Enhanced Security Technique for Wireless Sensor Network Nodes

The lightweight computational nodes being used in WSN pose particular challenge for many security applications. This paper investigates a number of security techniques and novel implementations appropriate for WSN nodes, including various trade-offs such as implementation complexity, power dissipation, security flexibility and scalabilit

Analysis of TCP Performance over a Low-Delay MAC Protocol Designed for Satellite-based Sensor Networks

Advances in terrestrial network technology such as fibre optic cables have significantly increased data rates and reduced cost, making it highly attractive for high-speed data networks. However, satellite communication remains competitive for certain applications where it has clear advantages over other technologies including fibre optic cables. The point to multipoint broadcast capability of a satellite is an important characteristic that allows multiple sub-networks or nodes to be controlled simultaneously by a single transmission. Similarly, multiple sub-networks or nodes can send data to a central point through a common channel, instead of using multiple point-to-point channels. This facilitates implementation of unique supervisory control and data acquisition systems such as a sensor network to monitor oil and gas pipelines or for agricultural purposes. One important problem in design of a satellite data network is how uncoordinated sources can share the common satellite channel. A multiple access control protocol is required to achieve efficient sharing of the channel while meeting the user traffic constraints. This paper investigates effects TCP performance when used with a new low-delay protocol that integrates Random Access and Bandwidth-on-Demand techniques

Sentiment computation of UK-originated Covid-19 vaccine Tweets: a chronological analysis and news effect.

This study aimed to analyse public sentiments of UK-originated tweets about COVID-19 vaccines using six chronological data periods between January and December 2021. The dates are based on six BBC news reports about the most significant developments in the three main vaccines administered in the UK - Pfizer-BioNTech, Moderna, and Oxford-AstraZeneca. Each data period spans seven days, starting from the news report. The study employed the Bidirectional Encoder Representations from Transformers (BERT) model to analyse the sentiments in the 4,172 extracted tweets. The BERT model adopts the transformer architecture and uses the 'Masked Language Model' and 'Next Sentence Prediction'. The results show that the overall sentiments for all three vaccines were negative across all six periods, with Moderna having the least negative tweets and the highest percentage of positive tweets overall, while AstraZeneca attracted the most negative tweets. However, for all the considered periods, period 3 (23 -29 May 2021) received the least negative and the most positive tweets, following the BBC report – COVID - Pfizer and AstraZeneca jabs work against Indian variant, despite reports of blood clot cases associated with AstraZeneca in the same period. Periods 5 to 6, where there was no breaking news relating to COVID Vaccines, had no significant changes. We, therefore, conclude that the BBC News reports on COVID Vaccines significantly impacted public sentiments regarding the COVID-19 Vaccines

A High Secured Steganalysis using QVDHC Model

Data compression plays a vital role in data security as it saves memory, transfer speed is high, easy to handle and secure. Mainly the compression techniques are categorized into two types. They are lossless, lossy data compression. The data format will be an audio, image, text or video. The main objective is to save memory of using these techniques is to save memory and to preserve data confidentiality, integrity. In this paper, a hybrid approach was proposed which combines Quotient Value Difference (QVD) with Huffman coding. These two methods are more efficient, simple to implement and provides better security to the data. The secret message is encoded using Huffman coding, while the cover image is compressed using QVD. Then the encoded data is embedded into cover image and transferred over the network to receiver. At the receiver end, the data is decompressed to obtain original message. The proposed method shows high level performance when compared to other existing methods with better quality and minimum error

Analysis and Comparison of Routing protocols in MANET using Simulation

In this paper a comparative analysis among Proactive, Reactive and Hybrid routing protocolis presented using simulation.  As we are well aware that a MANET is self-configuring network and most of the real world scenario involving MANET requires individual nodes to route data. Keeping in view MANET is infrastructure less and at times nodes are free to move in different direction, making routing protocol a vital component for network operational effectiveness and efficiency

An artificial intelligence based quorum system for the improvement of the lifespan of sensor networks.

