2692-4048ISSN (Online)
2770-0070ISSN (Print)
Unmanned aerial vehicles (UAVs) and swarms unmanned aerial vehicles (UAVs) have recently shown themselves capable of providing dependable and reasonably priced solutions for a variety of real-world issues. UAVs provide a wide range of services due to their autonomy, adaptability, mobility, and communications interoperability. Despite the fact that UAVs are frequently used to facilitate ground communications, data exchanges inside those networks are susceptible to security threats due to the ease with which radio and Wi-Fi signals can be hacked. However, there are many ways to stop cyberattacks. One of the potential methods to enhance user privacy, data security, and authentication—especially in peer-to-peer UAV networks—may be blockchain technology, which has lately gained prominence. Using the benefits of blockchain technology, several entities can communicate in a decentralized. This paper uses some supporting technologies to provide a thorough overview of privacy and security integration in blockchain-assisted swarm and UAV networks. For this goal, this work is compared to earlier research to find effective solutions, and blockchain technology is integrated to improve the capacity of swarm UAV networks and communication to move, manage, and exchange data. We conclude by talking about open research issues, the limitations of the UAV standards as they stand right now, and possible research paths in the future. This comprehensive review is an invaluable tool to know study and analyze a good number of reviews and research papers in recent years to overcome obstacles and find appropriate solutions for integrating UAV swarms with block chain Technology.
Read MoreDoi: https://doi.org/10.54216/FPA.180101
Vol. 18 Issue. 1 PP. 01-23, (2025)
The lack of practical teaching tools, such as a robotic arm, hinders students' understanding of complex concepts in robotics courses, where hands-on experience is essential for effective learning. This study introduced a 6DOF Robotic Arm as a teaching aid to address this issue, evaluating its impact through an experimental study with 30 computer science students. The findings revealed that the robotic arm effectively enhanced both basic and advanced Arduino programming skills, with students who used it performing better and expressing higher satisfaction than those who did not. The study also identified gaps in hardware control comprehension, leading to software development that could further aid in mastering programming concepts. The paper concludes with a discussion of the potential of the robotic arm as a valuable educational tool and its implications for future research and practical applications.
Read MoreDoi: https://doi.org/10.54216/FPA.180102
Vol. 18 Issue. 1 PP. 24-34, (2025)
Throughout a Wireless Sensor Network (WSN), information collected from the environment is continuously transmitted from one node to the next, and then the main collector or server receives and processes it. With the growth of a network, data transfers within the network also grow dramatically. Medical images increase traffic on a network if they are transmitted. An interlayer transmission protocol (WSN) was developed for this study. Pixels are used to create the medical image using the protocol. A gray-level medical image with 512x512 pixels provided by Brain was used to conduct the study. Medical image size is reduced from 256 KB to 192 KB, providing a 25% advantage. A study found SSIM of 51, 1365 and PSNR of 0,9976 for the structural similarity ratio (SSIM). The Advanced Encryption Standard (AES) encryption algorithm safeguards data during the transfer. By creating such a layer, transmissions became safer. In the WSNs, 12.5% and 25% of the data transfer has been reduced based on the information obtained from the study without changing the medical image.
Read MoreDoi: https://doi.org/10.54216/FPA.180103
Vol. 18 Issue. 1 PP. 35-48, (2025)
Recently, the complex network has become popular use as it can transfer huge amounts of multimedia, text, ideas, and other information, encouraging many participant connections. Social media is one of these networks that make the most connections. Predicting the formation or dissolution of links between nodes presents a problem for social network analysis researchers. Since social networks are dynamic, this task is exciting as it may also forecast lost network links with less information. On the other way, current link prediction methods use simply node similarity to find links. This study proposes a new technique that relies on node attributes and similarity measures. Nodes are labeled by their centrality and similarity. The network's edges are negative and positive samples. A well-defined dataset for link prediction comprises the features of the nodes at the edges labeled either positive or negative. The dataset is passed to multiple machine learning classifiers. On several real-world networks. The experiments conducted during the research show that Gradient Boosting gave the highest accuracy of 99% compared with other methods.
Read MoreDoi: https://doi.org/10.54216/FPA.180104
Vol. 18 Issue. 1 PP. 41-55, (2025)
Monitoring and analyzing athletes' jumps system using Electromyography (EMG) signals based on Virtual Instruments (LabVIEW) is presented in this paper. This system was prototyped using the virtual instrument workbench (LabVIEW) to display the jumping pattern. In Jump analysis hardware (JA-H/W), there are sensory boards, ultrasonics, and wireless communication systems. To measure the minimum foot clearance (MFC) and orientation, there have been two types of systems used to simulate Jump Analysis Software Ultrasonic (JAS-UltSnc) as well as Inertial Measurement Unit (JAS-IntMeUnt). Combining JAS-UltSnc with JAS-IntMeUnt provided a complete solution with error correction. LabVIEW is used to display the jump patterns generated by the system and analyze the jump patterns of the athlete.
Read MoreDoi: https://doi.org/10.54216/FPA.180105
Vol. 18 Issue. 1 PP. 56-65, (2025)