IEEE 802.11 Wi-Fi networks are prone to many denial of service(DoS)attacks due to vulnerabilities at the media access control(MAC)layer of the 802.11 protocol.Due to the data transmission nature of the wireless local ...IEEE 802.11 Wi-Fi networks are prone to many denial of service(DoS)attacks due to vulnerabilities at the media access control(MAC)layer of the 802.11 protocol.Due to the data transmission nature of the wireless local area network(WLAN)through radio waves,its communication is exposed to the possibility of being attacked by illegitimate users.Moreover,the security design of the wireless structure is vulnerable to versatile attacks.For example,the attacker can imitate genuine features,rendering classificationbased methods inaccurate in differentiating between real and false messages.Althoughmany security standards have been proposed over the last decades to overcome many wireless network attacks,effectively detecting such attacks is crucial in today’s real-world applications.This paper presents a novel resource exhaustion attack detection scheme(READS)to detect resource exhaustion attacks effectively.The proposed scheme can differentiate between the genuine and fake management frames in the early stages of the attack such that access points can effectively mitigate the consequences of the attack.The scheme is built through learning from clustered samples using artificial neural networks to identify the genuine and rogue resource exhaustion management frames effectively and efficiently in theWLAN.The proposed scheme consists of four modules whichmake it capable to alleviates the attack impact more effectively than the related work.The experimental results show the effectiveness of the proposed technique by gaining an 89.11%improvement compared to the existing works in terms of detection.展开更多
With the rapid development of wireless local area network （WLAN） technology, an important target of indoor positioning systems is to improve the positioning accuracy while reducing the online calibration effort to o...With the rapid development of wireless local area network （WLAN） technology, an important target of indoor positioning systems is to improve the positioning accuracy while reducing the online calibration effort to overcome signal time-varying. A novel fingerprint positioning algorithm, known as the adaptive radio map with updated method based on hidden Markov model （HMM）, is proposed. It is shown that by using a collection of user traces that can be cheaply obtained, the proposed algorithm can take advantage of these data to update the labeled calibration data to further improve the position estimation accuracy. This algorithm is a combination of machine learning, information gain theory and fingerprinting. By collecting data and testing the algorithm in a realistic indoor WLAN environment, the experiment results indicate that, compared with the widely used K nearest neighbor algorithm, the proposed algorithm can improve the positioning accuracy while greatly reduce the calibration effort.展开更多
For indoor location estimation based on received signal strength( RSS) in wireless local area networks( WLAN),in order to reduce the influence of noise on the positioning accuracy,a large number of RSS should be colle...For indoor location estimation based on received signal strength( RSS) in wireless local area networks( WLAN),in order to reduce the influence of noise on the positioning accuracy,a large number of RSS should be collected in offline phase. Therefore,collecting training data with positioning information is time consuming which becomes