A Parallel Military-Dog-Based Algorithm for Clustering Big Data in Cognitive Industrial Internet of Things

Tripathi, Ashish; Sharma, Kapil; Bala, Manju; Kumar, Akshi; Menon, Varun G.; Bashir, Ali Kashif

doi:10.1109/tii.2020.2995680

Cited by 70 publications

(26 citation statements)

References 28 publications

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“…e MR-MDBO is compared to 5 other advanced methods with F measurement and computing time. e experimental results indicate that the clustering based on MR-MDBO is superior to the other considered algorithms in terms of cluster precision and calculation time [31].…”

Section: Big Data and Iot Paradigmmentioning

confidence: 95%

Big Data and Its V’s with IoT to Develop Sustainability

Chu

Nazir

Wang

et al. 2021

Scientific Programming

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Internet of Things (IoT) has been considered as one of the emerging network and information technologies that can comprehend automatic monitoring, identification, and management through a network of smart IoT devices. The effective use of IoT in different areas has improved efficiency and reduced errors. The rapid growth of smart devices such as actuators, sensors, and wearable devices has made the IoT enable for smart and sustainable developments in the area. Physical objects are interlinked with these smart devices for the progression to analyse, process, and manage the surroundings data. Such data can then be further utilised for smarter decisions and postanalysis for different purposes. However, with the limited IoT resources, the management of data is difficult due to the restrictions of transmission power place and energy consumption, and the processing can put pressure on these smart devices. The network of IoT is connected with big data through Internet for manipulating and storing huge bulk of data on cloud storage. The secure framework based on big data through IoT is the awful need of modern society which can be energy efficient in a sustainable environment. Due to the intrinsic characteristics of sensors nodes in the IoT, like data redundancy, constrained energy, computing capabilities, and limited communication range, the issues of data loss are becoming among the main issues which mostly depend on the completeness of data. Various approaches are in practice for the recovery problem of data, such as spatiotemporal correlation and interpolation. These are used for data correlation and characteristics of sensory data. Extracting correlation data became difficult specifically as the coupling degree between diverse perceptual attributes is low. The current study has presented a comprehensive overview on big data and its V’s with Internet of Things to describe the research into the area with in-depth review of existing literature.

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Section: Big Data and Iot Paradigmmentioning

confidence: 95%

Big Data and Its V’s with IoT to Develop Sustainability

Chu

Nazir

Wang

et al. 2021

Scientific Programming

View full text Add to dashboard Cite

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“…On the other side, swarm-based algorithms behave like the swarm of agents, such as fishes or birds, to achieve optimal results. Some algorithms of this category are particle swarm optimization (PSO) [42], ant colony optimization (ACO) [29], gravitational search algorithm (GSA) [54], spider monkey optimization (SMO) [7], grey-wolf optimizer (GWO) [75], cuckoo search (CS) [60], and military dog based optimizer (MDO) [76].…”

Section: Metaheuristic-based Methodsmentioning

confidence: 99%

A comprehensive survey of image segmentation: clustering methods, performance parameters, and benchmark datasets

Mittal

Pandey

Saraswat

et al. 2021

Multimed Tools Appl

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Image segmentation is an essential phase of computer vision in which useful information is extracted from an image that can range from finding objects while moving across a room to detect abnormalities in a medical image. As image pixels are generally unlabelled, the commonly used approach for the same is clustering. This paper reviews various existing clustering based image segmentation methods. Two main clustering methods have been surveyed, namely hierarchical and partitional based clustering methods. As partitional clustering is computationally better, further study is done in the perspective of methods belonging to this class. Further, literature bifurcates the partitional based clustering methods into three categories, namely K-means based methods, histogram-based methods, and meta-heuristic based methods. The survey of various performance parameters for the quantitative evaluation of segmentation results is also included. Further, the publicly available benchmark datasets for image-segmentation are briefed.

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“…A set cover algorithm is used to construct weighted DS in the first stage (Chvatal, 1979), a Steiner tree algorithm is applied in the second stage (Klein and Ravi, 1995). For other studies related to clustering big data in IoT systems are given in (Palaniswami et al, 2020), (Tripathi et al, 2021) and (Wang et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

BICOT: Big Data Analysis Approach for Clustering Cloud based IoT Systems

Dagdeviren¹,

Dağdeviren²

2021

European Journal of Science and Technology

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Internet of Things (IoT) envisions the connection of billions of devices over the Internet. The data produced by these huge amount of devices grow exponentially, so analyzing this big data with traditional methods is not viable. Recent cloud computing and virtualization technologies cope with these issues by processing and storing IoT data. Wireless sensor networks (WSNs) are big data sources of IoT systems which provides data collection from the environment. WSNs are used in various applications such as habitat monitoring, military surveillance and smart agriculture. Data transmission to the sink node is one of the essential requirements for WSNs. Clustering is a fundamental technique that is used for efficient data transmission, time synchronizaion, load balancing and security services. In this paper, we propose a clustering framework that we call BICOT for WSNs tailored for IoT systems. BICOT inputs large scale node position, transmission range and node energy data and outputs clustering information. Our first algorithm (BICOT-CDS) is based on connected dominating set (CDS) structure and aims to reduce the cluster count. Our second algorithm uses a weighted CDS (WCDS) approach that targets to select nodes with high energy as cluster heads. We implement these algorithms in ns2 simulator environment and measure cluster count and total weight of cluster head values. The algorithms are tested against node counts and average node degrees. From extensive simulation measurements, we obtain that the cluster count generated by BICOT-CDS is far more better than its counterparts and as the network size increases the proposed algorithm performs better. The cost of dominators produced by the BICOT-WCDS algorithm is significantly lower than its competitors. These findings show us that our proposed algorithms are favorable big data analysis approaches for cloud based IoT systems.

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A Parallel Military-Dog-Based Algorithm for Clustering Big Data in Cognitive Industrial Internet of Things

Cited by 70 publications

References 28 publications

Big Data and Its V’s with IoT to Develop Sustainability

Big Data and Its V’s with IoT to Develop Sustainability

A comprehensive survey of image segmentation: clustering methods, performance parameters, and benchmark datasets

BICOT: Big Data Analysis Approach for Clustering Cloud based IoT Systems

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