Browsing by Author "Pramod Kumar Mishra"

Now showing 1 - 20 of 38

A clustering algorithm for multiprocessor environments using dynamic priority of modules
(2011) Pramod Kumar Mishra; Kamal Sheel Mishra; Abhishek Mishra
In this paper, we propose a task allocation algorithm on a fully connected homogeneous multiprocessor environment using dynamic priority of modules. This is a generalization of our earlier work in which we used static priority of modules. Priority of modules is dependent on the computation and the communication times associated with the module as well as the current allocation. Initially the modules are allocated in a single cluster. We take out the modules in decreasing order of priority and recalculate their priorities. In this way we propose a clustering algorithm of complexity O(|V| 2(|V|+|E|)log(|V|+|E|)), and compare it with Sarkar's algorithm.
A Contemporary Survey on IoT Based Smart Cities: Architecture, Applications, and Open Issues
(Springer, 2022) Shwet Ketu; Pramod Kumar Mishra
Internet of Things (IoT) is one of the emerging technologies, which is widely used across the globe. As the idea of a smart city was founded, IoT has been acknowledged as the key foundation in smart city paradigms. Technology makes a person smart, and to make the world smart, we have to make the country smart. To make the country smart, we have to make cities smart and to make smart cities, we have to be smart. In short, to create a smart environment, one must be equipped and familiar with the current trends. The integration of various smart devices and systems facilitates IoT for a smart city. The interdependent and interwoven nature of smart cities puts notable legislative, socioeconomic, and technical challenges for integrators, organizations, and designers committed to administrating these novel entities. The goal of this paper is to illustrate a contemporary survey of IoT-based smart cities with their potential, current trends and developments, amenity architecture, application area, real-world involvement, and open challenges. In addition, key elements with potential implementation constraints and integration of various IoT-based application areas that play a key role in building a smarter city have also been discussed. This extensive study contributes a useful panorama on various key points and gives a critical direction for forthcoming investigations. This study will also provide a reference point for practitioners and academics in the near future. © 2022, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.
A hybrid deep learning model for COVID-19 prediction and current status of clinical trials worldwide
(Tech Science Press, 2020) Shwet Ketu; Pramod Kumar Mishra
Infections or virus-based diseases are a significant threat to human societies and could affect the whole world within a very short time-span. Corona Virus Disease-2019 (COVID-19), also known as novel coronavirus or SARSCoV- 2 (Severe Acute Respiratory Syndrome-Coronavirus-2), is a respiratory based touch contiguous disease. The catastrophic situation resulting from the COVID-19 pandemic posed a serious threat to societies globally. The whole world is making tremendous efforts to combat this life-threatening disease. For taking remedial action and planning preventive measures on time, there is an urgent need for efficient prediction models to confront the COVID-19 outbreak. A deep learning-based ARIMA-LSTM hybrid model is proposed in this article for predicting the COVID-19 outbreak by utilizing real-time information from the WHO's daily bulletin report as well as provides information regarding clinical trials across the world. To evaluate the suitability and performance of our proposed model compared to other well-established prediction models, an experimental study has been performed. To estimate the prediction results, the three performance measures, i.e., Root Mean Square Error (RMSE), Coefficient of determination (R2 Score), and Mean Absolute Percentage Error (MAPE) have been employed. The prediction results of fifty countries substantiated the fact that the proposed ARIMA-LSTM hybrid model performs very well as compared to other models. The proposed model archives the lowest RMSE, lowest MAPE, and highest R2 Score throughout the testing, under varied selection criteria (country- wise). This article aims to contribute a deep learning-based solution for the wellbeing of livings and to provide the current status of clinical trials across the globe. © 2021 Tech Science Press. All rights reserved.
