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Abstract Cardiac monitoring systems in Internet of Things (IoT) healthcare, reliant on limited battery and computational capacity, need efficient local processing and wireless transmission for comprehensive analysis. Due to the power-intensive wireless transmission in IoT devices, ECG signal compression is essential to minimize data transfer. This paper presents a real-time, low-complexity algorithm for compressing electrocardiogram (ECG) signals. The algorithm uses just nine arithmetic operations per ECG sample point, generating a hybrid Pulse Width Modulation (PWM) signal storable in a compact 4-bit resolution format. Despite its simplicity, it performs comparably to existing methods in terms of Percentage Root-Mean-Square Difference (PRD) and space-saving while significantly reducing complexity and maintaining robustness against signal noise. It achieves an average Bit Compression Ratio (BCR) of 4 and space savings of 90.4% for ECG signals in the MIT-BIH database, with a PRD of 0.33% and a Quality Score (QS) of 12. The reconstructed signal shows no adverse effects on QRS complex detection and heart rate variability, preserving both the signal amplitude and periodicity. This efficient method for transferring ECG data from wearable devices enables real-time cardiac activity monitoring with reduced data storage requirements. Its versatility suggests potential broader applications, extending to compression of various signal types beyond ECG.

Abstract Traffic in urban areas contributes significantly to congestion and air pollution, which contributes to climate change issues and causes economic losses and fuel wastage. Agent-based models have significant advantages for analyzing urban transportation and its sustainability. The main objective of this paper is to provide a critical review of research on agent-based models for traffic simulation in urban areas. This article reviews the literature on the subject and examines earlier case studies that dealt with agent-based models for micro-mobility and traffic simulation considering six criteria. The study analyzes multiple publications obtained from databases such as Google Scholar, Scopus, and Web of Science. These publications span from 2014 to 2022 and are scrutinized to fulfill the stated objectives. Furthermore, a thorough critical evaluation is performed on a chosen set of 16 publications. The research also proposes traffic simulation tools based on insights gathered from case studies. Further, it discusses how to choose a decent data set through a balanced and objective summary of study findings on the topic and recommends future work in this topic.

Streamflow forecasting is crucial for effective water resource planning and early warning systems, especially in regions with complex hydrological behaviors and uncertainties. While machine learning (ML) has gained popularity for streamflow prediction, many studies have overlooked the predictability of future events considering anthropogenic, static physiographic, and dynamic climate variables. This study, for the first time, used a modified generative adversarial network (GAN) based model to predict streamflow. The adversarial training concept modifies and enhances the existing data to embed featureful information enough to capture extreme events rather than generating synthetic data instances. The model was trained using (sparse data) a combination of anthropogenic, static physiographic, and dynamic climate variables obtained from an ungauged basin to predict monthly streamflow. The GAN-based model was interpreted for the first time using local interpretable model-agnostic explanations (LIME), explaining the decision-making process of the GAN-based model. The GAN-based model achieved R2 from 0.933 to 0.942 in training and 0.93–0.94 in testing. Also, the extreme events in the testing period have been reasonably well captured. The LIME explanations generally adhere to the physical explanations provided by related work. This approach looks promising as it worked well with sparse data from an ungauged basin. The authors suggest this approach for future research work that focuses on machine learning-based streamflow predictions.

Water is a natural and essential resource for humans, animals, and plants to persist. However, only ⁓2.5 % of the freshwater resources are available, while the remaining ⁓97.5 % is saline water, which is unsuitable for humanity. According to the WHO, water scarcity will worsen by 2050. As a result, numerous researchers, scientists, and engineers are working in this field to improve water resources with advanced treatment technologies. Aside from the multiple water resources, desalination is critical in converting saline water to fresh water. In line with a recent update from the International Desalination Association (IDA, Reuse Handbook 2022–23), approximately ⁓22,757 desalination plants are operating worldwide, providing ⁓107.95 million cubic meters of freshwater per day (m3/day). Furthermore, in this digital age, artificial intelligence (AI) techniques, such as gray wolf optimization (GWO), sine cosine algorithm (SCA), artificial neural networks (ANN), multi-verse optimizer (MVO), fuzzy logic systems (FLS), moth flame optimizer (MFO), particle swarm optimization (PSO), artificial hummingbird algorithm (AHA) and genetic algorithms (GA), are playing a vital role and capable of deep analysis of real-time desalination plant for saving time, energy, human efforts, and money. This study focuses on the critical review and various aspects of current-age PSO-ANN techniques for desalination plants. In this regard, recent datasets of the Web of Science (WoS), provided by Clarivate Analytics, state that about >54,856 records (1965–2023) of desalination and around > 180 records (2008–2023) of PSO-ANN techniques are available globally. These records involve research articles, reviews, proceedings, letters, books, chapters, and editorial materials. Finally, this review article is specific and analyzes the various perspectives of PSO-ANN techniques in the water desalination process, promoting plant engineers and researchers to improve plant performance with minimum effort and time.

The term “Swarm intelligence” outlines a broad scope, which is generally defined as the collective behaviour of many individuals towards a certain task, each operating autonomously in a decentralised manner. Swarms are inspired by the type of biological behaviours of animals and this technology involves decentralised local control and communication, which ensures the problem-solving process is more efficient through modification and infusion into swarm robotic technology. Swarm robotics related experiments and applications are relatively expensive compared to other types of robotics experiments. The most common solution is to use computer based virtual simulations and accordingly, this study introduces a reliable and cost-effective hybrid solution using Mixed Reality (MR) technologies to perform swarm experiments on real robots and virtually deployed robots simultaneously. MR bridges the gap between the virtual and physical realms through Augmented Reality (AR) and Augmented Virtuality (AV) and addressing the weaknesses of simulators and providing a more accurate representation of real-world performance. This approach provides a platform for researchers to explore swarm robotics with enhanced realism and interactivity and conduct experiments that mimic real-world conditions. To validate this method we conducted experiments with both physical and virtual robots in near real-time in a mixed reality environment. In addition, an open-source, distributed, and modular mixed reality simulation framework was implemented with support libraries and applications. Our work not only demonstrates the potential of MR in swarm robotics but also emphasizes its value in advancing the understanding and application of swarm intelligence principles in practical contexts.

