Internet of Medical Things (IoMT) and Reflective Belief Design-Based Big Data Analytics with Convolution Neural Network-Metaheuristic Optimization Procedure (CNN-MOP)

Saini

Wireless Communications and Mobile Computing

et al. 2022

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.

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

confidence: 99%

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

Saini

Wireless Communications and Mobile Computing

et al. 2022

“…In addition, the advent of super-large-scale data generated from various sources has led to the emergence of big data, which has ignited a new competition among swarm intelligence algorithms. In [ 91 ], an optimization algorithm for big data analysis in IoMT using gravitational search optimization and the reflection-belief network with convolutional neural networks has been presented. In this algorithm, data optimization was performed on input data using gravitational search optimization.…”

Section: Application Of Si In Iot/iomtmentioning

confidence: 99%

Swarm Intelligence in Internet of Medical Things: A Review

Alizadehsani

Roshanzamir

Izadi

et al. 2023

Sensors

Continuous advancements of technologies such as machine-to-machine interactions and big data analysis have led to the internet of things (IoT) making information sharing and smart decision-making possible using everyday devices. On the other hand, swarm intelligence (SI) algorithms seek to establish constructive interaction among agents regardless of their intelligence level. In SI algorithms, multiple individuals run simultaneously and possibly in a cooperative manner to address complex nonlinear problems. In this paper, the application of SI algorithms in IoT is investigated with a special focus on the internet of medical things (IoMT). The role of wearable devices in IoMT is briefly reviewed. Existing works on applications of SI in addressing IoMT problems are discussed. Possible problems include disease prediction, data encryption, missing values prediction, resource allocation, network routing, and hardware failure management. Finally, research perspectives and future trends are outlined.

“…Furthermore, these schemes consume extremely little energy and have very low communication costs [30][31][32][33][34][35]. The previous approach contributes about the use of IoT in the different fields to improve the performance [36][37][38]. This section examines and analyses a complete assessment of related research work [39][40][41] offered by writers for effective key management in WSN.…”

Section: Related Workmentioning

confidence: 99%

Improved Secure Encryption with Energy Optimization Using Random Permutation Pseudo Algorithm Based on Internet of Thing in Wireless Sensor Networks

Nagaraj¹,

Kathole²,

Arya

et al. 2022

Energies

Self Cite

The use of wireless and Internet of Things (IoT) devices is growing rapidly. Because of this expansion, nowadays, mobile apps are integrated into low-cost, low-power platforms. Low-power, inexpensive sensor nodes are used to facilitate this integration. Given that they self-organize, these systems qualify as IoT-based wireless sensor networks. WSNs have gained tremendous popularity in recent years, but they are also subject to security breaches from multiple entities. WSNs pose various challenges, such as the possibility of numerous attacks, their innate power, and their unfeasibility for use in standard security solutions. In this paper, to overcome these issues, we propose the secure encryption random permutation pseudo algorithm (SERPPA) for achieving network security and energy consumption. SERPPA contains a major entity known as a cluster head responsible for backing up and monitoring the activities of the nodes in the network. The proposed work performance is compared with other work based on secure IoT devices. The calculation metrics taken for consideration are energy, overheads, computation cost, and time consumption. The obtained results show that the proposed SERPPA is very significant in comparison to the existing works, such as GKA (Group Key Agreement) and MPKE (Multipath Key Establishment), in terms of data transfer rate, energy consumption and throughput.