A randomized scheduling algorithm for multiprocessor environments
(2012) Pramod Kumar Mishra; Kamal Sheel Mishra; Abhishek Mishra; Anil Kumar Tripathi
In this paper, we propose a randomized scheduling algorithm on a fully connected homogeneous multiprocessor environment. This is a randomized version of our earlier algorithm in which we used priority of modules that was dependent on the computation and the communication times associated with the modules. First we propose a generalization of our earlier scheduling algorithm with restricted number of clusters to reduce the time complexity. Then we apply randomization to the generalized algorithm and demonstrate its superiority over our previous work. We show the complexity of our proposed algorithm as O(ab |V| (|V|+|E|) log (|V|+|E|)). Here a is the number of randomization steps, and b is a limit on the number of clusters formed. If we use a and b as constants, then this gives a better complexity in comparison with the complexity of our previous algorithm that was O(|V|2(|V|+|E|) log (|V|+|E|)). In comparison with our previous work we get a performance improvement of up to 6.63% and a performance improvement of up to 12.56% when compared with Sarkar's Edge Zeroing algorithm. © 2012 World Scientific Publishing Company.
A Randomized Scheduling Algorithm for Multiprocessor Environments Using Local Search
(World Scientific Publishing Co. Pte Ltd, 2016) Abhishek Mishra; Pramod Kumar Mishra
The LOCAL(A, B) randomized task scheduling algorithm is proposed for fully connected multiprocessors. It combines two given task scheduling algorithms (A, and B) using local neighborhood search to give a hybrid of the two given algorithms. Objective is to show that such type of hybridization can give much better performance results in terms of parallel execution times. Two task scheduling algorithms are selected: DSC (Dominant Sequence Clustering as algorithm A), and CPPS (Cluster Pair Priority Scheduling as algorithm B) and a hybrid is created (the LOCAL(DSC, CPPS) or simply the LOCAL task scheduling algorithm). The LOCAL task scheduling algorithm has time complexity O(|V
A Survey on DDoS Attacks on Network and Application Layer in IoT
(Springer Science and Business Media Deutschland GmbH, 2022) Nimisha Pandey; Pramod Kumar Mishra
IoT makes the devices remotely accessible and these devices are used to collect data for analysis at a later stage. IoT was expected to bring revolutionary changes in the way people use technology. However, security vulnerabilities in IoT make it insecure towards the possible attacks like DoS/DDoS attacks. Detection of DDoS attacks is required to protect the IoT systems from attackers and evade financial losses. This paper presents a review of solutions proposed for the detection of DDoS attacks in the network layer and application layer of IoT. Application layer attacks have been increasing because they are sophisticated and are difficult to differentiate from real users. A huge number of papers have contributed to the network layer but issues are still faced in application layer in IoT. We have reviewed the issues in application layer protocols in IoT as well. The need of development of countering DDoS in application layer of IoT is also addressed. © 2022, Springer Nature Switzerland AG.
A Survey on WSN Issues with its Heuristics and Meta-Heuristics Solutions
(Springer, 2021) Ankita Srivastava; Pramod Kumar Mishra
A wireless sensor networks (WSN’s) has stimulated significant research work among the researchers in monitoring and tracking tasks. It’s a quite challenging task that needs to cope up with various conflicting issues such as energy efficiency, network lifetime, connectivity, coverage, etc. in WSN’s for designing various applications. This paper explores the recent work and efforts done in addressing the various issues in WSN’s. This paper focused on basic concepts regarding the WSN’s and discusses meta-heuristics and heuristics algorithms for solving these issues with recent investigations. Various optimization algorithms in the context of WSN, routing algorithms, and clustering algorithms were discussed with details of earlier work done. This paper delivers various Multi-Objective Optimization approaches deeply for solving issues and summarizes the recent research work and studies. It provides researchers an understanding of the various issues, trade-offs between them, and meta-heuristics and heuristics approach for solving these issues. A glimpse of open research challenges has also been provided which will be helpful for researchers. This paper also gives an insight into various issues, open challenges that still exist in WSN’s with their heuristics and meta-heuristics solutions and also focuses on various conflicting issues as well. © 2021, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.
