A vehicular ad hoc network (VANET) is a sophisticated wireless communication infrastructure incorporating centralized and decentralized control mechanisms, orchestrating seamless data exchange among vehicles. This intricate communication system relies on the advanced capabilities of 5G connectivity, employing specialized topological arrangements to enhance data packet transmission. These vehicles communicate amongst themselves and establish connections with roadside units (RSUs). In the dynamic landscape of vehicular communication, disruptions, especially in scenarios involving high-speed vehicles, pose challenges. A notable concern is the emergence of black hole attacks, where a vehicle acts maliciously, obstructing the forwarding of data packets to subsequent vehicles, thereby compromising the secure dissemination of content within the VANET. We present an intelligent cluster-based routing protocol to mitigate these challenges in VANET routing. The system operates through two pivotal phases: first, utilizing an artificial neural network (ANN) model to detect malicious nodes, and second, establishing clusters via enhanced clustering algorithms with appointed cluster heads (CH) for each cluster. Subsequently, an optimal path for data transmission is predicted, aiming to minimize packet transmission delays. Our approach integrates a modified ad hoc on-demand distance vector (AODV) protocol for on-demand route discovery and optimal path selection, enhancing request and reply (RREQ and RREP) protocols. Evaluation of routing performance involves the BHT dataset, leveraging the ANN classifier to compute accuracy, precision, recall, F1 score, and loss. The NS-2.33 simulator facilitates the assessment of end-to-end delay, network throughput, and hop count during the path prediction phase. Remarkably, our methodology achieves 98.97% accuracy in detecting black hole attacks through the ANN classification model, outperforming existing techniques across various network routing parameters.

In this study, a theoretical examination is conducted to investigate the biosensing capabilities of different surface plasmon resonance (SPR) based hybrid multilayer structures, which are composed of two-dimensional (2D) materials. The transfer matrix formulation is implemented to calibrate the results of this study. A He–Ne laser of wavelength = 632.8 nm is used to simulate the results. Many permutations and combinations of layers of silver (Ag), aluminum oxynitride (AlON), and 2D materials were utilized to obtain the optimized structure. Ten dielectrics and twelve 2D materials were tested for a highly sensitive multilayer hybrid sensing design, which is composed of the prism (Ohara S-FPL53)/Ag/AlON/WS₂/AlON/sensing medium. The optimized biosensing design is capable of sensing and detecting analytes whose refractive variation is limited between 1.33 and 1.34. The maximum sensitivity, which is achieved by using the proposed design is 488.2° per RIU. Additionally, the quality factor, figure of merit, detection limit, and qualification limit values of the optimized design were also calculated to obtain a true picture of the sensing capabilities. The designing approach based on the multilayer hybrid SPR biosensors has the potential to develop various plasmonic biosensors that are related to food, chemical, and biomedical engineering fields.

The intelligent transportation system (ITS) relies heavily on the vehicular ad hoc network (VANET) and the internet of vehicles (IoVs), which combine cloud and fog to improve task processing capabilities. As a cloud extension, the fog processes’ infrastructure is close to VANET, fostering an environment favorable to smart cars with IT equipment and effective task management oversight. Vehicle processing power, bandwidth, time, and high-speed mobility are all limited in VANET. It is critical to satisfy the vehicles’ requirements for minimal latency and fast reaction times while offloading duties to the fog layer. We proposed a fuzzy logic-based task scheduling system in VANET to minimize latency and improve the enhanced response time when offloading tasks in the IoV. The proposed method effectively transfers workloads to the fog computing layer while considering the constrained resources of car nodes. After choosing a suitable processing unit, the algorithm sends the job and its associated resources to the fog layer. The dataset is related to crisp values for fog computing for system utilization, latency, and task deadline time for over 5000 values. The task execution, latency, deadline of task, storage, CPU, and bandwidth utilizations are used for fuzzy set values. We proved the effectiveness of our proposed task scheduling framework via simulation tests, outperforming current algorithms in terms of task ratio by 13%, decreasing average turnaround time by 9%, minimizing makespan time by 15%, and effectively overcoming average latency time within the network parameters. The proposed technique shows better results and responses than previous techniques by scheduling the tasks toward fog layers with less response time and minimizing the overall time from task submission to completion.

This study aims to theoretically address the design and analysis of an efficient pressure sensor designed using a polymer-based defective 1D annular photonic crystal (APC). The 1D APC comprises an alternate arrangement of Si and SiO₂ in a cylindrical fashion, incorporating a central defect layer. The investigation of the reflectance characteristics of the proposed structure is conducted by separately considering the polystyrene (PS) and the polymethyl methacrylate (PMMA) polymer materials as the defect layer. The pressure-sensitive refractive index of the polymers and the constituent materials of the APC play a vital role in envisaging the pressure-sensing application. The cornerstone of this study is represented by the shift analysis regarding the wavelength of the defect mode inside the band gap using different applied pressures, employing the modified transfer matrix method (MTMM). Various geometrical parameters like the defect polymer layer’s thickness and the APC period were carefully optimized to achieve an improved sensing performance. The proposed design demonstrated a remarkable pressure sensitivity and FoM of 51.29 nm/GPa and 301.7 GPa⁻¹, respectively, which is considerably high in the current research scenario. It is believed that the proposed structure can be an apt candidate for an innovative high-performance pressure sensor, and could play a key role in photonic integrated circuits.