Nov 23
The IUP Journal of Telecommunications
Focus
This issue contains three papers. The first two papers discuss data transmission on wireless media and vehicle detection and identification modeling applicable for intelligent transport systems, respectively. The last paper is a review of various technologies used in the deployment of wireless sensor nodes and their performance characterization.
The first paper, "Design and Implementation of Efficient Transmission of Cloud Data on Wireless Media" by Virendra P Nikam and Sheetal S Dhande, proposes different techniques to provide data integrity, security, authentication and sensitivity to ensure reliable and secure communication in a wireless environment. Data security becomes a crucial issue especially when the data is shared on public clouds, and therefore needs cryptography, steganography and compression algorithms. The paper suggests an entirely new approach to make secret data invisible behind a carrier object, and claims that it provides better protection compared to the existing conventional techniques.
In a modern vehicular communication system, vehicle detection and classification is of significance to ensure a reliable vehicle surveillance system, incorporating various control and management operations tools. Recent research has focused on designing and developing suitable vehicle identification algorithms under noisy visual environment to ensure its location in real time for automatic updation in the data server to realize a robust intelligent transport system. The second paper, "Detecting and Classifying Vehicles Under Uncontrolled Environmental Conditions: A Transfer Learning-Based Approach" by Agha Asim Husain, Tanmoy Maity, R K Yadav and Mritunjay Rai, presents a dataset of vehicle images taken under uncontrolled varied environmental visual conditions. The captured dataset is used to develop an efficient algorithm for computer vision-based traffic surveillance system. The study further compares and analyzes the accuracy and precision of the two transfer learning techniques discussed and uses machine learning classifiers for classifying the vehicles as per respective models.
Deployment of sensor nodes at optimum locations to ensure minimum delay and lower energy consumption during node data transfer is a crucial issue in the construction of a wireless sensor network. The deployment of sensor nodes in the target area requires a predetermined location to ensure guaranteed quality of service for a selected network topology to route the data from source to destination under a minimum energy consumption protocol. The last paper, "Deployment Techniques for Sensor Nodes in Traditional and 5G Wireless Networks" by P Anjani Kumar, discusses the research findings related to wireless sensor deployment and confirms the compatibility of 5G Networks with the traditional wireless sensor networks.
Consulting Editor
| Article | Price (₹) | ||
| Design and Implementation of Efficient Transmission of Cloud Data on Wireless Media |
100
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| Detecting and Classifying Vehicles Under Uncontrolled Environmental Conditions: A Transfer Learning-Based Approach |
100
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| Deployment Techniques for Sensor Nodes in Traditional and 5G Wireless Networks |
100
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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.