the bottleneck of WLAN indoor localization. In this paper,the traditional semisupervised learning method based on k-NN and ε-NN graph for reducing collection workload of offline phase are analyzed,and the result shows that the k-NN or ε-NN graph are sensitive to data noise,which limit the performance of semi-supervised learning WLAN indoor localization system. Aiming at the above problem,it proposes a l1-graph-algorithm-based semi-supervised learning( LG-SSL) indoor localization method in which the graph is built by l1-norm algorithm. In our system,it firstly labels the unlabeled data using LG-SSL and labeled data to build the Radio Map in offline training phase,and then uses LG-SSL to estimate user's location in online phase. Extensive experimental results show that,benefit from the robustness to noise and sparsity ofl1-graph,LG-SSL exhibits superior performance by effectively reducing the collection workload in offline phase and improving localization accuracy in online phase.展开更多
为了满足用户在各类场景下对无线业务日益增长的要求,高密集部署的无线局域网(wireless local area network,WLAN)是未来发展的趋势。但由于频率资源有限,相同信道必然存在多个WLAN无线接入点(access point,AP),然而处于同一信道的AP会...为了满足用户在各类场景下对无线业务日益增长的要求,高密集部署的无线局域网(wireless local area network,WLAN)是未来发展的趋势。但由于频率资源有限,相同信道必然存在多个WLAN无线接入点(access point,AP),然而处于同一信道的AP会互相干扰,造成网络中小区间吞吐量的公平性下降,无法为用户提供良好的服务质量。为了提高网络公平性,改善用户体验,需要制定合理的网络参数调优方法,给出了一种基于神经网络和遗传算法对WLAN参数优化的方法。采用神经网络构建网络参数与网络吞吐量公平性之间的映射,将训练完成的模型作为遗传算法的适应度评估函数,通过遗传算法求解优化参数组合配置来改善WLAN吞吐量公平性问题。仿真结果表明所提出算法能够使得高密集WLAN吞吐量公平性得到提升。展开更多
随着移动通信技术的快速发展,无线局域网(Wireless Local Area Network,WLAN)技术成为人们日常生活中的重要组成部分。然而由于无线信道的不稳定性和带宽资源的有限性,如何提高多媒体传输的效率和质量成为一个重要的研究课题。跨层设计...随着移动通信技术的快速发展,无线局域网(Wireless Local Area Network,WLAN)技术成为人们日常生活中的重要组成部分。然而由于无线信道的不稳定性和带宽资源的有限性,如何提高多媒体传输的效率和质量成为一个重要的研究课题。跨层设计作为一种有效的解决方案,可以在不同的网络层之间进行信息交互,从而提升整个系统的性能。基于此,文章主要分析跨层设计在WLAN无线网络通信技术中的应用意义和策略,并展望未来的发展方向。展开更多
由于无线网络主要在开放的空间环境内展开数据信息传输,而不是固定在物理线缆上,存在一定的安全隐患。因此,必须要优化落实对无线网络的安全防护。文章强调了无线局域网(Wireless Local Area Network,WLAN)安全防护的必要性,并对恶意攻...由于无线网络主要在开放的空间环境内展开数据信息传输,而不是固定在物理线缆上,存在一定的安全隐患。因此,必须要优化落实对无线网络的安全防护。文章强调了无线局域网(Wireless Local Area Network,WLAN)安全防护的必要性,并对恶意攻击、非法用户入侵、重传攻击等影响计算机无线网络安全的主要因素进行了分析。在此基础上,从无线与有线一体化关联分析技术、无线网络安全保障机制、WLAN安全技术这几方面入手,着重阐述了基于WLAN的网络安全防护技术的具体应用,以营造更为安全的WLAN无线网络运行环境。展开更多
针对现有的双局域网(LAN)太赫兹无线局域网(Dual-LAN THz WLAN)相关介质访问控制(MAC)协议中存在的某些节点会在多个超帧内重复发送相同的信道时隙请求帧以申请时隙资源以及网络运行的一些时段存在空闲时隙等问题,提出一种基于自发数据...针对现有的双局域网(LAN)太赫兹无线局域网(Dual-LAN THz WLAN)相关介质访问控制(MAC)协议中存在的某些节点会在多个超帧内重复发送相同的信道时隙请求帧以申请时隙资源以及网络运行的一些时段存在空闲时隙等问题,提出一种基于自发数据传输的高效MAC协议——SDTE-MAC(high-Efficiency MAC protocol based on Spontaneous Data Transmission)。SDTE-MAC通过让各节点都维护一张或多张时间单元链表,使各节点与其余节点在网络运行时间上达到同步,从而获悉各节点应该在信道空闲时隙的什么位置开始发送数据帧,优化了传统的信道时隙分配和信道剩余时隙再分配的流程,提高了网络吞吐量和信道时隙利用率,降低了数据时延,能够进一步提升双LAN太赫兹无线局域网的性能。仿真结果表明,网络饱和时,相较于AHT-MAC(Adaptive High Throughout multi-pan MAC protocol)中的N-CTAP(Normal Channel Time Allocation Period)时段时隙资源分配新机制以及自适应缩短超帧时段机制,SDTE-MAC的MAC层吞吐量提升了9.2%,信道时隙利用率提升了10.9%,数据时延降低了22.2%。展开更多
近年来,城市轨道交通建设加速增长,对城市轨道交通车地通信系统的可靠运行提出了更高的要求。随着第四代移动网络(4th Generation mobile networks,4G)具体化的长期演进(Long Term Evolution,LTE)系统已经广泛地进行了部署,而且技术也...近年来,城市轨道交通建设加速增长,对城市轨道交通车地通信系统的可靠运行提出了更高的要求。随着第四代移动网络(4th Generation mobile networks,4G)具体化的长期演进(Long Term Evolution,LTE)系统已经广泛地进行了部署,而且技术也逐渐趋于成熟,地铁长期演进(Long Term Evolution for Metro,LTE-M)车地通信系统具有较强的抗干扰能力、支持快速移动状态下的列车通信、资源调度灵活等优点,突破融合不同通信制式的关键技术,研制出信号通信设备样机,并根据列车控制系统的业务需求对新的通信制式进行测试,保障城铁列车的安全稳定运行。展开更多
基金The manuscript APC is supported by the grant name(UMS No.DFK2005)“Smart Vertical farming Technology for Temperate vegetable cultivation in Sabah:practising smart automation system using IR and AI technology in agriculture 4.0”.
