Abstract The promising technology of IoT in the fourth industrial revolution connects everything to the Internet in the digital world of the current era. Though the gigantic connectivity among things through the blend of multifarious technologies offers potential opportunities, it also increases the overhead to optimise the energy consumption in IoT networks. Energy optimisation has become the major concern in the IoT realm because of the continuous sensing of the constrained sensor nodes and data transmission to longer distances. The state‐of‐the‐art studies do not take into account the management of duty‐cycling and the process of efficient route discovery hand in hand. Also, the prevalent studies focus on the management of the sensor node's duty cycling (DC) solely instead of adaptive DC scheduling of the communication unit of the sensor node that consumes more energy during transmission. Therefore, keeping in view the existing tribulations regarding energy consumption, the authors attempt to devise an energy‐efficient routing approach using On‐Demand Duty Cycling and Ant‐Colony optimisation (DC‐ACO) for IoT. The ACO‐based routing approach is applicable in IoT networks because the ants’ environment is conceptualised as a distributed set of interconnected graph nodes. The proposed approach poses the empirical notion to manage the energy consumption of the IoT network by considering the key performance indicators (KPI) like energy consumption, packet delivery rate, average residual energy, mobility factor, distance, throughput, and network lifespan to accomplish the tangible outputs. The proposed approach is modelled using Data flow Diagrams (DFDs) and algorithms supported by results. Experimental results show significant improvement in relative throughput, network lifetime, and energy efficiency by 45%, 78%, and 68%, respectively, after simulating for successive iterations.

Abstract With time smart services have become more domineering than ever before however, the pertinent security considerations fade to correspond with growing heterogeneity in the internet of things (IoT) devices and new technologies coupled with resource constraints, crafting IoT‐based systems more susceptible to cyber‐attacks. To ensure a secure IoT environment, pro‐active security mechanisms, like scanning vulnerabilities and prioritizing to remediate them timely, should be embedded in the system. Motivated by the facts, we in this paper, highlight the state of the art of several works trading with a common vulnerability scoring system (CVSS), its limitations, and the emendations recommended to conclude its maturity. CVSS is an industry standard that has been adopted worldwide to quantify the vulnerabilities in organizations for IT and IoT‐based systems. The vulnerabilities mathematical score coalesces with environmental knowledge for finding attack paths and apt score for prioritization. The specific functionality and exclusive dynamics of IoT and cyber‐physical systems in comparison to traditional computer networks, make the legacy cyber‐security exemplars unfit for these advanced networks. This paper studies the relevance of CVSS for smart systems and present an intelligent vulnerability quantification framework for IoT systems grounded on the CVSS v3.1 framework with threat intelligence and machine learning models. Further by applying blockchain technology in the proposed framework, the issues concerning security, lack of trust, and privacy possibly will resolve by hiring a smart contract.

Abstract In the field of medical science, accurate prediction is a difficult and challenging task. But, the presence of missing values and outliers can make the prediction task more complicated. Many researchers address the issue of missing value in medical data, either detect the missing value and delete the respective data instances from the dataset or adopt some default methods such as mean, median, neighbour etc., for filling the missing value. However, both methods are lacking to produce optimal results. Furthermore, outliers are also presented in data and degraded the performance of classifier. Few researchers also focus on the outlier detection in medical dataset, but it is not fully explored till date. This work considers the two well‐known problems of data that is, (i) missing value imputation, and (ii) outlier. The missing value imputation issue is addressed through K‐Mean++ based data imputation technique. This technique also validates the data through clustering and also compute the values for missing data. The outlier can be detected through an ABC based outlier detection technique. Further, the final outcome is determined using LS‐SVM classifiers. Hence, this work presents a hybrid disease diagnosis framework for diabetes prediction, called hybrid diabetes prediction framework. The reason behind to choose the diabetes dataset for implementation as it contains 763 missing values and several outliers. The simulation results showed that proposed hybrid framework effectively determines the missing values and outliers in diabetes dataset. Further, the performance of proposed hybrid diabetes prediction framework is evaluated using accuracy, sensitivity, specificity, kappa and AUC parameters and compared with 34 state of art techniques. Results confirmed that proposed hybrid framework obtains 96.57%, 93.37%, 98.12%, 98.17%, and 95.43% accuracy, sensitivity, specificity, kappa and AUC rate respectively.

Abstract One of the capacious applications of data science could be its use in bioinformatics. With its proper implementation, chronic diseases like diabetes mellitus, responsible for millions of deaths worldwide, could be diagnosed and predicted with high efficacy. But if not attended, could lead to fatal issues such as kidney failures, heart diseases, and even limb amputation. Diabetic cases have only elevated in numbers in the recent past. The authors use various machine learning, deep learning, and data dimensionality reduction techniques to detect diabetes mellitus. The research is principally conducted on two datasets, first from the Frankfurt Hospital, Germany, second from the University of California, Irvine repository. Models such as support vector machines, Naïve Bayes, and Random Forests were implemented to classify diabetic patients from non‐diabetic ones. Subsequently, after hyperparameter tuning, a comparative study on the results was done and the most prominent model was promoted. This process was repeated for the datasets with reduced dimensionality using linear discriminant analysis and principal component analysis. For the Frankfurt, Germany, dataset, K‐nearest neighbours showed the best accuracy of 98.2%, and the Random Forest classifier for the University of California, Irvine, repository showed 99.2%. With such proficiency, the authors thereby propose a statistical approach for the prediction of diabetes in its early stages. They hope to counter the concern of undiagnosed diabetic cases in developing nations where there is a lack of a basic healthcare system.