This is a collection of the research work that I have done till now.

2022:

Multiview gait recognition on unconstrained path using graph convolutional neural network

  • Journal: IEEE Access
  • Publication Year: 2022
  • Authors: Md Shopon, Gee-Sern Jison Hsu, Marina L Gavrilova
  • Link: https://ieeexplore.ieee.org/abstract/document/9779706
  • Click Here for Abstract Automatic decision-making, especially when dealing with crucial cybersecurity and privacy issues, has become one of the key directions in digital identity research. Trustworthy human-AI collaboration highlights emergent multifaceted role of AI and deep learning in biometric online security. Biometrics are also increasingly used in a variety of government programs, intended to ensure cybersecurity and to mitigate inherent risks associated with online activities. This chapter provides a comprehensive overview of the current state-of-the-art approaches in the biometric domain, including physiological, behavioral, and social behavioral biometrics. It further establishes connection among biometric online security, privacy, and human psychological traits, including emotions, psychology and aesthetics. This in turn leads to new insights on ensuring safe and secure digital space through artificial intelligence methods, including most recent deep learning approaches. In addition, this chapter discusses emerging paradigms to ensure privacy and trustworthiness of biometric systems: cancelability, de-identification, and information fusion. The chapter is concluded with future research directions in the vibrant domain of behavioral biometrics and online security.

A Multifaceted Role of Biometrics in Online Security, Privacy, and Trustworthy Decision Making

  • Book Chapter: Breakthroughs in Digital Biometrics and Forensics
  • Publication Year: 2022
  • Authors: Marina L Gavrilova, Fahim Anzum, ASM Hossain Bari, Yajurv Bhatia, Fariha Iffath, Quwsar Ohi, Md Shopon, Zaman Wahid
  • Link: https://link.springer.com/chapter/10.1007/978-3-031-10706-1_14
  • Click Here for Abstract Automatic decision-making, especially when dealing with crucial cybersecurity and privacy issues, has become one of the key directions in digital identity research. Trustworthy human-AI collaboration highlights emergent multifaceted role of AI and deep learning in biometric online security. Biometrics are also increasingly used in a variety of government programs, intended to ensure cybersecurity and to mitigate inherent risks associated with online activities. This chapter provides a comprehensive overview of the current state-of-the-art approaches in the biometric domain, including physiological, behavioral, and social behavioral biometrics. It further establishes connection among biometric online security, privacy, and human psychological traits, including emotions, psychology and aesthetics. This in turn leads to new insights on ensuring safe and secure digital space through artificial intelligence methods, including most recent deep learning approaches. In addition, this chapter discusses emerging paradigms to ensure privacy and trustworthiness of biometric systems: cancelability, de-identification, and information fusion. The chapter is concluded with future research directions in the vibrant domain of behavioral biometrics and online security.

2021:

Biometric System De-identification: Concepts, Applications, and Open Problems

  • Book Chapter: Handbook of Artificial Intelligence in Healthcare
  • Publication Year: 2021
  • Authors: Md Shopon, ASM Hossain Bari, Yajurv Bhatia, Pavan Karkekoppa Narayanaswamy, Sanjida Nasreen Tumpa, Brandon Sieu, Marina Gavrilova
  • Link: https://link.springer.com/chapter/10.1007/978-3-030-83620-7_17
  • Click Here for Abstract This chapter advances information security research by integrating privacy concepts with the most recent biometric developments. Analytical discussions on how physiological, behavioral and social behavioral biometric data can be protected in various authentication applications will be presented. The chapter starts with introducing new de-identification classification, including complete de-identification, soft biometric preserving de-identification, soft biometric preserving utility retained de-identification, and traditional biometric de-identification. It then proceeds to introduce additional types of de-identification, related to emerging biometric research domains which include social behavioral biometrics, aesthetic identification, sensor-based biometrics, spatial and temporal patterns, and psychological user profiles. This chapter also provides some insights into current and emerging research in the multi-modal biometric domain and proposes for the first time multi-modal biometric system de-identification based on deep learning. It concludes with formulating open questions and investigating future directions in this vibrant research field. Answers to those questions will assist not only in the establishment of the new methods in the biometric security and privacy domains, but also provide insights into the future emerging topics in big data analytics and social network research.

