Internet of Things (IoT) technologies have permeated many of the devices used in daily life. Nowhere is this more evident than in smart homes, where regular home appliances are now connected to the internet to gain additional functionality, control and automation of routine tasks. However, like most technologies, convenience often comes at the price of risking user privacy. This paper aims to remedy this security risk by providing the user with a scheme to verify their identity in an anonymous manner. The solution needs to be lightweight and fast since smart home devices are resource constraint. The proposed scheme uses two stages to authenticate remote users, the first is using biometric fingerprints locally on the user’s mobile device and the second relies on user identity (username and password) that are sent to the smart home gateway over a secure VPN tunnel between the mobile device and the gateway. The security of the proposed scheme is verified using the scyther tool which finds security flaws in internet protocols. The performance of the scheme is compared with similar existing schemes in terms of computational cost, and it was found that the proposed scheme achieves a computational cost of 0.02 ms and outperforms all previous schemes in those metrics.
Brain Tumors (BTs) are serious medical conditions characterized by abnormal cellular growth in the brain. Magnetic Resonance Imaging (MRI) may be difficult and time-consuming for brain tumor identification and separation. This research automates the brain tumor classification, focusing on 4 diseases: gliomas, meningioma, and pituitary adenomas. Before employing Discrete Wavelet Transform (DWT) for tumor segmentation, MRI images should be converted to grayscale to improve speed and reduce computer complexity. PCA and Gray-Level Co-occurrence Matrix features are used to maximize feature extraction. The obtained features are classified using supervised K-Nearest Neighbours (KNN). The approach was tested using 2870 images, categorized as: 826 glioma, 822 meningioma, 827 pituitary adenoma, and 395 cancer-free images. The recommended approach used PCA and KNN to achieve 83% accuracy, however DWT and PCA together yielded 85% accuracy. The results show that the automated technique can quickly and accurately diagnose brain tumors.
The rapid proliferation of the Internet of Things (IoT) has significantly transformed modern control systems by enabling real-time data acquisition, intelligent decision-making, and seamless connectivity across distributed environments. This paper presents a systematic review of IoT-based control systems, covering studies published between 2015 and 2025, with emphasis on architectural foundations, enabling technologies, control strategies, and major application domains. The review paper analyses five key domains, namely industrial automation, smart manufacturing, healthcare, smart homes and buildings, transportation, and energy and smart grids, while examining major control and optimization approaches integrated with IoT, including data-driven, adaptive, intelligent, and distributed control methods. It also identifies four major cross-cutting challenge categories, namely security and privacy, interoperability and standardization, latency and reliability, and energy efficiency and sustainability, which continue to limit large-scale deployment. In addition, the study highlights eight emerging research directions, including edge-fog-cloud co-design, federated learning, TinyML, digital twins, zero-trust security, and safe learning- based control. The novelty of this review lies in its unified perspective that connects IoT architecture, embedded intelligence, control and optimization frameworks, and cross-domain applications within a single analytical structure. By synthesizing existing literature and revealing key research gaps, this work provides a clearer foundation for developing secure, scalable, and resilient next-generation IoT-driven control systems
Microgrids linked with renewable energy systems represent an essential solution to build sustainable power distribution systems with high resilience. The proposed research contributes new findings to existing SCADA system studies by creating a dynamic power-sharing optimization algorithm that solves energy overproduction issues and enhances inter-microgrid coordination. The authors developed a new framework which uses solar PV systems coupled to batteries and diesel generators to maintain stable power output while solar conditions fluctuate. The designed SCADA system functions through MATLAB/Simulink to operate and optimize power distribution throughout four linked houses microgrids. The suggested optimization algorithm distributes surplus power generated by overproducing microgrids to deficit nodes while keeping real-time demand-supply equilibrium as the top priority. The simulation output shows increased power reliability because total power delivery to households rises by 25.1–42.4% during low-irradiance times (sunrise/sunset). The system achieves lowering dependency on generators by 30-45% through its operations of battery optimization and microgrid power exchange techniques. The research demonstrates how SCADA coordination enables better renewable energy network scalability and energy distribution equality and waste minimization within regions like Baghdad that experience varying solar resources.
