International Journal of Sensors Wireless Communications and Control - Online First

In a recent study, a generalized implicit transmission (GIT) technique that can transmit multiple implicit sequences while transmitting a single explicit sequence over a channel has been introduced. Instead of considering all sequences as independent, as in the previous study, this study considers the explicit sequence and all implicit sequences of a GIT collectively as a single code, referred to as a GIT coding scheme.

The overall code rate Roverall and the inherent coding gain achieved by a GIT coding scheme due to the transmission of information implicitly, are discussed.

The overall code rate and the inherent coding gain achieved by a GIT coding scheme, due to the transmission of information, is discussed implicitly. A GIT coding scheme constructed from a rate code to function as a rate code, on average, transmits number of codewords of a rate code for every single codeword transmitted over the channel by transmitting number of codewords over all implicit sequences. A simple way to convert existing practical codes into GIT coding schemes is also discussed.

The numerical results presented with the LDPC codes employed in the WiFi and the 5G standards demonstrate that GIT coding schemes can achieve very high coding gains over conventional codes while functioning as high-rate codes.

Due to its ability to transmit the majority of information implicitly, which does not require any additional bandwidth or transmitted power, it is demonstrated here that GIT coding schemes can operate in the so-called unreachable region relative to the Shannon-Hartley bound.

Mobility-assisted localization remains a critical challenge in wireless sensor networks (WSNs), particularly for accurately determining sensor node positions. A localization framework that utilizes a single mobile anchor node (MAN) has been proposed in this paper to enhance localization performance in WSNs. The paper also investigates the effect of multiple MAN path-planning models on localization accuracy and computation time.

The proposed approach integrates two error minimization techniques—trilateration and the Harmony Search Algorithm (HSA)—to estimate sensor node positions. Five distinct MAN path-planning models are investigated: Random, Scan, Double Scan, Hilbert, and Circular. These models define the MAN’s trajectory, and the resulting anchor points are used as inputs for distributed localization using both trilateration and HSA. MATLAB simulations are conducted to evaluate the framework based on localization accuracy, node coverage, and computational time.

Simulation outcomes indicate that the Hilbert path-planning model achieves the highest localization accuracy and node coverage among all trajectories. Furthermore, the HSA-based localization method surpasses trilateration in terms of precision by effectively minimizing localization error, though it requires more computational time.

The findings reveal a clear trade-off between localization accuracy and computational efficiency. While HSA provides enhanced precision, it incurs a higher computational cost compared to trilateration. These results underscore the importance of selecting appropriate path-planning and optimization strategies to balance performance metrics in real-world WSN deployments.

The study demonstrates that the combination of MAN-based mobility, Hilbert path-planning, and HSA optimization yields superior localization performance in WSNs. These insights contribute to the development of efficient and accurate localization strategies suitable for dynamic and resource-constrained wireless sensor environments.

Early and precise landslide prediction remains a critical challenge for mitigating their devastating impacts. Traditional methods often struggle to integrate both spatial and temporal data effectively, leading to limited prediction accuracy. This study aims to develop a deep learning model that combines Convolutional Autoencoders (CAEs) for spatial feature extraction with Recurrent Neural Networks (RNNs) to capture temporal dynamics.

The proposed model leverages CAEs to learn robust spatial representations from the 14-band Landslide4Sense dataset, while the RNN component captures the temporal patterns crucial for landslide detection. This integrated approach enhances the prediction capability by considering both spatial and temporal factors.

The approach demonstrates an impressive landslide prediction accuracy of 0.988, with performance metrics of precision: 0.987, recall: 0.972, and F1-score: 0.982, highlighting its effectiveness in landslide prediction.

The model successfully integrates spatial and temporal dimensions, outperforming traditional prediction methods. Its deep learning design enhances robustness and adaptability across geospatial terrains.

This work paves the way for the application of advanced deep learning models in real-world landslide prediction. By integrating spatial and temporal data, the model offers a promising solution for mitigating landslide-related risks, potentially saving lives and infrastructure.

The introduction of 5G technology has revolutionized Wireless Sensor Networks (WSNs) by significantly enhancing connectivity, reducing latency, and improving scalability.

This paper presents a performance analysis framework for 5G-based WSNs, focusing on key parameters such as numerology, deployment strategies (e.g., Multi-Access Edge Computing and centralized models), traffic loads, and link capacity allocations. The framework evaluates their impact on critical performance metrics, including latency, throughput, energy efficiency, and reliability.

Simulation results reveal that deploying edge computing significantly reduces latency (e.g., 5.325 ms compared to 26.725 ms in centralized architectures), while optimized link capacity allocation and numerology configurations enhance overall network efficiency.

These findings demonstrate how 5G parameters can be fine-tuned to optimize WSN performance for diverse applications such as industrial automation, smart cities, and healthcare. The study provides practical insights into achieving real-time, scalable, and energy-efficient operations in 5G-enabled WSNs.

There are various concerns in Fiber wireless access networks, such as ONU placement, survivability, network planning andand its cost, architectural developments, etc. In this paper, we have focused on front end survivability of the network, which mainly depends on the routing algorithms employed at the front end of the network.

The effectiveness of the proposed scheme lies in finding the next alternate path in case of link/component failure with minimum delay. Hence, delay is the appropriate parameter to evaluate the efficacy of the proposed technique. In this paper, RSSI-based routing (RBRA) is proposed for various cases of link failures. Delay performance for each of the cases has been compared with two previous approaches maximum protection minimum link cost (MPMLC) andand minimum hop routing algorithm (MHRA).