文摘IEEE 802.11 Wi-Fi networks are prone to many denial of service(DoS)attacks due to vulnerabilities at the media access control(MAC)layer of the 802.11 protocol.Due to the data transmission nature of the wireless local area network(WLAN)through radio waves,its communication is exposed to the possibility of being attacked by illegitimate users.Moreover,the security design of the wireless structure is vulnerable to versatile attacks.For example,the attacker can imitate genuine features,rendering classificationbased methods inaccurate in differentiating between real and false messages.Althoughmany security standards have been proposed over the last decades to overcome many wireless network attacks,effectively detecting such attacks is crucial in today’s real-world applications.This paper presents a novel resource exhaustion attack detection scheme(READS)to detect resource exhaustion attacks effectively.The proposed scheme can differentiate between the genuine and fake management frames in the early stages of the attack such that access points can effectively mitigate the consequences of the attack.The scheme is built through learning from clustered samples using artificial neural networks to identify the genuine and rogue resource exhaustion management frames effectively and efficiently in theWLAN.The proposed scheme consists of four modules whichmake it capable to alleviates the attack impact more effectively than the related work.The experimental results show the effectiveness of the proposed technique by gaining an 89.11%improvement compared to the existing works in terms of detection.
基金supported by the National Natural Science Foundation of China(61571162)the Major National Science and Technology Project(2014ZX03004003-005)
文摘With the rapid development of wireless local area network （WLAN） technology, an important target of indoor positioning systems is to improve the positioning accuracy while reducing the online calibration effort to overcome signal time-varying. A novel fingerprint positioning algorithm, known as the adaptive radio map with updated method based on hidden Markov model （HMM）, is proposed. It is shown that by using a collection of user traces that can be cheaply obtained, the proposed algorithm can take advantage of these data to update the labeled calibration data to further improve the position estimation accuracy. This algorithm is a combination of machine learning, information gain theory and fingerprinting. By collecting data and testing the algorithm in a realistic indoor WLAN environment, the experiment results indicate that, compared with the widely used K nearest neighbor algorithm, the proposed algorithm can improve the positioning accuracy while greatly reduce the calibration effort.
基金Sponsored by the National Natural Science Foundation of China(Grant No.61101122)the National High Technology Research and Development Program of China(Grant No.2012AA120802)the National Science and Technology Major Project of the Ministry of Science and Technology of China(Grant No.2012ZX03004-003)
文摘For indoor location estimation based on received signal strength( RSS) in wireless local area networks( WLAN),in order to reduce the influence of noise on the positioning accuracy,a large number of RSS should be collected in offline phase. Therefore,collecting training data with positioning information is time consuming which becomes the bottleneck of WLAN indoor localization. In this paper,the traditional semisupervised learning method based on k-NN and ε-NN graph for reducing collection workload of offline phase are analyzed,and the result shows that the k-NN or ε-NN graph are sensitive to data noise,which limit the performance of semi-supervised learning WLAN indoor localization system. Aiming at the above problem,it proposes a l1-graph-algorithm-based semi-supervised learning( LG-SSL) indoor localization method in which the graph is built by l1-norm algorithm. In our system,it firstly labels the unlabeled data using LG-SSL and labeled data to build the Radio Map in offline training phase,and then uses LG-SSL to estimate user's location in online phase. Extensive experimental results show that,benefit from the robustness to noise and sparsity ofl1-graph,LG-SSL exhibits superior performance by effectively reducing the collection workload in offline phase and improving localization accuracy in online phase.