Artificial Intelligence-based Quorum systems are used to solve the energy crisis in real-time wireless sensor networks. They tend to improve the coverage, connectivity, latency, and lifespan of the networks where millions of sensor nodes need to be deployed in a smart grid system. The reality is that sensors may consume more power and reduce the lifetime of the network. This paper proposes a quorum-based grid system where the number of sensors in the quorum is increased without actually increasing quorums themselves, leading to improvements in throughput and latency by 14.23%. The proposed artificial intelligence scheme reduces the network latency due to an increase in time slots over conventional algorithms previously proposed. Secondly, energy consumption is reduced by weighted load balancing, improving the network’s actual lifespan. Our experimental results show that the coverage rate is increased on an average of 11% over the conventional Coverage Contribution Area (CCA), Partial Coverage with Learning Automata (PCLA), and Probabilistic Coverage Protocol (PCP) protocols respectively

Smart Security Implementation for Wireless Sensor Network Nodes

In the territory of concurrent systems such as wireless sensor networks (WSN), the computational nodes being used in wireless sensor networks faces challenges with security applications. Many different security protocols have been proposed that allow some form of security enhancement but not implemented. This article investigates and implements a number of smart security techniques appropriate for WSN nodes with various trade-off such as power consumption and scalability. We provide a brief survey of the major approaches to security prerogative and methods that could reduce if not eliminate algorithmic complexity and denial of service attacks to sensor nodes

A scientometric analysis of deep learning approaches for detecting Fake News

The unregulated proliferation of counterfeit news creation and dissemination that has been seen in recent years poses a constant threat to democracy. Fake news articles have the power to persuade individuals, leaving them perplexed. This scientometric study examined 569 documents from the Scopus database between 2012 and mid-2022 to look for general research trends, publication and citation structures, authorship and collaboration patterns, bibliographic coupling, and productivity patterns in order to identify fake news using deep learning. For this study, Biblioshiny and VOSviewer were used. The findings of this study clearly demonstrate a trend toward an increase in publications since 2016, and this dissemination of fake news is still an issue from a global perspective. Thematic analysis of papers reveals that research topics related to social media for surveillance and monitoring of public attitudes and perceptions, as well as fake news, are crucial but underdeveloped, while studies on deep fake detection, digital contents, digital forensics, and computer vision constitute niche areas. Furthermore, the results show that China and the USA have the strongest international collaboration, despite India writing more articles. This paper also examines the current state of the art in deep learning techniques for fake news detection, with the goal of providing a potential roadmap for researchers interested in undertaking research in this fiel

Time-series data modelling using advanced machine learning and AutoML – experimental work

A prominent area of data analytics is "time-series modeling" where it is possible to forecast future values for the same variable using previous data. Numerous usage examples, including the economy, the weather, stock prices, and the development of a corporation, demonstrate its significance. Experiments with time series forecasting utilizing machine learning (ML), deep learning (DL), and AutoML are conducted in this paper. Its primary contribution consists of addressing the forecasting problem by experimenting with additional ML and DL models and AutoML frameworks and expanding the AutoML experimental knowledge. In addition, it contributes by breaking down barriers found in past experimental studies in this field by using more sophisticated methods. The datasets this empirical research utilized were secondary quantitative of the real prices of the currently most used cryptocurrencies. We found that AutoML for time-series is still in the development stage and necessitates more study to be a viable solution since it was unable to outperform manually designed ML and DL models. The demonstrated approaches may be utilized as a baseline for predicting time-series data

Realizing an Efficient IoMT-Assisted Patient Diet Recommendation System Through Machine Learning Model

Recent studies have shown that robust diets recommended to patients by Dietician or an Artificial Intelligent automated medical diet based cloud system can increase longevity, protect against further disease, and improve the overall quality of life. However, medical personnel are yet to fully understand patient-dietician’s rationale of recommender system. This paper proposes a deep learning solution for health base medical dataset that automatically detects which food should be given to which patient base on the disease and other features like age, gender, weight, calories, protein, fat, sodium, fiber, cholesterol. This research framework is focused on implementing both machine and deep learning algorithms like, logistic regression, naive bayes, Recurrent Neural Network (RNN), Multilayer Perceptron (MLP), Gated Recurrent Units (GRU), and Long Short-Term Memory (LSTM). The medical dataset collected through the internet and hospitals consists of 30 patient’s data with 13 features of different diseases and 1000 products. Product section has 8 features set. The features of these IoMT data were analyzed and further encoded before applying deep and machine and learning-based protocols. The performance of various machine learning and deep learning techniques was carried and the result proves that LSTM technique performs better than other scheme with respect to forecasting accuracy, recall, precision, and $F1$ -measures. We achieved 97.74% accuracy using LSTM deep learning model. Similarly 98% precision, 99% recall and $99\%~F1$ -measure for allowed class is achieved, and for not-allowed class precision is 89%, recall score is 73% and $F1$ Measure score is 80%