22 results on '"Xiaojiang Chen"'
Search Results
2. Caring: Towards Collaborative and Cross-domain Wi-Fi Sensing
- Author
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Xinyi Li, Fengyi Song, Mina Luo, Kang Li, Liqiong Chang, Xiaojiang Chen, and Zheng Wang
- Subjects
Computer Networks and Communications ,Electrical and Electronic Engineering ,Software - Published
- 2023
3. AllSpark: Enabling Long-Range Backscatter for Vehicle-to-Infrastructure Communication
- Author
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Xuan Wang, Xin Kou, Haoyu Li, Fuwei Wang, Dingyi Fang, Yunfei Ma, and Xiaojiang Chen
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Computer Networks and Communications ,Hardware and Architecture ,Signal Processing ,Computer Science Applications ,Information Systems - Published
- 2022
4. Study on Schottky Al x Ga1-x N/GaN IMPATT Diodes for Millimeter-Wave Application
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Yang Dai, Jiangtao Dang, Xiaoyi Lei, Yunyao Zhang, Junfeng Yan, Wu Zhao, Xiaojiang Chen, and Shenglei Zhao
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Electrical and Electronic Engineering ,Electronic, Optical and Magnetic Materials - Published
- 2022
5. Exploiting Interference Fingerprints for Predictable Wireless Concurrency
- Author
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Tianzhang Xing, Xiaojiang Chen, Dingyi Fang, Meng Jin, Xiaolong Zheng, Yuan He, and Xu Dan
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Exploit ,Computer Networks and Communications ,business.industry ,Computer science ,Concurrency ,ComputerSystemsOrganization_COMPUTER-COMMUNICATIONNETWORKS ,020206 networking & telecommunications ,Throughput ,02 engineering and technology ,Interference (wave propagation) ,Link-state routing protocol ,0202 electrical engineering, electronic engineering, information engineering ,Concurrent computing ,Wireless ,Electrical and Electronic Engineering ,business ,Protocol (object-oriented programming) ,Software ,Computer network - Abstract
Operating in unlicensed ISM bands, ZigBee devices often yield poor performance due to the interference from ever increasing wireless devices in the 2.4 GHz band. Our empirical results show that, a specific interference is likely to have different influence on different outbound links of a ZigBee sender, which indicates the chance of concurrent transmissions . Based on this insight, we propose Smoggy-Link, a practical protocol to exploit the potential concurrency for adaptive ZigBee transmissions under harsh interference. Smoggy-Link maintains an accurate link model to quantify and trace the relationship between interference and link qualities of the sender’s outbound links. With such a link model, Smoggy-Link can translate low-cost interference information to the fine-grained spatiotemporal link state. The link information is further utilized for adaptive link selection and intelligent transmission schedule. We implement and evaluate a prototype of our approach with TinyOS and TelosB motes. The evaluation results show that Smoggy-Link has consistent improvements in both throughput and packet reception ratio under interference from various interferers.
- Published
- 2021
6. Simultaneous Material Identification and Target Imaging with Commodity RFID Devices
- Author
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Xiaojiang Chen, Jie Xiong, Hongbo Jiang, Dingyi Fang, Rajesh Krishna Balan, and Ju Wang
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Computer Networks and Communications ,Computer science ,business.industry ,Mobile computing ,020206 networking & telecommunications ,02 engineering and technology ,Signal ,Image stitching ,Identification (information) ,0202 electrical engineering, electronic engineering, information engineering ,Key (cryptography) ,Computer vision ,Radio frequency ,Artificial intelligence ,Electrical and Electronic Engineering ,business ,Software ,Multipath propagation - Abstract
Material identification and target imaging play an important role in many applications. This paper introduces TagScan, a system that can identify the material type and image the horizontal cut of a target simultaneously with cheap commodity Radio-Frequency IDentification (RFID) devices. The key intuition is that different materials and/or target sizes cause different amounts of phase and RSS (Received Signal Strength) changes, when radio frequency (RF) signal penetrates through the target. Multiple challenges need to be addressed before we can turn the idea into a functional system, including (i) indoor environments exhibit rich multipath which breaks the linear relationship between the phase change and the propagation distance inside a target; (ii) without knowing either material type or target size, trying to obtain these two information simultaneously is challenging; and (iii) stitching pieces of the propagation distances inside a target for an image estimate is non-trivial. We propose solutions to all the challenges and evaluate the system's performance in three different environments. TagScan is able to achieve higher than 94 percent material identification accuracies for 10 liquids and differentiates even very similar objects such as Coke and Pepsi. TagScan can accurately estimate the horizontal cut images of more than one target behind a wall.
