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ABSTRACT The rapid proliferation of Internet of Things (IoT) devices has underscored the critical need to safeguard the data they store and transmit. Among various data types, digital images often carry highly sensitive information, making their protection against breaches essential. This study introduces a novel image encryption algorithm specifically designed to bolster the security of images in resource‐constrained IoT ecosystems. Leveraging the randomness of a 5D multi‐wing hyperchaotic map, the proposed method employs pairs of non‐overlapping rectangles to induce confusion by swapping the pixels they encompass. Repeated iterations of this operation achieve significant confusion effects, enhancing encryption strength. To validate the robustness of the proposed algorithm, standard benchmark images were utilized, and rigorous security metrics —including information entropy, correlation coefficient, histogram uniformity, and resistance to differential attacks —were analyzed. Results demonstrate that the algorithm not only ensures strong protection against unauthorized access but also maintains low computational complexity, making it ideal for IoT applications. This research provides a foundational step toward ensuring the confidentiality and integrity of visual data in an increasingly interconnected digital world.

ABSTRACT Malware growth has accelerated due to the widespread use of Android applications. Android smartphone attacks have increased due to the widespread use of these devices. While deep learning models offer high efficiency and accuracy, training them on large and complex datasets is computationally expensive. Hence, a method that effectively detects new malware variants at a low computational cost is required. A transfer learning method to detect Android malware is proposed in this research. Because of transferring known features from a source model that has been trained to a target model, the transfer learning approach reduces the need for new training data and minimizes the need for huge amounts of computational power. We performed many experiments on 1.2 million Android application samples for performance evaluation. In addition, we evaluated how well our framework performed in comparison with traditional deep learning and standard machine learning models. In comparison with state‐of‐the‐art Android malware detection methods, the proposed framework offers improved classification accuracy of 98.87%, a precision of 99.55%, recall of 97.30%, F1‐measure of 99.42%, and a quicker detection rate of 5.14 ms using the transfer learning strategy.

The Internet of Things (IoT) can enable seamless communication between millions of billions of objects. As IoT applications continue to grow, they face several challenges, including high latency, limited processing and storage capacity, and network failures. To address these stated challenges, the fog computing paradigm has been introduced, purpose is to integrate the cloud computing paradigm with IoT to bring the cloud resources closer to the IoT devices. Thus, it extends the computing, storage, and networking facilities toward the edge of the network. However, data processing and storage occur at the IoT devices themselves in the fog-based IoT network, eliminating the need to transmit the data to the cloud. Further, it also provides a faster response as compared to the cloud. Unfortunately, the characteristics of fog-based IoT networks arise traditional real-time security challenges, which may increase severe concern to the end-users. However, this paper aims to focus on fog-based IoT communication, targeting real-time security challenges. In this paper, we examine the layered architecture of fog-based IoT networks along working of IoT applications operating within the context of the fog computing paradigm. Moreover, we highlight real-time security challenges and explore several existing solutions proposed to tackle these challenges. In the end, we investigate the research challenges that need to be addressed and explore potential future research directions that should be followed by the research community.

Abstract The health of the hospital associated persons, particularly those dealing directly with insertion of devices, are serious cause of concern for hospitals. In this study, the most prevalent organism on the surface of medical devices in PICU were CoNS (16.66%) and Staphylococcus aureus (16.66%), while in NICU the most prevalent organism was Klebsiella spp. (11.25%) among Entero-bacteriaceae group followed by Acinetobacter baumannii (10%), Escherichia coli (2.5%), CoNS (6.25%), S. aureus (6.25%) and Enterococcus faecalis (6.25%). The most common species identified from blood specimen of clinical samples shows the maximum presence of Candida sp. (60/135) followed by A. baumannii (21/135), Klebsiella Pneumoniae (20/135), Enterococci (12/135), Burkholderia cepacia complex (8/135), S. aureus (6/135), E. coli (5/135), Pseudomonas aeruginosa (3/135). Different antibiotics have been used against these micro-organisms; but Cotrimoxazole, Vancomycin have been found more effective against CoNS bacteria, Clindamycin, Tetracycline for S. aureus, Nitofurantoin for Acinetobacter, and for E. faecalis, A. baumanii, and Klebsiella, erythromycin, Colistin, and Ceftriaxone have been found more effective respectively.