It is found that RBRA performs better than MPMLC andand MHRA. For single link failure and two source destination pairs in the network with 50 wireless routers delay decreases by 58% andand 37% in RBRA as compared to MHRA andand MPMLC, respectively.

Hence, these algorithms can be integrated with RSSI-based route selection. This could be an attractive future direction for research.

Timely detection of catastrophic natural disasters, such as forest fires, is critical to minimizing losses and ensuring rapid response. Artificial intelligence is increasingly being recognized as a valuable tool in enhancing various stages of disaster management.

This paper presents the development of a smart framework utilizing machine learning techniques for real-time detection and monitoring of natural disasters, specifically forest fires. The proposed approach employs a 10-layer convolutional neural network (CNN) that classifies aerial images into Fire, Non-Fire, and Smoke categories with high precision and speed. In addition to this, a CNN-based feature extraction process is performed and integrated with various ML classifiers, including support vector machine, k-nearest neighbor, decision tree, random forest, and extra trees.

Extensive performance analysis reveals that the proposed 10-layer CNN model outperforms other classifiers, achieving an accuracy of 97.64% in the binary classification of fire vs. non-fire and 95.61% in the three-class classification of Fire, Non-Fire and Smoke classes. Furthermore, a comparative study with existing state-of-the-art methods demonstrates the proposed model's superior performance in both accuracy and computational complexity.

These results demonstrate the potential of the proposed CNN-based framework to serve as a reliable and effective tool for real-time disaster management across various applications, providing valuable support to emergency response teams in mitigating the impact of natural disasters.

Vehicular-Adhoc-Networks (VANETs) have gained a lot of attention in the past ten years. Used extensively in intelligent transport systems (ITS), it facilitates the sharing of traffic data between vehicles and their immediate surroundings, resulting in a more pleasant driving experience. When it comes to VANET security, privacy and security are the two biggest obstacles. To improve security in VANET, authentication and privacy-preserving methods are required because any specific disclosure of vehicle specifics, including route data, might have devastating consequences.

In light of this need, this research introduces a novel framework for VANETs called enhanced timed efficient stream loss-tolerant authentication (ETESLTA), which enables a new kind of lightweight authentication while simultaneously protecting users' privacy. Initialisation, registration, mutual authentication, broadcasting and verification, and vehicle revocation are all parts of the suggested model. Furthermore, the ETESLTA method requires very little memory while yet providing excellent broadcast authentication, just like TESLTA. In addition, the ETESLTA method incorporates a cuckoo filter to record the real details of cars inside the RSU's detection range.

The suggested approach provides strong anonymity to achieve privacy and resists common assaults, and it features lightweight mutual authentication between the parties. A wide scale of experiments are conducted and the outcomes are evaluated in terms of numerous metrics to ensure the ETESLTA technique performs well.

The experimental results demonstrated that the ETESLTA method was superior to the most current state-of-the-art approaches.

Image dehazing is an essential task in computer-vision, aimed at enhancing the clarity and visibility of images degraded by fog and haze. Traditional methods often struggle with handling the complex scattering effects of haze and maintaining the natural colours of the scene.

To address these challenges, we propose a novel approach termed Dual Colour Space Attentional Deep Network (DCSADN) for efficient image dehazing. Our method leverages the advantages of two-colour spaces: RGB and YCbCr, to boost the network's skill to capture both the luminance and chrominance information, thereby improving the dehazing performance. We employ a multi-stage training strategy to optimize the performance of the DCSADN. This multi-stage approach ensures that the network generalizes well across different types of haze conditions. Extensive experiments demonstrate the superiority of our method over state-of-the-art dehazing techniques.

The results indicate that our approach not only achieves higher quantitative scores in terms of Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index (SSIM) but also produces dehazed images with more natural colours and details.

The findings reveal that the dual colour space processing and attentional modules are crucial for the enhanced performance of our method and providing a valuable tool for improving image quality in hazy conditions.

As a communication paradigm that bridges the gap between virtual and physical spaces, the Internet of Things (IoT) has quickly gained popularity in recent years. In order to supplement the provisions made by security protocols, a network-based intrusion detection system (IDS) has emerged as a standard component of network architecture. IDSs monitor and detect cyber threats continuously throughout the network lifetime.

The main contribution is the development of a two-level neural network model that optimizes the number of neurons and features in the hidden layers, achieving superior accuracy in anomaly detection. Its include the utilization of deep anomaly detection (DAD) to protect networks from unknown threats without requiring rule modifications. The research also emphasizes the importance of feature extraction and selection, employing parallel deep models for segmenting attacks. The proposed supervised technique enables simultaneous feature selection using parallel models, enhancing the accuracy of IDS designs. A novel hybrid technique combining Deep Auto Encoders (DAEs) and DeepNets is proposed for intrusion detection.

The research present a data preprocessing stage, converting symbolic and quantitative data into real-valued vectors and normalizing them. Feature extraction involves utilizing DAEs to learn concise representations of datasets. The deep network architecture, including multilayer perceptrons and activation functions, is employed for feature extraction and classification. The proposed approach utilizes a DeepNets in the final stage in order to improve the rate of 97% accurate outputs and make it possible to achieve greatly accelerated execution durations.

When measured against the performances of other approaches to the extraction of features, the performance of the deep network platform is superior.