Wireless sensor network (WSN) sensor nodes should have adequate energy. Reduced energy usage is essential to maximize the endurance of WSNs. Combining WSN with a more significant energy source, a cluster head (CH), is another effective strategy for extending WSN durability. A CH is dependent on the communication inside and between clusters. A CH’s energy level extends the cluster’s life for the complete WSN. Determining the energy required in WSNs while developing clustering algorithms is challenging. For maintaining energy efficiency in WSNs, this research offers K-medoids with sunflower-based clustering and a cross-layer-based optimal routing approach. An efficient fitness function generated from diverse objectives is used to choose the CH. After CH selection, sunflower optimization (SFO) indicates the best data transmission line to the sink node. The proposed protocol, SFO-CORP, increased the network lifetime by 19.6%, 13.63%, 11.13%, and 4.163% compared to the LEACH, EECRP, FEEC-IIR, and CL-IoT protocols, respectively. The experimental results showed that it performed better for packet delivery ratio, energy consumption, end-to-end delay, network lifetime, and computation efficiency.

/pdf/hybrid-k-medoids-with-energy-efficient-sunflower-34zipaud.pdf

Hybrid K-Medoids with Energy-Efficient Sunflower Optimization Algorithm for Wireless Sensor Networks

The study and practice of recognizing and resolving physiological and biologic processes that prevent plants from reaching their full hereditary potential for applications such as food, decorations, lumber, or other uses is known as plant health care. One of most crucial tasks in any setting based on agriculture is crop management. The establishment of a programmed for monitoring plant health is covered in this study. which will evaluate certain environmental factors that impact plants, such as temperature, humidity, and light intensity. Restore soil moisture as well. The Arduino Uno development boards transmit all of this data to the Ubidot IoT (Iot) cloud platform. If there is a discrepancy between the number of sensor nodes in the data and the actual number, the user’s smartphone will be notified. 

A Novel Approach to IoT based Plant Health Monitoring System in Smart Agriculture

The study and practice of recognizing and resolving physiological and biologic processes that prevent plants from reaching their full hereditary potential for applications such as food, decorations, lumber, or other uses is known as plant health care. One of most crucial tasks in any setting based on agriculture is crop management. The establishment of a programmed for monitoring plant health is covered in this study. which will evaluate certain environmental factors that impact plants, such as temperature, humidity, and light intensity. Restore soil moisture as well. The Arduino Uno development boards transmit all of this data to the Ubidot IoT (Iot) cloud platform. If there is a discrepancy between the number of sensor nodes in the data and the actual number, the user’s smartphone will be notified.

Abstract Due to the characteristics of vehicular ad hoc networks, the increased mobility of nodes and the inconsistency of wireless communication connections pose significant challenges for routing. As a result, researchers find it to be a fascinating topic to study. Furthermore, since these networks are vulnerable to various assaults, providing an authentication method between the source and destination nodes is crucial. How to route in such networks more efficiently, taking into account node mobility characteristics and accompanying massive historical data, is still a matter of discussion. Fuzzy logic‐based Trusted Routing Protocol for vehicular cloud networks (FTRP) is proposed in this study that determines the secure path for data dissemination. Fuzzy Logic determines the node candidacy value and selects or rejects a path accordingly. The cloud assigns a confidence score to each vehicle based on the data it collects from nodes after each interaction. Our study identifies the secure path on the basis of trust along with factors such as speed, closeness to other nodes, signal strength and distance from the neighbouring nodes. Simulations of the novel protocol demonstrate that it can keep the packet delivery ratio high with little overhead and low delay. FTRP has significant implications for deploying Vehicular Cloud Networks using electric vehicle technologies in smart cities. The routing data is collected with the help of Internet of Technology (IOT) sensors. The information is transmitted between vehicles using IOT gateways. 

Fuzzy logic‐based trusted routing protocol using vehicular cloud networks for smart cities

The introduction of the study primarily emphasises the significance of utilising block chain technologies with the possibility of privacy and security benefits from the 5G Network. One may state that the study’s primary focus is on all the advantages of adopting block chain technology to safeguard everyone’s access to crucial data by utilizing intelligent contracts to enhance the 5G network security model on information security operations.Our literature evaluation for the study focuses primarily on the advantages advantages of utilizing block chain technology advance data security and privacy, as well as their development and growth. The whole study paper has covered both the benefits and drawbacks of employing the block chain technology. The literature study part of this research article has, on the contrary hand, also studied several approaches and tactics for using the blockchain technology facilities. To fully understand the circumstances in this specific case, a poll was undertaken. It was possible for the researchers to get some real-world data in this specific situation by conducting a survey with 51 randomly selected participants. 

