Identifying a device and detecting a change in its position is critical for secure devices management in the Internet of Things(IoT).In this paper,a device management system is proposed to track the devices by using a...Identifying a device and detecting a change in its position is critical for secure devices management in the Internet of Things(IoT).In this paper,a device management system is proposed to track the devices by using audio-based location distinction techniques.In the proposed scheme,traditional cryptographic techniques,such as symmetric encryption algorithm,RSA-based signcryption scheme,and audio-based secure transmission,are utilized to provide authentication,non-repudiation,and confidentiality in the information interaction of the management system.Moreover,an audio-based location distinction method is designed to detect the position change of the devices.Specifically,the audio frequency response(AFR)of several frequency points is utilized as a device signature.The device signature has the features as follows.(1)Hardware Signature:different pairs of speaker and microphone have different signatures;(2)Distance Signature:in the same direction,the signatures are different at different distances;and(3)Direction Signature:at the same distance,the signatures are different in different directions.Based on the features above,amovement detection algorithmfor device identification and location distinction is designed.Moreover,a secure communication protocol is also proposed by using traditional cryptographic techniques to provide integrity,authentication,and non-repudiation in the process of information interaction between devices,Access Points(APs),and Severs.Extensive experiments are conducted to evaluate the performance of the proposed method.The experimental results show that the proposedmethod has a good performance in accuracy and energy consumption.展开更多
This paper reviews and analyses various simplified-RFID (Radio Frequency Identification) indoor location systems, and proposes an improved implementation based on the propagation channel "fingerprinting" pri...This paper reviews and analyses various simplified-RFID (Radio Frequency Identification) indoor location systems, and proposes an improved implementation based on the propagation channel "fingerprinting" principle. The focus of the design aims to provide accurate location estimation, while minimising infrastructural requirements. The proposed approach is based on the LANDMARC (Indoor Location Sensing Using Active RFID) with Virtual Reference tags (VIRE) and implemented with dynamic linear interpolation and lagrange interpolation schemes. According to the simulation results, the proposed dynamic linear interpolation ensures much better performance on location-estimation error.展开更多
WLAN mdoor location method based on artificial neural network （ANN） is analyzed. A three layer feed-forward ANN model offers the benefits of reducing time cost of the layout of an indoor location system, saving stor...WLAN mdoor location method based on artificial neural network （ANN） is analyzed. A three layer feed-forward ANN model offers the benefits of reducing time cost of the layout of an indoor location system, saving storage cost of the radio map establishment and enhancing real-time capacity in the on-line phase. According to the analysis of SNR distributions of recorded beacon signal samples and discussion about the multi-mode phenomenon, the one map method is proposed for the purpose of simplifying ANN input values and increasing location performances. Based on the simulations and comparison analysis with other two typical indoor location methods, K-nearest neighbor （KNN） and probability, the feasibility and effectiveness of ANN-based indoor location method are verified with average location error of 2.37m and location accuracy of 78.6% in 3m.展开更多
A Wi-Fi fingerprinting localization approach has attracted increasing attention in recent years due to the ubiquity of Access Point( AP). However,typical fingerprinting localization methods fail to resist accidental e...A Wi-Fi fingerprinting localization approach has attracted increasing attention in recent years due to the ubiquity of Access Point( AP). However,typical fingerprinting localization methods fail to resist accidental environmental changes,such as AP movement. In order to address this problem,a robust fingerprinting indoor localization method is initiated. In the offline phase,three attributes of Received Signal Strength Indication( RSSI) —average,standard deviation and AP's response rate—are computed to prepare for the subsequent computation. In this way,the underlying location-relevant information can be captured comprehensively. Then in the online phase, a three-step voting scheme-based decision mechanism is demonstrated, detecting and eliminating the part of AP where the signals measured are severely distorted by AP 's movement. In the following localization step,in order to achieve accuracy and efficiency simultaneously,a novel fingerprinting localization algorithm is applied. Bhattacharyya distance is utilized to measure the RSSI distribution distance,thus realizing the optimization of MAximum Overlapping algorithm( MAO). Finally,experimental results are displayed,which demonstrate the effectiveness of our proposed methods in eliminating outliers and attaining relatively higher localization accuracy.展开更多
