Purpose For achieving the profitable human activity recognition (HAR) method, this paper solves the HAR problem under wireless body area network (WBAN) using a developed ensemble learning approach. The purpose of this study is,to solve the HAR problem under WBAN using a developed ensemble learning approach for achieving the profitable HAR method. There are three data sets used for this HAR in WBAN, namely, human activity recognition using smartphones, wireless sensor data mining and Kaggle. The proposed model undergoes four phases, namely, “pre-processing, feature extraction, feature selection and classification.” Here, the data can be preprocessed by artifacts removal and median filtering techniques. Then, the features are extracted by techniques such as “t-Distributed Stochastic Neighbor Embedding”, “Short-time Fourier transform” and statistical approaches. The weighted optimal feature selection is considered as the next step for selecting the important features based on computing the data variance of each class. This new feature selection is achieved by the hybrid coyote Jaya optimization (HCJO). Finally, the meta-heuristic-based ensemble learning approach is used as a new recognition approach with three classifiers, namely, “support vector machine (SVM), deep neural network (DNN) and fuzzy classifiers.” Experimental analysis is performed. Design/methodology/approach The proposed HCJO algorithm was developed for optimizing the membership function of fuzzy, iteration limit of SVM and hidden neuron count of DNN for getting superior classified outcomes and to enhance the performance of ensemble classification. Findings The accuracy for enhanced HAR model was pretty high in comparison to conventional models, i.e. higher than 6.66% to fuzzy, 4.34% to DNN, 4.34% to SVM, 7.86% to ensemble and 6.66% to Improved Sealion optimization algorithm-Attention Pyramid-Convolutional Neural Network-AP-CNN, respectively. Originality/value The suggested HAR model with WBAN using HCJO algorithm is accurate and improves the effectiveness of the recognition.

Wireless body area network (WBAN) is a novel technology with the incorporation of numerous types of devices, which is also employed in health monitoring applications.Human activity recognition (HAR) receives more interest in recent times along with wearable sensors. HAR system provides information about a person's identity, personality, and psychological state. In the present scenario, it becomes important to model the active learning paradigms with the help of wearable sensors for analysing human activities. Although various deep learning models and existing algorithms secure better outcomes through the sensor data analysis regarding HAR, the decision-making evaluation seems to be a complex one. The main intent of this paper is to implement the WBAN-based HAR system using the improved deep learning model. By connecting the wearable sensors, the signals are gathered regarding human activity from diverse benchmark sources. After collecting the required signals, the pre-processing of the input signals is done using artefact removal and median filtering. Further, the feature extraction is performed, which intends to extract a set of features by utilizing short-time Fourier transform (STFT) and statistical features. For reducing the feature-length, a multi-objective-based optimal feature selection is adopted. Human activities such as 'walking, walking upstairs, walking downstairs, sitting, standing, lying, and jogging' are recognized with help of selected optimal features. The optimized probabilistic neural network (PNN) and convolutional neural network (CNN) are combined and named as adaptive probabilistic-based CNN (AP-CNN). The effective performance of optimal feature selection and recognition is accomplished by incorporating the developed backward updating position-based sea lion optimization algorithm (BU-SLnO). Finally, the performance of the BU-SLnO-AP-CNN-based suggested model is analysed, which shows 1.001%, 1.237%, 0.811%, and 0.859% advanced than SLnO-AP-CNN, feed-forward-AP-CNN (FF-AP-CNN), grey wolf optimization-AP-CNN (GWO-AP-CNN), particle swarm optimization-AP-CNN (PSO-AP-CNN) when observing the dataset 1. The experimental outcomes from comparison with various classification techniques demonstrate the efficiency of the developed technique.

Improving existing animal husbandry practices is essential before introducing grazing animals to vineyards. In order to provide this type of assistance, it is necessary to monitor and condition the animals’ whereabouts and actions, especially their feeding posture. Using this strategy, sheep could graze in agricultural areas (such vineyards and orchards) without fear of harming them. Based on these findings, we have created an IoT-based platform for tracking animal habits. To facilitate unattended shepherding of ovine within vineyard areas, the system integrates a local Internet of Things network for data collection from the animals with a cloud platform with data dispensationalso storage competences. As a result, the system can tend to ovine flocks. Easy analysis and interpretation of Internet of Things (IoT) data is made possible by the machine learning capabilities built into the cloud platform. Therefore, we shall not only outline the platform but also supply some machine learning platform-specific results. To be more specific, testing looked at how well this platform could identify and characterize disorders related to animal posture. This page offers a comparison of the tested approaches because multiple algorithms were used.

No abstract

Wireless sensor networks (WSN) are a collection of autonomous collection of motes. Sensor motes are usually Low computational and low powered. In WSN Sensor motes are used to collect environmental data collection and pass that data to the base station. Data aggregation is a common technique widely used in wireless sensor networks. [2] Data aggregation is the process of collecting the data from multiple sensor nodes by avoiding the redundant data transmission and that collected data has been sent to the base station (BS) in single route. Secured data aggregation deals with Securing aggregated data collected from various sources. Many secured data aggregation algorithms has been proposed by many researchers. Symmetric key based cryptography schemes are not suitable when wireless sensor network grows. Here we are proposing an approach to secured data aggregation in wireless sensor networks using Asymmetric key based Elliptic Curve cryptography technique. Elliptic curve cryptography (ECC) [1] is an approach to public-key cryptography based on the algebraic structure of elliptic curves over finite fields. Elliptic Curve Cryptography requires smaller keys compared to non-Elliptic curve cryptography (based on plain Galois fields) to provide equivalent security. The proposed technique of secure data aggregation is used to improve the sensor network lifetime and to reduce the energy consumption during aggregation process.