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Data quality and the Internet of Things

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Abstract

The Internet of Things (IoT) is driving technological change and the development of new products and services that rely heavily on the quality of the data collected by IoT devices. There is a large body of research on data quality management and improvement in IoT, however, to date a systematic review of data quality measurement in IoT is not available. This paper presents a systematic literature review (SLR) about data quality in IoT from the emergence of the term IoT in 1999 to 2018. We reviewed and analyzed 45 empirical studies to identify research themes on data quality in IoT. Based on this analysis we have established the links between data quality dimensions, manifestations of data quality problems, and methods utilized to measure data quality. The findings of this SLR suggest new research areas for further investigation and identify implications for practitioners in defining and measuring data quality in IoT.

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References

  1. Georgakopoulos D, Jayaraman PP (2016) Internet of Things: from internet scale sensing to smart services. Computing 98(10):1041–1058

    Article  MathSciNet  Google Scholar 

  2. Ramirez ARG, González-Carrasco I, Jasper GH, Lopez AL, Lopez-Cuadrado JL, García-Crespo A (2017) Towards human smart cities: Internet of Things for sensory impaired individuals. Computing 99(1):107–126

    Article  MathSciNet  Google Scholar 

  3. Elijah O, Rahman TA, Orikumhi I, Leow CY, Hindia MN (2018) An overview of Internet of Things (IoT) and data analytics in agriculture: benefits and challenges. IEEE Internet Things J 5(5):3758–3773

    Article  Google Scholar 

  4. Liao Y, Loures EDFR, Deschamps F (2018) Industrial Internet of Things: a systematic literature review and insights. IEEE Internet Things J (to be published). https://doi.org/10.1109/JIOT.2018.2834151

    Article  Google Scholar 

  5. Manyika J, Chui M, Bughin J, Dobbs R, Bisson P, Marrs A (2013) Disruptive technologies: advances that will transform life, business, and the global economy. McKinsey Global Institute, San Francisco

    Google Scholar 

  6. Minteer A (2017) Analytics for the Internet of Things (IoT): intelligent analytics for your intelligent devices. Packt Publishing, Birmingham

    Google Scholar 

  7. Fruehe J (2015) The Internet of Things is about data, not things. Forbes. https://www.forbes.com/sites/moorinsights/2015/07/30/the-internet-of-things-is-about-data-not-things/3178bdd827cf. Accessed 12 Nov 2018

  8. Villasanta A (2019) Tesla model 3 autopilot feature to blame for death of driver in crash. Int Bus Times. https://www.ibtimes.com/tesla-model-3-autopilot-feature-blame-death-driver-crash-2792690. Accessed 3 June 2019

  9. Karkouch A, Mousannif H, Moatassime HAI, Noel T (2016) Data quality in Internet of Things: a state-of-the-art survey. J Netw Comput Appl 73:57–81

    Article  Google Scholar 

  10. Karkouch A, Moatassime HAI, Mousannif H, Noel T (2015) Data quality enhancement in Internet of Things environment. In: Proceedings of the 12th international conference on computer systems applications. ACS/IEEE, pp 1–8

  11. Qin Y, Sheng QZ, Falkner NJ, Dustdar S, Wang H, Vasilakos AV (2016) When things matter: a survey on data-centric Internet of Things. J Netw Comput Appl 64:137–153

    Article  Google Scholar 

  12. Wolfswinkel JF, Furtmueller E, Wilderom CP (2013) Using grounded theory as a method for rigorously reviewing literature. Eur J Inform Syst 22(1):45–55

    Article  Google Scholar 

  13. Kitchenham BA, Budgen D, Brereton P (2015) Evidence-based software engineering and systematic reviews. CRC Press, Boca Raton

    Google Scholar 

  14. Ashton K (2009) That ’Internet of Things’ thing in the real world, things matter more than ideas. RFID J. https://www.rfidjournal.com/articles/view?4986. Accessed 12 Nov 2018

  15. De Feo JA, Juran JM (2017) Juran’s quality handbook: the complete guide to performance excellence, 7th edn. McGraw-Hill Education, New York

    Google Scholar 

  16. Tilly R, Posegga O, Fischbach K, Schoder D (2017) Towards a conceptualization of data and information quality in social information systems. Bus Inf Syst Eng 59(1):3–21

    Article  Google Scholar 

  17. Wang RY, Strong DM (1996) Beyond accuracy: what data quality means to data consumers. J Manag Inf Syst 12(4):5–33

    Article  Google Scholar 

  18. Cai L, Zhu Y (2015) The challenges of data quality and data quality assessment in the big data era. Data Sci J 14:2. https://doi.org/10.5334/dsj-2015-002

    Article  Google Scholar 

  19. Whitmore A, Agarwal A, Xu L (2015) The Internet of Things—a survey of topics and trends. Inf Syst Front 17(2):261–274