- Published
- 2021
7. Cantor: Improving Goodput in LoRa Concurrent Transmission
- Author
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Tao Gu, Xiaojiang Chen, Xu Dan, Jing Zhang, Nannan Zhang, Dingyi Fang, and Nana Ding
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Computer Networks and Communications ,Computer science ,business.industry ,Goodput ,ComputerSystemsOrganization_COMPUTER-COMMUNICATIONNETWORKS ,010401 analytical chemistry ,020206 networking & telecommunications ,Throughput ,02 engineering and technology ,Energy consumption ,01 natural sciences ,0104 chemical sciences ,Computer Science Applications ,Transmission (telecommunications) ,Hardware and Architecture ,Duty cycle ,Default gateway ,Signal Processing ,Telecommunications link ,0202 electrical engineering, electronic engineering, information engineering ,business ,Information Systems ,Computer network - Abstract
Long range (LoRa) is an attractive low-power wide-area networks (LPWANs) technology for its features of low power, long range, and support for concurrent transmission. Our study reveals LoRa concurrent transmission suffer from the mismatch between the sender’s reception (RX) and gateway’s transmission (TX) window, which leads to the decline of goodput even the throughput is improved. Our experiment shows that goodput only accounts for two-fifths of the throughput in concurrent transmissions with 48 nodes at a duty cycle of 20%. This article presents a window match scheme named Cantor which improves the goodput of LoRa concurrent transmission by controlling the RX window size. Cantor does not require the frequent exchange of controlling information. Instead, it introduces a novel concurrent transmission model to estimate the downlink packet reception rate (PRR) with different network parameters, and a regression model is used to make the result more realistic. Then, we propose a simple optimization algorithm to select optimal RX window sizes in which nodes are able to receive acknowledgments. We implement and evaluate Cantor with commodity LoRa gateway and nodes, and conduct experiments in different scenarios. The experimental results show that Cantor increases the goodput by 70% and reduces energy consumption by 30% in LoRa concurrent transmissions with 48 nodes operate at a duty cycle of 20%.
- Published
- 2021
8. SitR: Sitting Posture Recognition Using RF Signals
- Author
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Chen Liu, Xiao Yin, Xiaojiang Chen, Dingyi Fang, Ziyi Li, and Lin Feng
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Computer Networks and Communications ,Computer science ,business.industry ,010401 analytical chemistry ,Work (physics) ,Sitting posture ,Wearable computer ,020206 networking & telecommunications ,02 engineering and technology ,Human body ,Sitting ,01 natural sciences ,0104 chemical sciences ,Computer Science Applications ,Hardware and Architecture ,Close relationship ,Signal Processing ,0202 electrical engineering, electronic engineering, information engineering ,Computer vision ,Artificial intelligence ,business ,Information Systems - Abstract
Sitting posture has a close relationship with our health, and keeping a healthy sitting posture is critical to each of us. Poor sitting postures often inevitably increase the risk of modern health musculoskeletal disorders. Previous works either used a camera to record the image or attached wearable sensors on the human body to recognize sitting postures. However, video-based approaches may face privacy issues while the wearable sensor-based approaches may cause uncomfortable to the user. In this work, we propose SitR, the first sitting posture recognition system using RF signals alone, which neither compromises the privacy nor requires wearing various sensors on the human body. We demonstrate that SitR can successfully recognize seven habitual sitting postures with just three lightweight and low-cost RFID tags pasted to the user’s back. Our design exploits the correlation between the phase change of RFID tags and the sitting postures. By extracting effective features of the measured phase sequences and employing appropriate machine learning algorithm, SitR can achieve robust and high performance. We evaluate the performance of SitR with ten volunteers in two different scenarios. Extensive experiments show SitR can recognize seven sitting postures with a high accuracy across different scenarios and various conditions. SitR can further detect the abnormal respiration, stand up, and sit down and provide sitting posture history for sedentary people.
