Vikash Kumar Mishra

Image steganography is an information-hiding technique, aiming to achieve confidentiality and data privacy while transmitting the data in a digital environment. Over the last two decades, steganography has evolved from classical spatial domain embedding to intelligent and adaptive steganographic systems capable of balancing imperceptibility, embedding capacity, robustness and security. This paper presents a review by categorizing the existing techniques into spatial domain-based, transform domain-based, hybrid and adaptive intelligent techniques. The review follows the PRISMA approach to make the selection process transparent for the inclusion and exclusion of papers in the study. Initially, the reviews include the spatial domain-based methods focusing on higher embedding capacity and simple embedding strategy, followed by transform-domain based techniques, including discrete cosine transform, discrete wavelet transform, and other multi-resolution wavelet transforms aiming to enhance robustness and imperceptibility by embedding the data into frequency coefficients. This paper further explores the methods that combine these techniques with other recent trends to develop adaptive and hybrid techniques. These techniques mainly integrate chaotic theory to enhance the security of secret data before embedding and optimization algorithms such as genetic algorithm, particle swarm optimization, Firefly, etc., for adaptive embedding to achieve an improved tradeoff. Finally, intelligent and adaptive techniques based on deep learning models such as convolutional neural networks, autoencoders, and generative adversarial networks are examined, highlighting their ability to learn intelligent embedding strategies and resist modern steganalysis. A comparative analysis is presented, including the technique, strengths, and limitations, together with the discussion of performance evaluation metrics and vulnerability analysis under image processing attacks. The review highlights the current trends and outlines the future direction to develop next-generation secure image steganographic systems.

In this study, we propose a wearable fall detection system that combines wearable sensors, TinyML model, and IoT-based communication for real-time monitoring and detection of falls. The system is designed for resource-constrained IoT devices where memory, power, and processing capacity are limited. The system works by collecting body motion data using accelerometer sensors placed on the human body. The data is then processed using a feedforward neural network trained on preprocessed signals. The trained model is quantized so that it can run on low-power embedded hardware with small memory size. The model performs inference directly on the device. This reduces latency and avoids sending raw sensor data to the cloud. When a fall is detected, the result is sent through Bluetooth to a gateway. The gateway forwards the data to a cloud server using the MQTT protocol. The cloud stores the data and supports monitoring and analysis. The experimental results show that the quantized TinyML model achieves 98.40% accuracy with more than 80% F1-score and more than 99% recall. The deployed model uses only ∼5 KB of RAM and ∼40 KB of flash memory. The inference time is 7 ms per class. These results show that wearable sensing with quantized TinyML models and IoT communication can provide fast and reliable fall detection for real-world safety monitoring systems.

The red tides directly damage marine species and ecosystems and have negative consequences affecting the fishing industry, fishing communities, seafood consumers and many others indirectly. This article deals with the early prediction of red tide incidents using satellite images. Due to the high spatial resolution, Sentinel-2 imagery is used to estimate the effective period of the red tide. The higher temporal resolution of Moderate Resolution Imaging Spectroradiometer (MODIS) motivates using it to find the exact date when the red tide started. A novel spectral index Normalized Diffrence Red Tide Index i.e., NDRTI_Sentinel-2 is proposed for Sentinel-2 imagery, and is compared with the following spectral indices: Algal Bloom Detection Index (ABDI), Adjusted Floating Algae Index (AFAI), Floating Algae Index (FAI), and Surface Algal Bloom Index (SABI). The indices were compared for the extraction of red tide and similar features. It is observed that the proposed index outperforms the other indices on the extraction of the red tide with an accuracy of 94.47% and kappa coefficient K=0.92. It is also found that the proposed index has a broader range of thresholds than others, which makes it efficient in segmenting the two features, namely red tide and water. An algorithm using the spectral response curve (SRC) of Sentinel-2 data is designed and implemented to find the threshold for water and red tide features. The effective period using NDRTI_Sentinel-2 and the Sentinel-2 data set is identified as February 02 to February 12, 2023. Another novel index, Normalized Difference Red Tide Index i.e., NDRTIMODIS using SRC, is proposed for the MODIS data. The existing indices ABDI, AFAI, FAI, and SABI cannot enhance the red tide feature in MODIS imagery. In contrast, the proposed index enhances the red tide feature and provides a broader suitable threshold range for the red tide feature. The NDRTIMODIS feature is applied on the periodic data, backtracking from the reference date, and red tide is enhanced and extracted until the red tide is not detected. The NDRTI_MODIS and MODIS data sets predict the occurrence of red tide eleven days before the reference date.

