Optimized clustering-based discovery framework on Internet of Things

Abstract

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.

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Abbreviations

\(D_{L}\) :

Description logics

TBox :

Terminology

ABox :

Assertions

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

Concept

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

Domain of concept

\(M_{R}\) :

Semantic distance

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

Positive real number

\(SS_{d}\) :

Sum of subsume distance

\(DF_{d}\) :

Definition distance

\(C_{s}\) :

Class

\(DS_{c}\) :

Direct superclass

\(RS_{c}\) :

Restrictions

SUP :

Superclass

\(S_{P}\) :

Transitive

\(R_{c}\) :

Resource

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

Weights

\(m_{l}\) :

Term

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

Resources with maximum attribute match

\(Y_{m}\) :

Record list

\(M_{max}\) :

Resources

s :

LNN Distance

\(\varrho\) :

Density

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

\(\varTheta\) :

Attributes

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

Pheromone value

\(Slc_{p}\) :

Solution component

\(\kappa _{s}\) :

Heuristic value

\(SS_{upd}\) :

Solution set

\(\chi\) :

Pheromone decay coefficient

O(N):

Total iterations

AFKM :

Agglomerative Fuzzy K-Means

ACO :

Ant colony optimization

ABC :

Artificial bee colony

Bar-ID :

Barcode Number

BGA :

Binary genetic algorithm

BS :

Bubble sort

CCSA :

Cluster center selection algorithm

CoAP :

Constrained Application Protocol

CoRE :

Constrained RESTful Environments

CASOF-IoT :

Context Aware Search Optimization Framework on Internet of Things

CA :

Cultural algorithm

DL :

Discovery layer

DNS :

Domain name system

ETC :

Electronic toll connection

ENO :

Escape nearest outlier

FCM :

Fuzzy c-means

FKM :

Fuzzy k-means

GWO :

Gray wolf optimizer

HGAPSO :

Hybrid genetic algorithm and particle swarm optimization

IRIF-IoT :

Intelligent Resource Inquisition Framework on Internet of Things

ITS :

Intelligent transport system

IoT :

Internet of Things

IAFKM :

Iterative agglomerative fuzzy k-means

IGD :

Iterative gradient descent

KQML :

Knowledge query and manipulation

LNN :

L-nearest neighbors

6LoWPAN :

Low-power wireless personal area networks

M2M :

Machine-to-machine

MDS :

Molecular dynamics simulations

OCDF-IoT :

Optimized clustering-based discovery framework on Internet of Things

OAS :

Order acceptance and scheduling

QAP :

Quadratic assignment problem

RBFN :

Radial basis function network

RFID :

Radio frequency identification

REST :

REpresentational State Transfer

RDF :

Resource description framework

SMEBG :

Semantic Matchmaking Engine using Bipartite Graph

SAL :

Sensor actuator layer

TS :

Tabu search

TEDI :

Traffic network editor

trans-ID :

Transaction IDentification

URI :

Universal Resource Identifier

UAV :

Unmanned autonomous vehicles

UDP :

User Datagram Protocol

UQ :

User query

vehicle-ID :

Vehicle registration IDentification

VM :

Virtual machine

OWL :

Web Ontology Language

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Correspondence to Himanshu Jindal.

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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

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Keywords

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