- Published
- 2020
9. Structured Optimal Graph-Based Clustering With Flexible Embedding
- Author
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Pengzhen Ren, Yun Xiao, Xiaojun Chang, Xiaojiang Chen, Xin Wang, Mahesh Prakash, and Feiping Nie
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Computer Networks and Communications ,Computer science ,Graph partition ,Duality (optimization) ,02 engineering and technology ,Graph ,Manifold ,Computer Science Applications ,Artificial Intelligence ,0202 electrical engineering, electronic engineering, information engineering ,Task analysis ,Bipartite graph ,Embedding ,Artificial Intelligence & Image Processing ,020201 artificial intelligence & image processing ,Cluster analysis ,Algorithm ,Software ,Subspace topology - Abstract
In the real world, the duality of high-dimensional data is widespread. The coclustering method has been widely used because they can exploit the co-occurring structure between samples and features. In fact, most of the existing coclustering methods cluster the graphs in the original data matrix. However, these methods fail to output an affinity graph with an explicit cluster structure and still call for the postprocessing step to obtain the final clustering results. In addition, these methods are difficult to find a good projection direction to complete the clustering task on high-dimensional data. In this article, we modify the flexible manifold embedding theory and embed it into the bipartite spectral graph partition. Then, we propose a new method called structured optimal graph-based clustering with flexible embedding (SOGFE). The SOGFE method can learn an affinity graph with an optimal and explicit clustering structure and does not require any postprocessing step. Additionally, the SOGFE method can learn a suitable projection direction to map high-dimensional data to a low-dimensional subspace. We perform extensive experiments on two synthetic data sets and seven benchmark data sets. The experimental results verify the superiority, robustness, and good projection direction selection ability of our proposed method.
- Published
- 2020
10. Monostatic MIMO Backscatter Communications
- Author
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Chen He, Xiaojiang Chen, Shangdong Chen, Z. Jane Wang, and Huixu Luan
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Block code ,Backscatter ,Computer Networks and Communications ,Computer science ,Monte Carlo method ,MIMO ,020206 networking & telecommunications ,Data_CODINGANDINFORMATIONTHEORY ,02 engineering and technology ,Unitary matrix ,Unitary state ,Bistatic radar ,0202 electrical engineering, electronic engineering, information engineering ,Electrical and Electronic Engineering ,Algorithm ,Computer Science::Information Theory ,Communication channel - Abstract
Backscatter communications have two major antenna configurations: the bistatic configuration, in which the reader employs two different sets of antennas to transmit and receive, and the monostatic configuration, in which the reader employs one set of antennas for both transmitting and receiving. In this paper, we provide a comprehensive study on the MIMO techniques for the $M \times L$ monostatic channel. Particularly we study on the joint design of the query and the coding matrices. We show that the maximum achievable diversity order of the monostatic channel is the diversity order achieved by the block-lever unitary query and orthogonal space-time block code (BUTQ-OSTBC) design pair, which is $\frac {ML}{2}$ , exactly the half of the diversity order of the conventional MIMO channel. Then we show that uniform query, the simplest query approach, cannot achieve the maximum achievable diversity order in the monostatic channel. We generalize BUTQ-OSTBC to the general augmenting approach, and show that unitary matrix is optimal in terms of SER performance among all possible query matrices, when OSTBC is employed. The above results further indicate that in the backscatter channel, additional diversity can be obtained by varying the query signals over time slots within the channel coherent time, which is quite different from the results from the conventional MIMO channels. We verify our results by Monte Carlo simulations.
- Published
- 2020
11. Parallel Backscatter in the Wild: When Burstiness and Randomness Play With You
- Author
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Yuan He, Xin Meng, Dingyi Fang, Xiaojiang Chen, and Meng Jin
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Signal processing ,Backscatter ,Computer Networks and Communications ,Computer science ,05 social sciences ,050801 communication & media studies ,020206 networking & telecommunications ,02 engineering and technology ,Signal ,Computer Science Applications ,symbols.namesake ,0508 media and communications ,Parallel communication ,Burstiness ,0202 electrical engineering, electronic engineering, information engineering ,symbols ,Time domain ,Electrical and Electronic Engineering ,Error detection and correction ,Gaussian process ,Throughput (business) ,Algorithm ,Decoding methods ,Software - Abstract
Parallel backscatter is a promising technique for high throughput, low power communications. The existing approaches of parallel backscatter are based on a common assumption, i.e. the states of the collided signals are distinguishable from each other in either the time domain or the IQ (the In-phase and Quadrature) domain. We in this paper disclose the superclustering phenomenon, which invalidates that assumption and seriously affects the decoding performance. Then we propose an interstellar travelling model to capture the bursty Gaussian process of a collided signal. Based on this model, we design Hubble, a reliable signal processing approach to support parallel backscatter in the wild. Hubble addresses several technical challenges: (i) a novel scheme based on Pearson’s Chi-Square test to extract the collided signals’ combined states, (ii) a Markov driven method to capture the law of signal state transitions, and (iii) error correction schemes to guarantee the reliability of parallel decoding. Theoretically, Hubble is able to decode all the backscattered data, as long as the signals are detectable by the receiver. The experiment results demonstrate that the median throughput of Hubble is $11.7\times $ higher than that of the state-of-the-art approach.
