电力线和无线混合通信可以优势互补提升电力物联网室内覆盖的综合性能。针对无线接入和电力线中继的混合通信场景,文章提出一种适用于两跳混合中继的通用MAC层算法。综合考虑物理层信道参数和MAC层的载波侦听多路访问(carrier sense mul...电力线和无线混合通信可以优势互补提升电力物联网室内覆盖的综合性能。针对无线接入和电力线中继的混合通信场景,文章提出一种适用于两跳混合中继的通用MAC层算法。综合考虑物理层信道参数和MAC层的载波侦听多路访问(carrier sense multiple access,CSMA)退避流程,建立了该MAC层算法的跨层性能分析模型,推导了两跳传输系统归一化吞吐量以及系统时延等性能;最后仿真验证了算法有效性和模型准确性,分析了物理层和MAC层关键参数影响系统性能的机理。与Basic-RTS/CTS算法相比,所提算法可有效提升系统吞吐量和时延性能。展开更多
Hybrid Power-line/Visible-light Communication(HPVC)network has been one of the most promising Cooperative Communication(CC)technologies for constructing Smart Home due to its superior communication reliability and har...Hybrid Power-line/Visible-light Communication(HPVC)network has been one of the most promising Cooperative Communication(CC)technologies for constructing Smart Home due to its superior communication reliability and hardware efficiency.Current research on HPVC networks focuses on the performance analysis and optimization of the Physical(PHY)layer,where the Power Line Communication(PLC)component only serves as the backbone to provide power to light Emitting Diode(LED)devices.So designing a Media Access Control(MAC)protocol remains a great challenge because it allows both PLC and Visible Light Communication(VLC)components to operate data transmission,i.e.,to achieve a true HPVC network CC.To solve this problem,we propose a new HPC network MAC protocol(HPVC MAC)based on Carrier Sense Multiple Access/Collision Avoidance(CSMA/CA)by combining IEEE 802.15.7 and IEEE 1901 standards.Firstly,we add an Additional Assistance(AA)layer to provide the channel selection strategies for sensor stations,so that they can complete data transmission on the selected channel via the specified CSMA/CA mechanism,respectively.Based on this,we give a detailed working principle of the HPVC MAC,followed by the construction of a joint analytical model for mathematicalmathematical validation of the HPVC MAC.In the modeling process,the impacts of PHY layer settings(including channel fading types and additive noise feature),CSMA/CA mechanisms of 802.15.7 and 1901,and practical configurations(such as traffic rate,transit buffer size)are comprehensively taken into consideration.Moreover,we prove the proposed analytical model has the solvability.Finally,through extensive simulations,we characterize the HPVC MAC performance under different system parameters and verify the correctness of the corresponding analytical model with an average error rate of 4.62%between the simulation and analytical results.展开更多
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
Detecting sophisticated cyberattacks,mainly Distributed Denial of Service(DDoS)attacks,with unexpected patterns remains challenging in modern networks.Traditional detection systems often struggle to mitigate such atta...Detecting sophisticated cyberattacks,mainly Distributed Denial of Service(DDoS)attacks,with unexpected patterns remains challenging in modern networks.Traditional detection systems often struggle to mitigate such attacks in conventional and software-defined networking(SDN)environments.While Machine Learning(ML)models can distinguish between benign and malicious traffic,their limited feature scope hinders the detection of new zero-day or low-rate DDoS attacks requiring frequent retraining.In this paper,we propose a novel DDoS detection framework that combines Machine Learning(ML)and Ensemble Learning(EL)techniques to improve DDoS attack detection and mitigation in SDN environments.Our model leverages the“DDoS SDN”dataset for training and evaluation and employs a dynamic feature selection mechanism that enhances detection accuracy by focusing on the most relevant features.This adaptive approach addresses the limitations of conventional ML models and provides more accurate detection of various DDoS attack scenarios.Our proposed ensemble model introduces an additional layer of detection,increasing reliability through the innovative application of ensemble techniques.The proposed solution significantly enhances the model’s ability to identify and respond to dynamic threats in SDNs.It provides a strong foundation for proactive DDoS detection and mitigation,enhancing network defenses against evolving threats.Our comprehensive runtime analysis of Simultaneous Multi-Threading(SMT)on identical configurations shows superior accuracy and efficiency,with significantly reduced computational time,making it ideal for real-time DDoS detection in dynamic,rapidly changing SDNs.Experimental results demonstrate that our model achieves outstanding performance,outperforming traditional algorithms with 99%accuracy using Random Forest(RF)and K-Nearest Neighbors(KNN)and 98%accuracy using XGBoost.展开更多
Introduction: The cicatricial acceleration method (MAC®) promotes photobiological effects of an anti-inflammatory and healing nature. Its therapeutic radiation is emitted, producing photobiostimulant effects that...Introduction: The cicatricial acceleration method (MAC®) promotes photobiological effects of an anti-inflammatory and healing nature. Its therapeutic radiation is emitted, producing photobiostimulant effects that result in rapid tissue repair and better tissue quality. The treatment of burns has always been a challenge, which involves both performing surgery and controlling and guiding scar regeneration, avoiding possible morbidities. Objective: To evaluate the effects of applying the MAC methodology with an AlGa (aluminum, gallium arsenide) laser on the time and quality of tissue repair in the skin of rats after induced chemical burns. Method: 22 adult male rats were subjected to a second-degree chemical burn on the back using 50% trichloroacetic acid. After the burns, the animals were randomly separated into 2 groups: control and experimental. The control group (G1) received placebo laser therapy and the laser group (G2) underwent laser irradiation with an energy density of 100 J/cm2. Histological analysis and macroscopic evaluation were carried out by means of the paper template method. Results: Group G1 showed (53%) of the necrosis area and group G2 showed (11%) necrosis area. Conclusion: The cicatricial acceleration method (MAC®) favored the repair of wounds caused by a 2nd-degree chemical burn, optimizing time and improving quality.展开更多
文摘电力线和无线混合通信可以优势互补提升电力物联网室内覆盖的综合性能。针对无线接入和电力线中继的混合通信场景,文章提出一种适用于两跳混合中继的通用MAC层算法。综合考虑物理层信道参数和MAC层的载波侦听多路访问(carrier sense multiple access,CSMA)退避流程,建立了该MAC层算法的跨层性能分析模型,推导了两跳传输系统归一化吞吐量以及系统时延等性能;最后仿真验证了算法有效性和模型准确性,分析了物理层和MAC层关键参数影响系统性能的机理。与Basic-RTS/CTS算法相比,所提算法可有效提升系统吞吐量和时延性能。
基金supported by the National Natural Science Foundation of China(No.61772386)National Key Research and Development Project(No.2018YFB1305001)Fundamental Research Funds for the Central Universities(No.KJ02072021-0119).