An intelligent hybrid classification model for heart disease detection using imbalanced electrocardiogram signals
(Springer, 2024) Shwet Ketu; Pramod Kumar Mishra
Cardiovascular disease (CVD) is among one of the notable menaces to society worldwide. CVD causes the highest number of deaths each year making it one of the most life-threatening diseases across the globe. Most deaths from CVD are sudden therefore patients do not have a chance to get medical assistance in time. Consequently, an immense need for a smart real-time system arises that can be used to monitor heart patients’ activities affecting their cardiac health. This system acts as a life-saving tool during serious health emergencies. Data analysis in real-time will proves to be a substantial enhancement in innovative healthcare practices, by which in the near future we can develop an effective, faster, and smarter diagnosis system for doctors. If we talk about real-time data monitoring possibilities, Internet of Things (IoT) empowered systems can provide one of the better solutions. IoT-enabled intelligent healthcare system include a variety of applications, such as Blood Pressure (BP) check, Heart Rate (HR) monitoring, Electrocardiography (ECG) observation, etc. This paper recommends an IoT-enabled ECG monitoring system for data generation (with the help of Node MCU ESP32 and heart rate sensor AD8232) and an intelligent hybrid classification model for data classification. The dataset used has two classes where class 1 represents healthy patients and class 2 represents cardiac ill patients. A comparison among state-of-the-art algorithms and recommended hybrid models has been carried out to establish the accurateness and suitableness of our recommended model. The recommended model attains the highest accuracy of 99.7% under different validation criteria among all the state-of-the-art algorithms, i.e. Adaboost (91.88%), Bagging (92.40%), random forest (92.48%), K-Nearest Neighbor (92.38%), and support vector machine (91.98%). The recommended hybrid model not only handles the complexities of class imbalance for electrocardiogram datasets but will also help in building intelligent and accurate IoT-enabled healthcare systems. © 2023, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.
Benchmarking the clustering algorithms for multiprocessor environments using dynamic priority of modules
(2012) Pramod Kumar Mishra; Abhishek Mishra; Kamal Sheel Mishra; Anil Kumar Tripathi
In this paper we give some extensive benchmark results for some dynamic priority clustering algorithms for homogeneous multiprocessor environments. By dynamic priority we mean a priority function that can change with every step of the algorithm. Using dynamic priority can give us more flexibility as compared to static priority algorithms. Our objective in this paper is to compare the dynamic priority algorithms with some well known algorithms from the literature and discuss their strengths and weaknesses. For our study we have selected two recently proposed dynamic priority algorithms: CPPS (Cluster Pair Priority Scheduling algorithm) having complexity O(|V
Cloud, Fog and Mist Computing in IoT: An Indication of Emerging Opportunities
(Taylor and Francis Ltd., 2022) Shwet Ketu; Pramod Kumar Mishra
In the last couple of years, massive growth has been seen in the Internet of Things (IoT). It is being adopted by all the application areas worldwide and playing a vital role in making smart environments. In IoT based paradigms, millions of sensors and intelligent devices are linked with each other and produce a massive volume of data. For data computation and analysis, we need computing resources that will able to provide the services at the edge levels. These computing resources may also be capable of dealing with bandwidth, storage, and latency related issues. The computing technologies in IoT may offer the services at the end-user levels, which will increase the system's overall performance with higher throughput in real-time. The developing rate of IoT based smart devices or sensors needs mobility with extensive geographical distribution, which is only possible by computing technologies. In this paper, we have discussed the emerging opportunities of Cloud, Fog, and Mist computing in IoT paradigms and also described the layer-wise architecture of these computing technologies. Apart from this, various characteristics and significance toward the problem-solving in the IoT paradigm have also been deeply discussed. © 2022 IETE.
