RFID(Radio Frequency IDentification)系统射频标签结构简单,且与阅读器间采用无线方式传输数据,易产生隐私泄露和受到安全攻击。针对该问题,文中提出通过双向轻权认证协议来保护RFID系统的安全性和隐私。该协议通过随机化标签的秘密...RFID(Radio Frequency IDentification)系统射频标签结构简单,且与阅读器间采用无线方式传输数据,易产生隐私泄露和受到安全攻击。针对该问题,文中提出通过双向轻权认证协议来保护RFID系统的安全性和隐私。该协议通过随机化标签的秘密信息再哈希的方法生成会话消息,标签与阅读器间采用二次相互认证,提升了协议的安全性。该协议通过哈希运算确保认证过程中会话信息的保密传输和完整性,通过对标签端每次发出会话消息的随机化确保了消息的新鲜性,系统秘密信息的更新确保协议满足前向安全性。RFID认证协议不仅能抵抗窃听、追踪、重放、去同步化等攻击,还能满足RFID系统的安全性和隐私保护需要。展开更多
目的确保物品包装上的超高频射频识别(Radio Frequency Identification,RFID)标签一致性关键指标符合相关标准,解决现有文献对一致性关键指标阐述不全面的问题,基于ISO/IEC 18000-63和ISO/IEC 18047-63对一致性关键指标的测试方法展开...目的确保物品包装上的超高频射频识别(Radio Frequency Identification,RFID)标签一致性关键指标符合相关标准,解决现有文献对一致性关键指标阐述不全面的问题,基于ISO/IEC 18000-63和ISO/IEC 18047-63对一致性关键指标的测试方法展开研究。方法在对现有一致性测试方法进行研究阐述的基础上,改进了状态跳转和截断响应的测试方法,提升了测试准确性;设计了一种时隙计数器测试方法,该方法通过改变Q值和重复发送QueryRep命令,验证时隙计数器在非0到0的变化过程中,标签有且仅有一次响应,从而避免出现多个标签同时应答的现象。结果应用改进及新设计的测试方法对指定标签进行测试,结果符合标准。结论较为全面地实现了对RFID标签的客观验证和有效评估,对提升RFID标签在实际应用中的可靠性具有重要意义。展开更多
In this paper,we present a Deep Neural Network(DNN)based framework that employs Radio Frequency(RF)hologram tensors to locate multiple Ultra-High Frequency(UHF)passive Radio-Frequency Identification(RFID)tags.The RF h...In this paper,we present a Deep Neural Network(DNN)based framework that employs Radio Frequency(RF)hologram tensors to locate multiple Ultra-High Frequency(UHF)passive Radio-Frequency Identification(RFID)tags.The RF hologram tensor exhibits a strong relationship between observation and spatial location,helping to improve the robustness to dynamic environments and equipment.Since RFID data is often marred by noise,we implement two types of deep neural network architectures to clean up the RF hologram tensor.Leveraging the spatial relationship between tags,the deep networks effectively mitigate fake peaks in the hologram tensors resulting from multipath propagation and phase wrapping.In contrast to fingerprinting-based localization systems that use deep networks as classifiers,our deep networks in the proposed framework treat the localization task as a regression problem preserving the ambiguity between fingerprints.We also present an intuitive peak finding algorithm to obtain estimated locations using the sanitized hologram tensors.The proposed framework is implemented using commodity RFID devices,and its superior performance is validated through extensive experiments.展开更多
文摘RFID(Radio Frequency IDentification)系统射频标签结构简单,且与阅读器间采用无线方式传输数据,易产生隐私泄露和受到安全攻击。针对该问题,文中提出通过双向轻权认证协议来保护RFID系统的安全性和隐私。该协议通过随机化标签的秘密信息再哈希的方法生成会话消息,标签与阅读器间采用二次相互认证,提升了协议的安全性。该协议通过哈希运算确保认证过程中会话信息的保密传输和完整性,通过对标签端每次发出会话消息的随机化确保了消息的新鲜性,系统秘密信息的更新确保协议满足前向安全性。RFID认证协议不仅能抵抗窃听、追踪、重放、去同步化等攻击,还能满足RFID系统的安全性和隐私保护需要。
文摘目的确保物品包装上的超高频射频识别(Radio Frequency Identification,RFID)标签一致性关键指标符合相关标准,解决现有文献对一致性关键指标阐述不全面的问题,基于ISO/IEC 18000-63和ISO/IEC 18047-63对一致性关键指标的测试方法展开研究。方法在对现有一致性测试方法进行研究阐述的基础上,改进了状态跳转和截断响应的测试方法,提升了测试准确性;设计了一种时隙计数器测试方法,该方法通过改变Q值和重复发送QueryRep命令,验证时隙计数器在非0到0的变化过程中,标签有且仅有一次响应,从而避免出现多个标签同时应答的现象。结果应用改进及新设计的测试方法对指定标签进行测试,结果符合标准。结论较为全面地实现了对RFID标签的客观验证和有效评估,对提升RFID标签在实际应用中的可靠性具有重要意义。
基金supported in part by the U.S.National Science Foundation(NSF)under Grants ECCS-2245608 and ECCS-2245607。
文摘In this paper,we present a Deep Neural Network(DNN)based framework that employs Radio Frequency(RF)hologram tensors to locate multiple Ultra-High Frequency(UHF)passive Radio-Frequency Identification(RFID)tags.The RF hologram tensor exhibits a strong relationship between observation and spatial location,helping to improve the robustness to dynamic environments and equipment.Since RFID data is often marred by noise,we implement two types of deep neural network architectures to clean up the RF hologram tensor.Leveraging the spatial relationship between tags,the deep networks effectively mitigate fake peaks in the hologram tensors resulting from multipath propagation and phase wrapping.In contrast to fingerprinting-based localization systems that use deep networks as classifiers,our deep networks in the proposed framework treat the localization task as a regression problem preserving the ambiguity between fingerprints.We also present an intuitive peak finding algorithm to obtain estimated locations using the sanitized hologram tensors.The proposed framework is implemented using commodity RFID devices,and its superior performance is validated through extensive experiments.