文摘Hybrid Power-line/Visible-light Communication(HPVC)network has been one of the most promising Cooperative Communication(CC)technologies for constructing Smart Home due to its superior communication reliability and hardware efficiency.Current research on HPVC networks focuses on the performance analysis and optimization of the Physical(PHY)layer,where the Power Line Communication(PLC)component only serves as the backbone to provide power to light Emitting Diode(LED)devices.So designing a Media Access Control(MAC)protocol remains a great challenge because it allows both PLC and Visible Light Communication(VLC)components to operate data transmission,i.e.,to achieve a true HPVC network CC.To solve this problem,we propose a new HPC network MAC protocol(HPVC MAC)based on Carrier Sense Multiple Access/Collision Avoidance(CSMA/CA)by combining IEEE 802.15.7 and IEEE 1901 standards.Firstly,we add an Additional Assistance(AA)layer to provide the channel selection strategies for sensor stations,so that they can complete data transmission on the selected channel via the specified CSMA/CA mechanism,respectively.Based on this,we give a detailed working principle of the HPVC MAC,followed by the construction of a joint analytical model for mathematicalmathematical validation of the HPVC MAC.In the modeling process,the impacts of PHY layer settings(including channel fading types and additive noise feature),CSMA/CA mechanisms of 802.15.7 and 1901,and practical configurations(such as traffic rate,transit buffer size)are comprehensively taken into consideration.Moreover,we prove the proposed analytical model has the solvability.Finally,through extensive simulations,we characterize the HPVC MAC performance under different system parameters and verify the correctness of the corresponding analytical model with an average error rate of 4.62%between the simulation and analytical results.
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Detecting sophisticated cyberattacks,mainly Distributed Denial of Service(DDoS)attacks,with unexpected patterns remains challenging in modern networks.Traditional detection systems often struggle to mitigate such attacks in conventional and software-defined networking(SDN)environments.While Machine Learning(ML)models can distinguish between benign and malicious traffic,their limited feature scope hinders the detection of new zero-day or low-rate DDoS attacks requiring frequent retraining.In this paper,we propose a novel DDoS detection framework that combines Machine Learning(ML)and Ensemble Learning(EL)techniques to improve DDoS attack detection and mitigation in SDN environments.Our model leverages the“DDoS SDN”dataset for training and evaluation and employs a dynamic feature selection mechanism that enhances detection accuracy by focusing on the most relevant features.This adaptive approach addresses the limitations of conventional ML models and provides more accurate detection of various DDoS attack scenarios.Our proposed ensemble model introduces an additional layer of detection,increasing reliability through the innovative application of ensemble techniques.The proposed solution significantly enhances the model’s ability to identify and respond to dynamic threats in SDNs.It provides a strong foundation for proactive DDoS detection and mitigation,enhancing network defenses against evolving threats.Our comprehensive runtime analysis of Simultaneous Multi-Threading(SMT)on identical configurations shows superior accuracy and efficiency,with significantly reduced computational time,making it ideal for real-time DDoS detection in dynamic,rapidly changing SDNs.Experimental results demonstrate that our model achieves outstanding performance,outperforming traditional algorithms with 99%accuracy using Random Forest(RF)and K-Nearest Neighbors(KNN)and 98%accuracy using XGBoost.
文摘Introduction: The cicatricial acceleration method (MAC®) promotes photobiological effects of an anti-inflammatory and healing nature. Its therapeutic radiation is emitted, producing photobiostimulant effects that result in rapid tissue repair and better tissue quality. The treatment of burns has always been a challenge, which involves both performing surgery and controlling and guiding scar regeneration, avoiding possible morbidities. Objective: To evaluate the effects of applying the MAC methodology with an AlGa (aluminum, gallium arsenide) laser on the time and quality of tissue repair in the skin of rats after induced chemical burns. Method: 22 adult male rats were subjected to a second-degree chemical burn on the back using 50% trichloroacetic acid. After the burns, the animals were randomly separated into 2 groups: control and experimental. The control group (G1) received placebo laser therapy and the laser group (G2) underwent laser irradiation with an energy density of 100 J/cm2. Histological analysis and macroscopic evaluation were carried out by means of the paper template method. Results: Group G1 showed (53%) of the necrosis area and group G2 showed (11%) necrosis area. Conclusion: The cicatricial acceleration method (MAC®) favored the repair of wounds caused by a 2nd-degree chemical burn, optimizing time and improving quality.
基金Supported by the National High-Tech Research and Development Plan of China under Grant No.2005AA121570(国家高技术研究发展计划(863))the National Basic Research Program of China under Grant No.2003CB314802(国家重点基础研究发展计划(973))