Conditional entropy-based hybrid DDoS detection model for IoT networks
(Elsevier Ltd, 2025) Nimisha Pandey; Pramod Kumar Mishra
In a distributed denial-of-service (DDoS) attack, an attacker channelizes the resources of a botnet to launch denial of service attack on the victim. The increased use of IoT devices and dependence of users on e-services like online shopping and online payments have elevated the liability risks. The entropy provides a significant measure of randomness. The variation in entropy of traffic features determines the presence of abrupt traffic. This paper uses entropy and conditional entropy to achieve insights on data and feeds it to the proposed 2-stage detection approach for multi-class classification. The proposed model employs four classifiers for first hand classification. Further, stacking generalization-based second stage achieves the final detection process. The recently launched CIC IoT 2023 dataset is used to illustrate the findings of the study. The proposed approach produces an accuracy of 99.86%. Further, this paper utilizes relative entropy for the determination of deflection of traffic behavior between the attack and legitimate samples. Comparisons have been made among symmetric versions of information divergence, ϕ-divergence and Kullback–Leibler divergence along with, Hellinger distance and total variation distance. It is found that the information distance gives a better differentiation between the entropy of legitimate traffic and attack traffic. Significance Statement Entropy has been manipulated to define the nature of incoming traffic for any rule-based detection. This work explores the significance of conditional entropy for the ML-based detection of DDoS attacks in a recently launched IoT-based dataset. Additionally, the effectiveness of KL-divergence, information divergence, ϕ-divergence, Hellinger distance and total variation distance is compared for differentiating between legitimate traffic and attack traffic. © 2024 Elsevier Ltd
Covid-MANet: Multi-task attention network for explainable diagnosis and severity assessment of COVID-19 from CXR images
(Elsevier Ltd, 2022) Ajay Sharma; Pramod Kumar Mishra
The devastating outbreak of Coronavirus Disease (COVID-19) cases in early 2020 led the world to face health crises. Subsequently, the exponential reproduction rate of COVID-19 disease can only be reduced by early diagnosis of COVID-19 infection cases correctly. The initial research findings reported that radiological examinations using CT and CXR modality have successfully reduced false negatives by RT-PCR test. This research study aims to develop an explainable diagnosis system for the detection and infection region quantification of COVID-19 disease. The existing research studies successfully explored deep learning approaches with higher performance measures but lacked generalization and interpretability for COVID-19 diagnosis. In this study, we address these issues by the Covid-MANet network, an automated end-to-end multi-task attention network that works for 5 classes in three stages for COVID-19 infection screening. The first stage of the Covid-MANet network localizes attention of the model to the relevant lungs region for disease recognition. The second stage of the Covid-MANet network differentiates COVID-19 cases from bacterial pneumonia, viral pneumonia, normal and tuberculosis cases, respectively. To improve the interpretation and explainability, three experiments have been conducted in exploration of the most coherent and appropriate classification approach. Moreover, the multi-scale attention model MA-DenseNet201 proposed for the classification of COVID-19 cases. The final stage of the Covid-MANet network quantifies the proportion of infection and severity of COVID-19 in the lungs. The COVID-19 cases are graded into more specific severity levels such as mild, moderate, severe, and critical as per the score assigned by the RALE scoring system. The MA-DenseNet201 classification model outperforms eight state-of-the-art CNN models, in terms of sensitivity and interpretation with lung localization network. The COVID-19 infection segmentation by UNet with DenseNet121 encoder achieves dice score of 86.15% outperforming UNet, UNet++, AttentionUNet, R2UNet, with VGG16, ResNet50 and DenseNet201 encoder. The proposed network not only classifies images based on the predicted label but also highlights the infection by segmentation/localization of model-focused regions to support explainable decisions. MA-DenseNet201 model with a segmentation-based cropping approach achieves maximum interpretation of 96% with COVID-19 sensitivity of 97.75%. Finally, based on class-varied sensitivity analysis Covid-MANet ensemble network of MA-DenseNet201, ResNet50 and MobileNet achieve 95.05% accuracy and 98.75% COVID-19 sensitivity. The proposed model is externally validated on an unseen dataset, yields 98.17% COVID-19 sensitivity. © 2022
Deep Learning Approaches for Automated Diagnosis of COVID-19 Using Imbalanced Training CXR Data
(Springer Science and Business Media Deutschland GmbH, 2022) Ajay Sharma; Pramod Kumar Mishra
Due to the exponential rise of COVID-19 worldwide, it is important that the artificial intelligence community address to analyze CXR images for early classification of COVID-19 patients. Unfortunately, it is very difficult to collect data in such epidemic situations, which is essential for better training of deep convolutional neural networks. To address the limited dataset challenge, the author makes use of a deep transfer learning approach. The presence of limited number of COVID-19 samples may lead to biased learning due to class imbalance. To resolve class imbalance, we propose a new class weighted loss function that reduces biasness and improves COVID-19 sensitivity. Classification and preprocessing are two concrete components of this study. For classification, we compare five pre-trained deep neural networks architectures i.e. DenseNet169, InceptionResNetV2, MobileNet, Vgg19 and NASNetMobile as a baseline to achieve transfer learning. This study is conducted using two fused datasets where samples are collected from four heterogeneous data resources. Based on number of classes we make four different classification scenarios to compare five baseline architectures in two stages. These scenarios are COVID-19 vs non- COVID-19, COVID-19 vs Pneumonia vs Normal, COVID-19 pneumonia vs Viral vs Bacterial pneumonia vs Normal and COVID19 vs Normal vs Virus + Bacterial pneumonia. The primary goal of this study is to improve COVID-19 sensitivity. Experimental outcomes show that DenseNet169 achieves the highest accuracy and sensitivity for COVID-19 detection with score of 95.04% and 100% for 4-class classification and 99.17% and 100% for 3 class-classification. © 2022, Springer Nature Switzerland AG.
