We propose an algorithm to detect wormhole attacks based on the Address Resolution Protocol (ARP) for mobile networks which use On-demand Distance Vector (AODV) routing protocol. This novel approach differs from all existing methods, requiring no extra hardware or software equipment, and offers a simple detection method by avoiding complex calculations. Wormhole attacks, being among the most harmful, can result in data theft, discarding, tampering, and can even facilitate other attacks. Current defense algorithms typically require additional hardware or software and involve complex calculations at each node, sometimes failing in certain scenarios. Our algorithm aims to overcome these limitations.
The application of reflective intelligent surfaces (RISs) combined with the vast bandwidth available in the THz spectrum band is an enabling technology that can substantially enhance the performance of industrial networks in the sixth generation of wireless communications (6G). In this context, this work analyzes the performance improvement achieved by deploying multiple RISs in a system operating at 140 GHz in an indoor factory (InF) scenario. The analysis is based on ray tracing simulations using a geometric model representing an InF environment. The effects of specular reflection, diffraction, diffuse scattering, and atmospheric molecular absorption are considered in the propagation modeling. The performance of the networks is measured based on the achievable data rate (ADR), and the gain from using RISs is quantified by comparing it to the performance of a system without the RIS aid. According to the simulation results, the application of five RISs with 1000 reflecting elements promotes an increase of 4.1 bit/s/Hz in the average achievable rate when compared to the non-RIS-assisted system.
Single-pixel imaging is a promising technique for Terahertz (THz) imaging, because it can largely reduce the numbers of expensive THz emitter and detector. Single-pixel imaging uses compressive sensing (CS) to measure the compressed THz signal and to recover the object image. This work presents a low-complexity and high-quality total variation augmented Lagrangian method (2D-TVALM) algorithm based on the well-known total variation augmented Lagrangian alternating-direction algorithm (TVAL3). TVAL3 is a convex optimization algorithm used to solve the problem of total variation (TV) regularization. By designing the sampling matrix and some mathematic approximation, this work largely reduces the computational complexity and speed up the convergence. The simulation results show that the reconstruction quality of the proposed algorithm is about 7 dB higher than TVAL3 in PSNR and the performance is much better than that of the other CS algorithms. In the computation time, the proposed algorithm is about 31 times faster than TVAL3. Finally, the average PSNR and elapsed time of the proposed algorithm are 38.43 dB and 0.0558 s respectively with 200 iterations in the 15 test patterns.
Filter banks play a crucial role in optimizing signal allocation and quality in wireless communication systems, addressing challenges like channelization and interference. However, traditional filter bank schemes suffer from high computational complexity and hardware costs. This paper proposes a novel approach for the hardware implementation of multidimensional filter banks, which significantly reduces computational complexity while maintaining high throughput. The underlying idea is to employ the in-memory computing (IMC) technique to perform filtering computations in a fully parallel manner using memristive devices. Additionally, this scheme offers scalability and flexibility, allowing it to realize multiple filters with diverse impulse responses and filter sizes.
In non-cooperative contexts, a receiver must estimate the transmitter’s communication parameters to recover information. Particularly in multi-user direct sequence spread spectrum systems, it is crucial to estimate the number of users and determine the length and pattern of each spreading sequence. Various studies estimating the spreading sequence have assumed that the number of users is known beforehand. And, the methods employed by these studies in an attempt to justify this assumption have inherent issues, such as peak ambiguity and threshold setting. To address these issues, this paper proposes a deep learning-based blind estimation algorithm for determining the number of users using the autocorrelation fluctuation extracted from the input signal. Through computer simulations, we show that the proposed method achieves high estimation accuracy even at low signal-to-noise ratio.
Assistive mobile applications play a pivotal role for visually impaired individuals worldwide. These applications often face challenges in currency recognition due to varying perspectives, inconsistent illumination, and background clutter. This issue is especially pressing in developing countries like Thailand, where there is a notable gap in robust currency recognition systems, particularly for the new Thai currency notes. This study employs a deep learning approach using a convolutional neural network (CNN) to automate the recognition of the new Thai currency notes. Using transfer learning, we fine-tuned the CNN using the Xception model, renowned for its depth-wise separable convolution. The network trained on a meticulously curated dataset comprising 3600 images (without data augmentation) of five different denominations of the new Thai currency (20, 50, 100, 500, and 1000 baht) notes, captured under various conditions. The resulting model achieved an average training accuracy of 99.5% and a validation accuracy of 99.8%. Given its robustness and high accuracy, the model can be integrated into an Android application. Such an application would offer a user-friendly and reliable tool for visually impaired individuals to effortlessly identify the new Thai currency notes in their everyday transactions.