    Article  Google Scholar 

  20. Sadiq S, Yeganeh NK, Indulska M (2011) 20 years of data quality research: themes, trends and synergies. In: Proceedings of the 22nd Australasian datebase conference, pp 153–162

  21. Wang RY (1998) A product perspective on total data quality management. Commun ACM 41(2):58–65

    Article  Google Scholar 

  22. ISO 25000 Portal (2019) ISO/IEC 25012. https://iso25000.com/index.php/en/iso-25000-standards/iso-25012. ISO 25000 Standards. Accessed 27 May 2019

  23. De Faria MLL, Cugnasca CE, Amazonas JRA (2018) Insights into IoT data and an innovative DWT-based technique to denoise sensor signals. IEEE Sens J 18(1):237–247

    Article  Google Scholar 

  24. Subramaniam S, Palpanas T, Papadopoulos D, Kalogerakiand V, Gunopulos D (2006) Online outlier detection in sensor data using nonparametric models. In: Proceedings of the 32nd international conference on very large data bases, pp 187–198

  25. Yoon I, Joung H, Lee J (2016) Zynq-based reconfigurable system for real-time edge detection of noisy video sequences. J Sens. https://doi.org/10.1155/2016/2654059

    Google Scholar 

  26. Spachos P, Song L, Plataniotis KN (2017) Wireless noise prevention for mobile agents in smart home. In: Proceedings of the 2017 international conference on communities. IEEE, pp 1–6

  27. Adelantado F, Vilakaosana X, Tuset-Peiro P, Martinez B, MeliàSeguí J, Watteyne T (2017) Understanding the limits of LoRaWAN. IEEE Commun Mag 55(9):34–40

    Article  Google Scholar 

  28. Priller P, Aldrian A, Ebner T (2014) Case study: from legacy to connectivity migrating industrial devices into the world of smart services. In: Proceedings of the 2014 IEEE emerging technology and factory automation (ETFA), pp 1–8

  29. Olteanu AM, Huguenin K, Shokri R, Humbert M, Hubaux JP (2017) Quantifying interdependent privacy risks with location data. IEEE Trans Mobile Comput 16(3):829–842

    Article  Google Scholar 

  30. Williams SP, Hardy CA, Nitschke P (2018) Configuring the Internet of Things (IoT): a review and implications for Big Data Analytics. In: Proceedings of the 52nd Hawaii international conference on system sciences (to be published)

  31. Wu M, Lu TJ, Ling FY, Sun J, Du HY (2010) Research on the architecture of Internet of Things. In: Proceedings of the 3rd international conference on advanced computer theory and engineering, vol 5, pp 484–487

  32. Khan R, Khan SU, Zaheer R, Khan S (2012) Future internet: the Internet of Things architecture, possible applications and key challenges. In: Proceedings of the 10th international conference on frontiers of information technology. IEEE, pp 257–260

  33. Farzana AF, Neduncheliyan S (2017) Ant-based routing and QoS-effective data collection for mobile wireless sensor network. Wirel Netw 23(6):1697–1707

    Article  Google Scholar 

  34. Cheung MH, Hou F, Huang J (2018) Delay-sensitive mobile crowdsensing: algorithm design and economics. IEEE Trans Mobile Comput 17(12):2761–2774

    Article  Google Scholar 

  35. ISO (2019) ISO/IEC 25024:2015 Systems and software engineering—systems and software quality requirements and evaluation (SQuaRE)—measurement of data quality. https://www.iso.org/standard/35749.html. International Organization for Standardization. Accessed 31 May 2019

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Authors and Affiliations

  1. Faculty of Engineering and Information Technology, University of Technology Sydney, Broadway 15, Ultimo, NSW, 2007, Australia

    Caihua Liu & Didar Zowghi

  2. Faculty of Computer Science, University of Koblenz-Landau, Universitätsstraße 1, 56070, Koblenz, Germany

    Patrick Nitschke & Susan P. Williams

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  1. Caihua Liu
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  2. Patrick Nitschke
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Correspondence to Caihua Liu.