Biometric Systems De-Identification: Current Advancements and Future Directions

  • Journal: Journal of Cybersecurity and Privacy
  • Publication Year: 2021
  • Authors: Md Shopon, Sanjida Nasreen Tumpa, Yajurv Bhatia, KN Kumar, Marina L Gavrilova
  • Link: https://www.mdpi.com/2624-800X/1/3/24
  • Click Here for Abstract Biometric de-identification is an emerging topic of research within the information security domain that integrates privacy considerations with biometric system development. A comprehensive overview of research in the context of authentication applications spanning physiological, behavioral, and social-behavioral biometric systems and their privacy considerations is discussed. Three categories of biometric de-identification are introduced, namely complete de-identification, auxiliary biometric preserving de-identification, and traditional biometric preserving de-identification. An overview of biometric de-identification in emerging domains such as sensor-based biometrics, social behavioral biometrics, psychological user profile identification, and aesthetic-based biometrics is presented. The article concludes with open questions and provides a rich avenue for subsequent explorations of biometric de-identification in the context of information privacy.

Residual connection-based graph convolutional neural networks for gait recognition

  • Journal: The Visual Computer
  • Publication Year: 2021
  • Authors: Md Shopon, ASM Bari, Marina L Gavrilova
  • Link: https://link.springer.com/article/10.1007/s00371-021-02245-9
  • Click Here for Abstract The walking manner of a person, also known as gait, is a unique behavioral biometric trait. Existing methods for gait recognition predominantly utilize traditional machine learning. However, the performance of gait recognition can deteriorate under challenging conditions including environmental occlusion, bulky clothing, and different viewing angles. To provide an effective solution to gait recognition under these conditions, this paper proposes a novel deep learning architecture using Graph Convolutional Neural Network (GCNN) that incorporates residual connections for gait recognition from videos. The optimized feature map of the proposed GCNN architecture exhibits the invariant property to viewing angle and subject’s clothing. The residual connection is used to capture both spatial and temporal features of a gait sequence. The kinematic dependency extracted from shallower network layer is propagated to deeper layer using residual connection-based GCNN architecture. The proposed method is validated on CASIA-B gait dataset and outperforms all recent state-of-the-art methods.

A Graph Convolutional Neural Network for Reliable Gait-Based Human Recognition

  • Conference: 2021 IEEE International Conference on Autonomous Systems (ICAS)
  • Publication Year: 2021
  • Authors: Md Shopon, Svetlana Yanushkevich, Yingxu Wang, Marina Gavrilova
  • Link: https://ieeexplore.ieee.org/abstract/document/9551170/
  • Click Here for Abstract In a domain of human-machine autonomous systems, gait recognition provides unique advantages over other biometric modalities. It is an unobtrusive, widely-acceptable way of identity, gesture and activity recognition, with applications to surveillance, border control, risk prediction, military training and cybersecurity. Trustworthy and reliable person identification from videos under challenging conditions, when a subject’s walk is occluded by environmental elements, bulky clothing or a viewing angle, is addressed in this paper. It proposes a novel deep learning architecture based on Graph Convolutional Neural Network (GCNN) for accurate and reliable gait recognition from videos. The optimized feature map of the proposed GCNN architecture ensures that recognition remains accurate and invariant to viewing angle, type of clothing or other conditions.

Age-Style and Alignment Augmentation for Facial Age Estimation

  • Conference: International Conference on Computer Analysis of Images and Patterns
  • Publication Year: 2021
  • Authors: Yu-Hong Lin, Chia-Hao Tang, Zhi-Ting Chen, Gee-Sern Jison Hsu, Md Shopon, Marina Gavrilova
  • Link: https://link.springer.com/chapter/10.1007/978-3-030-89131-2_27
  • Click Here for Abstract Facial age is an important soft biometric trait for better identification of a human subject. The development of a facial age estimation system requires a large collection of age-labeled data. However, the imbalanced data distribution across age poses a major challenge to making a decent model to describe the variation of facial appearance caused by age. The cross-age data imbalance can be observed in common facial age datasets, for example, the MORPH [8], FG-NET [7] and the MIVIA dataset [3] considered in the GTA Contest. It can be often seen that insufficient data are provided for younger ages and senior ages, and the insufficiency becomes worsened as the age moves close to both ends. To deal with the data imbalance issues, many approaches implement various data augmentation schemes. In our approach, we propose a data augmentation scheme built upon the Age-Style GAN (ASGAN), which we propose for facial age regression and progression. In addition to the ASGAN-based data augmentation, we leverage the mean-variance loss to improve the age classification accuracy, and exploit face alignment as an auxiliary scheme to augment the whole dataset with an aligned subset. We conducted extensive experiments on the MIVIA dataset for verifying the performance of our approach.