- Published
- 2020
12. cDeepArch: A Compact Deep Neural Network Architecture for Mobile Sensing
- Author
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Dingyi Fang, Yang Liu, Xiaojiang Chen, Xiaoqing Gong, Zhenjiang Li, Kang Yang, and Tianzhang Xing
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Context model ,Artificial neural network ,Computer Networks and Communications ,Computer science ,business.industry ,Deep learning ,Distributed computing ,020206 networking & telecommunications ,02 engineering and technology ,010501 environmental sciences ,01 natural sciences ,Computer Science Applications ,Data modeling ,Task analysis ,Neural network architecture ,0202 electrical engineering, electronic engineering, information engineering ,Central processing unit ,Artificial intelligence ,Latency (engineering) ,Electrical and Electronic Engineering ,business ,Mobile device ,Processing delay ,Software ,0105 earth and related environmental sciences - Abstract
Mobile sensing is a promising sensing paradigm in the era of Internet of Things (IoT) that utilizes mobile device sensors to collect sensory data about sensing targets and further applies learning techniques to recognize the sensed targets to correct classes or categories. Due to the recent great success of deep learning, an emerging trend is to adopt deep learning in this recognition process, while we find an overlooked yet crucial issue to be solved in this paper — The size of deep learning models should be sufficiently large for reliably classifying various types of recognition targets, while the achieved processing delay may fail to satisfy the stringent latency requirement from applications. If we blindly shrink the deep learning model for acceleration, the performance cannot be guaranteed. To cope with this challenge, this paper presents a compact deep neural network architecture, namely cDeepArch . The key idea of the cDeepArch design is to decompose the entire recognition task into two lightweight sub-problems: context recognition and the context-oriented target recognitions. This decomposition essentially utilizes the adequate storage to trade for the CPU and memory resource consumptions during execution. In addition, we further formulate the execution latency for decomposed deep learning models and propose a set of enhancement techniques, so that system performance and resource consumption can be quantitatively balanced. We implement a cDeepArch prototype system and conduct extensive experiments. The result shows that cDeepArch achieves excellent recognition performance and the execution latency is also lightweight.
- Published
- 2019
13. Material Identification and Target Imaging with RFIDs [IoT Connection]
- Author
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Xiaojiang Chen, Dingyi Fang, Hongbo Jiang, Rajesh Krishna Balan, Jie Xiong, and Ju Wang
- Subjects
General Computer Science ,business.industry ,Computer science ,010401 analytical chemistry ,Cognitive neuroscience of visual object recognition ,020206 networking & telecommunications ,Image processing ,02 engineering and technology ,Object (computer science) ,01 natural sciences ,0104 chemical sciences ,Connection (mathematics) ,Identification (information) ,0202 electrical engineering, electronic engineering, information engineering ,business ,Internet of Things ,Computer hardware - Abstract
TagScan is a system that determines the material type and shape of an object with inexpensive commercial RFID technology. Real-world experiments show that TagScan can identify 10 common liquids with accuracy greater than 94%.
