Skip to main content

Advertisement

Springer Nature Link
Log in

A heterogeneous soft-hard fusion framework on fog based private SaS model for smart monitoring of public restrooms

  • Original Research
  • Published:
Journal of Ambient Intelligence and Humanized Computing Aims and scope Submit manuscript

Abstract

Usage of public restrooms, provided by the Indian government under “Clean India Mission”, is limited due to negligence in manual supervision. This paper proposes an Internet of Things-based smart application for sanitation to keep check on geographically distributed restrooms autonomously through soft-hard sensors. Due to a substantial increase in the number of sensing devices in recent years, the application aims to re-utilize data from hard sensors co-located near the restroom locations. A private Sensing as a Service (SaS) paradigm is proposed on the fog node that provides sensor data as a service at the network edge to reduce new sensor deployments. The data from re-utilized sensors is vendor-specific, and, therefore, have heterogeneous protocols and file formats, unknown to the application vendor. A soft-hard fusion framework is proposed to handle data heterogeneity of re-utilized data and perform time-series fusion of hard-sensor data (vendor-specific or application-specific) with uncertain soft sensor data at the fog node for having complete and accurate information about the toilet. The proposed framework takes approximately 0.145 s to resolve heterogeneity, handle soft uncertainty and perform fusion with low resource consumption. Moreover, it has a good system as well as network performance with increased classification accuracy in predicting the cleaning requirement of every toilet.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

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

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

Data Availability

The datasets generated during and/or analysed during the current study are available in the GitHub repository, https://github.com/RajasiG/soft-hard-fusionDataset.git.

References

  • Alves MP, Delicato FC, Santos IL, Pires PF (2020) LW-CoEdge: a lightweight virtualization model and collaboration process for edge computing. World Wide Web 23(2):1127–1175

    Article  Google Scholar 

  • Biswas R, Arya K, Deshpande S (2020) More toilet infrastructures do not nullify open defecation: a perspective from squatter settlements in megacity Mumbai. Appl Water Sci 10(4):1–9

    Article  Google Scholar 

  • Chaudhari AA, Mulay P (2019) SCSI: real-time data analysis with Cassandra and Spark. Big Data Process Using Spark Cloud 43:237–264

    Article  Google Scholar 

  • Corral-Plaza D, Medina-Bulo I, Ortiz G, Boubeta-Puig J, Group USER et al (2020) A stream processing architecture for heterogeneous data sources in the Internet of Things. Comput Stand Interfaces 70:103426–103439

    Article  Google Scholar 

  • Dautov R, Distefano S, Buyya R (2019) Hierarchical data fusion for smart healthcare. J Big Data 6(1):1–23

    Article  Google Scholar 

  • Dayalan UK, Fezeu RA, Varyani N, Salo TJ, Zhang Z-L (2021) VeerEdge: towards an edge-centric IoT gateway. In: 2021 IEEE/ACM 21st international symposium on cluster, cloud and internet computing (CCGrid). IEEE, pp 690–695

  • Diván MJ, Sánchez-Reynoso ML, Gonnet SM (2022) Measurement project interoperability for real-time data gathering systems. Future Gener Comput Syst 129:298–314

    Article  Google Scholar 

  • Diyan M, Nathali Silva B, Han J, Cao Z, Han K (2022) Intelligent Internet of Things gateway supporting heterogeneous energy data management and processing. Trans Emerg Telecommun Technol 33(2):1–14

    Google Scholar 

  • Donta PK, Srirama SN, Amgoth T, Annavarapu CSR (2021) Survey on recent advances in IoT application layer protocols and machine learning scope for research directions. Digit Commun Netw 7:1–18

    Google Scholar 

  • Donta PK, Amgoth T, Annavarapu CSR (2022) Delay-aware data fusion in duty-cycled wireless sensor networks: a Q-learning approach. Sustain Comput Inform Syst 33:1–15

    Google Scholar 

  • Dwivedi R, Dey S (2019) A novel hybrid score level and decision level fusion scheme for cancelable multi-biometric verification. Appl Intell 49:1016–1035

