SJEE

Enhanced Secure and Efficient Routing Algorithm for Optimal Multimedia Data Transmission

Mayur Natwarlal Bhalia, Arjav Ambaram Bavarva — Wed, 16 Jul 2025 06:49:57 +0000

Wireless Multimedia Sensor Networks (WMSNs) are critical for various applications requiring reliable and secure data transmission. Enhancing routing protocols in WMSNs is essential to improve performance and security. Existing routing techniques, such as LEACH, Directed Diffusion, and AODV, often suffer from high energy consumption, limited throughput, and vulnerability to security breaches. These limitations hinder the overall efficiency and reliability of WMSNs. Conventional methods struggle to maintain low latency and high data integrity under increasing network loads, leading to performance degradation. This study proposes the Enhanced Minimum Distance Secure Routing Algorithm (EMDSRA), designed to optimize energy efficiency, increase throughput, and enhance security in WMSNs. The dataset comprises simulations with node densities of 100, 200, 300, 400, 500, and 600 nodes, evaluating metrics such as energy consumption, data throughput, latency, and security. Experimental results show that E-MDSRA reduces energy consumption, increases throughput and significantly improves security metrics compared to existing techniques. Specifically, E-MDSRA shows an improvement in data integrity and reduction in unauthorized access incidents. In comparison, Directed Diffusion and AODV also show improvements, but EMDSRA outperforms them across all evaluated metrics. In conclusion, E-MDSRA demonstrates substantial improvements in network efficiency and security, making it a robust solution for future WMSN deployments.

Advanced Stimulation and Dog Behavior Analysis System

Milan Stojanović, Dejan Stevanović, Slavimir Stošović — Wed, 16 Jul 2025 06:58:49 +0000

This paper describes a system for analyzing the behavior of a hunting dog using the detection of the dog’s barking and its speed of movement, as well as a system for sound and vibration stimulation of the dog and LED module. These systems are part of a more complex system for tracking hunting dogs that enables hunters to monitor the movement and behavior of their dog during the hunt. Monitoring a dog’s barking provides critical insight during a hunt, especially when the dog is out of sight and pursuing game. By tracking the number of barks per minute, the hunter can determine whether the dog is actively in pursuit. If the hunter needs the dog to return or to stop chasing prey, they can issue an audible or vibratory command, provided the dog has been adequately trained. Detecting the dog’s movement speed is equally important in bird hunting, since a dog freezes in place when it locates a bird. The GPS module can pinpoint the dog’s location but cannot reveal its behavior at that spot.

Developing Tunable Machine Learning Workflow for Traffic Analysis in SDN

Sama Salam Samaan, Hassan Awheed Jeiad — Wed, 16 Jul 2025 07:07:32 +0000

Traffic monitoring is a critical issue in networking in general, especially in SDN due to its layered architecture in which the control plane represents a single point of failure. Therefore, this paper is tailored to mitigate the control and mitigate the effect od the DDoS attacks in SDN networks. It presents a complete machine learning (ML) workflow that begins with data ingestion and end with a trained model that is capable of analyzing packets in a production network. Three ML pipelines are part of this workflow, where the training process is carried out on a distributed framework, i.e., Spark, to accomplish a near real time analysis for each flow of packets. To evaluate the performance of the suggested workflow, the LRHR DDoS 2024 dataset is employed. The decision tree model outperforms the remaining models with 99% of accuracy and 4 min 33 s of training time.

A PSO-Based Approach for Parameter Estimation in Synchronous Machines

Farid Leguebedj, Youcef Benmahamed, Djamel Boukhetala, Kamel Boughrara — Wed, 16 Jul 2025 07:21:34 +0000

This study employs the particle swarm optimization (PSO) approach using Stand Still Frequency Responses Testing (SSFR) to identify the time constants (poles and zeros) of the operational inductances along the d and q axes, as well as the parameters of the equivalent circuits for the SSFR1, SSFR2, and SSFR3 synchronous machine models. The difference between the frequency responses of the identified and simulated models at a standstill is minimized using a quadratic criterion in this method. The SSFR3 model accurately represents the synchronous machine, and simulation results show that the PSO approach is effective in terms of convergence rate and offers ideal solutions.