Identifying brute-force and dictionary-based login attempts in modern cybersecurity systems has become increasingly challenging, as advanced techniques often fail to detect large-scale intrusion attempts.The aim of this research is to determine the effectiveness of machine learning methods in identifying such attacks in an accurately and efficiently. Two classifiers SVM and GNB, are trained on authentication log data, both with and without PCA for dimensionality reduction.The experimental results indicate that SVM achieves the highest accuracy of 97.24% without PCA and 96.55% with PCA, demonstrating that SVM is robust in high-dimensional feature spaces. Conversely, GNB shows significant with PCA, with accuracy rising from 87.93% to 91.03%, highlighting the importance of feature decorrelation in probabilistic models. The key contribution of this work is the comparative study of lightweight machine learning models demonstrating that PCA improves the performance of correlation- sensitive classifiers without undermining the computational efficiency. The results provide a feasible and scalable solution to real-time intrusion detection systems.
The speedy improvement in Wireless Sensor Network (WSN) technology leads to various applications such as smart cities, industrial applications, and health care. Energy efficiency is one of the leading challenges in WSN because the major constraints for the process of communication are the routing protocol and energy efficiency. A new approach has been introduced to enhance network performance and quality, an improved Ant Colony Optimisation (ACO) with Elliptic Curve Cryptography (ECC) mechanism-based Clustered Routing Protocol for WSN. The major sections of the protocol are LEACH-based CH selection, Ant Colony Optimisation, and ECC mechanism. The protocol offers improved results in optimal path finding and wormhole attack protection. Simulation results indicate that the proposed scheme yields superior performance metrics in terms of Packet Delivery Ratio (PDR), network throughput, energy consumption, and security overhead.
Android malware creates a growing security risk due to the increasing number of applications that work on Android platform nowadays. The need for effective detection methods has made the use of machine learning and deep learning a viable solution. This study presents a comparison between different Machine Learning (ML) and Deep Learning (DL) models including Long Short-Term Memory (LSTM), Bidirectional Long Short-Term Memory (BiLSTM), Gated Recurrent Unit (GRU), Support Vector Machine (SVM), K Nearest Neighbor (KNN), and Logistic Regression (LR) on two datasets Android Malware Detection (AMD) and CICMalDroid2020 to evaluate their performance using a unified architecture with the same parameters. Random Forest method is used to select features from the AMD dataset, by calculating the most significant features that have a direct impact on the process of detecting Androidmalware samples, thereby increasing models’ accuracy. Result for both datasets shows that DL models, especially BiLSTM, outperform other models with accuracy metric that reach 99.77% with full features, 99.88% with selected features for AMD dataset and 89.70% for CICMalDroid2020 dataset.
Nowadays, data is the most valuable content in the world. Millions of data are generated every day in the form of text, images, videos, etc. Among them, images are widely used in daily communication. Due to the vulnerabilities of many data, it is difficult to transmit such images in a secure way. For this reason, a chaos-based data encryption algorithm is proposed where the image pixels are encrypted to generate a blurry image. In this paper, it is explained how to encrypt images using a 3D logistic chaotic map by generating chaotic keys where the image undergoes pixel scrambling and then XOR operation with the previously generated chaotic keys. To enhance the security and privacy of data, the proposed scheme uses different image sets to evaluate performance metrics such as Number of Pixel Change Rate (NPCR), Average Variable Intensity (UACI), correlation coefficients, and entropy in different attack scenarios. The proposed method achieved superior performance results in entropy (7.9993), key space, encryption pixel correlations, histogram contrast, UACI (35.2041%), and NPCR (99.6333%). The achieved results show that the proposed method can be used for image encryption with a high level of confidentiality.