The advancements of high resolution display technologies support applications ranging from mobile multimedia streaming to automotive cockpits and infotainment systems. Given the limited transmission and processing resources in a mobile communication context, a large number of upscaling methods for restoring high-resolution visual content from low-resolution representations have been proposed and assessed from a theoretical point of view. This paper, therefore, deals with the practical implementation of state-of-the-art upscaling methods on development boards. The proposed tool chain and solutions may be applied as a general framework for the implementation of current and future upscaling methods on resource limited devices.
While electromagnetic waves dominate the data communication arena in modern computing systems, they are unsuitable in environments requiring stringent electromagnetic interference control, such as covert operations, medical facilities, and sensitive research settings. Molecular communication (MC) is an alternative data communication paradigm that utilises the propagation of chemical molecules to carry modulated data. Implementation of MC systems demand expertise in specialised hardware and chemicals, thereby limiting its accessibility for researchers in communication and networks. This study addresses these challenges by developing a low-cost, ready-to-use research platform for rapid prototyping and experimentation. Utilizing Ethanol and Thinner as the chemical agents and off-the-shelf gas sensors as receivers, the study explores the feasibility of establishing a wireless diffusion-based MC channel between computers. Through a series of experiments, the response of sensors to chemical molecules under various conditions were evaluated, demonstrating the effectiveness of the platform. The proposed platform, suitable for indoor environments, provides a foundational tool for further research in molecular data communication.
Medical data analysis is crucial for advancing healthcare but is often hindered by the scarcity and high cost of medical data. This paper presents a novel approach to improve neural network performance in medical data analysis by introducing engineered features that capture the underlying relationships in the data. We demonstrate the effectiveness of this method and show significant improvements in model accuracy.
This paper presents an efficient approach for finding the optimal 3D deployment locations of multiple UAV base stations, to maximize the probability of connection for users uniformly distributed over a wide area. We consider covering a two-dimensional built urban environment with UAVs arranged in a regular equilateral triangle structure. We propose to provide a degree of overlapping coverage between the UAVs to provide diversity to UEs to overcome blockage from buildings. We optimize the distance between UAV flying locations as well as their flying height. Our analysis approach is based on an accurate two-tier connectivity model. We demonstrate that our deployment approach outperforms existing approaches that adopt average path loss models. We achieve a 10-20% improvement in connectivity performance. Index Terms—Unmanned aerial vehicles (UAVs), UAV Communication, Deployment, Aerial Base Stations, Drones, Probability of Line of Sight (LoS), Channel Modelling
The Internet of Medical Things has emerged as a transformative technology in healthcare, facilitating remote monitoring and management of medical devices. However, despite these benefits, there is a need for more vigilance in ensuring electromagnetic compatibility within the Internet of Medical Things networks. It is quite essential to prevent electromagnetic interference as it could compromise device efficacy and consequently patient safety. This study aims to enhance real-time detection and prediction of electromagnetic radiations on the Internet of Medical Things networks by leveraging the predictive capabilities of the Kalman filter. An experiment was conducted over 30 days to evaluate the performance of the algorithm in optimizing electromagnetic compatibility management thus mitigating electromagnetic interference events. The algorithm was implemented and validated with 2500 samples of radiation emissions, with coordinates of the device’s location. The results indicated that the implementation of Kalman filter algorithms could reduce sensor noise by up to 88% of accuracy. This research contributes to advancing electromagnetic compatibility monitoring techniques on the Internet of Medical Things networks, offering valuable implications for the design and implementation of resilient healthcare systems.