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Appendix

Appendix

List of articles included in the SLR:

  1. S1

    Borges Neto JB, Silva TH, Assunção RM, Mini RA, Loureiro AA (2015) Sensing in the collaborative Internet of Things. Sensors, 15(3): 6607–6632

  2. S2

    Hendrik Haan G, Hillegersberg JV, De Jong E, Sikkel K (2013) Adoption of wireless sensors in supply chains: A process view analysis of a pharmaceutical cold chain. J Theory Appl Electron Commer Res, 8(2): 138–154

  3. S3

    Dmitriev A, Efremova E, Gerasimov MY (2015) Multimedia sensor networks based on ultrawideband chaotic radio pulses. J Commun Technol Electron, 60(4): 393–401

  4. S4

    Gill S, Lee B, Neto E (2015) Context aware model-based cleaning of data streams. In: Proc the 26th Irish Signals Syst Conf, IEEE, pp 1–6

  5. S5

    Sicari S, Rizzardi A, Miorandi D, Cappiello C, Coen-Porisini A (2016) A secure and quality-aware prototypical architecture for the Internet of Things. Inform Syst, 58: 43–55

  6. S6

    Pravato L, Doyle TE (2017) IoT for remote wireless electrophysiological monitoring: Proof of concept. In: Proc the 27th Annu Int Conf Compt Sci Softw Eng, pp 254–258

  7. S7

    Javed N, Wolf T (2012) Automated sensor verification using outlier detection in the Internet of Things. In: Proc the 32nd Int Conf Distrib Comput Syst Workshop,IEEE, pp 291–296

  8. S8

    Kuemper D, Iggena T, Toenjes R, Pulvermueller E (2018) Valid. IoT: A framework for sensor data quality analysis and interpolation. In: Proc the 9th Multimedia Syst Conf, ACM, pp 294–303

  9. S9

    Liono J, Jayaraman PP, Qin A, Nguyen T, Salim FD (2018) QDaS: Quality driven data summarisation for effective storage management in Internet of Things. J Parallel Distrib Comput. DOI: https://doi.org/10.1016/j.jpdc.2018.03.013.

  10. S10

    Tariq M, Majeed H, Beg MO, Khan FA, Derhab A (2018) Accurate detection of sitting posture activities in a secure IoT based assisted living environment. Future Gener Comp Syst. DOI: https://doi.org/10.1016/j.future.2018.02.013.

  11. S11

    Turabieh H, Salem AA, Abu-El-Rub N (2018) Dynamic L-RNN recovery of missing data in IoMT applications. Future Gener Comp Syst, 89: 575–583

  12. S12

    Siegel JE, Kumar S, Sarma SE (2018) The future internet of things: Secure, efficient, and model-based. IEEE Internet Things J, 5(4): 2386–2398

  13. S13

    Karkouch A, Mousannif H, Al Moatassime H, Noel T (2016) A model-driven architecture-based data quality management framework for the Internet of Things. In: Proc the 2nd Int Conf Cloud Comput Technol Appl, pp 252–259

  14. S14

    Bijarbooneh FH, Du W, Ngai ECH, Fu X, Liu J (2016) Cloud-assisted data fusion and sensor selection for Internet of Things. IEEE Internet Things J, 3(3): 257–268

  15. S15

    Sotres P, Santana JR, Sánchez L, Lanza J, Mun̄oz L (2017) Practical lessons from the deployment and management of a smart city Internet-of-Things infrastructure: The SmartSantander testbed case. IEEE Access, 5: 14309–14322

  16. S16

    Guo Y, Fang L, Geng K, Yin L, Li F, Chen L (2018) Real-time data incentives for IoT searches. In: Proc 2018 Int Conf Comm, IEEE, pp 1–6

  17. S17

    Li F, Nastic S, Dustdar S (2012) Data quality observation in pervasive environments. In: Proc the 15th Int Conf Comput Sci Eng, IEEE, pp 602–609

  18. S18

    Nesa N, Ghosh T, Banerjee I (2018) Outlier detection in sensed data using statistical learning models for IoT. In: Proc 2018 Wireless Commun Netw Conf, IEEE, pp 1–6

  19. S19

    Gupta M, Holloway C, Heravi BM, Hailes S (2015) A comparison between smartphone sensors and bespoke sensor devices for wheelchair accessibility studies. In: Proc the 10th Int Conf Intelligent Sensors, Sensor Netw Inform Process, IEEE, pp 1–6

  20. S20

    Tao X, Song W (2018) Location-Dependent Task Allocation for Mobile Crowdsensing with Clustering Effect. IEEE Internet Things J. https://doi.org/10.1109/JIOT.2018.2866973.

  21. S21

    Kothari A, Boddula V, Ramaswamy L, Abolhassani N (2014) DQS-cloud: A data quality-aware autonomic cloud for sensor services. In: Proc the 10th Int Conf Collaborative Compt: Netw Appl Worksharing, IEEE, pp 295–303

  22. S22

    Candra ZM, Truong HL, Dustdar S (2016) On monitoring cyber-physical-social systems. In: Proc 2016 World Congr Serv, IEEE, pp 56–63

  23. S23

    Leonardi A, Ziekow H, Strohbach M, Kikiras P (2016) Dealing with data quality in smart home environments–Lessons learned from a smart grid pilot. J Senser Actuator Netw, 5(1): 5

