Wireless Body Area Network(WBAN)is a cutting-edge technology that is being used in healthcare applications to monitor critical events in the human body.WBAN is a collection of in-body and on-body sensors that monitor ...Wireless Body Area Network(WBAN)is a cutting-edge technology that is being used in healthcare applications to monitor critical events in the human body.WBAN is a collection of in-body and on-body sensors that monitor human physical parameters such as temperature,blood pressure,pulse rate,oxygen level,body motion,and so on.They sense the data and communicate it to the Body Area Network(BAN)Coordinator.The main challenge for the WBAN is energy consumption.These issues can be addressed by implementing an effective Medium Access Control(MAC)protocol that reduces energy consumption and increases network lifetime.The purpose of the study is to minimize the energy consumption and minimize the delay using IEEE 802.15.4 standard.In our proposed work,if any critical events have occurred the proposed work is to classify and prioritize the data.We gave priority to the highly critical data to get the Guarantee Tine Slots(GTS)in IEEE 802.15.4 standard superframe to achieve greater energy efficiency.The proposed MAC provides higher data rates for critical data based on the history and current condition and also provides the best reliable service to high critical data and critical data by predicting node similarity.As an outcome,we proposed a MAC protocol for Variable Data Rates(MVDR).When compared to existing MAC protocols,the MVDR performed very well with low energy intake,less interruption,and an enhanced packet-sharing ratio.展开更多
携手共赴金色十年壮阔新征程,合力共创智能时代可持续发展。2024年是“一带一路”倡议“下一个金色十年”的崭新起点,也是高质量共建“一带一路”八项行动的重要节点。推动科技创新作为“八项行动”的关键内容,为通用人工智能时代下世...携手共赴金色十年壮阔新征程,合力共创智能时代可持续发展。2024年是“一带一路”倡议“下一个金色十年”的崭新起点,也是高质量共建“一带一路”八项行动的重要节点。推动科技创新作为“八项行动”的关键内容,为通用人工智能时代下世界各国现代化发展带来无限可能,为一带一路可持续发展目标实现注入强劲动能。为此,中国人工智能学会联合IEEE China Council主办《2024 IEEE“一带一路”人工智能可持续发展大会》,携手打造国际化交流平台,构建全球性共创模式,为一带一路高质量、可持续发展凝共识、集众智、聚合力、谋未来。展开更多
信道预测是支撑变电站等电力物联网通信系统自适应传输的重要技术。为了解决过期信道状态信息降低通信系统自适应传输性能的问题,提出了一种基于自适应跳跃学习网络的信道状态信息预测方法。该方法主要包括递归微调算法和混合惩戒网络...信道预测是支撑变电站等电力物联网通信系统自适应传输的重要技术。为了解决过期信道状态信息降低通信系统自适应传输性能的问题,提出了一种基于自适应跳跃学习网络的信道状态信息预测方法。该方法主要包括递归微调算法和混合惩戒网络两部分。其中,前者主要用于微调学习网络的随机输入权重矩阵,后者主要通过两层惩戒网络来解决输出权重矩阵的病态解问题。由于具有oracle属性,自适应跳跃学习网络不仅具有良好的泛化能力,还可以生成稀疏性输出权重矩阵。仿真结果表明,自适应跳跃学习网络在IEEE802.11ah协议的正交频分复用(Orthogonal Frequency Division Multiplexing,OFDM)通信系统中具有良好的单步预测性能和多步预测性能。展开更多
文摘Wireless Body Area Network(WBAN)is a cutting-edge technology that is being used in healthcare applications to monitor critical events in the human body.WBAN is a collection of in-body and on-body sensors that monitor human physical parameters such as temperature,blood pressure,pulse rate,oxygen level,body motion,and so on.They sense the data and communicate it to the Body Area Network(BAN)Coordinator.The main challenge for the WBAN is energy consumption.These issues can be addressed by implementing an effective Medium Access Control(MAC)protocol that reduces energy consumption and increases network lifetime.The purpose of the study is to minimize the energy consumption and minimize the delay using IEEE 802.15.4 standard.In our proposed work,if any critical events have occurred the proposed work is to classify and prioritize the data.We gave priority to the highly critical data to get the Guarantee Tine Slots(GTS)in IEEE 802.15.4 standard superframe to achieve greater energy efficiency.The proposed MAC provides higher data rates for critical data based on the history and current condition and also provides the best reliable service to high critical data and critical data by predicting node similarity.As an outcome,we proposed a MAC protocol for Variable Data Rates(MVDR).When compared to existing MAC protocols,the MVDR performed very well with low energy intake,less interruption,and an enhanced packet-sharing ratio.
文摘携手共赴金色十年壮阔新征程,合力共创智能时代可持续发展。2024年是“一带一路”倡议“下一个金色十年”的崭新起点,也是高质量共建“一带一路”八项行动的重要节点。推动科技创新作为“八项行动”的关键内容,为通用人工智能时代下世界各国现代化发展带来无限可能,为一带一路可持续发展目标实现注入强劲动能。为此,中国人工智能学会联合IEEE China Council主办《2024 IEEE“一带一路”人工智能可持续发展大会》,携手打造国际化交流平台,构建全球性共创模式,为一带一路高质量、可持续发展凝共识、集众智、聚合力、谋未来。
文摘信道预测是支撑变电站等电力物联网通信系统自适应传输的重要技术。为了解决过期信道状态信息降低通信系统自适应传输性能的问题,提出了一种基于自适应跳跃学习网络的信道状态信息预测方法。该方法主要包括递归微调算法和混合惩戒网络两部分。其中,前者主要用于微调学习网络的随机输入权重矩阵,后者主要通过两层惩戒网络来解决输出权重矩阵的病态解问题。由于具有oracle属性,自适应跳跃学习网络不仅具有良好的泛化能力,还可以生成稀疏性输出权重矩阵。仿真结果表明,自适应跳跃学习网络在IEEE802.11ah协议的正交频分复用(Orthogonal Frequency Division Multiplexing,OFDM)通信系统中具有良好的单步预测性能和多步预测性能。