文摘为了满足用户在各类场景下对无线业务日益增长的要求,高密集部署的无线局域网(wireless local area network,WLAN)是未来发展的趋势。但由于频率资源有限,相同信道必然存在多个WLAN无线接入点(access point,AP),然而处于同一信道的AP会互相干扰,造成网络中小区间吞吐量的公平性下降,无法为用户提供良好的服务质量。为了提高网络公平性,改善用户体验,需要制定合理的网络参数调优方法,给出了一种基于神经网络和遗传算法对WLAN参数优化的方法。采用神经网络构建网络参数与网络吞吐量公平性之间的映射,将训练完成的模型作为遗传算法的适应度评估函数,通过遗传算法求解优化参数组合配置来改善WLAN吞吐量公平性问题。仿真结果表明所提出算法能够使得高密集WLAN吞吐量公平性得到提升。
文摘随着移动通信技术的快速发展,无线局域网(Wireless Local Area Network,WLAN)技术成为人们日常生活中的重要组成部分。然而由于无线信道的不稳定性和带宽资源的有限性,如何提高多媒体传输的效率和质量成为一个重要的研究课题。跨层设计作为一种有效的解决方案,可以在不同的网络层之间进行信息交互,从而提升整个系统的性能。基于此,文章主要分析跨层设计在WLAN无线网络通信技术中的应用意义和策略,并展望未来的发展方向。
文摘由于无线网络主要在开放的空间环境内展开数据信息传输,而不是固定在物理线缆上,存在一定的安全隐患。因此,必须要优化落实对无线网络的安全防护。文章强调了无线局域网(Wireless Local Area Network,WLAN)安全防护的必要性,并对恶意攻击、非法用户入侵、重传攻击等影响计算机无线网络安全的主要因素进行了分析。在此基础上,从无线与有线一体化关联分析技术、无线网络安全保障机制、WLAN安全技术这几方面入手,着重阐述了基于WLAN的网络安全防护技术的具体应用,以营造更为安全的WLAN无线网络运行环境。
文摘针对现有的双局域网(LAN)太赫兹无线局域网(Dual-LAN THz WLAN)相关介质访问控制(MAC)协议中存在的某些节点会在多个超帧内重复发送相同的信道时隙请求帧以申请时隙资源以及网络运行的一些时段存在空闲时隙等问题,提出一种基于自发数据传输的高效MAC协议——SDTE-MAC(high-Efficiency MAC protocol based on Spontaneous Data Transmission)。SDTE-MAC通过让各节点都维护一张或多张时间单元链表,使各节点与其余节点在网络运行时间上达到同步,从而获悉各节点应该在信道空闲时隙的什么位置开始发送数据帧,优化了传统的信道时隙分配和信道剩余时隙再分配的流程,提高了网络吞吐量和信道时隙利用率,降低了数据时延,能够进一步提升双LAN太赫兹无线局域网的性能。仿真结果表明,网络饱和时,相较于AHT-MAC(Adaptive High Throughout multi-pan MAC protocol)中的N-CTAP(Normal Channel Time Allocation Period)时段时隙资源分配新机制以及自适应缩短超帧时段机制,SDTE-MAC的MAC层吞吐量提升了9.2%,信道时隙利用率提升了10.9%,数据时延降低了22.2%。
文摘近年来,城市轨道交通建设加速增长,对城市轨道交通车地通信系统的可靠运行提出了更高的要求。随着第四代移动网络(4th Generation mobile networks,4G)具体化的长期演进(Long Term Evolution,LTE)系统已经广泛地进行了部署,而且技术也逐渐趋于成熟,地铁长期演进(Long Term Evolution for Metro,LTE-M)车地通信系统具有较强的抗干扰能力、支持快速移动状态下的列车通信、资源调度灵活等优点,突破融合不同通信制式的关键技术,研制出信号通信设备样机,并根据列车控制系统的业务需求对新的通信制式进行测试,保障城铁列车的安全稳定运行。
