Cuttlefish Optimization based Clustering Approach (COCA) to improve the Quality of Service (QoS) for Flying Ad-Hoc Network (FANET)

Pandey, Ankur; Shukla, Piyush Kumar; Agrawal, Ratish

doi:10.1109/idea49133.2020.9170667

Cited by 3 publications

(2 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…There were three parallel versions of Apriori suggested, which were designated as count distribution (CD), data distribution (DD), and candidate distribution (Cand. D), respectively [22,23]. In the literature, data parallelism algorithms (CD and DD algorithms) are classified as either data parallelism or task parallelism algorithms, respectively, whereas candidate distribution algorithms are classified as a hybrid of data parallelism and task parallelism algorithms [24].…”

Section: Multivariate Empirical Mode Decomposition-based Gradient Sup...mentioning

confidence: 99%

See 1 more Smart Citation

[Retracted] Big Data Analytics Model for Distributed Document Using Hybrid Optimization with K‐Means Clustering

Sharma

Saini

Sharma

et al. 2022

Wireless Communications and Mobile Computing

View full text Add to dashboard Cite

Clustering, also known as unsupervised learning, is one of the most significant topics of machine learning because it divides data into groups based on similarity with the aim of extracting or summarizing new information. It is one of the most often used machine learning techniques. The most significant problem encountered in this subject is the sheer volume of electronic text documents accessible, which is increasing at an exponential rate, necessitating the development of efficient ways for dealing with these papers. Furthermore, it is not practicable to consolidate all of the papers from numerous locations into a single area for processing. In this study, the primary goal is to enhance the performance of the distributed document clustering approach for clustering big, high-dimensional distributed document datasets. For distributed storage and analysis, one of the most prominent open-source implementations of the big data analytic-based MapReduce model, such as the Hadoop framework, is used in conjunction with a distributed file system and is known as the Hadoop Distributed File System, to achieve the desired results. This necessitates an improvement in the approach of the clustering operation with Elephant Herding Optimization, which will be accomplished by applying a hybridized clustering procedure. In conjunction with the MapReduce framework, this hybridized strategy is able to solve the obstacles associated with the K -means clustering method, including the initial centroids difficulty and the dimensionality problem. In this paper, we analyze the performance of the whole distributed document clustering technique for big document datasets by using a distributed document clustering framework such as Hadoop and the associated MapReduce methodology. In the end, this decides how quickly computations may be completed.

show abstract

Section: Multivariate Empirical Mode Decomposition-based Gradient Sup...mentioning

confidence: 99%

“…MEMF is a data-driven method for accomplishing multi-scale decomposition. MEMF divides the time series data into different component for further analysis [23]. Let consider, user rating series data " X = x 1 , x 2 , x 3 , ::x m " is used as input from MovieLens 1 M dataset.…”

Section: Multivariate Empirical Mode Decomposition-basedmentioning

confidence: 99%

[Retracted] Big Data Analytics Model for Distributed Document Using Hybrid Optimization with K‐Means Clustering

Sharma

Saini

Sharma

et al. 2022

Wireless Communications and Mobile Computing

View full text Add to dashboard Cite

show abstract

SCFS-securing flying ad hoc network using cluster-based trusted fuzzy scheme

Gupta,

Sharma

2024

Complex Intell. Syst.

View full text Add to dashboard Cite

Flying Ad hoc Networks have emerged as a promising technology for number of real-time applications. However, the flexible and unstructured characteristics of these networks make them vulnerable to security threats posed by malicious nodes, such as denial of service attacks, node impersonation, and information breaches. Another major issue is the consideration of those nodes being unable to prove their trustworthiness due to factors like hardware or software failure, or by link interruptions, during the processing of detection of false nodes in the network. The existing mechanisms encompassing encryption, authentication, and intrusion detection highlight limitations to secure real-time applications and services due to the high speed of flying nodes and the absence of fixed network structures. To overcome these constraints, this research paper incorporates a novel framework for evaluating and improving the security of network by introducing an innovative cluster-based approach. Moreover, it presents a fuzzy model that dynamically estimates the trust levels of both individual nodes and clusters, by assigning weight to the parameters to address vulnerabilities. Additionally, a trust reconfiguration mechanism is further proposed to address the issue of nodes unable to substantiate their trust by providing them with additional chances based on the collective trust from previous evaluations. Further, the paper incorporates a dynamic reputation system to proficiently identify and separate malicious and selfish nodes from the network. Simulation results indicate a significant improvement in performance metrics, with a considerable reduction in delay and drop ratio by 41.46% and 36.37%, respectively, while the sufficient rise of 54.71% and 46.05% in throughput and coverage, respectively, comparing with the considered state-of-art.

show abstract

Red deer optimization algorithm inspired clustering-based routing protocol for reliable data dissemination in FANETs

Tadkal¹,

Mallapur²

2022

Materials Today: Proceedings

View full text Add to dashboard Cite

Cuttlefish Optimization based Clustering Approach (COCA) to improve the Quality of Service (QoS) for Flying Ad-Hoc Network (FANET)

Cited by 3 publications

References 10 publications

[Retracted] Big Data Analytics Model for Distributed Document Using Hybrid Optimization with K‐Means Clustering

[Retracted] Big Data Analytics Model for Distributed Document Using Hybrid Optimization with K‐Means Clustering

SCFS-securing flying ad hoc network using cluster-based trusted fuzzy scheme

Red deer optimization algorithm inspired clustering-based routing protocol for reliable data dissemination in FANETs

Contact Info

Product

Resources

About