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The Industrial Internet of Things (IIoT) has become a transformative force in various healthcare applications, providing integrated services for daily life. The app healthcare based on the IIoT framework is broadly used to remotely monitor clients health using advanced biomedical sensors with wireless technologies, managing activities such as monitoring blood pressure, heart rate, and vital signs. Despite its widespread use, IIoT in healthcare faces challenges such as security concerns, inefficient work scheduling, and associated costs. To address these issues, this paper proposes and evaluates the Blockchain-Based Deep Reinforcement Learning System for Optimizing Healthcare (BDRL) framework. BDRL aims to enhance security protocols and maximize makespan efficiency in scheduling medical applications. It facilitates the sharing of legitimate and secure data among linked network nodes beyond the initial stages of data validation and assignment. This study presents the design, implementation, and statistical evaluation of BDRL using a new dataset and varying platform resources. The evaluation shows that BDRL is versatile and successfully addresses the security, privacy, and makespan needs of healthcare applications on distributed networks, while also delivering excellent performance. However, the framework utilizes high resources as the size of inserted data increases. [ABSTRACT FROM AUTHOR]

The present paper provides a technical overview about the most relevant vehicular communication technologies including IEEE 802.11p, IEEE 802.11bd, LTEV2X and NRV2X. IEEE 802.11p is the most matured one Wi-Fi based technology, and its successor, the IEEE 802.11bd is expected to be released in 2021. As CV2X (Cellular Vehicle to Everything) technologies, LTEV2X (Long Term Evolution V2X) and NRV2X (New Radio V2X) are discussed in this paper. The former one is already available, while the latter one's final specification will be finalized in 2020 by the 3GPP (Third Generation Partnership Project). These four technologies also motivate the already started transformation of vehicle industry by enabling basic safety features and more efficient traffic management, as well as cooperative maneuver execution towards high-level automated driving. The comparison of these technologies is essential to clarify their benefits completely. These technologies are considered as competitors, however, it is expected that they will coexist in the same geographical region. Thus, they will share on the same unlicensed frequency bands in 5.9 GHz domain. Therefore, different coexistence scenarios are also discussed in the paper to see how their advantages could be utilized. [ABSTRACT FROM AUTHOR]