Energy-efficient and reliable data gathering using highly stable links in underwater wireless sensor networks (UWSNs) is challenging because of time and location-dependent communication characteristics of the acoustic channel. In this paper, we propose a novel dynamic firefly mating optimization inspired routing scheme called FFRP for the internet of UWSNs-based events monitoring applications. The proposed FFRP scheme during the events data gathering employs a self-learning based dynamic firefly mating optimization intelligence to find the highly stable and reliable routing paths to route packets around connectivity voids and shadow zones in UWSNs. The proposed scheme during conveying information minimizes the high energy consumption and latency issues by balancing the data traffic load evenly in a large-scale network. In additions, the data transmission over highly stable links between acoustic nodes increases the overall packets delivery ratio and network throughput in UWSNs. Several simulation experiments are carried out to verify the effectiveness of the proposed scheme against the existing schemes through NS2 and AquaSim 2.0 in UWSNs. The experimental outcomes show the better performance of the developed protocol in terms of high packets delivery ratio (PDR) and network throughput (NT) with low latency and energy consumption (EC) compared to existing routing protocols in UWSNs.

Smart Grid Industry 4.0 (SGI4.0) defines a new paradigm to provide high-quality electricity at a low cost by reacting quickly and effectively to changing energy demands in the highly volatile global markets. However, in SGI4.0, the reliable and efficient gathering and transmission of the observed information from the Internet of Things (IoT)-enabled Cyber-physical systems, such as sensors located in remote places to the control center is the biggest challenge for the Industrial Multichannel Wireless Sensors Networks (IMWSNs). This is due to the harsh nature of the smart grid environment that causes high noise, signal fading, multipath effects, heat, and electromagnetic interference, which reduces the transmission quality and trigger errors in the IMWSNs. Thus, an efficient monitoring and real-time control of unexpected changes in the power generation and distribution processes is essential to guarantee the quality of service (QoS) requirements in the smart grid. In this context, this paper describes the dataset contains measurements acquired by the IMWSNs during events monitoring and control in the smart grid. This work provides an updated detail comparison of our proposed work, including channel detection, channel assignment, and packets forwarding algorithms, collectively called CARP [1] with existing G-RPL [2] and EQSHC [3] schemes in the smart grid. The experimental outcomes show that the dataset and is useful for the design, development, testing, and validation of algorithms for real-time events monitoring and control applications in the smart grid.

The pH-metric studies on the interaction of divalent metal cations, Co (II), Ni (II) and Cu (II) with lomatiol have been performed under the thermodynamic conditions achievable at constant ionic strength and infinite dilution in which a mole of divalent metal, combined with 02 mol of lomatiol. Lomatiol being bidentate could replace two water molecules at a time. The values of stepwise formation constants showed no conspicuous difference at 25 °C and 35 °C, and the complexes, registered a decrease in their stability values suggesting the low temperature as a favorable condition under which the chelation of metal with lomatiol is feasible. The reaction takes place without the use of any specific catalyst although under alkaline conditions. The reactions of lomatiol with metal cations in solution had been adjudged as the spontaneous reaction on account of the negative ΔG value in all the metal lomatiol systems. Also the study showed the positive experimental values of entropy changes in all the systems and negative value of free energy. The continuous fall in the values of the stepwise formation constants (k1 > k2 > k3) in the divalent metal-lomatiol system has been assigned to the statistical factor.

Aim of studyThe shape effects of nanoparticles are very significant in fluid flow and heat transfer. In this paper, we discuss the effects of nanoparticles shape in nanofluid flow between divergent-convergent channels theoretically. In this present study, various shapes of nanoparticles, namely sphere, column and lamina in zinc oxide-water nanofluid are used. The effect of the magnetic field and joule dissipation are also considered.Research methodologyThe system of nonlinear partial differential equations (PDEs) is converted into ordinary differential equations (ODES). The analytical solutions are successfully obtained and compared with numerical solutions. The Homotopy perturbation method and NDsolve method are used to compare analytical and numerical results respectively.ConclusionThe results show that the lamina shape nanoparticles have higher performance in temperature disturbance and rate of heat transfer as compared to other shapes of nanoparticles.

