International Journal of Sensors Wireless Communications and Control - Volume 15, Issue 4, 2025

Face identification and tracking have grown in importance in a variety of applications, including security, surveillance, and human-computer interaction. The goal of this paper is to create a real-time face detection and tracking system that uses a 2D convolutional neural network (CNN) model.

The model is trained on a dataset of facial images with accompanying bounding boxes, allowing it to recognize and localize faces in real-time video streams with high accuracy for 3D.VGG16 is a well-known CNN architecture that has shown excellent performance in image classification applications. Additional layers are added to the network to modify VGG16 for face detection: a fully connected layer for classification and two fully connected levels for bounding box regression.

Facial expressions are a great way to guide cursor movement, with average accuracies of about 95% and 95.5% for 2D and 3D models, respectively. Head movements are an additional intriguing technique with an accuracy rate of about 93% for 2D on average. Eye movements can be used to precisely control the cursor with average accuracy rates of about 94% and 93.5% for 2D and 3D models, respectively.

The different CNN models were compared based on the accuracies out of which VGG16 results better. The accuracy improvement of 0.05% and 1.58% was observed for the 3D model over the 2D model for facial expressions and head movements, respectively.

Enhancing localization accuracy while minimizing development costs poses significant challenges in deploying and managing wireless sensor networks (WSNs). This paper presents an advanced algorithm for node localization in indoor environments, integrating sophisticated optimization techniques. The hybrid algorithm, HSPPSO-TLBO, combines Hierarchical Structure Poly Particle Swarm Optimization (HSPPSO) with Teaching–Learning-Based Optimization (TLBO).

The proposed algorithm HSPPSO-TLBO aims to minimize the mean squared range error (MSRE) resulted by calculating internal distances between nodes using Received Signal Strength Indicator (RSSI). TLBO, with its robust global search capabilities, complements HSPPSO’s local search, preventing convergence to inappropriate local optima. HSPPSO-TLBO offers easy implementation and leverages the cost-free feature of RSSI, making it an attractive choice for enhancing localization precision.

Simulation results demonstrate the superior performance of HSPPSO-TLBO compared to other algorithms using different meta-heuristic optimization techniques. The outstanding performance of HSPPSO-TLBO is evident across various evaluation metrics, including localization error, localization rate, and simulation runtime.

The proposed algorithm utilizing HSPPSO and TLBO is exceptionally effective in improving localization precision in indoor WSNs due to several key characteristics. These include the seamless integration and easy implementation of both HSPPSO and TLBO, along with the cost-free advantage of using the RSSI technique. This combination makes the algorithm a highly functional solution for improving localization accuracy.

These days, communication is governed by scaled-down devices with extremely high data transfer rates. The days of data transfer rates in megabytes are long gone and the systems are touching data speeds of hundreds of gigabytes. To cater to this requirement, the evolution of metasurface-based antennas working in the THz frequency range is gaining pace.

Metasurface layers in antennas have unique mechanical and electrical properties that make them feasible. Graphene is a preferred metasurface material when designing antennas. In this paper, two novel designs of graphene-based metasurface antennas are proposed. These patch antennas with graphene metasurface layers have two configurations for slotted patches. Both graphene and gold-slotted patches are tested in this paper, and results are presented and validated for various performance parameters like return loss, peak gain, peak directivity, and radiation efficiency.

The design of the proposed Graphene Patch Antennas (GPA) is done in HFSS, and once the design is complete, the data is collected for variations in antenna parameters and is further processed via machine learning for better convergence in terms of return loss using ensemble learning. Optimal tuning of antenna components like substrate length, patch length, slot width, etc., is done using two regressor algorithms viz Histogram-based Gradient Boosting Regression Tree (HBDT) and Random Forest Tree (RFT) in machine learning. The radiation efficiencies of the two designs presented in this work are 89.04%, 97.54%, an average radiation efficiency of of 93.29%. The bandwidth of the two antenna designs is 8.63 THz and 8.69 THz, respectively.

Experimental results indicate that the proposed designs are achieved better bandwidth, and radiation efficiencies compared to state-of-art designs.

In the context of modern communication networks, the increasing demand for high data rates, minimal latency, and improved reliability—especially in scenarios involving Unmanned Aerial Vehicles (UAVs)—calls for innovative solutions. Millimeter-wave (mmWave) communication emerges as a fitting choice due to its capacity to offer ample bandwidth and high data rates.

This research paper presents a comprehensive study that compares the performance of mmWave communication links across four different frequencies (6, 28, 60, and 120 GHz) for UAV-to-Base Station (BS) communication. The investigation, conducted through simulations, adheres to atmospheric conditions such as rain and fog, different environments (urban and rural) scenarios. It evaluates the suitability of mmWave frequencies using critical performance metrics: latency, received power, Bit Error Rate (BER) and Signal-to-Interference plus Noise Ratio (SINR). These metrics play a fundamental role in assessing communication link quality.

Our findings reveal that leveraging higher mmWave frequencies—specifically 60 and 120 GHz—significantly enhances UAV-to-BS communication performance. Compared to lower frequencies, these higher bands exhibit reduced latency, higher data rates, and improved throughput. Consequently, mmWave frequencies beyond 60 GHz prove well-suited for UAV communication, facilitating efficient data exchange with minimal delay.

Furthermore, the potential for reduced interference and enhanced reliability at these elevated frequencies makes them particularly advantageous in applications requiring seamless connectivity. Examples include UAV-assisted emergency response, surveillance, and remote sensing. These insights hold substantial implications for the design and deployment of UAV communication systems, highlighting mmWave technology’s transformative potential in the emerging era of aerial communication.

The increasing expansions of IoT networks enforce the adoption of efficient resource allocation, energy management, and network congestion control.

In this regard, this paper proposes a brand-new hybrid quantum-driven optimization model integrating Pyramid Quantum Neural Network (Py-QNN), Deep Long Short-Term Memory (DLSTM), and Multi-fragmented Jaya Puzzle Optimization (FJPO). This optimizes energy consumption to the minimum, latency to the minimum, throughput to the maximum, and network lifetime by increasing the multi-layer architecture of cluster-based communication. A comparative study with models like LEACH, PEGASIS, and Direct Transmission shows better performance.

Simulation results show a reduction in energy consumption by up to 60%, 30-50% lower communication delay, and a throughput increase of 25%.

The proposed model is scalable and adaptable in real-time. Hence, it is suitable for large-scale dynamic IoT environments.