다중혈관 관상동맥 환자에서 y-문합을 이용하여 양쪽 내흉동맥만을 사용한 우회술의 조기 성적

[신간의 별자리x] 우리/미술, 그리고 ‘슬픔의 박물관’

The spatial features of emitted wireless signals are the basis of location distinction and determination for wireless indoor localization. Available in mainstream wireless signal measurements, the Received Signal Strength Indicator (RSSI) has been adopted in vast indoor localization systems. However, it suffers from dramatic performance degradation in complex situations due to multipath fading and temporal dynamics. Break-through techniques resort to finer-grained wireless channel measurement than RSSI. Different from RSSI, the PHY layer power feature, channel response, is able to discriminate multipath characteristics, and thus holds the potential for the convergence of accurate and pervasive indoor localization. Channel State Information (CSI, reflecting channel response in 802.11 a/g/n) has attracted many research efforts and some pioneer works have demonstrated submeter or even centimeter-level accuracy. In this article, we survey this new trend of channel response in localization. The differences between CSI and RSSI are highlighted with respect to network layering, time resolution, frequency resolution, stability, and accessibility. Furthermore, we investigate a large body of recent works and classify them overall into three categories according to how to use CSI. For each category, we emphasize the basic principles and address future directions of research in this new and largely open area.

From RSSI to CSI: Indoor Localization via Channel Response, A survey on indoor localization using PHY-layer information

Power delay profiles characterize multipath channel features, which are widely used in motion- or localization-based applications. Recent studies show that the power delay profile may be derived from the CSI traces collected from commodity WiFi devices, but the performance is limited by two dominating factors. The resolution of the derived power delay profile is determined by the channel bandwidth, which is however limited on commodity WiFi. The collected CSI reflects the signal distortions due to both the channel attenuation and the hardware imperfection. A direct derivation of power delay profiles using raw CSI measures, as has been done in the literature, results in significant inaccuracy. In this paper, we present Splicer, a software-based system that derives high-resolution power delay profiles by splicing the CSI measurements from multiple WiFi frequency bands. We propose a set of key techniques to separate the mixed hardware errors from the collected CSI measurements. Splicer adapts its computations within stringent channel coherence time and thus can perform well in presence of mobility. Our experiments with commodity WiFi NICs show that Splicer substantially improves the accuracy in profiling multipath characteristics, reducing the errors of multipath distance estimation to be less than $2m$. Splicer can immediately benefit upper-layer applications. Our case study with recent single-AP localization achieves a median localization error of $0.95m$.

/pdf/precise-power-delay-profiling-with-commodity-wifi-4dvfgt14vc.pdf

Precise Power Delay Profiling with Commodity WiFi

Intended for network-wide dissemination of commands, configurations and code binaries, flooding has been investigated extensively in wireless networks. However, little work has yet been done on low-duty-cycle wireless sensor networks in which nodes stay asleep most of time and wake up asynchronously. In this type of network, a broadcasting packet is rarely received by multiple nodes simultaneously, a unique constraining feature that makes existing solutions unsuitable. Combined with unreliable links, flooding in low-duty-cycle networks is a new challenging issue.In this paper, we introduce Opportunistic Flooding, a novel design tailored for low-duty-cycle networks with unreliable wireless links and predetermined working schedules. The key idea is to make probabilistic forwarding decisions at a sender based on the delay distribution of next-hop nodes. Only opportunistically early packets are forwarded using links outside the energy optimal tree to reduce the flooding delay and redundancy in transmission. To improve performance further, we propose a forwarder selection method to alleviate the hidden terminal problem and a link-quality-based backoff method to resolve simultaneous forwarding operations. We evaluate Opportunistic Flooding with extensive simulation and a test-bed implementation consisting of 30 MicaZ nodes. Evaluation shows our design is close to the optimal performance achievable by oracle flooding designs. Compared with improved traditional flooding, our design achieves significantly shorter flooding delay while consuming only 20% ~ 60% of the transmission energy in various low-duty-cycle network settings.

/pdf/opportunistic-flooding-in-low-duty-cycle-wireless-sensor-4707vvw6bx.pdf

Opportunistic flooding in low-duty-cycle wireless sensor networks with unreliable links

We study the ubiquitous data collection for mobile users in wireless sensor networks. People with handheld devices can easily interact with the network and collect data. We propose a novel approach for mobile users to collect the network-wide data. The routing structure of data collection is additively updated with the movement of the mobile user. With this approach, we only perform a local modification to update the routing structure while the routing performance is bounded and controlled compared to the optimal performance. The proposed protocol is easy to implement. Our analysis shows that the proposed approach is scalable in maintenance overheads, performs efficiently in the routing performance, and provides continuous data delivery during the user movement. We implement the proposed protocol in a prototype system and test its feasibility and applicability by a 49-node testbed. We further conduct extensive simulations to examine the efficiency and scalability of our protocol with varied network settings.

