Optimized clustering-based discovery framework on Internet of Things

Bharti, M.; Jindal, Himanshu

doi:10.1007/s11227-020-03315-w

Cited by 15 publications

(4 citation statements)

References 51 publications

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“…Service discovery technologies used till date are mainly based on service description language [8] such as XML, WSMO and OWL-S. [9] proposed a new semantic-aware web service discovery method, which was designed to provide relevant web services based on user queries. In addition, Bharti and Jindal [10] proposed a new search-based clustering strategy based on the heterogeneity of smart IoT devices and smart web services for service discovery methods in IoT environments. Although these methods are simple, their accuracy and recall rates are low, and there are still imperfections.…”

Section: Service Discovery and Compositionmentioning

confidence: 99%

“…In Fig. 1 An application scenario for the service multilevel index model addition, Bharti and Jindal [10] proposed a new searchbased clustering strategy based on the heterogeneity of smart IoT devices and smart web services for service discovery methods in IoT environments. Although these methods are simple, their accuracy and recall rates are low, and there are still imperfections.…”

Section: Service Discovery and Compositionmentioning

confidence: 99%

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The least-used key selection method for information retrieval in large-scale Cloud-based service repositories

Anjum

et al. 2022

J Cloud Comp

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As the number of devices connected to the Internet of Things (IoT) increases significantly, it leads to an exponential growth in the number of services that need to be processed and stored in the large-scale Cloud-based service repositories. An efficient service indexing model is critical for service retrieval and management of large-scale Cloud-based service repositories. The multilevel index model is the state-of-art service indexing model in recent years to improve service discovery and combination. This paper aims to optimize the model to consider the impact of unequal appearing probability of service retrieval request parameters and service input parameters on service retrieval and service addition operations. The least-used key selection method has been proposed to narrow the search scope of service retrieval and reduce its time. The experimental results show that the proposed least-used key selection method improves the service retrieval efficiency significantly compared with the designated key selection method in the case of the unequal appearing probability of parameters in service retrieval requests under three indexing models.

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Section: Service Discovery and Compositionmentioning

confidence: 99%

Section: Service Discovery and Compositionmentioning

confidence: 99%

The least-used key selection method for information retrieval in large-scale Cloud-based service repositories

Anjum

et al. 2022

J Cloud Comp

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“…To address the weakness of the MCDM riddles-based analytical hierarchy process (AHP), the fuzzy AHP strategy has been integrated with fuzzy set theory to produce more effective and accurate results. Bharti et al [23] utilised the technique of fuzzy AHP to optimise the process of friend selection within the SIoT environment through a novel framework called Optimal Resource Discovery and Selection (ORDS). To represent the knowledge parameter, the authors utilised the capability of ontology-based semantic description.…”

Section: Related Workmentioning

confidence: 99%

Development of a Model for Trust Management in the Social Internet of Things

et al. 2022

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The Internet of Things (IoT) has evolved at a revolutionary pace in the last two decades of computer science. It is becoming increasingly fashionable for the IoT to be rebranded as the “Social Internet of Things” (SIoT), and this is drawing the attention of the scientific community. Smart items in the Internet of Things (IoT) ecosystem can locate relevant services based on the social ties between neighbors. As a result, SIoT displays the interplay between various items as a problem in the context of the social IoT ecosystem. Navigating a network can be difficult because of the number of friends and the complexity of social ties. By identifying difficulties with standard SIoT devices’ interaction with social objects, truthful friend computing (TFC) is a new paradigm for tracing such difficulties by utilising a relationship management component to improve network navigability. The concept of trust management can be useful as a strategy during collaborations among social IoT nodes. As a result, the trustor can use a variety of measures to evaluate a smart object’s trustworthiness. Hence, this article demonstrates the need for the trustor to evaluate the extent to which a given metric has contributed to the overall trust score and illustrates profitability when engaging in a transaction with other nodes. With the help of the SIoT, this paper used a unified fuzzy-based computational technique and a multiple-criteria decision-making approach to evaluate the trust weights. The statistical findings show that the computing of “truthful friends” is the biggest challenge for successful SIoT implementation at the initial level.

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“…The procedure is effectively cast-off e.g . in management for hazard appraisal Bharti & Jindal (2020) and Jain (2010) or in portfolio management ( Sanchez-Silva, 2009 ). Even though several clustering approaches have been suggested in the late periods, see e.g .…”

Section: Introductionmentioning

confidence: 99%

A new affinity matrix weighted k-nearest neighbors graph to improve spectral clustering accuracy

Ahmed

Saeed

Paul

et al. 2021

PeerJ Computer Science

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Researchers have thought about clustering approaches that incorporate traditional clustering methods and deep learning techniques. These approaches normally boost the performance of clustering. Getting knowledge from large data-sets is quite an interesting task. In this case, we use some dimensionality reduction and clustering techniques. Spectral clustering is gaining popularity recently because of its performance. Lately, numerous techniques have been introduced to boost spectral clustering performance. One of the most significant part of these techniques is to construct a similarity graph. We introduced weighted k-nearest neighbors technique for the construction of similarity graph. Using this new metric for the construction of affinity matrix, we achieved good results as we tested it both on real and artificial data-sets.

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Optimized clustering-based discovery framework on Internet of Things

Cited by 15 publications

References 51 publications

The least-used key selection method for information retrieval in large-scale Cloud-based service repositories

The least-used key selection method for information retrieval in large-scale Cloud-based service repositories

Development of a Model for Trust Management in the Social Internet of Things

A new affinity matrix weighted k-nearest neighbors graph to improve spectral clustering accuracy

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