A crucial component of the Internet of Things (IoT) is the wireless sensor network (WSN), which comprises numerous sensor nodes detecting various atmospheric variations such as temperature, pressure and humidity. These sensor nodes rely on internal batteries to power their sensors and communicate data with other nodes within the network. Given the significance of energy consumption in WSN nodes, there is a growing focus on developing advanced models to address network challenges by minimizing overhead and optimizing energy efficiency. This paper introduces a novel approach that combines fuzzy logic with the Energy‐Efficient Fuzzy Grey Wolf Optimization (EE‐FGO) to facilitate cluster formation in determining optimal solutions for selecting aggregation points using cluster heads (CH) and computing the most efficient data transmission path from CHs to the base station (BS) inside the network. By strategically selecting multiple aggregation points, the proposed approach aims to maximise the node’s lifetime. Simulation results illustrate that the EE‐FGO approach outperforms several existing protocols, leading to significant network lifetime extension.

Water pollution is a growing concern for aquatic ecosystems worldwide, with threats like plastic waste, nutrient pollution, and oil spills harming biodiversity and impacting human health, fisheries, and local economies. Traditional methods of monitoring water quality, such as ground sampling, are often limited in how frequently and widely they can collect data. Satellite imagery is a potent tool in offering broader and more consistent coverage. This review explores how Multispectral Imagery (MSI) and Synthetic Aperture Radar (SAR), including polarimetric SAR (PolSAR), are utilised to monitor harmful algal blooms (HABs) and other types of aquatic pollution. It looks at recent advancements in satellite sensor technologies, highlights the value of combining different data sources (like MSI and SAR), and discusses the growing use of artificial intelligence for analysing satellite data. Real-world examples from places like Lake Erie, Vembanad Lake in India, and Korea’s coastal waters show how satellite tools such as the Geostationary Ocean Colour Imager (GOCI) and Environmental Sample Processor (ESP) are being used to track seasonal changes in water quality and support early warning systems. While satellite monitoring still faces challenges like interference from clouds or water turbidity, continued progress in sensor design, data fusion, and policy support is helping make remote sensing a key part of managing water health.

Algal blooms are ecological phenomena with long-lasting effects on the ecosystem and on the climate. Often, they reduce the oxygen level underwater, creating adverse circumstances for aquatic species’ survival, development, and reproduction. In this article, the mapping of algal bloom incidents and their daily monitoring is automated using Python script and the Earthdata website. The automation is carried out in eight separate modules and then integrated. Test site dictionary, configuration, query data, download MODIS data, open image data, clip data, implementing a novel Normalized Difference MODIS Algae Index (NDMAI), and threshold are the eight modules used for automating the extraction and daily monitoring. This automation requires two inputs: firstly, the bounding box, i.e., lower left coordinate (LLC) and upper right coordinate (URC) of the test site, and secondly, the date range. In this article, eight test sites are used to extract algal bloom incidents, and a ninth test site is used for the extraction and daily monitoring, which are reported by the NASA Earth Observatory (NEO). The proposed framework automates the process of enhancing algal bloom features in MODIS imagery, and daily monitoring is successfully accomplished, and the results perfectly match the algal bloom region in the test sites reported by the NEO.

Advancements in intelligent vehicular networks and computing systems have created new possibilities for innovative approaches that enhance traffic safety, comfort, and transportation performance. Machine Learning (ML) has become widely employed for boosting conventional data-driven methodologies in various scientific study domains. The integration of a Vehicle-to-Everything (V2X) system with ML enables the acquisition of knowledge from multiple places, enhances the operator’s awareness, and predicts future crashes to prevent them. The information serves multiple functions, such as determining the most efficient route, increasing the driver’s knowledge, forecasting movement strategy to avoid risky circumstances, and eventually improving user convenience, security, and overall highway experiences. This article thoroughly examines Artificial Intelligence (AI) and ML methods that are now investigated through different study endeavors in vehicular ad hoc networks (VANETs). Furthermore, it examines the benefits and drawbacks accompanying such intelligent methods in the context of the VANETs system and simulation tools. Ultimately, this study pinpoints prospective domains for vehicular network development that can utilize the capabilities of AI and ML.

In wireless sensor networks (WSNs), optimising energy efficiency while maintaining coverage and managing resource constraints remains a critical challenge. This paper introduces a novel Region‐Based Multilevel Energy Efficiency Protocol (RBMEEP), which innovatively partitions the network into regions and sub‐regions to enhance energy utilisation through optimised clustering and communication with the base station (BS). Unlike conventional protocols, RBMEEP significantly extends network lifetime, outperforming the Stable Election Protocol (SEP). The novelty lies in the integration of a Regression Prediction Model (RPM), which accurately predicts network lifetime based on node density and packet size. Simulation results demonstrate the model's high prediction accuracy, with up to 99.94% in smaller network areas and 99.87% in larger areas. This predictive capability allows for adaptable and efficient WSN design, tailored to specific user requirements. The proposed approach presents a significant advancement in extending the operational life of WSNs, offering a robust solution for energy and coverage optimisation. This work not only improves the theoretical understanding of WSN energy efficiency but also provides a practical framework that can be deployed in real‐world scenarios.