2020:

Air Quality Index Prediction Using Azure IoT & Machine Learning for Smart Cities

  • Conference: International Conference on Computational Intelligence, Data Science and Cloud Computing
  • Publication Year: 2020
  • Authors: Shaon Hossain Sani, Md Shopon, Sakhawat Hossen Rakib
  • Link: https://link.springer.com/chapter/10.1007/978-981-33-4968-1_56
  • Click Here for Abstract In this paper, we are concerned about the air pollution & Air Quality Index (AQI) level. Today, every major city around the world is facing a worsened situation about public health & environmental disturbance. So, we have proposed an IoT-based solution that evolved with cloud computing platform likes Microsoft Azure, Azure IoT Hub and Azure machine learning (AML). These enhanced the scalability and life cycle of the system. The system classifies the health effects of human life from the air quality index level and gives interactive real-time data visualization results and push notification through the system. We have worked on Dhaka’s air quality dataset. Among major criteria pollutants, we have focused on PM 2.5 , PM 10 , CO, SO 2 , NO 2 that can help us to classify the air quality index level. Multiple classification algorithm is used to train the dataset. Among them, we have acquired the highest accuracy 99.89% with multiclass decision forest.

Short-term and Long-term Air Quality Forecasting Technique Using Stacked LSTM

  • Conference: 6th International Conference on Communication and Information Processing
  • Publication Year: 2020
  • Authors: Shaon Hossain Sani, Md Shopon, Mashrur Hossain Khan, Moenul Hasan, MF Mridha
  • Link: https://dl.acm.org/doi/abs/10.1145/3442555.3442582
  • Click Here for Abstract For the entire globe, air pollution has been a worrying issue. Earth's Atmosphere contains numerous toxic gases and harmful solid particles are caused by Air pollution. Contaminated Air have been many mischievous effects on human health. Asthma, emphysema, chronic obstructive pulmonary disease and lung cancer can happen due to air contamination. Among other enlisted polluted cities, Dhaka lies in a hazardous problem for air pollution. This paper has approached two Long Short-Term Memory (Vanilla LSTM, Stacked LSTM) model and Gated Recurrent Unit (GRU) model to Predict air Quality Indexing with different hyper-parameter tuning. And analyze future the health effects based on Air Quality Index Level. We have worked on Dhaka Air Quality data which was collected by the United States Environmental Protection Agency (EPA). Among the two models, we have acquired the highest accuracy of 91.61% for Short-term prediction and 90.83% for Long-term prediction. And RMSE value of 4.65 and 16.19 for Air Quality Index value prediction on Stacked LSTM tuned with 200 hidden nodes on the first layer and 100 nodes on the second layer.

Classification of Bangla News Articles Using Bidirectional Long Short Term Memory

  • Conference: IEEE TENSYMP (2020 IEEE Region 10 Symposium)
  • Publication Year: 2020
  • Authors: Shahin.M, Ahmed.T, Rahman.S, Shopon.M,
  • Link: Will Publish Soon
  • Click Here for Abstract Classification is a method of assigning input vectors to one of the discrete classes. This problem can be used to identify related content such as E-commerce, news agencies, content cura- tors, blogs, directories, and likes can use automated technologies. In this paper, we have proposed a method of classification using bi-directional LSTM to classify the Bangla news headline. We have used Bangla stop word corpus to removing stop words to get a better result in our method of classification. We have used Gensim and fastText model to vectorized our text to compatible with our machine learning model. We have built a dataset that contains around 10 lakh articles from the different renowned newspapers of Bangladesh and 8 different categories. Then we trained this data in 3 different models. Among those models, Bi- LSTM has achieved 85.14 percent accuracy, which is better than any other method.

