From M-Ary Query to Bit Query: A New Strategy for Efficient Large-Scale RFID Identification

Su, Jian; Chen, Yongrui; Sheng, Zhengguo; Huang, Zhong; Liu, Alex X.

doi:10.1109/tcomm.2020.2968438

Cited by 56 publications

(55 citation statements)

References 41 publications

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“…To evaluate our method on the dataset, we use the confusion matrix (CM) to evaluate the Accuracy, Recall, Precision, and F-measure. Equations (4) to (7), where TP represents the number of true positives, TN represents the number of true negatives, FP represents the number of false positives, and FN represents the number of false negatives [25], [26], [27]. Accuracy: the proportion of correctly classified instance; Recall: the proportion of elements correctly classified as positive out of all positive elements; Precision: the proportion of elements correctly classified as true alarms out of all the elements the detection model classified as positive; F-measure: the average of the sum of the detection rate and the recall rate; Detection time: the time taken for the test sample to complete the test.…”

Section: A Experimental Settingsmentioning

confidence: 99%

SLGBM: An Intrusion Detection Mechanism for Wireless Sensor Networks in Smart Environments

Jiang

Zhao

2020

IEEE Access

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Wireless sensor network (WSN), which serves as one of the key components of the cyber physical system, is a wireless network composed of many stationary or moving sensors in a self-organizing and multi-hop manner. It cooperatively senses, collects, processes and transmits the information of the perceived objects in the geographical area covered by the network, and finally sends this information to the owner of the network. There are some typical attacks in WSN, such as Blackhole, Grayhole, Flooding and Scheduling attacks, which may cause damages to the WSN system in a short time. Moreover, intrusion detection methods for WSN suffer from the disadvantages of low detection rate, large calculation overhead, and high false alarm rate due to the limited resources of sensor nodes and a large amount of redundancy as well as high correlation of network data. In view of the above problems, we propose SLGBM, an intrusion detection method for wireless sensor networks. First, the sequence backward selection (SBS) algorithm is applied to reduce the data dimension on the feature space of the original traffic data so as to reduce the computational overhead. A LightGBM algorithm is then utilized to detect different network attacks. The experimental results based on the WSN-DS dataset show that F-measure of our proposed SLGBM are obviously superior to current typical detection methods, which are 99.8%, 99.4%, 99.1%, 96.5%, and 96.1% in Normal, Blackhole, Grayhole, Flooding, and Scheduling (TDMA) attack detections respectively.

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Section: A Experimental Settingsmentioning

confidence: 99%

SLGBM: An Intrusion Detection Mechanism for Wireless Sensor Networks in Smart Environments

Jiang

Zhao

2020

IEEE Access

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“…As an important branch, hand gesture recognition [2] is of great significance for promoting the development of HCI. Meanwhile, the development of hand gesture recognition also promotes the progress of many fields, such as smart home [3], industrial internet of things [4][5][6], sign language interaction [7], radio frequency identification [8,9], etc. As a result, hand gesture recognition has become a research hotspot recently [9][10][11][12][13].…”

Section: Introductionmentioning

confidence: 99%

A Novel Detection and Recognition Method for Continuous Hand Gesture Using FMCW Radar

et al. 2020

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In this paper, a novel method for continuous hand gesture detection and recognition is proposed based on a frequency modulated continuous wave (FMCW) radar. Firstly, we adopt the 2-Dimensional Fast Fourier Transform (2D-FFT) to estimate the range and Doppler parameters of the hand gesture raw data, and construct the range-time map (RTM) and Doppler-time map (DTM). Meanwhile, we apply the Multiple Signal Classification (MUSIC) algorithm to calculate the angle and construct the angletime map (ATM). Secondly, a hand gesture detection method is proposed to segment the continuous hand gestures using a decision threshold. Thirdly, the central time-frequency trajectory of each hand gesture spectrogram is clustered using the k-means algorithm, and then the Fusion Dynamic Time Warping (FDTW) algorithm is presented to recognize the hand gestures. Finally, experiments show that the accuracy of the proposed hand gesture detection method can reach 96.17%. The hand gesture average recognition accuracy of the proposed FDTW algorithm is 95.83%, while its time complexity is reduced by more than 50%. INDEX TERMS FMCW radar, continuous hand gesture recognition, detection, time-frequency trajectory, FDTW.

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“…Besides, in the actual indoor environment, due to the obstruction of the furniture, walls, and many other obstacles including the human body shadowing, indoor wireless signals are severely affected by the signal strength attenuation and multi-path fading during the propagation, which limits the development of indoor positioning technologies. At present, there are a large number of common indoor positioning technologies such as Ultra-wide Band (UWB) [4] [5], Infrared Ray (IR) [6], Radio Frequency Identification (RFID) [7] [8] [9], Bluetooth [10], Ultrasonic Wave (UW) [11], ZigBee [12], Visible Light (VL) [13], and Wi-Fi [14] indoor positioning technologies. Most of them involve the high deployment cost due to the additional hardware requirement, which makes them difficult to be widely applied.…”

Section: Introductionmentioning

confidence: 99%

AP Optimization for Wi-Fi Indoor Positioning-Based on RSS Feature Fuzzy Mapping and Clustering

Yang

Nie

et al. 2020

IEEE Access

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In indoor environments, Access Points (APs) are widely deployed in various locations of buildings, and thereby the AP optimization-based Wi-Fi indoor positioning technology is of great significance for achieving the satisfactory indoor Location-based Services (LBSs). However, the current Wi-Fi indoor positioning methods rarely pay attention to the diversity of Received Signal Strength (RSS) features for AP optimization, which may result in the low positioning accuracy and high positioning overhead. In order to deal with such issues, this paper proposes a new concept of multi-dimensional RSS feature fuzzy mapping and clustering for AP optimization in Wi-Fi indoor positioning. Besides, the extensive experiments conducted in an actual indoor environment show that compared with the existing positioning methods, the proposed method can not only achieve higher positioning accuracy by using the optimized APs but also reduce the positioning overhead in the online phase. INDEX TERMS Wi-Fi indoor positioning, AP optimization, multi-dimensional RSS feature, fuzzy mapping and clustering

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From M-Ary Query to Bit Query: A New Strategy for Efficient Large-Scale RFID Identification

Abstract: Alex X (2020) From M-ary Query to Bit Query: a new strategy for efficient large-scale RFID identification. IEEE Transactions on Communications. p. 1.

Cited by 56 publications

References 41 publications

SLGBM: An Intrusion Detection Mechanism for Wireless Sensor Networks in Smart Environments

SLGBM: An Intrusion Detection Mechanism for Wireless Sensor Networks in Smart Environments

A Novel Detection and Recognition Method for Continuous Hand Gesture Using FMCW Radar

AP Optimization for Wi-Fi Indoor Positioning-Based on RSS Feature Fuzzy Mapping and Clustering

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