Optimized clustering-based discovery framework on Internet of Things


With the proliferation of technology, a system of connected and interconnected devices, henceforth referred to as Internet of Things, is emerging as a viable method for automated interactions between users and environment in day-to-day life. However, such proliferation leads to an impractical task with respect to interactions among humans and devices. The major reason behind this impractical task is that domain of human’s eye for interaction is limited and devices have their own obligations and prohibitions in context. Motivated by this observation, the paper has proposed four-layered framework, namely, Optimized Clustering-based Discovery Framework on Internet of Things (OCDF-IoT), that (1) automatically discovers resources and their associated services using ontology, (2) governs resources using knowledge formation and representation, (3) provides efficient procedures to index resources on the basis of maximum similarity match, and (4) delegates the selection of the near optimal resource among indexed resources. The framework’s efficiency is evaluated using toll datasets that are gathered from Shambhu Toll Plaza, Panipat–Jalandhar section, Haryana, India. The obtained results support the framework’s efficacy providing more accurate similarity searches, consuming less search time. It is found that framework is stable in providing accurate erred parametric resources and helps in finding the rightful resource with computation of maximum resources. The framework takes minimum CPU throughput for processing queries and increases CPU’s efficiency with less load on server.

This is a preview of subscription content, access via your institution.

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
Fig. 19
Fig. 20


\(D_{L}\) :

Description logics

TBox :


ABox :


\(C_{k_{i}}\) :


\(D_{m}(C_{k_{i}})\) :

Domain of concept

\(M_{R}\) :

Semantic distance

\(R_{N}^{+}\) :

Positive real number

\(SS_{d}\) :

Sum of subsume distance

\(DF_{d}\) :

Definition distance

\(C_{s}\) :


\(DS_{c}\) :

Direct superclass

\(RS_{c}\) :




\(S_{P}\) :


\(R_{c}\) :


\(wt_{R_{c},l}\), \(wt_{UQ,l}\) :


\(m_{l}\) :


\(max\left( sim(R_{c}, UQ)\right)\) :

Resources with maximum attribute match

\(Y_{m}\) :

Record list

\(M_{max}\) :


s :

LNN Distance

\(\varrho\) :


q :

Centered resource

\(Y_{j}\) :

Resource set

G :

Degree of membership

\(B_{j, l}\) :

Dissimilarity measurement

\(\beta\) :

Lagrangian multiplier

\(CBO_{m}\) :

Combinatorial optimization model

\(I_{p}\) :

Search space

\(idx_{i}\) :

Discrete best resources

\(f_{x}\) :

Objective function

\(\varTheta\) :


\(P_{v_{s}}\) :

Pheromone value

\(Slc_{p}\) :

Solution component

\(\kappa _{s}\) :

Heuristic value

\(SS_{upd}\) :

Solution set

\(\chi\) :

Pheromone decay coefficient


Total iterations


Agglomerative Fuzzy K-Means


Ant colony optimization


Artificial bee colony

Bar-ID :

Barcode Number


Binary genetic algorithm

BS :

Bubble sort


Cluster center selection algorithm

CoAP :

Constrained Application Protocol

CoRE :

Constrained RESTful Environments


Context Aware Search Optimization Framework on Internet of Things

CA :

Cultural algorithm

DL :

Discovery layer


Domain name system


Electronic toll connection


Escape nearest outlier


Fuzzy c-means


Fuzzy k-means


Gray wolf optimizer


Hybrid genetic algorithm and particle swarm optimization


Intelligent Resource Inquisition Framework on Internet of Things


Intelligent transport system

IoT :

Internet of Things


Iterative agglomerative fuzzy k-means


Iterative gradient descent


Knowledge query and manipulation


L-nearest neighbors


Low-power wireless personal area networks

M2M :



Molecular dynamics simulations


Optimized clustering-based discovery framework on Internet of Things


Order acceptance and scheduling


Quadratic assignment problem


Radial basis function network


Radio frequency identification


REpresentational State Transfer


Resource description framework


Semantic Matchmaking Engine using Bipartite Graph


Sensor actuator layer

TS :

Tabu search


Traffic network editor

trans-ID :

Transaction IDentification


Universal Resource Identifier


Unmanned autonomous vehicles


User Datagram Protocol

UQ :

User query

vehicle-ID :

Vehicle registration IDentification

VM :

Virtual machine


Web Ontology Language


  1. 1.

    Gartner, Gartner’s hype cycle special report for 2014 (2014) https://www.gartner.com/newsroom/id/2819918

  2. 2.

