education, Science and Technology of Rio Grande do Sul) for the financial support. This study was financed partially by the CAPES—Brazil—Finance Code 001.

Author information

Authors and Affiliations

  1. Instituto Federal de Educação, Ciência e Tecnologia (IFRS), Alvorada, Brazil

    Leonardo Vianna do Nascimento

  2. Institute of Informatics, Federal University of Rio Grande do Sul (UFRGS), Porto Alegre, Brazil

    Guilherme Medeiros Machado & José Palazzo M. de Oliveira

  3. Laboratory of Ubiquitous, Mobile and Applied Computing (LUMAC), Universidade Federal de Santa Maria, Cachoeira do Sul, Brazil

    Vinícius Maran

Authors
  1. Leonardo Vianna do Nascimento
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Guilherme Medeiros Machado
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Vinícius Maran
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. José Palazzo M. de Oliveira
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Leonardo Vianna do Nascimento.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

do Nascimento, L.V., Machado, G.M., Maran, V. et al. Context recognition and ubiquitous computing in smart cities: a systematic mapping. Computing 103, 801–825 (2021). https://doi.org/10.1007/s00607-020-00878-7

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00607-020-00878-7

Keywords

Mathematics Subject Classification

Search

Navigation