/pdf/ubiquitous-data-collection-for-mobile-users-in-wireless-5954ddhjfd.pdf

Ubiquitous data collection for mobile users in wireless sensor networks

To save energy, wireless sensor networks often run in a low duty cycle mode, where the radios of sensor nodes are scheduled between ON and OFF states. For nodes to communicate with each other, Low Power Listening (LPL) and Low Power Probing (LPP) are two types of rendezvous mechanisms. Nodes with LPL or LPP rely on signal strength or probe packets to detect potential transmissions, and then keep the radio-on for communications. Unfortunately, in many cases, signal strength and probe packets are susceptible to interference, resulting in undesirable radio on time when the signal strength of interference is above a threshold or a probe packet is interfered. To address the issue, we propose ZiSense, an energy efficient rendezvous mechanism which is resilient to interference. Instead of checking the signal strength or decoding the probe packets, ZiSense detects the existence of ZigBee transmissions and wakes up nodes accordingly. On sensor nodes with limited information and resource, we carefully study and extract short-term features purely from the time-domain RSSI sequence, and design a rule-based approach to efficiently identify the existence of ZigBee. We theoretically analyze the benefit of ZiSense in different environments and implement a prototype in TinyOS with TelosB motes. We examine ZiSense performance under controlled interference and office environments. The evaluation results show that, compared with state-of-the-art rendezvous mechanisms, ZiSense significantly reduces the energy consumption.

/pdf/zisense-towards-interference-resilient-duty-cycling-in-nisteca6gm.pdf

ZiSense: towards interference resilient duty cycling in wireless sensor networks

We study the ubiquitous data collection for mobile users in wireless sensor networks. People with handheld devices can easily interact with the network and collect data. We propose a novel approach for mobile users to collect the network-wide data. The routing structure of data collection is additively updated with the movement of the mobile user. With this approach, we only perform a limited modification to update the routing structure while the routing performance is bounded and controlled compared to the optimal performance. The proposed protocol is easy to implement. Our analysis shows that the proposed approach is scalable in maintenance overheads, performs efficiently in the routing performance, and provides continuous data delivery during the user movement. We implement the proposed protocol in a prototype system and test its feasibility and applicability by a 49-node testbed. We further conduct extensive simulations to examine the efficiency and scalability of our protocol with varied network settings.

Exploiting Ubiquitous Data Collection for Mobile Users in Wireless Sensor Networks

User identified gesture recognition is a fundamental step towards ubiquitous device-free sensing. We propose WiHF, which first simultaneously enables cross-domain gesture recognition and user identification using WiFi in a real-time manner. The basic idea of WiHF is to derive a cross-domain motion change pattern of arm gestures from WiFi signals, rendering both unique gesture characteristics and the personalized user performing styles. To extract the motion change pattern in realtime, we develop an efficient method based on the seam carving algorithm. Moreover, taking as input the motion change pattern, a Deep Neural Network (DNN) is adopted for both gesture recognition and user identification tasks. In DNN, we apply splitting and splicing schemes to optimize collaborative learning for dual tasks. We implement WiHF and extensively evaluate its performance on a public dataset including 6 users and 6 gestures performed across 5 locations and 5 orientations in 3 environments. Experimental results show that WiHF achieves 97.65% and 96.74% for in-domain gesture recognition and user identification accuracy, respectively. The cross-domain gesture recognition accuracy is comparable with the state-of-the-art methods, but the processing time is reduced by 30×.

WiHF: Enable User Identified Gesture Recognition with WiFi

Low-Power Wide-Area Networks (LPWANs) are an emerging Internet-of-Things (IoT) paradigm marked by low-power and long-distance communication. Among them, LoRa is widely deployed for its unique characteristics and open-source technology. By adopting the Chirp Spread Spectrum (CSS) modulation, LoRa enables low signal-to-noise ratio (SNR) communication. However, the standard demodulation method does not fully exploit the properties of chirp signals, thus yields a sub-optimal SNR threshold under which the decoding fails. Consequently, the communication range and energy consumption have to be compromised for robust transmission. This paper presents NELoRa, a neural-enhanced LoRa demodulation method, exploiting the feature abstraction ability of deep learning to support ultra-low SNR LoRa communication. Taking the spectrogram of both amplitude and phase as input, we first design a mask-enabled Deep Neural Network (DNN) filter that extracts multi-dimension features to capture clean chirp symbols. Second, we develop a spectrogram-based DNN decoder to decode these chirp symbols accurately. Finally, we propose a generic packet demodulation system by incorporating a method that generates high-quality chirp symbols from received signals. We implement and evaluate NELoRa on both indoor and campus-scale outdoor testbeds. The results show that NELoRa achieves 1.84-2.35 dB SNR gains and extends the battery life up to 272% (~0.38-1.51 years) in average for various LoRa configurations.

https://dl.acm.org/doi/pdf/10.1145/3485730.3485928

Zhichao Cao

Papers

Ubiquitous data collection for mobile users in wireless sensor networks

ZiSense: towards interference resilient duty cycling in wireless sensor networks

Exploiting Ubiquitous Data Collection for Mobile Users in Wireless Sensor Networks

WiHF: Enable User Identified Gesture Recognition with WiFi

NELoRa: Towards Ultra-low SNR LoRa Communication with Neural-enhanced Demodulation