  24. S24

    Chacko V, Bharati V (2017) Data validation and sensor life prediction layer on cloud for IoT. In: Proc 2017 Int Conf Internet Things, IEEE, pp 906–909

  25. S25

    Atmoko R, Riantini R, Hasin M (2017) IoT real time data acquisition using MQTT protocol. J Phys: Conf Ser, 853(1): 012003

  26. S26

    Alduais N, Abdullah J, Jamil A, Audah L, Alias R (2017) Sensor node data validation techniques for realtime IoT/WSN application. In: Proc 14th Int Multi-Conf Syst, Signals and Devices, pp 760-765

  27. S27

    Balestrini M, Diez T, Marshall P, Gluhak A, Rogers Y (2015) IoT community technologies: Leaving users to their own devices or orchestration of engagement? EAI Endorsed Trans Internet Things, 1(1): e7

  28. S28

    Ma Y, Jin J, Huang Q, Dan F (2018) Data preprocessing of agricultural IoT based on time series analysis. In: Proc Int Conf Intelligent Comput, pp 219–230

  29. S29

    Bharti M, Saxena S, Kumar R (2017) Intelligent resource inquisition framework on Internet-of-Things. Compt Elect Eng, 58: 265-281

  30. S30

    Jang B, Park S, Lee J, Han SG (2018) Three hierarchical levels of Big-Data market model over multiple data sources for Internet of Things. IEEE Access, 6: 31269–31280

  31. S31

    Moon A, Kim J, Zhang J, Son SW (2018) Evaluating fidelity of lossy compression on spatiotemporal data from an IoT enabled smart farm. Comput Elect Agriculture, 154: 304–313

  32. S32

    Gorenflo C, Golab L, Keshav S (2017) Managing Sensor Data Streams: Lessons Learned from the WeBike Project. In: Proc the 29th Int Conf Sci Statistical Database Manage, pp 1–11

  33. S33

    Dong R, Ratliff LJ, Cárdenas AA, Ohlsson H, Sastry S (2018) Quantifying the utility–privacy tradeoff in the Internet of Things. ACM Trans Cyber-Physical Syst, 2(2): 1–28

  34. S34

    Huang Z, Xie T, Zhu T, Wang J, Zhang Q (2016) Application-driven sensing data reconstruction and selection based on correlation mining and dynamic feedback. In Proc 2016 Int Conf Big Data, IEEE, pp 1322–1327

  35. S35

    Gao Y, Li X, Li J, Gao Y (2017) A dynamic-trust-based recruitment framework for mobile crowd sensing. In: Proc 2017 Int Conf Commun, IEEE, pp 1–6

  36. S36

    Fekade B, Maksymyuk T, Kyryk M, Jo M (2018) Probabilistic recovery of incomplete sensed data in IoT. IEEE Internet Things J, 5(4): 2282–2292

  37. S37

    Sta HB (2017) Quality and the efficiency of data in “Smart-Cities”. Future Gener Comp Syst, 74: 409–416

  38. S38

    Yan X, Xiong W, Hu L, Wang F, Zhao K (2015) Missing value imputation based on gaussian mixture model for the Internet of Things. Mathematical Problems Eng, 2015: 1–8

  39. S39

    Mary IPS, Arockiam L (2017) Imputing the missing data in IoT based on the spatial and temporal correlation. In: Proc 2017 Int Conf Current Trends Advanced Compt, IEEE, pp 1–4

  40. S40

    Gill S, Lee B (2015) A framework for distributed cleaning of data streams. Procedia Compt Sci, 52: 1186–1191

  41. S41

    Zhang Y, Szabo C, Sheng QZ (2014) Cleaning environmental sensing data streams based on individual sensor reliability. In: Proc Int Conf Web Inform Syst Eng, pp 405–414

  42. S42

    Pouryazdan M, Kantarci B, Soyata T, Foschini L, Song H (2017) Quantifying user reputation scores, data trustworthiness, and user incentives in mobile crowd-sensing. IEEE Access, 5: 1382–1397

  43. S43

    Kos A, Tomažič S, Umek A (2016) Evaluation of smartphone inertial sensor performance for cross-platform mobile applications. Sensors, 16(4): 477-493

  44. S44

    Casado-Vara R, de la Prieta F, Prieto J, Corchado JM (2018) Blockchain framework for IoT data quality via edge computing. In: Proc 1st Workshop Blockchain-enabled Netw Sensor Syst, pp 19-24

  45. S45

    Ukil A, Bandyopadhyay S, Pal A (2015) IoT data compression: Sensor-agnostic approach. In: Proc 2015 Data Compression Conf, pp 303–312

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Liu, C., Nitschke, P., Williams, S.P. et al. Data quality and the Internet of Things. Computing 102, 573–599 (2020). https://doi.org/10.1007/s00607-019-00746-z

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