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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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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DOI: https://doi.org/10.1007/s11227-020-03315-w