The Internet of Things (IoT) is rapidly expanding into critical healthcare, industrial, and commercial do- mains, yet its resource-constrained devices remain vulnerable to cyberattacks. IoT devices have resource constraints, making it challenging to execute standard security algorithms. To address these limitations, the National Institute of Standards and Technology (NIST) selected 10 Lightweight Cryptographic (LWC) finalist algorithms in 2023 to provide suitable confidentiality for constrained environments. This review focuses exclusively on these finalists and highlights their importance in modern IoT security. A systematic search was conducted across IEEE Xplore, ScienceDirect, Springer, and the Cryptology ePrint Archive, using the PRISMA methodology, defined keywords, and strict inclusion/exclusion criteria. In the initial 2118 retrieved studies, 40 high-quality contributions were selected after title, abstract, and full-text screening. The selected works were categorized into four themes: performance evaluation across hardware and software platforms, cryptanalytic and security assessments, algorithmic optimization, and integration of LWC algorithms into existing systems and communication protocols. Performance analysis research indicates that TinyJambu is the most energy-efficient among the NIST block-cipher-based algorithms. Xoodyak and ASCON demonstrated the best energy efficiency among permutation-based algorithms. On the other hand, the set of Elephant, ISAP, and Grain128-AEAD was the least energy-efficient, consuming up to 10 to 25 times more energy than the most efficient set, TinyJambu. In particular, the first reported cryptanalytic break of the 7-round Xoodyak, presented in a recent article, substantially expands the threat model for the NIST LWC finalist. Some experimental reports indicate a full key-recovery attack with success rates exceeding 90%. In contrast, adapted variants of the attack have proven effective against multiple Elephant-family ciphers, illustrating the importance of updated security assessments and implementation countermeasures. Finally, this study identifies critical research gaps that require further investigation, emphasizing the importance of addressing these challenges through targeted research efforts and developing adaptive solutions in future studies.
The heavy reliance on the internet for secure data transmission require strong and efficient methods to ensure confidentiality. This study suggests an enhanced steganographic method that embedded secret messages into grayscale images using a four slice Two Bit Plane Slicing (2-BPS) technique, random key generation and XOR-based embedding. The proposed method minimize complexity by dividing the cover image into four segments, thereby improving efficiency and security, unlike the traditional bit-plane slicing methods that depend on eight planes. The method was analyzed using the metrics performance like: Peak Signal-to-Noise Ratio (PSNR), Mean Squared Error (MSE), entropy, correlation, and histogram analysis. The results show high degree of non-perceptually, low deformation and flexibility against statistical and visual steganalysis. Furthermore, execution time tests confirm the method’s computational efficiency, making it suitable for real-time applications. This approach provides a practical balance between visual quality, data security, and processing speed, with potential extensions to color and high- resolution images.
Modern transportation systems are severely hampered by urban traffic congestion, which causes delays and fuel consumption. Proactive control techniques and intelligent traffic management depend on accurate congestion prediction. In order to predict congestion across urban edge networks, current study presents a deep learning-based framework that combines an attention mechanism with a bidirectional Long Short-Term Memory (LSTM) network with custom learnable attention layer, flowed by focal loss for addressing the data imbalance. A numerous dataset was generated by using SUMO, therefore over 2 million sequence was generated, including 12 spatiotemporal features that were extracted from the dataset. A large scale map was used and the prediction was based on edge level. The model can efficiently learn temporal dependencies and spatial patterns thanks to our preprocessing pipeline, which consists of temporal windowing, edge ID encoding, and cyclical time transformations. Trained with a 30-step sliding window, the model achieved low error metrics (MAE: 0.0744, RMSE: 0.2728), an F1-score of 0.90, and a classification accuracy of 92.56%. Our architecture performs better at detecting congestion events than recent state-of-the-art models. Thus the potential for scalable implementation in urban traffic forecasting systems of deep spatiotemporal learning models trained on realistic but synthetic simulation data.
The RC4 stream cipher has known security weaknesses due to its weak keystream distribution and biased key scheduling. In this article, an extended RC4X is proposed to enhance the security of the original RC4 stream cipher by combining sophisticated mixing methods into its Key Scheduling Algorithm (KSA-X) and Pseudo-Random Generation Algorithm (PRGA-X) process. The proposed KSA-X uses two full-shuffle operations to eliminate predictable key-byte relationships: a first RC4-style permutation and a nonlinear mixing function that performs bitwise shifts and XOR operations. The internal state becomes more unpredictable through an additional permutation phase, which diffuses the state elements and increases the randomness. The PRGA-X update mechanism performs state-value rotations to reduce linear dependencies and minimize distribution-related weaknesses in the generated keystream. The experimental results show that RC4X produces keystreams with improved higher-order correlation properties, enhanced resistance to key-recovery attacks, and fast operation and efficient memory usage. The study proves the fact that RC4X is a lightweight, secure substitute of RC4, which is utilized where maximum efficiency and security are needed and minimum resources are at hand. The experimental findings indicate that RC4X provides security against many known security weaknesses and offers equivalent performance as the traditional stream ciphers. The distance-equalities statistical test will assist us in determining structural flaws in algorithmic keystreams created by similar algorithms such as RC4. This test has been done to identify the statistical bias in RC4. The largest RC4 bias is subsequently fixed by RC4X. Precisely, the most critical RC4 deviation (Event-2 bias) was narrowed down to -11.59X -0.14X in RC4X.