The authentication of digital images poses a significant challenge due to the wide range of image forgery techniques employed, with one notable example being a copy-move forgery. This form of forgery involves duplicating and relocating segments of an image within the same image, often accompanied by geometric transformations to deceive viewers into perceiving the forged image as authentic. Furthermore, additional processing techniques like scaling, rotation, JPEG compression, and the application of Additive White Gaussian Noise (AWGN) are frequently employed to further obscure any traces of forgery, making the detection and verification process even more complex. This paper presents a novel approach for detecting copy-move forgery in digital images using the self-supervised image keypoint detector, SuperPoint. Our approach leverages the advanced capabilities of SuperPoint, which combines keypoint detection and descriptor extraction, to identify and localize copy-move forgery accurately. One important aspect of our approach is its ability to handle images with different textures, including smooth and self-similar structural images. The proposed approach is able to produce stable results in images with various attacks, making it a functional and reliable tool for detecting copy-move forgery in a diverse range of forged images. Comparative analysis with existing forgery detection methods shows the superior performance of our proposed approach. Furthermore, the computational efficiency of our algorithm enables real-time forgery detection. Our approach using SuperPoint offers an effective solution for detecting copy-move forgery in digital images, making it valuable for image forensics and authenticity

The paper manufacturing process is a large-scale process in which the control performance is required to be optimal in the machine direction (MD) and cross direction (CD). This work focuses on CD control, which is more complicated than MD control. Uncertainties in the process arise from different sources, so there is a requirement for better modeling techniques. The control of paper profile properties, namely basis weight, moisture, and caliper, is done with the help of actuators that are distributed spatially. Basis weight is taken as the profile parameter in this work. The work aims to improve robust performance and simplify the control design problem. Here, $\mu $ synthesis methods are used for controller design, as this method provides guaranteed robust performance. To simplify the design problem two-dimensional modeling is explained explicitly. The conventional methods of $\mu $ analysis are extended to spatial coordinates. The performance of the $\mu $ synthesis designed method is compared with the open loop shaping method. Control performance is judged in terms of sensitivity, stability margin, $\sigma $ performance, and robust performance. The effectiveness of the developed $\mu $ synthesis designed method is demonstrated using the industrial example from the pulp and paper industry.

The development of smart grids has paved the way for sustainable energy infrastructure to transition towards decentralized energy trading. As intelligent agents, energy sources engage in energy trading based on their energy surplus or deficit. Buyers and sellers (participants) should achieve maximum payoffs in which buyers cut costs and sellers improve their utilities, and the security of sensitive information of smart agents must be guaranteed. This paper provides a blockchain-based energy trading network where intelligent agents can exchange energy in a secure manner, without the intervention of third parties. We model energy trading as a Stackelberg game, ensuring that the platform maximizes social welfare while participants increase their payoffs. Using the inherited characteristics of blockchain technology, a novel decentralized swarm intelligence technique is applied to solve the game while ensuring the privacy of the smart agents’ sensitive information. The numerical analysis demonstrates that the suggested method outperforms the present methods (Constant Utility Optimization, average method...) for optimizing the objectives of each agent by maximizing the sellers’ utilities and reducing the buyers’ costs. In addition, the experimental results demonstrate that it significantly reduces carbon footprint (15%) by enhancing energy exchange between intelligent agents.

A two-party authenticated key exchange (AKE) protocol allows each of the two parties to share a common secret key over insecure channels, even in the presence of active adversaries who can actively control and modify the exchanged messages. To capture the malicious behaviors of the adversaries, there have been many efforts to define security models. Amongst them, the extended Canetti–Krawczyk (eCK) security model is considered one of the strongest security models and has been widely adopted. In this paper, we present a simple construction of a pairing-based eCK-secure AKE protocol in the standard model. Our protocol can be instantiated with a suitable signature scheme (i.e., an existentially unforgeable signature scheme against adaptive chosen message attacks). The underlying assumptions of our construction are the decisional bilinear Diffie–Hellman assumption and the existence of a pseudorandom function. Note that the previous eCK-secure protocol constructions either relied on random oracles for their security or used somewhat strong assumptions, such as the existence of strong-pseudorandom functions, target collision-resistant functions, etc., while our protocol construction uses fewer and more-standard assumptions in the standard model. Furthermore, preserving the same security argument, our protocol can be instantiated with any appropriate signature scheme that comes in the future with better efficiency.

Lattice-based cryptography is centered around the hardness of problems on lattices. A lattice is a grid of points that stretches to infinity. With the development of quantum computers, existing cryptographic schemes are at risk because the underlying mathematical problems can, in theory, be easily solved by quantum computers. Since lattice-based mathematical problems are hard to be solved even by quantum computers, lattice-based cryptography is a promising foundation for future cryptographic schemes. In this paper, we focus on lattice-based public-key encryption schemes. This survey presents the current status of the lattice-based public-key encryption schemes and discusses the existing implementations. Our main focus is the learning with errors problem (LWE problem) and its implementations. In this paper, the plain lattice implementations and variants with special algebraic structures such as ring-based variants are discussed. Additionally, we describe a class of lattice-based functions called lattice trapdoors and their applications.