    Article  Google Scholar 

  • Foukalas F (2020) Cognitive IoT platform for fog computing industrial applications. Comput Electr Eng 87:1–13

    Article  Google Scholar 

  • Garcia-de Prado A, Ortiz G, Boubeta-Puig J (2017) COLLECT: collaborative context-aware service oriented architecture for intelligent decision-making in the Internet of Things. Expert Syst Appl 85:231–248

    Article  Google Scholar 

  • Gopikrishnan S, Priakanth P, Awangga RM (2019) HSIR: hybrid architecture for sensor identification and registration for IoT applications. J Supercomput 75(8):5000–5018

    Article  Google Scholar 

  • Halim DK, Hutagalung S (2022) Towards data sharing economy on Internet of Things: a semantic for telemetry data. J Big Data 9(1):1–24

    Article  Google Scholar 

  • Jain A, Wagner A, Snell-Rood C, Ray I (2020) Understanding open defecation in the age of Swachh Bharat Abhiyan: agency, accountability, and anger in rural Bihar. Int J Environ Res Public Health 17(4):1–13

    Article  Google Scholar 

  • Jaybal Y, Ramanathan C, Rajagopalan S (2018) HDSanalytics: a data analytics framework for heterogeneous data sources. In: Proceedings of the ACM India joint international conference on data science and management of data. ACM, pp 11–19

  • Jogunola O, Adebisi B, Hoang KV, Tsado Y, Popoola SI, Hammoudeh M, Nawaz R (2022) CBLSTM-AE: a hybrid deep learning framework for predicting energy consumption. Energies 15(3):1–16

    Article  Google Scholar 

  • Kapsalis A, Kasnesis P, Venieris IS, Kaklamani DI, Patrikakis CZ (2017) A cooperative fog approach for effective workload balancing. IEEE Cloud Comput 4(2):36–45

    Article  Google Scholar 

  • Kashani MH, Madanipour M, Nikravan M, Asghari P, Mahdipour E (2021) A systematic review of IoT in healthcare: applications, techniques, and trends. J Netw Comput Appl 192:1–41

    Google Scholar 

  • Kenda K, Kažič B, Novak E, Mladenić D (2019) Streaming data fusion for the Internet of Things. Sensors 19(8):1–16

    Article  Google Scholar 

  • Khan FA, ur Rehman M, Khalid A, Ali M, Imran M, Nawaz M, Rahman A (2018) An intelligent data service framework for heterogeneous data sources. J Grid Comput 17(3):1–13

    Google Scholar 

  • Li C (2020) Information processing in Internet of Things using big data analytics. Comput Commun 160:718–729

    Article  Google Scholar 

  • Liu G, Xiao F (2019) Time series data fusion based on evidence theory and OWA operator. Sensors 19(5):1–14

    Article  Google Scholar 

  • Manzoni P, Hernández-Orallo E, Calafate CT, Cano J-C (2017) A proposal for a publish/subscribe, disruption tolerant content island for fog computing. In: Proceedings of the 3rd workshop on experiences with the design and implementation of smart objects. ACM, pp 47–52

  • Ogawa K, Kanai K, Nakamura K, Kanemitsu H, Katto J, Nakazato H (2019) IoT device virtualization for efficient resource utilization in smart city IoT platform. In: IEEE international conference on pervasive computing and communications workshops (PerCom Workshops). IEEE, pp 419–422

  • Peng Y, Wu I-C (2021) A cloud-based monitoring system for performance analysis in IoT industry. J Supercomput 77(8):9266–9289

    Article  Google Scholar 

  • Pereira J, Batista T, Cavalcante E, Souza A, Lopes F, Cacho N (2022) A platform for integrating heterogeneous data and developing smart city applications. Future Gener Comput Syst 128:552–566

    Article  Google Scholar 

  • Perera C, Zaslavsky A, Liu CH, Compton M, Christen P, Georgakopoulos D (2013) Sensor search techniques for sensing as a service architecture for the Internet of Things. IEEE Sens J 14(2):406–420