A review of signal processing algorithms employing Wi-Fi signals for positioning and recognition of human activities is presented.The principles of how channel state information(CSI)is used and how the Wi-Fi sensing s...A review of signal processing algorithms employing Wi-Fi signals for positioning and recognition of human activities is presented.The principles of how channel state information(CSI)is used and how the Wi-Fi sensing systems operate are reviewed.It provides a brief introduction to the algorithms that perform signal processing,feature extraction and recognitions,including location,activity recognition,physiological signal detection and personal identification.Challenges and future trends of Wi-Fi sensing are also discussed in the end.展开更多
In recent years, indoor localization becomes more and more essential in our daily life thanks to its interesting applications that cover all domains including security, tourism. Unfortunately, the existing outdoor loc...In recent years, indoor localization becomes more and more essential in our daily life thanks to its interesting applications that cover all domains including security, tourism. Unfortunately, the existing outdoor localization systems fails in indoor environment, which has motivated researchers to develop new localization systems that challenge the indoor environments. In our work, we propose a 3D fingerprinting-based localization system that estimates a source position using acoustic signals. The latter has the advantage of being used in almost roaming devices. No dedicated infrastructure is necessary and the existing infrastructures can then be reused for indoor purposes. The proposed system has been evaluated in experimental tests in an area of dimensions 1.5 m * 1.5 m * 2 m when four microphones were placed at known positions and an artificial fan is turned on. Results show that turbulence affects the precision of estimating the source position by 7% for an accuracy of 8.5 cm.展开更多
基金This work is supported by Demonstration of Scientific and Technology Achievements Transform in Sichuan Province under Grant 2022ZHCG0036National Natural Science Foundation of China(62002047).
文摘Identifying a device and detecting a change in its position is critical for secure devices management in the Internet of Things(IoT).In this paper,a device management system is proposed to track the devices by using audio-based location distinction techniques.In the proposed scheme,traditional cryptographic techniques,such as symmetric encryption algorithm,RSA-based signcryption scheme,and audio-based secure transmission,are utilized to provide authentication,non-repudiation,and confidentiality in the information interaction of the management system.Moreover,an audio-based location distinction method is designed to detect the position change of the devices.Specifically,the audio frequency response(AFR)of several frequency points is utilized as a device signature.The device signature has the features as follows.(1)Hardware Signature:different pairs of speaker and microphone have different signatures;(2)Distance Signature:in the same direction,the signatures are different at different distances;and(3)Direction Signature:at the same distance,the signatures are different in different directions.Based on the features above,amovement detection algorithmfor device identification and location distinction is designed.Moreover,a secure communication protocol is also proposed by using traditional cryptographic techniques to provide integrity,authentication,and non-repudiation in the process of information interaction between devices,Access Points(APs),and Severs.Extensive experiments are conducted to evaluate the performance of the proposed method.The experimental results show that the proposedmethod has a good performance in accuracy and energy consumption.
基金as part of a final-year project ID100139:Indoor Item finder,by Miss Chen Xu
文摘This paper reviews and analyses various simplified-RFID (Radio Frequency Identification) indoor location systems, and proposes an improved implementation based on the propagation channel "fingerprinting" principle. The focus of the design aims to provide accurate location estimation, while minimising infrastructural requirements. The proposed approach is based on the LANDMARC (Indoor Location Sensing Using Active RFID) with Virtual Reference tags (VIRE) and implemented with dynamic linear interpolation and lagrange interpolation schemes. According to the simulation results, the proposed dynamic linear interpolation ensures much better performance on location-estimation error.