The Block Chain Technology to protect Data Access using Intelligent Contracts Mechanism Security Framework for 5G Networks

Wireless sensor networks (WSNs) and the Internet of Things (IoT) are increasingly making an impact in a wide range of domain-specific applications. In IoT-integrated WSNs, nodes generally function with limited battery units and, hence, energy efficiency is considered as the main design challenge. For homogeneous WSNs, several routing techniques based on clusters are available, but only a few of them are focused on energy-efficient heterogeneous WSNs (HWSNs). However, security provisioning in end-to-end communication is the main design challenge in HWSNs. This research work presents an energy optimizing secure routing scheme for IoT application in heterogeneous WSNs. In our proposed scheme, secure routing is established for confidential data of the IoT through sensor nodes with heterogeneous energy using the multipath link routing protocol (MLRP). After establishing the secure routing, the energy and network lifetime is improved using the hybrid-based TEEN (H-TEEN) protocol, which also has load balancing capacity. Furthermore, the data storage capacity is improved using the ubiquitous data storage protocol (U-DSP). This routing protocol has been implemented and compared with two other existing routing protocols, and it shows an improvement in performance parameters such as throughput, energy efficiency, end-to-end delay, network lifetime and data storage capacity.

/pdf/secure-routing-based-energy-optimization-for-iot-application-2tqkf04x.pdf

Secure Routing-Based Energy Optimization for IoT Application with Heterogeneous Wireless Sensor Networks

In a variety of medical applications, electrocardiogram (ECG) records are essential for the identification of cardiovascular disorders. Cardiovascular issues such cardiac arrhythmias and coronary heart disease (CHD) are among the most prevalent and can result in cardiac arrest or sudden cardiac death. This study employs ECG signal preprocessing and feature extraction to identify cardiac arrhythmias and evaluate CHD risk. This research stresses the use of a Support Vector Machine (SVM) classifier for cardiac arrhythmia identification after ECG signal preprocessing. The preprocessed ECG signal is then subjected to arrhythmic beat classification to find anomalies. Extracted R-peaks from the ECG signal are divided into normal and arrhythmic risk subjects using the SVM classification-based approach for abnormality identification. When compared to other similar classifiers, the K-Nearest Neighbor (KNN) classifier offers the highest classification accuracy of 97.5%.

ECG Signal Feature Extraction and SVM Classifier Based Cardiac Arrhythmia Detection

This paper primarily designates the isolated words transfiguration in the Kannada language based on a few techniques like Mel Frequency Cepstral Coefficients (MFCCs), Euclidean distance measure, Wavelet Transforms and Vector Quantization (VQ) technique. The contiguous match to the test speech exclamation is found by comparing the Euclidean distance value of the MFCC coefficients with that of the utterances present in the training set. The matched sampled word is detected by the display of its text equivalent message trained during the test phase. MATLAB R2019b software is employed to build the framework.

Speech-to-text Transfiguration in Language Numerals for Perpetual Deaf Patients

A botnet is a malware application controlled from a distance by a programmer with the assistance of a botmaster. Botnets can launch enormous cyber-attacks like Denial-of-Service (DOS), phishing, spam, data stealing, and identity theft. The botnet can also affect the security and privacy of the systems. The conventional approach to detecting botnets is made by signature-based analysis, which cannot discover botnets that are not visible. The behavior-based analysis appears to be an appropriate solution to the current botnet characteristics that are constantly developing. This paper aims to develop an efficient botnet detection algorithm using machine learning with traffic reduction to increase accuracy. Based on behavioural analysis, a traffic reduction strategy is applied to reduce network traffic to improve overall system performance. Several network devices are typically used to retrieve network traffic information. With a detection accuracy of 98.4%, the known and unknown botnet activities are measured using the supplied datasets. The machine learning-based traffic reduction system has achieved a high rate of traffic reduction, about 82%, and false-positive rates range between 0% to 2%. Both findings demonstrate that the suggested technique is efficient and accurate. 

Hybrid Machine Learning Technique to Detect Active Botnet Attacks for Network Security and Privacy

Enhanced Network Performance in WSNs through the Improved PSO-MBO Coverage Algorithm

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