- Published
- 2018
14. Low Human-Effort, Device-Free Localization with Fine-Grained Subcarrier Information
- Author
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Hongbo Jiang, Dingyi Fang, Xiaojiang Chen, Jie Xiong, Chen Wang, Kyle Jamieson, and Ju Wang
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SIMPLE (military communications protocol) ,Computer Networks and Communications ,Computer science ,business.industry ,020208 electrical & electronic engineering ,Real-time computing ,020206 networking & telecommunications ,02 engineering and technology ,Subcarrier ,Non-line-of-sight propagation ,Channel state information ,Computer Science::Networking and Internet Architecture ,0202 electrical engineering, electronic engineering, information engineering ,Key (cryptography) ,Wireless ,Electrical and Electronic Engineering ,Transceiver ,business ,Software ,Multipath propagation ,Computer Science::Information Theory - Abstract
Device-free localization of objects not equipped with RF radios is playing a critical role in many applications. This paper presents LIFS, a Low human-effort, device-free localization system with fine-grained subcarrier information, which can localize a target accurately without offline training. The basic idea is simple: channel state information (CSI) is sensitive to a target’s location and thus the target can be localized by modelling the CSI measurements of multiple wireless links. However, due to rich multipath indoors, CSI can not be easily modelled. To deal with this challenge, our key observation is that even in a rich multipath environment, not all subcarriers are affected equally by multipath reflections. Our CSI pre-processing scheme tries to identify the subcarriers not affected by multipath. Thus, CSI on the “clean” subcarriers can still be utilized for accurate localization. Without the need of knowing the majority transceivers’ locations, LiFS achieves a median accuracy of 0.5 m and 1.1 m in line-of-sight (LoS) and non-line-of-sight (NLoS) scenarios, respectively, outperforming the state-of-the-art systems.
- Published
- 2018
15. Adaptive Client Clustering for Efficient Federated Learning over Non-IID and Imbalanced Data
- Author
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Biyao Gong, Tianzhang Xing, Zhidan Liu, Wei Xi, and Xiaojiang Chen
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Information Systems and Management ,Information Systems - Published
- 2022
16. Resource Allocation in Reverse TDD Wireless Backhaul HetNets With 3D Massive Antennas
- Author
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Xiaojiang Chen, Dingyi Fang, Geoffrey Ye Li, Xun Li, Jinping Niu, and Jie Zheng
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Optimization problem ,Logarithm ,business.industry ,Iterative method ,Computer science ,Wireless backhaul ,ComputerSystemsOrganization_COMPUTER-COMMUNICATIONNETWORKS ,020302 automobile design & engineering ,020206 networking & telecommunications ,02 engineering and technology ,Backhaul (telecommunications) ,Base station ,0203 mechanical engineering ,Control and Systems Engineering ,Computer Science::Networking and Internet Architecture ,0202 electrical engineering, electronic engineering, information engineering ,Wireless ,Macrocell ,Electrical and Electronic Engineering ,business ,Computer Science::Information Theory ,Computer network - Abstract
This letter investigates resource allocation for users and small cell wireless backhaul in a reverse time division duplexing heterogenous network with 3D massive antennas at the macrocell base station. We first formulate the problem to maximize the summation of the logarithmic throughputs of all users, including the small cell users and macrocell users, considering backhaul limitation and resource allocation. To address the non-convex and non-linear optimization problem, we develop an iterative algorithm based on primal decomposition to find its solution. Simulation results demonstrate that the proposed algorithm benefits both cell edge and cell average performance.
- Published
- 2018
17. R&P: An Low-Cost Device-Free Activity Recognition for E-Health
- Author
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Xiaojiang Chen, Binbin Xie, Li Liyao, Wei Wang, Anwen Wang, Bo Jiang, Jian Liang, Yao Peng, and Bai Rui
- Subjects
Dynamic time warping ,General Computer Science ,business.industry ,Computer science ,Real-time computing ,General Engineering ,020206 networking & telecommunications ,02 engineering and technology ,Activity recognition ,0202 electrical engineering, electronic engineering, information engineering ,Radio-frequency identification ,020201 artificial intelligence & image processing ,General Materials Science ,business ,Wearable technology - Abstract
Activity recognition is important for taking care of patients and old men especially in e-Health. The activity recognition system without carrying any wearable devices is widely used in our daily life. Current methods employing uneconomical equipment or even dedicated devices lead to cost-inefficiency for large-scale deployments. This paper introduces R&P, a device-free activity recognition system only using cheap radio frequency identification devices (RFID) tags. Based on the analysis of RFID signals, we extract received signal strength fingerprints and phase fingerprints for each activity and synthesize these two kinds of fingerprints to accurately recognize activities. Moreover, we also modify the dynamic time warping (DTW) algorithm and propose T-DTW method to improve the recognition efficiency. We use commercial passive RFID hardware and verify R&Pin three different environments with different targets and six activities. The results demonstrate that our solution can recognize activities with an average accuracy of 87.9%.
