通过使用通过网络连接的“智能”设备和应用程序,可以提高个人和团队的绩效。在体育运动中,物联网(IoT)是指通过网络连接的所有“智能”设备和应用程序,以将伤害降至最低,发展先进的培训技术,并应用分析性先进的运动改进方法来提高总体运动表现。体育中的物联网(IoT)与体育中的安全和隐私目标密切相关,这已经成为近年来体育界非常关注的话题,体育年采用物联网证明了这一点。出于这个原因,安全漏洞可能会带来灾难性的后果,包括个人数据的披露,操纵统计结果,损害组织的声誉,以及体育组织的巨大经济损失。一种或多种后果,如前所述,与体育组织和这些组织成员的运动员有关,它们对相应的体育相关集合有直接影响,与医疗有关的,和辅助医疗企业,特别是那些提供专业运动器材和相关服务的人。在采用或构建安全可靠的体育物联网基础设施时,人们早已认识到检测和量化威胁的迫切需要,以更好地支持决策。这变得越来越普遍。使用先进的机器学习算法,这项研究为网络安全防御中的技术优化提供了一种方法,然后在一个独特的案例研究中使用排球运动员来证明其有效性。结合蒙特卡罗优化技术,更详细地介绍了模糊认知图(FCM)的升级变体。该模型用于排球行业风险识别的特定场景,评估,和物联网体育网络的优化。
Individual and team performance can be improved by utilizing \"smart\" devices and applications that are connected through networks. In sports, the Internet of Things (IoT) refers to all of the \"smart\" devices and applications linked through networks to reduce injuries to the bare minimum, develop advanced training techniques, and apply analytical advanced sports improvement methodologies to improve sports performance in general. The Internet of Things (IoT) in sports is closely related to the objective of both security and privacy in sports, which has become a topic of crucial concern for the sports business in recent years, as evidenced by the adoption of IoT in sports years. For this reason, security flaws can have catastrophic consequences, including the disclosure of personal data, the manipulation of statistical findings, the harming of organizations\' reputations, and enormous financial losses for the sporting organization. One or more of the consequences, as previously mentioned, is related to sports organizations and the athletes who are members of those organizations, and they have a direct impact on the corresponding set of sports-related, medical-related, and paramedical enterprises, specifically those that provide specialized sports equipment and associated services. A critical need to detect and quantify threats has long been recognized to better support decision-making when adopting or constructing a safe and reliable sports Internet-of-Things infrastructure, which is becoming increasingly common. Using advanced machine learning algorithms, this research provides a methodology for technology optimization in cybersecurity defenses that is then used in a unique
case study utilizing
volleyball players to demonstrate its effectiveness. In conjunction with a Monte Carlo optimization technique, an upgraded variant of fuzzy cognitive maps (FCM) is presented in greater detail. This model is utilized for a specific scenario of risk identification of
volleyball industry, assessment, and optimization for IoT sports networks.