    Datta SK, Bonnet C (2016) Describing things in the internet of things: From core link format to semantic based descriptions. In: 2016 IEEE International Conference on Consumer Electronics-Taiwan (ICCE-TW). IEEE, pp 1–2

  3. 3.

    Datta SK, Da Costa RPF, Bonnet C (2015) Resource discovery in internet of things: current trends and future standardization aspects. In: 2015 IEEE 2nd World Forum on Internet of Things (WF-IoT). IEEE, pp 542–547

  4. 4.

    Zaslavsky A, Jayaraman PP (2015) Discovery in the internet of things: the internet of things (ubiquity symposium), Ubiquity 2015, pp 2:1–2:10

  5. 5.

    Shafiq MZ, Ji L, Liu AX, Pang J, Wang J (2012) A first look at cellular machine-to-machine traffic: large scale measurement and characterization. ACM SIGMETRICS Perform Eval Rev 40:65–76

    Article  Google Scholar 

  6. 6.

    Delicato FC, Pires PF, Batista T (2017) The activities of resource discovery and resource estimation. Springer, Cham, pp 33–44. https://doi.org/10.1007/978-3-319-54247-8_4

    Google Scholar 

  7. 7.

    Omar NA, Kasim S, Fudzee MFM (2019) A review on feature based approach in semantic similarity for multiple ontology. In: Proceedings of the International Conference on Data Engineering 2015 (DaEng-2015). Springer, pp 457–465

  8. 8.

    Jiang W, Lin Y, Li Y (2018) Concept alignment of product taxonomies based on semantic similarity. In: 2018 IEEE 3rd International Conference on Cloud Computing and Big Data Analysis (ICCCBDA). IEEE, pp 517–521

  9. 9.

    Singh N (1993) A common LISP API and facilitator for ABSI: version 2.0. 3, Technical Report, Technical Report logic-93-4, Logic Group, Computer Science Department, Stanford University

  10. 10.

    Sycara K, Widoff S, Klusch M, Lu J (2002) Larks: dynamic matchmaking among heterogeneous software agents in cyberspace. Auton Agents Multi-agent Syst 5:173–203

    Article  Google Scholar 

  11. 11.

    Nodine M, Bohrer W, Ngu AHH (1999) Semantic brokering over dynamic heterogeneous data sources in InfoSleuth/sup TM. In: Proceedings of 15th International Conference on Data Engineering. IEEE, pp 358–365

  12. 12.

    Tangmunarunkit H, Decker S, Kesselman C (2003) Ontology-based resource matching in the grid-the grid meets the semantic web. In: International Semantic Web Conference, volume 2870. Springer, pp 706–721

  13. 13.

    Sharma Y, Goyal N (2008) An efficient multi-component indexing embedded bitmap compression for data reorganization. Inf Technol J 7:160–164

    Article  Google Scholar 

  14. 14.

    Bharti M, Kumar R, Saxena S (2018) Clustering-based resource discovery on Internet-of-Things. Int J Commun Syst 31:e3501

    Article  Google Scholar 

  15. 15.

    Fanian F, Rafsanjani MK (2019) Cluster-based routing protocols in wireless sensor networks: a survey based on methodology. J Netw Comput Appl 142:111–142

    Article  Google Scholar 

  16. 16.

    Jindal H, Kasana SS, Saxena S (2016) A novel image zooming technique using wavelet coefficients. In: Proceedings of the International Conference on Recent Cognizance in Wireless Communication & Image Processing. Springer, pp 1–7

  17. 17.

    Dunkels A, Gronvall B, Voigt T (2004) Contiki-a lightweight and flexible operating system for tiny networked sensors. In: 29th Annual IEEE International Conference on Local Computer Networks. IEEE, pp 455–462

  18. 18.

    Cao Q, Abdelzaher T, Stankovic J, He T (2008) The liteos operating system: towards unix-like abstractions for wireless sensor networks. In: 2008 International Conference on Information Processing in Sensor Networks (IPSN 2008). IEEE, pp 233–244

  19. 19.

    Mourya G, Jindal H, Saxena S (2015) Software perspective to underwater acoustic sensors network. In: 2015 1st International Conference on Next Generation Computing Technologies (NGCT). IEEE, pp 187–191

  20. 20.

    Gantz J, Reinsel D (2007) The digital universe in 2020: big data, bigger digital shadows, and biggest growth in the far east. IDC iView: IDC Anal Future 2012:1–16

    Google Scholar 

  21. 21.