- Published
- 2018
18. Human Behavior Recognition Using Wi-Fi CSI: Challenges and Opportunities
- Author
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Xiaojiang Chen, Dingyi Fang, Lili Chen, Yao Peng, and Ni Ligang
- Subjects
Computer Networks and Communications ,business.industry ,Computer science ,Feature extraction ,Fingerprint (computing) ,020206 networking & telecommunications ,02 engineering and technology ,Fingerprint recognition ,Behavior recognition ,Data science ,Computer Science Applications ,0202 electrical engineering, electronic engineering, information engineering ,020201 artificial intelligence & image processing ,Electrical and Electronic Engineering ,Hidden Markov model ,Telecommunications ,business - Abstract
Human behavior recognition (HBR) has emerged as a core research area in human-computer interaction. In this article, we give a comprehensive introduction to HBR using Wi-Fi channel state information. We first comprehensively review the state-of-art of HBR, based on the two techniques that drove recent progress in Wi-Fi channel-state-information-based HBR -- fingerprint-based and model-based. Specifically, we describe their corresponding characteristics, general architectures, and provide a performance comparison of the two mechanisms. We then provide insights into the future directions of HBR research, and propose two possible new schemes, and the technical challenges coming with them.
- Published
- 2017
19. SmartMTra: Robust Indoor Trajectory Tracing Using Smartphones
- Author
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Hongbo Jiang, Ma Yang, Yanyan Wu, Zhanyong Tang, Xiaojiang Chen, Xiaoqiang Ma, Dingyi Fang, and Pengyan Zhang
- Subjects
Engineering ,Data processing ,business.industry ,media_common.quotation_subject ,010401 analytical chemistry ,Real-time computing ,020206 networking & telecommunications ,02 engineering and technology ,Pedestrian ,Tracing ,01 natural sciences ,Adaptability ,0104 chemical sciences ,Inertial measurement unit ,Phone ,Robustness (computer science) ,Dead reckoning ,0202 electrical engineering, electronic engineering, information engineering ,Electrical and Electronic Engineering ,business ,Instrumentation ,media_common - Abstract
Using smartphones for indoor motion trajectory tracing has attracted a lot of attention in recent years, which offers great potential to support a broad spectrum of applications in indoor environment, including elder care, business analysis, and navigation. Yet most existing approaches only work for certain pedestrian’s motion modes or smartphone’s carrying patterns, which lack the robustness and adaptability to general scenarios. In this paper, we propose SmartMTra, a comprehensive, robust, and accurate solution for indoor motion trajectory tracing based on smartphone’s built-in inertial sensors. Through analyzing the data from inertial sensors, we extract a set of features that are found to be highly related to human’s physical activities, which can help to identify motion mode and phone’s carrying pattern through a decomposition model. After that, SmartMTra utilizes the pedestrian dead reckoning technique, which involves estimating step counts, step-length, and heading direction, to achieve accurate trajectory tracing. We have conducted extensive experiments to evaluate the performance of SmartMTra in a campus building, and the results demonstrate the robustness of SmartMTra in various scenarios, as well as the superiority of SmartMTra over the state-of-the-art solutions.
- Published
- 2017
20. E-HIPA: An Energy-Efficient Framework for High-Precision Multi-Target-Adaptive Device-Free Localization
- Author
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Dingyi Fang, Tianzhang Xing, Hongbo Jiang, Zhe Yang, Xiaojiang Chen, Lin Cai, and Ju Wang
- Subjects
Computer Networks and Communications ,business.industry ,Computer science ,010401 analytical chemistry ,Testbed ,020206 networking & telecommunications ,02 engineering and technology ,Intrusion detection system ,Energy consumption ,01 natural sciences ,0104 chemical sciences ,Compressed sensing ,Computer engineering ,Embedded system ,0202 electrical engineering, electronic engineering, information engineering ,Electrical and Electronic Engineering ,business ,Wireless sensor network ,Software ,Efficient energy use - Abstract
Device-free localization (DFL), which does not require any devices to be attached to target(s), has become an appealing technology for many applications, such as intrusion detection and elderly monitoring. To achieve high localization accuracy, most recent DFL methods rely on collecting a large number of received signal strength (RSS) changes distorted by target(s). Consequently, the incurred high energy consumption renders them infeasible for resource-constraint networks, such as wireless sensor networks. This paper introduces an e nergy-efficient framework for high-precision multi-target-a daptive device-free localization (E-HIPA). Compared with the existing methods, E-HIPA demands fewer transceivers, applies the compressive sensing (CS) theory to guarantee high localization accuracy with less RSS change measurements. The motivation behind the proposed E-HIPA is the sparse nature of multi-target locations in the spatial domain. Before taking advantage of this intrinsic sparseness, we theoretically prove the validity of the proposed CS-based framework problem formulation. Based on the formulation, the proposed E-HIPA primarily includes an adaptive orthogonal matching pursuit (AOMP) algorithm, by which it is capable of recovering the precise location vector with high probability, even for a more practical scenario with unknown target number. Experimental results via real testbed demonstrate that, compared with the previous state-of-the-art solutions, i.e., RTI, SCPL, and RASS approaches, E-HIPA reduces the energy consumption by up to 69 percent with meter-level localization accuracy.
