International Journal of Sensors Wireless Communications and Control - Online First

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.

Several wireless technologies are assumed to be operating in cooperation for next-generation networks. These networks offer various services to mobile users; however, efficient handover between different networks is the most challenging task in high mobility scenarios. The traditional signal strength-based handover algorithms are not able to cope with mobile users' high-quality requirements.

In this paper, multiple criteria-based intelligent techniques are proposed to deal with inefficiencies related to handover. This technique makes use of artificial neural networks that take multiple parameters as inputs in order to predict the degradation of parameters. These predictions are further used to design rules for initiating handover procedures prior to service quality degradation.

Based on the prediction results obtained by deep neural networks, the handover decisions are recommended according to the type of application: conversational or streaming.

The simulation results demonstrate the efficacy of the proposed method as there is an improvement of up to 40% and 25% in terms of handover rate and service disruption time, respectively, with an acceptable prediction error of 0.05.

Network security detection has become increasingly complex due to the proliferation of Internet nodes and the ever-changing nature of network architecture. To address this, a multi-layer feedforward neural-network has been employed to construct a model for security threat detection, which has enhanced network security protection.

Improving prediction accuracy and real-time performance, this research suggests an optimal strategy based on Clockwork Recurrent-Neural-Networks(CW-RNNs) to handle nonlinearity and temporal dynamics in network security circumstances. We get the model to pick up on both the short-term and long-term temporal aspects of network-security situations by using the clock-cycle RNN. To further improve the network security scenario prediction model, we tune the network hyperparameters using the Grey-Wolf-Optimization(GWO) technique. By incorporating a clock-cycle for hidden units, the model can improve its pattern recognition capabilities by learning short-term knowledge from high-frequency update modules and preserving long-term memory from low-frequency update modules.

The optimized clock-cycle RNN achieves better prediction accuracy than competing network models when it comes to extracting nonlinear and temporal characteristics of network security scenarios, according to the experimental data.

In addition, our method is perfect for tracking massive amounts of data transmitted by sensor networks because of its minimal time complexity and outstanding real-time performance.

The integration of Cooperative Communications (CC), Cognitive Radio (CR) technology, and Non-Orthogonal Multiple Access (NOMA) techniques, termed Cooperative Cognitive Radio used NOMA (CCR-NOMA) systems, has emerged as a promising solution to address spectrum scarcity and connectivity challenges anticipated in sixth generation (6G) networks.

This studyaims to investigate the Bit Error Rate (BER) performance of underlay CCR-NOMA systems.

We derive precise closed-form expressions for BER at distant users under perfect and imperfect Channel State Information (CSI) conditions. These mathematical formulations are validated through Monte Carlo simulations.

Our results indicate that the near user 𝐶𝑈2 exhibits superior performance compared to the far user 𝐶𝑈2. Additionally, distant users utilizing the CCR-OMA protocol demonstrate better BER performance than those employing the CCR-NOMA protocol.

The presence of imperfect CSI adversely affects BER performance. Moreover, the derived closed-form expressions for BER in the investigated system align well with Monte Carlo simulations. These findings provide valuable insights for optimizing the performance of CCR-NOMA systems in real-world scenarios.