Bidirectional LSTM with Attention Mechanism for Automatic Bangla News Categorization In Terms of News Captions

  • Conference: International Conference on Electronic Systems and Intelligent Computing
  • Publication Year: 2020
  • Authors: Shopon, M
  • Link: Will Publish Soon
  • Click Here for Abstract The aim of any classification problem is to create a set of models that can classify the class of different texts and objects. Text classification is known as one such application. This problem can be used in various classification task, e.g. news category classification, identifying language, classification of text genre, recommendation systems etc. In this paper we propose a text classification method using Bidirectional LSTM with Attention mechanism to classify Bangla news articles. This news articles are collected from a renowned a news portal Prothom-Alo. The dataset consist of in total 383304 news articles and there were total number of 12 different categories. Traditionally news classification task is done in terms of news content. But in our work we have performed classification based on the news captions. Which takes lesser amount of training time. We have achieved 91.37\% accuracy using our approach. This is the state of the art result that has achieved on this dataset.

Automatic Violence Detection Method Using Convolution Neural Network

  • Conference: International Conference On Sentimental Analysis And Deep Learning (ICSADL 2020)
  • Publication Year: 2020
  • Authors: Karim.A, Razin,J, Ahmed,N. Shopon, M. Alam.T
  • Link: Will Publish Soon
  • Click Here for Abstract Automatic violence detection using video surveillance system is a mandatory things for everyday life. There are frequent incidents of snatch, fights, murders and many other misdeeds in various important places of the country such as bus stand, railway station, launch gateway, deserted highway, universities, hospitals and many more areas. For this purpose, a violent dataset has been proposed for automatic detecting a situation which is violence or nonviolence. All the data has been collected based on Bangladeshi context. Which includes both types of data like violence and nonviolence. However, different types of machine learning and deep learning algorithms have been applied in this field and detect different results. Here Convolution neural network model is used for detecting violence automatically.80\% data have been used for training the model and 20\% have been used for testing. Near about 96.16\% accuracy has achieved by our mod

2019:

Hand Sign to Bangla Speech: A Deep Learning in Vision based system for Recognizing Hand Sign Digits and Generating Bangla Speech.

  • Conference: International Conference on Sustainable Computing in Science, Technology & Management (SUSCOM-2019)
  • Publication Year: 2019
  • Authors: Ahmed, S., Islam, M., Hassan, J., Ahmed, M. U., Ferdosi, B. J., Saha, S., & Shopon, M
  • Link: https://arxiv.org/abs/1901.05613
  • Click Here for Abstract Recent advancements in the field of computer vision with the help of deep neural networks have led us to explore and develop many existing challenges that were once unattended due to the lack of necessary technologies. Hand Sign/Gesture Recognition is one of the significant areas where the deep neural network is making a substantial impact. In the last few years, a large number of researches has been conducted to recognize hand signs and hand gestures, which we aim to extend to our mother-tongue, Bangla (also known as Bengali). The primary goal of our work is to make an automated tool to aid the people who are unable to speak. We developed a system that automatically detects hand sign based digits and speaks out the result in Bangla language. According to the report of the World Health Organization (WHO), 15% of people in the world live with some kind of disabilities. Among them, individuals with communication impairment such as speech disabilities experience substantial barrier in social interaction. The proposed system can be invaluable to mitigate such a barrier. The core of the system is built with a deep learning model which is based on convolutional neural networks (CNN). The model classifies hand sign based digits with 92% accuracy over validation data which ensures it a highly trustworthy system. Upon classification of the digits, the resulting output is fed to the text to speech engine and the translator unit eventually which generates audio output in Bangla language.

End to End Optical Character Recognition Using Sythetic Dataset Generator For Noisy Conditions

  • Conference: International Joint Conference on Computational Intelligence, IJCCI 2019
  • Publication Year: 2019
  • Authors: Shopon, M., Diput,N.H., & Mohammed, N.
  • Link: https://link.springer.com/chapter/10.1007/978-981-15-3607-6_41
  • Click Here for Abstract Optical Character Recognition is one of the most prevailing research fields since 1970's. Numerous research work has been conducted on Optical Character Recognition. The problem of Optical Character Recognition is to convert images of texts into editable texts. Recent advances in Deep Learning has accelerated the improvements in this field, particularly with languages with large annotated datasets. Bangla, a language with large number of character classes and complex cursive alphabet shapes, is unfortunately not included in these advancements due to the lack of a large annotated dataset. This work concentrates on attempting to perform OCR in noisy conditions for Bangla text. We have created a dataset of 5000 noisy Bangla text samples. To augment this small collection we use a strategy to pre-train our proposed End-to-End model on synthetically generated data and then optionally fine-tune on a part of the collected dataset. Our results indicate that attempting to perform noisy OCR is an extremely challenging task and the best results are obtained when models trained on synthetic data are fine tuned with some real world data.