This study addresses beam squint mitigation in millimeter-Wave (mmWave) systems using a 1024- element Reconfigurable Intelligent Surface (RIS) optimized via gradient descent. The proposed approach achieves ±0.2° squint correction and over 90% accuracy within 200 iterations, with gain variation maintained below 0.5 dB. A supervised Machine Learning (ML) model, trained on simulation data, demonstrates an 83% reduction in training error and a 60% drop in validation error over 1000 epochs, converging by epoch 700. The integration of early stopping and L2 regularization is suggested to further reduce generalization error. These results indicate that RIS, combined with ML optimization, offers a scalable and effective solution for wideband mmWave systems, paving the way for real-time beamforming in next-generation wireless networks.
This paper presents a flexible and adjustable antenna design that reduces interference in different practical applications. The antenna, shaped like a square with slots and integrated pin diodes, enables frequency adjustment by connecting or disconnecting components. Made of certain substrate material, the antenna operates at five frequency bands commonly used in 5G sub-6 GHz applications. Along with improving the connection with the Metamaterial (MTM) structure by using four PIN diodes without altering the radiation pattern, the new design replaces the via with a fractal technique, which cuts down on energy losses and boosts performance while also lowering costs. Simulation results demonstrate excellent impedance matching, multiple adjustable bands, and significant gain(>10 dBi). The antenna exhibits radiation patterns and achieves an efficiency greater than 75%. Analysis under various conditions shows the antenna’s performance on curved surfaces, making it suitable for adaptable electronic systems. A comparison with prior studies highlights the antenna’s potential within the specified frequency ranges.
Long Range Wide Area Network is among the foremost favored techniques for wireless communication in Internet of Things applications, owing to its simplicity and versatility. Long Range Wide Area Network utilizes an adaptive data rate approach at both the network server and end device levels. The network server could employ a resource allocation approach to return the requested radio parameters of the downlink by regulating the transmit power and spreading factor. we suggest Network Server controlled adaptive data rate, a Uniform Distribution Adaptive Data Rate to deal with fast fluctuating of signal-noise ratio of arriving packets at the network server. The suggested strategy seeks to most effective allocation of radio parameters to the end devices to reduce the network energy consumption and improve average packet delivery ratio of the network. The findings of the simulation demonstrated that the suggested approach enhance the average packet delivery ratio by 102% and 77.94% for a single and two gateways deployment in an urban scenario respectively while in sub-urban improved by 65.14% and 33.53%. The total network energy consumption reduced by 9% and 16.55% for single and two gateways in urban scenario while in sub-urban scenario reduced by 15.92% and 12.53%.
The Internet of Things (IoT) constitutes an expanding network of interconnected gadgets that enable intelligent systems to gather, analyze, and disseminate data. However, this rapid growth raises cyber-attack risks due to poor configurations and outdated systems. Malware, which exploits system vulnerabilities, represents a significant threat to the information security of IoT systems. Thus, malware detection in IoT systems is a critical concern. Therefore, this research paper presents an IoT malware detection method based on an image dataset and the Chi-square method as well as applying the Convolutional Neural Network (CNN) deep learning model to detect the IoT malware. This study attempts to investigate the impact of the chi-square Feature Selection (FS) method on the effectiveness of CNNs for identifying IoT malware, by directly applying feature selection to the images to discern the most informative ones from the dataset before passing them to the CNN deep learning model, demonstrating robust outcomes and validating the efficacy and robustness of the suggested approach for identifying IoT malware. An experimental comparison was carried out between the suggested method that Involved training the CNN on the feature-selected dataset (FS+CNN Model) and the (CNN Model) that was trained on the full dataset and was also evaluated using the presented state-of-the-art to add to the method’s reliability. The accuracy of the (Fs + CNN Model) reached 98.19% while its precision, recall, and F1-score were 99.52%, 95.90 %, and 97.68 %, respectively, outperforming the CNN Model’s accuracy with 94.75 %, precision with 93.00 %, recall with 91.43 % and f1-score with 90.43 %. It also outperformed the state-of-the-art evaluation with an accuracy value of 97.93 %, a precision value of 98.64 %, a recall value of 88.73 %, and an f1-score value of 93.94 %.