Elliptic Curve Cryptography and Biometrics for IoT Authentication

Wed, 16 Jul 2025 07:34:15 +0000

The Internet of Things (IoT) is now present in every aspect of our daily lives because of its ability to offer remote services. Unfortunately, the insecure transmission of user data in open channels caused by this significant use of IoT networks makes it vulnerable to malicious use. Hence, the security of the user’s data is now a serious matter in an IoT environment. Since authentication may prevent hackers from recovering and using data transmitted between IoT devices, researchers have proposed many lightweight IoT authentication protocols over the past decades. Many of these protocols are built around two authentication factors. They cannot guarantee unlinkability and perfect forward secrecy, as well as withstand well-known attacks such as node capture, DOS attack, stolen verifier, Denning-Sacco attack, and GWN bypass. This paper proposes an Elliptic Curve Cryptography (ECC) -based authentication protocol that is anonymous and exploits three authentication factors to ensure all security services and withstand well-known attacks. Our provided protocol is secure and can resist known attacks, as demonstrated by both informal security analysis and formal security proof using ProVerif. Lastly, our protocol and other protocols are compared in terms of computational costs, communication costs, and security features.

A Grey Wolf Optimization Based Approach to Provide Ancillary Services for Battery Owners

Wed, 16 Jul 2025 07:44:51 +0000

As is known, batteries have started to be used increasingly in both power distribution and transmission networks. This study develops a near-optimal approach for ancillary services in power networks from the perspective of the battery owner. We first model the optimization algorithm for the battery owner, then utilize a grey wolf optimization approach, where near-optimal actions are selected daily from available services. We use real data of frequency, voltage magnitude, combined home and Photovoltaic system, and transformer load to perform the simulations. The simulation results show that battery owners may profit from these services and help the system operators solve the issues such as over-voltage, under-voltage, frequency, and similar.

UWB Slot-Loaded Antipodal Vivaldi Antenna for Through-the-Wall Radar Imaging (TWRI) Applications

Wed, 16 Jul 2025 07:59:47 +0000

The study presents an Ultra-Wideband Slot-loaded Antipodal Vivaldi Antenna (SL-AVA) designed for through-the-wall radar imaging (TWRI) applications. The antenna incorporates rectangular slots of varying lengths and widths, effectively extending its electrical length and suppressing surface waves. These variable-length slots play a crucial role in enhancing overall performance by improving bandwidth, impedance matching, and radiation characteristics. Fabricated on a Rogers 5880 substrate with dimensions of 60.50 × 66.10 mm², the SL-AVA operates efficiently across a wide frequency range of 3 GHz to 10 GHz. It achieves a peak gain of 11 dBi. Experimental fabrication and testing validate the SL-AVA antenna’s characteristics, including compact size, high gain, ultra-wide bandwidth, and directional radiation, making it an excellent choice for TWRI applications.

Redefining Dental Image Processing: De-Convolutional Component with Residual Prolonged Bypass for Enhanced Teeth Segmentation

Kumar Prasun, Anil Verma, Rajiv Mishra — Wed, 16 Jul 2025 08:08:53 +0000

Dental diseases have risen in the past few years due to improper hygiene. Early detection and diagnosis can control this rapid growth in dental diseases. Therefore, different traditional techniques are employed for the detection of dental problems. However, these classical techniques such as X-Ray and CT scans are considered to be time-consuming, ineffective, and prone to errors due to human intervention. Hence, AI techniques are used to obtaining precise outcomes for dental-related issues. The conventional ML (Machine Learning) techniques are inefficient for obtaining enhanced outcomes as the efficiency of ML techniques heavily depends on image processing approaches. They are performed and also the quality of the features that have been extracted. Further, ML techniques lack in producing better outcomes while dealing with huge datasets. Therefore, the proposed model employs DL (Deep Learning) techniques due to its capability to learn the features strongly from the data by using a general-purpose learning procedure. So, DL techniques can work efficiently on huge datasets. The proposed DC (De-convolution Component) with RES (Residual Prolonged Bypass) is employed in the present research work as it is responsible to increase the spatial resolution of the feature maps and helps in recovering lost spatial information during the down sampling process. Likewise, the RES model aids in proficiently proliferating both low-level and high-level features to the deep layers, which help in generating better-segmented images. RES model includes prolonged bypass paths that carry feature information across multiple layers. This ensures that features extracted at earlier layers (low-level features) are available at much deeper layers. Implementation of the present research work contributes to enhancing the overall performance and effectiveness in detecting and diagnosing various dental issues and possesses the capability to work on both small and massive datasets effectively. Also, the proposed work contributes to deliver better accuracy, IoU (Intersection Over Union) and Dice coefficient, compared to Multi-Headed CNN and Context Encoder-Net, thereby assisting dental professionals in the detection and diagnosis of various dental issues due to the effectiveness of the proposed model.