Detection of DDoS attack in IoT traffic using ensemble machine learning techniques
(American Institute of Mathematical Sciences, 2023) Nimisha Pandey; Pramod Kumar Mishra
A denial-of-service (DoS) attack aims to exhaust the resources of the victim by sending attack packets and ultimately stop the legitimate packets by various techniques. The paper discusses the consequences of distributed denial-of-service (DDoS) attacks in various application areas of Internet of Things (IoT). In this paper, we have analyzed the performance of machine learning(ML)-based classifiers including bagging and boosting techniques for the binary classification of attack traffic. For the analysis, we have used the benchmark CICDDoS2019 dataset which deals with DDoS attacks based on User Datagram Protocol (UDP) and Transmission Control Protocol (TCP) in order to study new kinds of attacks. Since these protocols are widely used for communication in IoT networks, this data has been used for studying DDoS attacks in the IoT domain. Since the data is highly unbalanced, class balancing is done using an ensemble sampling approach comprising random under-sampler and ADAptive SYNthetic (ADASYN) oversampling technique. Feature selection is achieved using two methods, i.e., (a) Pearson correlation coefficient and (b) Extra Tree classifier. Further, performance is evaluated for ML classifiers viz. Random Forest (RF), Naïve Bayes (NB), support vector machine (SVM), AdaBoost, eXtreme Gradient Boosting (XGBoost) and Gradient Boosting (GB) algorithms. It is found that RF has given the best performance with the least training and prediction time. Further, it is found that feature selection using extra trees classifier is more efficient as compared to the Pearson correlation coefficient method in terms of total time required in training and prediction for most classifiers. It is found that RF has given best performance with least time along with feature selection using Pearson correlation coefficient in attack detection. © 2023 the Author(s), licensee AIMS Press.
Devising a hybrid approach for near real-time DDoS detection in IoT
(Elsevier Ltd, 2024) Nimisha Pandey; Pramod Kumar Mishra
DDoS attacks have impacted businesses financially and hit their market reputation. Entropy variation and machine learning are two popular measures of DDoS detection in the literature. The entropy-based detection takes fewer resources yet a longer time to detect the attack and produces high false positive rate. Meanwhile, traditional machine learning classifiers churn out more accurate classification, however, need ample resources for processing huge data. Since IoT devices generate large amounts of data generally; therefore training ML classifiers with all data is impractical. This paper presents an overview of practical merits and demerits of entropy-based detection approach and ML-based detection. In this paper, we have proposed a two-tier hybrid approach for IoT networks that employs entropy variation to filter the attack traffic from benign traffic in first tier. Further, the remaining and reduced volume of supposedly benign data is fed to the second tier which is ML-based detection approach. We have utilized the CICDDoS2019 dataset to illustrate our notions, perform evaluation and findings. The proposed approach has yielded 99.99% f1-score in the second cycle of training and prediction. The proposed approach gives the first response in comparatively less duration as compared to the ML classifiers and significantly reduces the false positive rate as compared to entropy-based detection. It is found that the proposed detection process takes fewer resources too. The findings of the analysis were validated on the CICIoT2023 dataset, which resulted in similar performance. The proposed approach is compared with peer IDSs and results indicate the effectiveness of our approach. © 2024 Elsevier Ltd
DRI-UNet: dense residual-inception UNet for nuclei identification in microscopy cell images
(Springer Science and Business Media Deutschland GmbH, 2023) Ajay Sharma; Pramod Kumar Mishra