The issue of sensor coverage in Wireless Sensor Networks (WSNs) is crucial, particularly as these networks are deployed in military applications for the armed forces as well as in civilian health applications. Therefore, improving coverage and communication while minimizing interference between sensors is essential. This paper presents a hybrid meta-heuristic approach to optimizing node deployment in WSNs using a modified Particle Swarm Optimization (mPSO) and Ant Lion Optimization (ALO) algorithms. The Particle Swarm Optimization (PSO) algorithm was applied for global search, while the ALO focused on internal search within the Region ofInterest (ROI). Initially, nodes are deployed randomly within the ROI. The algorithm then detects uncovered gaps and iteratively enhances node placement, leading to an improved coverage ratio and minimized node overlap. The results of the hybrid meta-heuristic algorithm show improved performance compared to using PSO and ALO separately. This approach leads to an enhanced network lifetime and energy consumption of the WSN.
parse Code Multiple Access (SCMA) is an extremely effective non-orthogonal multiple access tech-nology that enables communication between users who have limited orthogonal resources currently. Traditional SCMA methods use manually designed codebooks, potentially leading to subpar performance owing to inadequate optimization for certain encoders. A Deep Neural Network (DNN) is used to produce a deep learning for SCMA codebook using Stochastic Gradient Descent (SGD). This enables the model to determine the optimal weights and biases for generating precise predictions. The proposed approach surpasses existing techniques in a Rayleigh fading channel due to its reduced Bit Error Rate (BER), enhanced Minimum Euclidean Distance (MED), and diminished complexity compared to prior SCMA frameworks.
Edge-cloud computing paradigms increase the Quality of Experience (QoE) for real-time applications by offering many benefits, such as reducing the response time. Many strategies are proposed to handle the data in the edge cloud environment. Hence, where to execute the data generated by the end device is considered an important issue. In this paper, an Artificial Intelligence (AI) model is proposed based on a neural network for workload allocation decisions. The model deals with an AI application that runs on an edge server and a cloud center. The model was trained using a pre-generated dataset based on several features. The features considered are the data size, model complexity, application priority, edge server utilization, and delay of execution on the edge and the cloud. The model decides where to perform the task generated by the end device, either in the edge server or on the cloud. Four AI applications are considered. The model has been implemented using Tensorflow platform with the required libraries. The proposed model employee multi-feature with multi-application addressing workload allocation decisions in a hybrid edge-cloud environment by creating and utilizing a dataset based on proposed algorithm. The proposed model achieved accuracy reached 98.3% and reduced the response time of task execution compared to the based line approach considered in this paper.
Image enhancement in low-light conditions has gained significant attention in recent years due to its importance in improving visual clarity and uncovering hidden details in poorly illuminated images. This study focuses on the application of classical methods for enhancing color and brightness, as these approaches provide a practical balance between efficiency and performance compared to computationally intensive modern techniques. Classical algorithms were applied, modified, and extended to develop lightweight solutions capable of improving image quality through relatively simple processes. In this work, a set of classical enhancement methods was tested on images captured in night conditions. Performance was evaluated using metrics such as the Structural Similarity Index Measure (SSIM) and the Signal-to-Noise Ratio (SNR), which assess improvements in brightness, color quality, and detail preservation. Additionally, three novel techniques were proposed: enhanced Hue, Saturation, Value (HSV) through scaling of the Value channel, Custom HSV-based Brightness and Saturation Scaling, and an Entropy- based Hybrid Enhancement combining HSV and Lab* color spaces. Results showed that the proposed methods outperformed conventional histogram equalization, with the best-performing approach achieving up to a 41.4% improvement in Structural Similarity Index Measure. Overall, the findings demonstrate that classical and improved lightweight methods remain effective and computationally efficient for low-light image enhancement.