    Article  Google Scholar 

  • Popović I, Radovanovic I, Vajs I, Drajic D, Gligorić N (2022) Building low-cost sensing infrastructure for air quality monitoring in urban areas based on fog computing. Sensors 22(3):1–21

    Article  Google Scholar 

  • Reyana A, Vijayalakshmi P (2021) Multisensor data fusion technique for energy conservation in the wireless sensor network application “condition-based environment monitoring”. J Ambient Intell Human Comput 12:1–10

  • Sachan S, Almaghrabi F, Yang J-B, Xu D-L (2021) Evidential reasoning for preprocessing uncertain categorical data for trustworthy decisions: an application on healthcare and finance. Expert Syst Appl 185:1–27

    Article  Google Scholar 

  • Saini K, Kalra S, Sood SK (2022) Disaster emergency response framework for smart buildings. Future Gener Comput Syst 131:106–120

    Article  Google Scholar 

  • Sarker IH (2021) Machine learning: algorithms, real-world applications and research directions. SN Comput Sci 2(3):1–21

    Article  MathSciNet  Google Scholar 

  • Schell Z, Samal A, Soh L-K (2021) An information fusion approach for conflating labeled point-based time-series data. GeoInformatica 25:1–41

    Article  Google Scholar 

  • Seoane V, Garcia-Rubio C, Almenares F, Campo C (2021) Performance evaluation of CoAP and MQTT with security support for IoT environments. Comput Netw 197:1–22

    Article  Google Scholar 

  • Song M, Sun C, Cai D, Hong S, Li H (2022) Classifying vaguely labeled data based on evidential fusion. Inf Sci 583:159–173

    Article  Google Scholar 

  • Verba N, Chao K-M, James A, Goldsmith D, Fei X, Stan S-D (2017) Platform as a service gateway for the fog of things. Adv Eng Inform 33:243–257

    Article  Google Scholar 

  • Wang P, Yang LT, Li J, Chen J, Hu S (2019) Data fusion in cyber-physical-social systems: state-of-the-art and perspectives. Inf Fusion 51:42–57

    Article  Google Scholar 

  • Wang T, Liu R, Qi G (2022) Multi-classification assessment of bank personal credit risk based on multi-source information fusion. Expert Syst Appl 191:1–15

    Article  Google Scholar 

  • Weddington J, Niu G, Chen R, Yan W, Zhang B (2021) Lithium- ion battery diagnostics and prognostics enhanced with Dempster–Shafer decision fusion. Neurocomputing 458:440–453

    Article  Google Scholar 

  • Wickramarathne T (2017) Evidence updating for stream-processing in big-data: robust conditioning in soft and hard data fusion environments. In: 2017 20th international conference on information fusion (fusion). IEEE, pp 1–7

  • Yigitoglu E, Mohamed M, Liu L, Ludwig H (2017) Foggy: a framework for continuous automated IoT application deployment in fog computing. In: IEEE international conference on AI & mobile services (AIMS). IEEE, pp 38–45

  • Zhang Y, Thorburn PJ (2022) Handling missing data in near real-time environmental monitoring: a system and a review of selected methods. Future Gener Comput Syst 128:63–72

    Article  Google Scholar 

Download references

Funding

This work is fully funded by the Ministry of Education (MoE), Government of India.

Author information

Authors and Affiliations

  1. Computer Science and Engineering Department, MNNIT Allahabad, Prayagraj, Uttar Pradesh, 211004, India

    Rajasi Gore, Shashwati Banerjea & Neeraj Tyagi

Authors
  1. Rajasi Gore
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Shashwati Banerjea
    View author publications

    You can also search for this author inPubMed Google Scholar

  3. Neeraj Tyagi
    View author publications

    You can also search for this author inPubMed Google Scholar

Corresponding author

Correspondence to Rajasi Gore.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Additional information

Publisher's Note

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

Rights and permissions

Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Gore, R., Banerjea, S. & Tyagi, N. A heterogeneous soft-hard fusion framework on fog based private SaS model for smart monitoring of public restrooms. J Ambient Intell Human Comput 14, 8957–8984 (2023). https://doi.org/10.1007/s12652-022-04401-y

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12652-022-04401-y

Keywords