    Jindal H, Saxena S, Singh S (2014) Challenges and issues in underwater acoustics sensor networks: a review. In: 2014 International Conference on Parallel, Distributed and Grid Computing. IEEE, pp 251–255

  22. 22.

    Almagbile A (2019) Estimation of crowd density from uavs images based on corner detection procedures and clustering analysis. Geo-spatial Inf Sci 22:23–34

    Article  Google Scholar 

  23. 23.

    Jindal H, Saxena S, Kasana SS (2017) Sewage water quality monitoring framework using multi-parametric sensors. Wirel Pers Commun 97:881–913

    Article  Google Scholar 

  24. 24.

    Zhu C, Miao D (2019) Influence of kernel clustering on an RBFN. CAAI Trans Intell Technol 4:255–260

    Article  Google Scholar 

  25. 25.

    Jindal H, Kasana SS, Saxena S (2018a) Underwater pipelines panoramic image transmission and refinement using acoustic sensors. International J Wavelets Multiresolut Inf Process 16:1850013

    MathSciNet  Article  Google Scholar 

  26. 26.

    Jindal H, Saxena S, Kasana SS (2018b) A sustainable multi-parametric sensors network topology for river water quality monitoring. Wirel Netw 24:3241–3265

    Article  Google Scholar 

  27. 27.

    Mousavi S, Lee D, Griffin T, Steadman D, Mockus A (2020) Collaborative learning of semi-supervised clustering and classification for labeling uncurated data. arXiv preprint arXiv:2003.04261

  28. 28.

    Jindal H (2019) Procreation of energy efficient topologies for data transmission in underwater wireless sensor network. Ph.D. thesis, TIET

  29. 29.

    Aljarah I, Mafarja M, Heidari AA, Faris H, Mirjalili S (2020) Clustering analysis using a novel locality-informed grey wolf-inspired clustering approach. Knowl Inf Syst 62:507–539

    Article  Google Scholar 

  30. 30.

    Mander K, Jindal H (2017) An improved image compression–decompression technique using block truncation and wavelets. Int J Image Graph Signal Process 9:17

    Article  Google Scholar 

  31. 31.

    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, vol 180. McKinsey Global Institute, San Francisco

    Google Scholar 

  32. 32.

    Kumar CA, Srinivas S (2010) Concept lattice reduction using fuzzy k-means clustering. Expert Syst Appl 37:2696–2704

    Article  Google Scholar 

  33. 33.

    Rayward-Smith V (2000) Fuzzy cluster analysis: methods for classification, data analysis and image recognition. J Oper Res Soc 51:769–769

    Article  Google Scholar 

  34. 34.

    Webster F (1958) Traffic signal settings, road research technical paper no. 39, Road Research Laboratory

  35. 35.

    Jindal H, Singh H, Bharti M (2018) Modified cuckoo search for resource allocation on social Internet-of-Things. In: 2018 Fifth International Conference on Parallel, Distributed and Grid Computing (PDGC). IEEE, pp 465–470

  36. 36.

    Robertson DI (1969) ‘Tansyt’ method for area traffic control. Traffic Eng Control 8(8)

  37. 37.

    Wallace CE, Courage K, Reaves D, Schoene G, Euler G (1984) TRANSYT-7F user’s manual. Technical Report

  38. 38.

    Baskan O, Haldenbilen S, Ceylan H, Ceylan H (2009) A new solution algorithm for improving performance of ant colony optimization. Appl Math Comput 211:75–84

    MathSciNet  MATH  Google Scholar 

  39. 39.

    Kaur S, Jindal H (2017) Enhanced image watermarking technique using wavelets and interpolation. Int J Image Graph Signal Process 11:23

    Article  Google Scholar 

  40. 40.

    Li D, Li K, Liang J, Ouyang A (2019) A hybrid particle swarm optimization algorithm for load balancing of MDS on heterogeneous computing systems. Neurocomputing 330:380–393

    Article  Google Scholar 

  41. 41.

    Shuka R, Brehm J (2019) A parallel adaptive swarm search framework for solving black-box optimization problems. In: International Conference on Architecture of Computing Systems. Springer, pp 100–111

  42. 42.

    Dokeroglu T, Sevinc E, Cosar A (2019) Artificial bee colony optimization for the quadratic assignment problem. Appl Soft Comput 76:595–606

    Article  Google Scholar 

  43. 43.

    Chaurasia SN, Kim JH (2019) An artificial bee colony based hyper-heuristic for the single machine order acceptance and scheduling problem. In: Decision science in action. Springer, pp 51–63

  44. 44.

