Nov 23
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The IUP Journal of Telecommunications
ISSN: 0975-5551
A 'peer reviewed' journal indexed on Cabell's Directory,
and also distributed by EBSCO and Proquest Database
It is a quarterly journal that publishes multidisciplinary research papers encompassing conceptual, theoretical and empirical studies relating to: Modeling, analysis, design and management of telecommunication systems; Transmission systems and Signaling system; Time division switching systems; Radio waves, Radar imaging, Satellite communication; Artificial Intelligence; Fibre optics and Photonic switching; Performance evaluation of Wide Area and Local Networks; Security issues of Mobile Networks; Standardization and regulatory issues, etc.
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Design and Implementation of Efficient Transmission of Cloud Data on Wireless Media
Nowadays, information security is a challenge, especially when the data is transmitted or shared on public clouds. Researchers have proposed various techniques which fail to provide data integrity, security, authentication and data sensitivity. The most common techniques used to protect data during transmission on public clouds are cryptography, steganography and compression. The current study suggests an entirely new approach that completely makes secret data invisible behind a carrier object and cannot be detected with image performance parameters like PSNR, MSE, entropy, etc. The proposed technique has better outcome than any other existing techniques as a security mechanism on a public cloud. The primary focus of the suggested technique is to minimize integrity loss of public storage data due to unrestricted access rights of users. To improve reusability of carrier, even after data is concealed, is really a challenging task and can be achieved through the proposed approach.
Detecting and Classifying Vehicles Under Uncontrolled Environmental Conditions: A Transfer Learning-Based Approach
Vehicle detection and classification is an active area of research in vehicle surveillance system and has numerous applications in intelligent transportation system. In vehicle surveillance system, identifying the make and model of a vehicle is crucial for traffic monitoring. Due to intraclass diversity, viewpoint variation, and variable illumination conditions, identifying vehicle make and model is a difficult process. Earlier researchers have used different datasets for the detection and classification of vehicle as per their make and model. However, a majority of these studies have used datasets of clear high-quality images. The present study uses dataset with vehicle images taken under uncontrolled environmental conditions such as images with different illumination, shadowing, reflection from vehicle surface, etc. and employs transfer learning approach to develop an efficient algorithm for computer vision based on traffic surveillance system that can detect and classify vehicles. The study further compares and analyzes the accuracy and precision of the two transfer learning techniques. The study deploys machine learning (ML) classifiers for classifying the vehicles as per their models.
Deployment Techniques for Sensor Nodes in Traditional and 5G Wireless Networks
Deployment of sensor nodes is an essential issue in wireless sensor networks (WSNs). The first step involves effective deployment of sensor nodes in the target area based on predetermined location to ensure guaranteed quality of surveillance. One of the hardest tasks in a WSN is figuring out how to route data from source to destination. Sensors gather data from the sensor network area and transmit it to various locations, and a large number of nodes must be deployed in a proper way. There are numerous deployment techniques available to enhance localization success rates and accuracy. The paper examines significant research findings related to sensor deployment, while also outlining key findings with examples of supporting applications. Finally, the unique characteristics and requirements of these deployment techniques for sensor nodes in 5G-integrated traditional WSNs are discussed.
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