Nuclei segmentation has great significance in biomedical applications as the preliminary step for disease diagnosis and treatment analysis. In this study, we propose a model for automated nuclei identification of varying cell shapes and types from microscopy images. Identifying nuclei helps to understand the underlying mechanism of various diseases in their early stages and provides solutions to enable faster cures. The foremost aim of the study is to develop a lightweight model, capable of segmenting varied shapes and sizes. The proposed architecture exploits multi-scale low-level features following dense high-level feature extraction with multi-feature fusion and special skip connections resulting in enhanced learning capability. The multi-scale feature extractor module extracts low-level information which is further processed using attention-based dense connections to extract semantically meaningful information. The special short-skip residual connections replacing long-skip connections reduced the semantic gap between encoder–decoder features. Moreover, the context encoder module extracts higher-level contextual information of different receptive fields using dilated convolutions making the model robust to different shapes and sizes. The higher-level feature maps propagate upward the decoder connections following the shared attention mechanism of an encoder to decoder features to reconstruct a better segmentation map. Moreover, the evaluation scheme following the proposed test-time augmentation operations improved the mean segmentation performance. The experiments on KDSB18, Synthetic cells, Triple-negative breast cancer (TNBC), MoNuSeg, CryoNuSeg, and BUS datasets demonstrate the suitability of the model for the nuclei segmentation tasks. The DRI-UNet model holds good segmentation performance outperforming baseline architecture by 8.12%, 4.71%, 10.19%, 2.46%, 3.14%, 8.91%, and 9.32% on KDSB18, synthetic cells, TNBC, MoNuSeg, CryoNuSeg, CVC-ClinicDB, and BUS datasets, respectively. We further conducted generalization tests of the proposed model for cross-dataset validation, and two independent MIS datasets confirm model effectiveness for nuclei cell and biomedical image segmentation. © 2023, The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature.
Effect of eranda taila and ruksha (baluka) sweda in the treatment of amavata with reference to rheumatoid arthritis
(International Journal of Research in Ayurveda and Pharmacy, 2014) Pramod Kumar Mishra; N.P. Rai
Rheumatoid arthritis, an autoimmune inflammatory disorder, has similar clinical presentation as amavata. Modern system of medicine is effective in alleviating agony of pain but there is no complete remission of this disease. Researchers have proved that Ayurvedic management is effective in curing and checking progression of the disease. The objective of this study was to evaluate the effectiveness of eranda taila and ruksha baluka sweda in the management of amavata vis-à-vis rheumatoid arthritis. Present clinical trial was carried out on 16 patients selected from the Sir Sundar Lal Hospital, IMS, BHU, Varanasi, India; for this study, eranda oil and dry fomentation by sand (ruksha baluka sweda) were used which is described in Chakradutta. There was significant improvement in symptoms and it was evident clinically and stastically too. The therapy was proved quite effective in the management of amavata (Rheumatoid arthritis).
Empirical Analysis of Machine Learning Algorithms on Imbalance Electrocardiogram Based Arrhythmia Dataset for Heart Disease Detection
(Springer Science and Business Media Deutschland GmbH, 2022) Shwet Ketu; Pramod Kumar Mishra
Living beings are subjected to many hazards during their course of life. Owing to high mortality rate, heart disease (HD) is among leading hazards for living being. It is world’s one of the critical disease due to its complex diagnosis and expansive treatment. It has predominantly affected the health care sector of developing as well as developed countries. Inadequate preventive measures, diagnosis shortcomings, inefficient medical support, lack of medical staff and advancements have led to severe impacts on developing countries. The paper exhibits state-of-the-art of various intelligent solutions for HD detection with an empirical analysis of machine learning algorithms on electrocardiogram-based arrhythmia dataset for disease detection. A critical investigation is being performed using eight machine learning algorithms, Support Vector Machine, K-Nearest Neighbors, Random Forest, Extra Tree, Bagging, Decision Tree, Linear Regression, and Adaptive Boosting, under imbalanced and balanced class paradigms. The performance of these algorithms is tested with four metrics namely, precision, recall, accuracy, and f1-score. The empirical analysis presents an interesting insight on the structure of dataset. Initially for binary class balancing problem majority class have more accuracy than the minority class because model’s training dataset is crowded with majority class tuples than minority class. The paper uses Synthetic Minority Over-sampling Technique for data balancing. It has not only increased the overall accuracy of the algorithm but also the individual accuracy of the classes. Hence, the accuracy of the minority class will not be sacrificed. © 2021, King Fahd University of Petroleum & Minerals.