    Mittal A, Jindal H (2017) Novelty in image reconstruction using DWT and CLAHE. Int J Image Graph Signal Process 9:28

    Article  Google Scholar 

  45. 45.

    Dixit A, Kumar S, Pant M, Bansal R (2019) CA-DE: Hybrid algorithm based on cultural algorithm and DE. In: Machine Intelligence and Signal Analysis. Springer, pp 185–196

  46. 46.

    Jindal H, Saxena S, Kasana SS (2017) Triangular pyramidal topology to measure temporal and spatial variations in shallow river water using ad-hoc sensors network. Ad Hoc Sens Wirel Netw 39:1–35

    Google Scholar 

  47. 47.

    Mohammadhosseini M, Haghighat AT, Mahdipour E (2019) An efficient energy-aware method for virtual machine placement in cloud data centers using the cultural algorithm. J Supercomput 75:6904–6933

    Article  Google Scholar 

  48. 48.

    Saxena S, Mehta D, Kaur J, Jindal H (2014) Acoustic communication characteristics in UWDBCSN. In: 2014 International Conference on Parallel, Distributed and Grid Computing. IEEE, pp 176–180

  49. 49.

    Li MJ, Ng MK, Cheung Y-M, Huang JZ (2008) Agglomerative fuzzy k-means clustering algorithm with selection of number of clusters. IEEE Trans Knowl Data Eng 20:1519–1534

    Article  Google Scholar 

  50. 50.

    Bharti M, Saxena S, Kumar R (2017) Intelligent resource inquisition framework on internet-of-things. Comput Electr Eng 58:265–281

    Article  Google Scholar 

  51. 51.

    Bharti M, Kumar R, Saxena S (2018) Context-aware search optimization framework on the Internet of Things. Concurr Comput Pract Exp 30:e4426

    Article  Google Scholar 

  52. 52.

    Severi P, Rohrer E, Motz R (2019) A description logic for unifying different points of view. In: Iberoamerican Knowledge Graphs and Semantic Web Conference. Springer, pp 17–32

  53. 53.

    Shelby Z (2012) Constrained restful environments (core) link format

  54. 54.

    Fielding R (2000) Representational state transfer. Architectural Styles and the Design of Netowork-based Software. Architecture, pp 76–85

  55. 55.

    Kushalnagar N, Montenegro G, Schumacher C et al (2007) IPv6 over low-power wireless personal area networks (6LoWPANs): overview, assumptions, problem statement, and goals

  56. 56.

    Fielding R, Gettys J, Mogul J, Frystyk H, Masinter L, Leach P, Berners-Lee T (1999) Hypertext transfer protocol–http/1.1

  57. 57.

    Nottingham M (2010) RFC5988: Web linking, Internet Engineering Task Force (IETF) Request for Comments

  58. 58.

    Shelby Z, Hartke K, Bormann C, Frank B (2012) Constrained application protocol (CoAP), draft-ietf-core-coap-13. The Internet Engineering Task Force-IETF, Orlando

  59. 59.

    Freed N, Borenstein N (1996) Multipurpose internet mail extensions (MIME) part one: format of internet message bodies

  60. 60.

    Shelby Z (2012) Core link format, draft-ietf-core-link-format-11, Internet draft, IETF 2012 (in progress)

  61. 61.

    Compton M, Barnaghi P, Bermudez L, GarcíA-Castro R, Corcho O, Cox S, Graybeal J, Hauswirth M, Henson C, Herzog A et al (2012) The SSN ontology of the W3C semantic sensor network incubator group. J Web Semant 17:25–32

    Article  Google Scholar 

  62. 62.

    Moussa M, Măndoiu II (2018) Single cell RNA-seq data clustering using TF-IDF based methods. BMC Genom 19:127

    Article  Google Scholar 

  63. 63.

    Mockapetris P (1987) RFC-1034 domain names-concepts and facilities. Network Working Group, 55

  64. 64.

    Kennedy J (2011) Particle swarm optimization. In: Encyclopedia of Machine Learning. Springer, pp 760–766

Download references

Author information



Corresponding author

Correspondence to Himanshu Jindal.

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

Verify currency and authenticity via CrossMark

Cite this article

Bharti, M., Jindal, H. Optimized clustering-based discovery framework on Internet of Things. J Supercomput 77, 1739–1778 (2021). https://doi.org/10.1007/s11227-020-03315-w

Download citation


  • Internet of Things (IoT)
  • Ontology
  • Semantic matchmaking
  • Clustering
  • Optimization
  • Sensors
  • Fuzzy
  • Ant colony
  • OCDF-IoT
  • PSO