文摘WLAN mdoor location method based on artificial neural network （ANN） is analyzed. A three layer feed-forward ANN model offers the benefits of reducing time cost of the layout of an indoor location system, saving storage cost of the radio map establishment and enhancing real-time capacity in the on-line phase. According to the analysis of SNR distributions of recorded beacon signal samples and discussion about the multi-mode phenomenon, the one map method is proposed for the purpose of simplifying ANN input values and increasing location performances. Based on the simulations and comparison analysis with other two typical indoor location methods, K-nearest neighbor （KNN） and probability, the feasibility and effectiveness of ANN-based indoor location method are verified with average location error of 2.37m and location accuracy of 78.6% in 3m.
基金Sponsored by the National High Technology Research and Development Program of China(Grant No.2014AA123103)
文摘A Wi-Fi fingerprinting localization approach has attracted increasing attention in recent years due to the ubiquity of Access Point( AP). However,typical fingerprinting localization methods fail to resist accidental environmental changes,such as AP movement. In order to address this problem,a robust fingerprinting indoor localization method is initiated. In the offline phase,three attributes of Received Signal Strength Indication( RSSI) —average,standard deviation and AP's response rate—are computed to prepare for the subsequent computation. In this way,the underlying location-relevant information can be captured comprehensively. Then in the online phase, a three-step voting scheme-based decision mechanism is demonstrated, detecting and eliminating the part of AP where the signals measured are severely distorted by AP 's movement. In the following localization step,in order to achieve accuracy and efficiency simultaneously,a novel fingerprinting localization algorithm is applied. Bhattacharyya distance is utilized to measure the RSSI distribution distance,thus realizing the optimization of MAximum Overlapping algorithm( MAO). Finally,experimental results are displayed,which demonstrate the effectiveness of our proposed methods in eliminating outliers and attaining relatively higher localization accuracy.
文摘在室内定位服务中,WiFi指纹技术因其覆盖面积广、定位精度高而受到人们的广泛关注.然而,对于在线阶段的位置查询,用户的个人敏感信息容易受到恶意攻击而造成位置隐私泄露.现有基于WiFi指纹的室内定位技术仅考虑室内单一空旷平面,这使得WiFi部署的灵活性受到限制.而当WiFi部署在多维场景时,空间位置隐私问题亟待解决.提出了一种基于地理不可区分性的WiFi指纹室内定位隐私保护方案,用户利用自身接收信号强度生成一个新的接收信号强度向量,并通过加噪混淆将得到的数据发送给位置服务提供商,同时引入数字签名技术,在混淆位置被发送给位置服务提供商实现定位之前确保客户端身份不被伪造.基于模拟实验平台的实验结果表明,该方案支持WiFi的灵活部署,能够在保护位置隐私的同时,首次实现12个WiFi接入点灵活部署情况下的高精度定位,保证定位误差小于1 m.
基金National Natural Science Foundation of China(NSFC)under Grant No.61401100Natural Science Foundation of Fuji⁃an Province under Grant No.2018J01805+1 种基金Youth Research Project of Fujian Provincial Department of Education under Grant No.JAT190011and Fuzhou University Scientific Research Fund Project under Grant No.GXRC-18074.
文摘A review of signal processing algorithms employing Wi-Fi signals for positioning and recognition of human activities is presented.The principles of how channel state information(CSI)is used and how the Wi-Fi sensing systems operate are reviewed.It provides a brief introduction to the algorithms that perform signal processing,feature extraction and recognitions,including location,activity recognition,physiological signal detection and personal identification.Challenges and future trends of Wi-Fi sensing are also discussed in the end.
文摘In recent years, indoor localization becomes more and more essential in our daily life thanks to its interesting applications that cover all domains including security, tourism. Unfortunately, the existing outdoor localization systems fails in indoor environment, which has motivated researchers to develop new localization systems that challenge the indoor environments. In our work, we propose a 3D fingerprinting-based localization system that estimates a source position using acoustic signals. The latter has the advantage of being used in almost roaming devices. No dedicated infrastructure is necessary and the existing infrastructures can then be reused for indoor purposes. The proposed system has been evaluated in experimental tests in an area of dimensions 1.5 m * 1.5 m * 2 m when four microphones were placed at known positions and an artificial fan is turned on. Results show that turbulence affects the precision of estimating the source position by 7% for an accuracy of 8.5 cm.