Unsupervised Pretraining and Transfer Learning Based Bangla Sign Language Recognition

  • Conference: International Joint Conference on Computational Intelligence, IJCCI 2019.
  • Publication Year: 2019
  • Authors: Nishat,Z.K. & Shopon, M. (2019)
  • Link: https://link.springer.com/chapter/10.1007/978-981-15-3607-6_42
  • Click Here for Abstract For hearing impaired peoples Bangladeshi Sign Language (BdSL) is a common medium in Bangladesh that is used for their day to day conversation. In this work we have developed a system for BdSL recognition. We have used transfer learning and unsupervised pre-training for recognition. A dataset of 2080 image was used for conducting the experiment. As the number of samples in the used dataset was very small we have performed augmentation to increase the amount of data samples. This dataset consist of 46 Bangla Characters Sign Language. Among them 10 are Bangla digits, 6 are vowels and 36 are consonants. We have conducted two different experiments on the dataset. In one we have used unsupervised pre training. It has shown excellent performance in the field of image classification. We have acquired 94.86 accuracy using unsupervised pre training. Our second experiment was done using transfer learning. Transfer learning is mostly used when the amount of data available is very limited. We have attained 96.57 state of the art accuracy using transfer learning.

Synthetic Class Specific Bangla Handwritten Character Generation Using Conditional Generative Adversarial Networks

  • Conference: International Conference on Bangla Speech and Language Processing, ICBSLP 2019
  • Publication Year: 2019
  • Authors: Nishat,Z.K. & Shopon, M.
  • Link: https://ieeexplore.ieee.org/abstract/document/9084031/
  • Click Here for Abstract Bangla handwritten character recognition is known to be one of the most classical problem in the field of machine learning. In order to solve a machine learning problem one must thing is dataset. The more varied data a model sees the better it learns. Generative adversarial networks (GANs) are a group of neural networks that are used in unsupervised machine learning. It helps to resolve many difficult operations such as image generation from description, transforming low resolution image into high resolution, retrieving image contents given a small pattern etc. GAN's have many other promising applications in machine learning. There are many variations available for GAN. One of the variation of GAN is Conditional Generative Adversarial Networks(cGAN). This kind of GAN is used for generating a specific type of image. In this work we have used cGAN for generating Class Based Character Generation. This work can help researchers to generate handwritten characters to enhance the perfomance of deep learning models. We have trained this model to generate 50 Basic Bangla Characters, 10 Bangla Numerals and 24 Compound characters.

2018:

An incremental clustered gradient method for wireless sensor networks

  • Conference: 21st Saudi Computer Society National Computer Conference
  • Publication Year: 2018
  • Authors: Mahmud, A., Akhtaruzzaman, A., & Shopon, M.
  • Link: https://ieeexplore.ieee.org/document/8593074
  • Click Here for Abstract In wireless sensor networks, clustering is a very crucial problem. Basically clustering means grouping some specific objects based on their behavior and functionality. Clustering can be formulated for different optimization problems, such as nonsmooth, nonconvex problems. This paper is based on the review of the optimization algorithm that was proposed in the paper A Convergent Incremental Gradient Method With Constant Step Size by Blatt et al called Incremental Aggregate Gradient method. A novel algorithm called Incremental Clustered Aggregate Gradient Method was proposed in this paper to counter the shortcomings of the previous one. It has many similarities with the earlier method but it is more efficient for wireless sensor networks. The main aim of Incremental Gradient Method was to minimize the sum of continuously differentiable functions and also it required a single gradient evaluation per iteration and used a constant step size. For quadratic functions, a global linear rate of convergence was proved. It was claimed that it is more suitable for sensor networks. Although the experiments performed in this work confirm the convergence properties of it, it was found that it is not suitable for sensor networks. The proposed method addresses the flaws of the previous method as regards to sensor networks. When both algorithms operate with their respective optimal step sizes, they require approximately the same number of gradient evaluations for convergence.