Citizen engagement is increasingly recommended or even legally mandated to connect citizens with the policy making process. In this context, public displays equipped with user input can enable voluntary and opportunistic forms of interaction such as questionnaires, interactive guides or voting. Furthermore, public displays are a proven means to target a well defined community by being physically located ‘in’ the context of interest and maintaining local engagement by conveying relevant information. However, the deployment of public displays remains complex and expensive due to the need to connect to mains power or deploy large arrays of solar panels in order to serve the significant power demands of conventional displays. We tackle this problem by introducing Citizen Dialog Kit (CDK), a low-power platform that enables fully-autonomous in-situ surveying using a novel combination of low-power NB-IoT and LTE-M cellular networking, an E-Ink display and a low-power embedded software stack based on FreeRTOS. A lab based evaluation in combination with extensive field trials demonstrate that CDK achieves multi-year battery lifetimes in realistic deployment scenarios, enabling extremely low cost and long-lived citizen engagement projects.
Traditional diagnostic methods such as Fundus cameras and slit-lamps that are used for diagnosing various ocular diseases are often limited by their cost and accessibility which hinders timely intervention especially in rural areas and indigent societies with limited resources. While the current research is mostly focused on classification of single diseases or expensive setups, the proposed work aims to address these limitations by developing a cost-effective and user-friendly system for primary diagnosis of various ocular diseases, including Diabetic Retinopathy, Age-Related Macular Degeneration and Glaucoma. The proposed work utilizes a smartphone, cloud-based deep learning model, and a 3D printed multi-lens holder for fundus imaging. This research leverages open-source datasets for model training and testing, added to new sets of images that were collected throughout this work for validation. The new set tested the quality of the captured images in actual setup and verified accurate classification. Based on the results, the system has been successfully implemented and the overall classification accuracy of the multi-classification ResNet-50 model is 74%. Preliminary results demonstrate the potential of this approach for multi-classification of ocular diseases, marking a significant advancement in portable ocular health primary diagnostics.
6G mobile networks are foreseen to integrate non-terrestrial networks (NTNs), such as unmanned aerial vehicle (UAV) networks, with conventional terrestrial networks to improve global coverage. UAVs offer flexible deployment and strong line-of-sight propagation links when placed at favorable altitudes. In this paper, we propose a hybrid amplify-and-forward (AF) decode-and-foward (DF) relaying based non-orthogonal multiple access (NOMA) UAV system. In this system, the base station uses NOMA to simultaneously communicate on the downlink with a number of hybrid AF-DF UAV relays which improves spectral efficiency. The UAVs select the AF or DF relaying mode such that the highest signal-to-interference-plus-noise ratio at the user equipments is obtained. Hence, either the low signal processing complexity in AF relaying mode or the potentially lower power dissipation in DF relaying mode is utilized. Analytical expressions of the outage probability and sum rate of the hybrid AF-DF relaying based NOMA UAV system are derived. Numerical results are provided revealing the performance gains offered by the proposed system in the presence of Nakagami-m fading and the system’s benefits over AF and DF based systems.
This paper proposes iterative colored noise cancellation for overloaded MIMO spatial multiplexing in uplinks of massive MIMO systems. The overloaded MIMO spatial multiplexing increases the number of the spatially multiplexed signal streams to that of the receive antennas for higher throughput, even though the number of the receive antennas is much more than that of the transmit antennas in the links. While the performance of conventional linear MIMO systems is seriously degraded due to lack of the freedom, the proposed iterative colored noise cancellation mitigates the performance degradation by removing the colored noise that causes the performance degradation. The performance of the proposed iterative colored noise cancellation is evaluated in uplinks of massive MIMO systems by computer simulation. The proposed colored noise cancellation achieves a gain of about 10 dB when the overloading ratio is set to 2. The proposed iterative colored noise cancellation makes the spatial multiplexing attain a diversity order of about 6, even if the overloading ratio is increased to 3 in a MIMO system where the number of the transmit antennas and that of the receive antennas are 2 and 6 respectively.
Medical image segmentation plays a critical role in diagnosing and treating disease; it is a challenging task due to the complexity and variety of medical images. One of the critical factors in enhancing the accuracy and reliability of segmentation results is denoising, which involves removing noise from input images. Deep Neural Networks (DNNs) have shown promising results in medical image segmentation, but the manual design of DNN models is time-consuming and requires domain knowledge. To address this problem, Neural Architecture Search (NAS) provides an automated approach for developing neural network structures, eliminating manual design requirements. This paper introduces two evolutionary DNN models for medical image denoising and segmentation. The denoising model serves as a preprocessing step for segmentation, enabling us to assess its impact on medical image segmentation performance compared to normal images. The proposed approach performed well, with Dice scores of 86.99% and 88.62% on the original images for the Spleen and Heart segmentation datasets, respectively. While it achieved Dice scores of 89.12% and 87.78% on denoised images for the Heart and Spleen segmentation datasets, respectively. The experimental findings show that the architecture derived from the proposed approach outperforms existing models.