Chrysoidine is a type of industrial azo dye and a well-known toxicant. Due to its good dyeing characteristics, it is widely used for dyeing leather, paper, feather, grass, wood, bamboo, etc. Hence, it is very important to remove or reduce its concentration below the contamination level in the waste line by using low-cost technologies. Sawdust is a plentiful material available very cheaply from sawmills and woodworks. Therefore, the present work was conducted to study sorption ability of both raw sawdust and chemically activated sawdust carbon on the removal of chrysoidine from the aqueous solutions. Adsorption isotherms of the dye on sawdust were determined and correlated with usual isotherm equations like Freundlich and Langmuir. Experimental results have shown that sawdust has a high adsorption efficiency, and the adsorption of chrysoidine followed Freundlich’s isotherm. Although raw sawdust proved to be slightly less efficient in comparison to chemically treated sawdust but in economic terms, raw sawdust is more cost-effective as the difference in the percent dye removal is less than the difference in the manufacturing costs. The influence of several parameters such as effect of temperature, adsorbent dose, adsorption time, etc., on the adsorption process was studied along with thermodynamic parameters such as enthalpy (ΔH°) and entropy (ΔS°).

Quality of service (QoS)-aware data gathering in static-channel based underwater wireless sensor networks (UWSNs) is severely limited due to location and time-dependent acoustic channel communication characteristics. This paper proposes a novel cross-layer QoS-aware multichannel routing protocol called QoSRP for the internet of UWSNs-based time-critical marine monitoring applications. The proposed QoSRP scheme considers the unique characteristics of the acoustic communication in highly dynamic network topology during gathering and relaying events data towards the sink. The proposed QoSRP scheme during the time-critical events data-gathering process employs three basic mechanisms, namely underwater channel detection (UWCD), underwater channel assignment (UWCA) and underwater packets forwarding (UWPF). The UWCD mechanism finds the vacant channels with a high probability of detection and low probability of missed detection and false alarms. The UWCA scheme assigns high data rates channels to acoustic sensor nodes (ASNs) with longer idle probability in a robust manner. Lastly, the UWPF mechanism during conveying information avoids congestion, data path loops and balances the data traffic load in UWSNs. The QoSRP scheme is validated through extensive simulations conducted by NS2 and AquaSim 2.0 in underwater environments (UWEs). The simulation results reveal that the QoSRP protocol performs better compared to existing routing schemes in UWSNs.

Network survivability endeavors to ensure the uninterrupted provisioning of services by the network operators in case of a disaster event. Studies and news reports show that network failures caused by physical attacks and natural disasters have significant impacts on the optical networks. Such network failures may lead to a section of a network to cease to function, resulting in non-availability of services and may increase the congestion within the rest of the network. Therefore, fault tolerant and disaster-resilient optical networks have grasped the attention of the research community and have been a critical concern in network studies during the last decade. Several studies on protection and restoration techniques have been conducted to address the network component failures. This study reviews related previous research studies to critically discuss the issues regarding protection, restoration, cascading failures, disaster-based failures, and congestion-aware routing. We have also focused on the problem of simultaneous cascading failures (which may disturb the data traffic within a layer or disrupt the services at upper layers) along with their mitigating techniques, and disaster-aware network survivability. Since traffic floods and network congestion are pertinent problems, they have therefore been discussed in a separate section. In the end, we have highlighted some open issues in the disaster-resilient network survivability for research challenges and discussed them along with their possible solutions.

The levels of selected heavy metals in popular cigarette brands sold and/or produced in Saudi Arabia were determined by graphite furnace-atomic absorption spectrometry (GFAAS). Average concentrations of Cadmium and Lead in different cigarette brands were 1.81 and 2.46 μg g−1 (dry weight), respectively. The results obtained in this study estimate the average quantity of Cd inhaled from smoking one packet of 20 cigarettes to be in the range of 0.22–0.78 μg. Results suggest that the quantity of Pb inhaled of smoking one packet of 20 cigarettes is estimated to be 0.97–2.64 μg. The concentrations of Cd and Pb in cigarettes were significantly different between cigarette brands tested. The results of the present study were compared with those of other regional and international studies.