- Published
- 2017
21. RSS Distribution-Based Passive Localization and Its Application in Sensor Networks
- Author
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Zhe Yang, Xiaojiang Chen, Tianzhang Xing, Wei Wang, Hongbo Jiang, Chen Liu, Dingyi Fang, and Lin Cai
- Subjects
Ground truth ,Radar tracker ,Computer science ,business.industry ,Applied Mathematics ,RSS ,Node (networking) ,Real-time computing ,020206 networking & telecommunications ,02 engineering and technology ,computer.file_format ,Computer Science Applications ,Embedded system ,Scalability ,0202 electrical engineering, electronic engineering, information engineering ,Wireless ,020201 artificial intelligence & image processing ,Electrical and Electronic Engineering ,business ,computer ,Wireless sensor network - Abstract
Passive localization is fundamental for many applications such as activity monitoring and real-time tracking. Existing received signal strength (RSS)-based passive localization approaches have been proposed in the literature, which depend on dense deployment of wireless communication nodes to achieve high accuracy. Thus, they are not cost-effective and scalable. This paper proposes the RSS distribution-based localization (RDL) technique, which can achieve high localization accuracy without dense deployment. In essence, RDL leverages the RSS and the diffraction theory to enable RSS-based passive localization in sensor networks. Specifically, we analyze the fine-grained RSS distribution properties at a variety of node distances and reveal that the structure of the triangle is efficient for low-cost passive localization. We further construct a unit localization model aiming at high accuracy localization. Experimental results show that RDL can improve the localization accuracy by up to 50%, compared to existing approaches when the error tolerance is less than 1.5 m. In addition, we apply RDL to facilitate the application of moving trajectory identification. Our moving trajectory identification includes two phases: an offline phase where the possible locations can be estimated by RDL and an online phase where we precisely identify the moving trajectory. We conducted extensive experiments to show its effectiveness for this application—the estimated trajectory is close to the ground truth.
- Published
- 2016
22. Transferring Compressive-Sensing-Based Device-Free Localization Across Target Diversity
- Author
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Zhe Yang, Xiaojiang Chen, Dingyi Fang, Tianzhang Xing, Ju Wang, and Chase Q. Wu
- Subjects
education.field_of_study ,Engineering ,business.industry ,Speech recognition ,RSS ,Feature vector ,Population ,Pattern recognition ,computer.file_format ,Compressed sensing ,Transferring (function) ,Signal strength ,Control and Systems Engineering ,Leverage (statistics) ,Artificial intelligence ,Electrical and Electronic Engineering ,business ,education ,Device free localization ,computer - Abstract
Device-free localization (DFL) plays an important role in many applications, such as wildlife population and migration tracking. Most of current DFL systems leverage the distorted received signal strength (RSS) changes to localize the target(s). However, they assume a fixed distribution of the RSS change measurements, although they are distorted by different types of targets. It inevitably causes the localization to fail if the targets for modeling and testing belong to different categories. This paper presents TLCS—a transferring compressive sensing based DFL approach—which employs a rigorously designed transferring function to transfer the distorted RSS changes across different categories of targets into a latent feature space, where the distributions of the distorted RSS change measurements from different categories of targets are unified. A benefit of this approach is that the same transferred sensing matrix can be shared by different categories of targets, leading to a substantial reduction in the human efforts. The results of experiments illustrate the efficacy of the TLCS.
- Published
- 2015
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