Millimeter wave (mm-Wave) frequencies provide high data rates while encountering issues like high penetration losses and obstruction. To overcome these challenges, large antenna arrays and analog beamforming with phase shifters are utilized. This can lead to overlapping coverage areas and interference between beams, making resource scheduling (user, beam, time, and data rate) a complex and critical issue. This paper proposes four algorithms for coordinated multi-BS, multibeam scheduling of users in the time domain. Next, we propose two approaches for rate management amongst the scheduled users in the time domain. Finally, we present some numerical results illustrating the effectiveness of the proposed strategies and algorithms.
Imperceptible image watermarking methods strive to balance visibility with robustness requirements in order to protect copyright images from copyright infringement without impacting users’ viewing experience. Additionally, image generators such as Stable Diffusion and Google’s Imagen utilize watermarking to enable the detection of synthetically generated images. To guarantee a sufficient level of robustness, watermarking methods are evaluated against traditional attacks. We recognize diffusion models as a potential disruptive technology in the watermark robustness field and therefore investigate this approach. This paper presents Diffusion Denoising Watermark Removal Models (DDWRM) as a watermark attack. Instead of generating images from noise, our approach strategically diffuses watermarked images, followed by a denoising process to effectively remove embedded watermarks while preserving the original content. The methodology is designed to be generic, showcasing its potential as a robust and versatile watermark removal technique. Experimental results highlight the proposed DDWRM’s success in removing watermarking information, outperforming the traditional JPEG compression attack method for specific watermarking techniques (DWT-DCT-SVD and Riva-GAN). However, variations in resilience, particularly with DWT-DCT watermarking method, prompt further exploration needs. In conclusion, our proposed attack emerges as an innovative and effective watermark removal method.
Software-Defined Networking (SDN) enhances flexibility, scalability, and innovation by decoupling the control plane from the data plane, managed through streamlined controller operations. However, Distributed Denial of Service (DDoS) attacks pose significant cybersecurity threats to SDNs, disrupting services by flooding targeted systems with traffic from multiple sources. Real-time detection of these attacks remains challenging, as traditional methods often lack the ability to accurately identify complex attack patterns due to limited feature sets. To address this, we propose an ensemble-based model that combines three classifiers (SGD, EBM, and MLP) for effective DDoS attack detection and mitigation in SDNs. Our approach integrates a reliable feature selection methodology, leveraging Principal Component Analysis (PCA) to identify the most informative features for attack detection. This enhancement significantly improves the accuracy and efficiency of the ensemble model. Evaluated using the CIC-DDoS2019 and InSDN datasets, our model demonstrates substantial improvements in detection rates and computational efficiency. Results show that the proposed approach achieves 99% accuracy, underscoring its potential as a resilient solution for DDoS attack detection in SDNs.
Spectrum occupancy prediction offers many advantages in Dynamic Spectrum Access (DSA) type applications and has been performed using conventional algorithms such as linear predictors, Bayesian prediction, etc. Deep learning (DL) algorithms such as Long Short Term Memory (LSTMs), Convolutional Neural Networks (CNNs), and its variants have been increasingly adopted for these applications over recent years. Moreover, these approaches can provide accurate spectrum occupancy forecasts for multiple predictive or future time steps. However, a performance analysis of different DL techniques for different input model conditions needs to be explored in the literature. To this end, we consider different DL algorithms viz., vanilla LSTM, Encoder-Decoder LSTM, and CNN models that perform multi-time step ahead spectrum occupancy prediction for Markovian input data. We analyse the performance of these models and extract performance characteristics for a range of predictive time steps and input model parameter scenarios. Our findings suggest that vanilla LSTM and 1D CNN models have the ability to perform closely with that of Bayesian techniques. These algorithms perform well in case highly correlated input data. However, there is a performance degradation when the predictive horizon width increases.
