Mode 4 of 3GPP Cellular Vehicle-to-Everything (C-V2X) uses a new Sensing-Based Semi-Persistent Scheduling (SB-SPS) algorithm to manage its radio resources. SB-SPS applies a probabilistic approach to provide the resource allocation in the system. The resource keep probability ($P_{rk}$) variable plays an essential role in the resource allocation mechanism. Most of the previous works used a fixed $P_{rk}$ value. However, the Packet Delivery Ratio (PDR) can be improved by adapting the optimal $P_{rk}$ value. Hence, we propose a Fuzzy Inference System (FIS) with two inputs, distance, and Channel State Information (CSI) to determine the suitable $P_{rk}$. The simulation results show that the proposed FIS method outperforms the other methods for sparse and congested road scenarios, with total numbers of vehicles at 200 and 400, respectively.

The modulation coding scheme (MCS) index is the essential configuration parameter in cellular vehicle-to-everything (C-V2X) communication. As referenced by the 3rd Generation Partnership Project (3GPP), the MCS index will dictate the transport block size (TBS) index, which will affect the size of transport blocks and the number of physical resource blocks. These numbers are crucial in the C-V2X resource management since it is also bound to the transmission power used in the system. To the authors’ knowledge, this particular area of research has not been previously investigated. Ultimately, this research establishes the fundamental principles for future studies seeking to use the MCS adaptability in many contexts. In this work, we proposed the application of the reinforcement learning (RL) algorithm, as we used the Q-learning approach to adaptively change the MCS index according to the current environmental states. The simulation results showed that our proposed RL approach outperformed the static MCS index and was able to attain stability in a short number of events.

Healthcare systems face growing challenges in protecting patient information, with more than 276 million healthcare records breached in 2024 alone. This study presents a proof-of-concept consortium blockchain framework that integrates Near Field Communication (NFC) based authentication with smart contract driven consensus to securely verify and synchronize Electronic Medical Records (EMRs) across multiple healthcare facilities. The system was tested in a simulated network of three Virtual Private Servers, achieving an average NFC verification time of 2.9 seconds and a consensus propagation time of 0.4 seconds, demonstrating stable performance suitable for near-real-time operations.Although these results are promising, the evaluation was limited to synthetic datasets, small-scale network conditions,and basic database security configurations. Future work will focus on scaling the system to larger and more diverse networks, strengthening cybersecurity measures, and ensuring full compliance with HIPAA and GDPR standards. By supporting the United Nations’3rd Sustainable Development Goal on Health and the 9th Sustainable Development Goal on Infrastructure and Innovation, this research contributes to the development of secure, interoperable, and sustainable healthcare information systems.

The use of a static modulation coding scheme (MCS), such as 7, and resource keep probability ($P_{rk}$) value, such as 0.8, was proven to be insufficient to achieve the best packet reception ratio (PRR) performance. Various adaptation techniques have been used in the following years. This work introduces a novel optimization algorithm approach called the fuzzy inference reinforcement learning (FIRL) sequence for adaptive parameter configuration in cellular vehicle-to-everything (C-V2X) mode-4 communication networks. This innovative method combines a Sugeno-type fuzzy inference system (FIS) control system with a Q-learning reinforcement learning algorithm to optimize the PRR as the key metric for overall network performance. The FIRL sequence generates adaptive configuration parameters for $P_{rk}$ and MCS index values each time the Long-Term Evolution (LTE) packet is generated. Simulation results demonstrate the effectiveness of this optimization algorithm approach, achieving up to a 169.83% improvement in performance compared to static baseline parameters.

The automotive industry is constantly evolving, demanding advanced technologies for growth. Among these is cellular technology's long-term evolution (LTE), which 3GPP is pioneering to provide vehicle-to-everything (V2X) connectivity. It concentrates on vehicle-to-vehicle (V2V) direct communications, essential for safety applications like traffic management or infotainment. Something significant here is having a system that allows them to understand each other and cooperate. All vehicles should be able to do this by sending messages about what they are doing and where they are so that it will be safer for drivers moving through town where there may be several hurdles that break the signal lines or block them out completely. Still, before this goal can be reached, there is a need for clever ways of using packets to maximize radio channels while ensuring the service works correctly. This task poses a considerable challenge while constructing algorithms that can handle it, which is an open development question. To solve this development question, a simulator named LTEV2Vsim has been introduced. Written in MATLAB, this simulator uses straightforward models for managing vehicle movement or more complex ones based on realistic urban environment input files. This simulator is helpful for scholars interested in validating and designing LTE-V2V networks. The ability to simulate urban environment scenarios and give example results will help develop a better system for carrying packets and thus ensure that cooperative awareness services are successfully implemented in V2X communication.