2017:

BanglaLekha-Isolated: A multi-purpose comprehensive dataset of Handwritten Bangla Isolated characters.(Journal)

  • Publisher: Elsevier Data in Brief, 12, 103-107.
  • Publication Year: 2017
  • Authors: Biswas, M., Islam, R., Shom, G. K., Shopon, M., Mohammed, N., Momen, S., & Abedin, A
  • Link: https://www.sciencedirect.com/science/article/pii/S2352340917301117
  • Click Here for Abstract BanglaLekha-Isolated, a Bangla handwritten isolated character dataset is presented in this article. This dataset contains 84 different characters comprising of 50 Bangla basic characters, 10 Bangla numerals and 24 selected compound characters. 2000 handwriting samples for each of the 84 characters were collected, digitized and pre-processed. After discarding mistakes and scribbles, 1,66,105 handwritten character images were included in the final dataset. The dataset also includes labels indicating the age and the gender of the subjects from whom the samples were collected. This dataset could be used not only for optical handwriting recognition research but also to explore the influence of gender and age on handwriting. The dataset is publicly available at https://data.mendeley.com/datasets/hf6sf8zrkc/2.

Image augmentation by blocky artifact in deep convolutional neural network for handwritten digit recognition.

  • Conference: Imaging, vision & pattern recognition (icivpr), 2017 ieee international conference on (pp. 1–6)
  • Publication Year: 2017
  • Authors: Shopon, M., Mohammed, N., & Abedin, M. A.
  • Link: https://ieeexplore.ieee.org/document/7890867/
  • Click Here for Abstract Deep Convolutional Neural Networks - also known as DCNN - are powerful models for different visual pattern classification problems. Many works in this field use image augmentation at the training phase to achieve better accuracy. This paper presents blocky artifact as an augmentation technique to increase the accuracy of DCNN for handwritten digit recognition, both English and Bangla digits, i.e., 0-9. This paper conducts a number of experiments on three different datasets: MNIST Dataset, CMATERDB 3.1.1 Dataset and Indian Statistical Institute (ISI) Dataset. For each dataset, DCNNs with the proposed augmentation technique give better results than those without such augmentation. Unsupervised pre-training with the blocky artifact achieves 99.56%, 99.83% and 99.35% accuracy respectively on MNIST, CMATERDDB and ISI datasets producing, in the process, so far the best accuracy rate for CMATERDB and ISI datasets.

2016:

Bangla handwritten digit recognition using autoencoder and deep convolutional neural network.

  • Conference: International Workshop on Computational Intelligence
  • Publication Year: 2016
  • Authors: Shopon, M., Mohammed, N., & Abedin, M. A
  • Link: https://ieeexplore.ieee.org/document/7860340
  • Click Here for Abstract Handwritten digit recognition is a typical image classification problem. Convolutional neural networks, also known as ConvNets, are powerful classification models for such tasks. As different languages have different styles and shapes of their numeral digits, accuracy rates of the models vary from each other and from language to language. However, unsupervised pre-training in such situation has shown improved accuracy for classification tasks, though no such work has been found for Bangla digit recognition. This paper presents the use of unsupervised pre-training using autoencoder with deep ConvNet in order to recognize handwritten Bangla digits, i.e., 0-9. The datasets that are used in this paper are CMATERDB 3.1.1 and a dataset published by the Indian Statistical Institute (ISI). This paper studies four different combinations of these two datasets-two experiments are done against their own training and testing images, other two experiments are done cross validating the datasets. In one of these four experiments, the proposed approach achieves 99.50% accuracy, which is so far the best for recognizing handwritten Bangla digits. The ConvNet model is trained with 19,313 images of ISI handwritten character dataset and tested with images of CMATERDB dataset.

Krill herd based clustering algorithm for wireless sensor networks

  • Conference: International Workshop on Computational Intelligence
  • Publication Date: 2016
  • Authors: Shopon, M., Adnan, M. A., & Mridha, M. F
  • Link: https://ieeexplore.ieee.org/document/7860346
  • Click Here for Abstract Wireless sensor networks are principally categorized by insufficient energy resource. Naturally, communication between the nodes is the utmost energy consuming act that they perform. Hence, development of a well-organized clustering algorithm can play a vital part in enhancing the lifetime of network. Currently, nature inspired methodologies are very common in dealing with it. This work presents a centralized approach that deals with energy-awareness of wireless sensor networks using the Krill Herd algorithm. The performance of the suggested algorithm is assessed with famous clustering protocols. The simulation results show that suggested approach can maximize sensor network lifetime over other algorithms of the same category.