A Hybrid Swarm Evolution Optimization for Solving Sensornet Node Localization

2021 ◽  
pp. 101-108
Author(s):  
Trong-The Nguyen ◽  
Tien-Wen Sung ◽  
Duc-Tinh Pham ◽  
Truong-Giang Ngo ◽  
Van-Dinh Vu
Keyword(s):  
Node Localization ◽  
Hybrid Swarm

scholarly journals Industrial Centric Node Localization and Pollution Prediction Using Hybrid Swarm Techniques

2022 ◽  
Vol 42 (2) ◽  
pp. 545-460
Author(s):  
R. Saravana Ram ◽  
M. Vinoth Kumar ◽  
N. Krishnamoorthy ◽  
A. Baseera ◽  
D. Mansoor Hussain ◽  
...  
Keyword(s):  
Node Localization ◽  
Hybrid Swarm ◽  
Pollution Prediction

A Hybrid Swarm Optimization Algorithm For Reducing Energy Consumption In The Cloud

2018 ◽  
Vol 11 ◽  
Author(s):  
Mohammed Mostafa Abdulghafoor ◽  
Raed Abdulkareem Hasan ◽  
Zeyad Hussein Salih ◽  
Hayder Ali Nemah Alshara ◽  
Nicolae Tapus
Keyword(s):  
Energy Consumption ◽  
Optimization Algorithm ◽  
Hybrid Swarm ◽  
Swarm Optimization ◽  
Reducing Energy

scholarly journals Distance Mapping Algorithm for Sensor Node Localization in WSNs

2019 ◽  
Vol 27 (2) ◽  
pp. 261-270
Author(s):  
Rong Tan ◽  
Yudong Li ◽  
Yifan Shao ◽  
Wen Si
Keyword(s):  
Sensor Node ◽  
Node Localization ◽  
Mapping Algorithm ◽  
Distance Mapping

SecRET

2021 ◽  
Vol 15 (1) ◽  
pp. 1-26
Author(s):  
Sudip Misra ◽  
Tamoghna Ojha ◽  
Madhusoodhanan P
Keyword(s):  
Sensor Networks ◽  
Error Rates ◽  
Location Information ◽  
Underwater Sensor Networks ◽  
Node Localization ◽  
Limited Bandwidth ◽  
Secure Localization ◽  
Localization Scheme ◽  
Monitoring Application ◽  
Fundamental Requirement

Node localization is a fundamental requirement in underwater sensor networks (UWSNs) due to the ineptness of GPS and other terrestrial localization techniques in the underwater environment. In any UWSN monitoring application, the sensed information produces a better result when it is tagged with location information. However, the deployed nodes in UWSNs are vulnerable to many attacks, and hence, can be compromised by interested parties to generate incorrect location information. Consequently, using the existing localization schemes, the deployed nodes are unable to autonomously estimate the precise location information. In this regard, similar existing schemes for terrestrial wireless sensor networks are not applicable to UWSNs due to its inherent mobility, limited bandwidth availability, strict energy constraints, and high bit-error rates. In this article, we propose SecRET , a <underline>Sec</underline>ure <underline>R</underline>ange-based localization scheme empowered by <underline>E</underline>vidence <underline>T</underline>heory for UWSNs. With trust-based computations, the proposed scheme, SecRET , enables the unlocalized nodes to select the most reliable set of anchors with low resource consumption. Thus, the proposed scheme is adaptive to many attacks in UWSN environment. NS-3 based performance evaluation indicates that SecRET maintains energy-efficiency of the deployed nodes while ensuring efficient and secure localization, despite the presence of compromised nodes under various attacks.

scholarly journals A Hybrid Swarm and Gravitation-based feature selection algorithm for handwritten Indic script classification problem

2021 ◽  
Author(s):  
Ritam Guha ◽  
Manosij Ghosh ◽  
Pawan Kumar Singh ◽  
Ram Sarkar ◽  
Mita Nasipuri
Keyword(s):  
Feature Selection ◽  
Character Recognition ◽  
Optical Character Recognition ◽  
Classification Problem ◽  
Classification Model ◽  
Support Vector ◽  
Intermediate Step ◽  
Hybrid Swarm ◽  
Feature Vectors ◽  
Indic Script

AbstractIn any multi-script environment, handwritten script classification is an unavoidable pre-requisite before the document images are fed to their respective Optical Character Recognition (OCR) engines. Over the years, this complex pattern classification problem has been solved by researchers proposing various feature vectors mostly having large dimensions, thereby increasing the computation complexity of the whole classification model. Feature Selection (FS) can serve as an intermediate step to reduce the size of the feature vectors by restricting them only to the essential and relevant features. In the present work, we have addressed this issue by introducing a new FS algorithm, called Hybrid Swarm and Gravitation-based FS (HSGFS). This algorithm has been applied over three feature vectors introduced in the literature recently—Distance-Hough Transform (DHT), Histogram of Oriented Gradients (HOG), and Modified log-Gabor (MLG) filter Transform. Three state-of-the-art classifiers, namely, Multi-Layer Perceptron (MLP), K-Nearest Neighbour (KNN), and Support Vector Machine (SVM), are used to evaluate the optimal subset of features generated by the proposed FS model. Handwritten datasets at block, text line, and word level, consisting of officially recognized 12 Indic scripts, are prepared for experimentation. An average improvement in the range of 2–5% is achieved in the classification accuracy by utilizing only about 75–80% of the original feature vectors on all three datasets. The proposed method also shows better performance when compared to some popularly used FS models. The codes used for implementing HSGFS can be found in the following Github link: https://github.com/Ritam-Guha/HSGFS.

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