Energy efficient clustering using modified PROMETHEE-II and AHP approach in wireless sensor networks
(Springer, 2023) Ankita Srivastava; Pramod Kumar Mishra
In wireless sensor networks (WSNs), sensor nodes were considered to be an integral part of IoT (Internet of Things) for sensing and monitoring the environment. The IoT-based applications need to be optimized regarding the changing requirements of users as everything is connected via the internet. In today’s era, where every day new technologies were rebuilt where sensor nodes plays an important role on it. In every field, whether it is healthcare, smart agriculture, smart home appliances, smart traffic, or smart city sensors were deployed for sensing their environment, collecting data from them, and forwarding it to the servers. These sensor nodes were made up of non-rechargeable power batteries, as a fact efficient energy consumption of these batteries becomes vital. In WSN, efficient energy consumption is still an issue, and its solutions were given by many researchers among them, clustering is considered to be more effective in this domain. For efficient energy consumption, multi-attributes of cluster head selection need to be considered and proper coordination among the conflicting nature of multi-attributes needs to be done. In this paper, we have proposed PROMETHEE II and modified AHP together for cluster heads selection by considering multi-attributes. Twenty-one attributes were considered including connectivity, distance to the base station, residual energy, member nodes, and many more. Being conflicting in nature, proper coordination among these attributes has been done and optimal cluster heads were selected modified for data transmissions. In this paper, modified AHP has been compared with our proposed modified PROMETHEE II and AHP for understanding the significance of this integration. Results is evaluated in terms of energy consumption, network lifetime, and load balancing and it also validate that our proposed approach outperforms with modified AHP and other existing algorithms. Our proposed algorithm enriches network lifetime by balancing the load among sensor nodes which leads to efficient energy consumption. © 2023, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.
Enhanced Gaussian process regression-based forecasting model for COVID-19 outbreak and significance of IoT for its detection
(Springer, 2021) Shwet Ketu; Pramod Kumar Mishra
Virus based epidemic is one of the speedy and widely spread infectious disease which can affect the economy of the country as well as it is life-threatening too. So, there is a need to forecast the epidemic lifespan, which can help us in taking preventive measures and remedial action on time. These preventive measures and corrective action may consist of closing schools, closing malls, closing theaters, sealing of borders, suspension of public services, and suspension of traveling. Resuming such restrictions is depends upon the outbreak momentum and its decay rate. The accurate forecasting of the epidemic lifespan is one of the enormously essential and challenging tasks. It is a challenging task because the lack of knowledge about the novel virus-based diseases and its consequences with complicated societal-governmental factors can influence the widespread of this newly born disease. At this stage, any forecasting can play a vital role, and it will be reliable too. As we know, the novel virus-based diseases are in a growing phase, and we also do not have real-time data samples. Thus, the biggest challenge is to find out the machine learning-based best forecasting model, which could offer better forecasting with the limited training samples. In this paper, the Multi-Task Gaussian Process (MTGP) regression model with enhanced predictions of novel coronavirus (COVID-19) outbreak is proposed. The purpose of the proposed MTGP regression model is to predict the COVID-19 outbreak worldwide. It will help the countries in planning their preventive measures to reduce the overall impact of the speedy and widely spread infectious disease. The result of the proposed model has been compared with the other prediction model to find out its suitability and correctness. In subsequent analysis, the significance of IoT based devices in COVID-19 detection and prevention has been discussed. © 2021, Springer Science+Business Media, LLC, part of Springer Nature.