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Beamforming can improve the communication and sensing capabilities for a wide range of IoT applications. However, most existing IoT devices cannot perform beamforming due to form factor, energy, and cost constraints. This paper presents RFlens, a reconfigurable metasurface that empowers low-profile IoT devices with beamforming capabilities. The metasurface consists of many unit-cells, each acting as a phase shifter for signals going through it. By encoding the phase shifting values, RFlens can manipulate electromagnetic waves to "reshape" and resteer the beam pattern. We prototype RFlens for 5 GHz Wi-Fi signals. Extensive experiments demonstrate that RFlens can achieve a 4.6 dB median signal strength improvement (up to 9.3 dB) even with a relatively small 16 × 16 array of unit-cells. In addition, RFlens can effectively improve the secrecy capacity of IoT links and enable passive NLoS wireless sensing applications.

Plant sensing plays an important role in today's smart agriculture and intelligent forestry. However, existing plant sensing work based on wireless IoT mostly focuses on the environment that plants live like soil moisture and temperature of a greenhouse or a farm. The information about the surrounding environment does not reflect the physical condition of the plant itself. Taking ambient RF signal and commodity RFID tag as the fundamental sensing platform, we explore the Plant-Keeper, a real-time monitor system, which traces the physical state and biological activities of plants by perceiving Ion transmission in plants, like water content and the reaction of plants to an external stimulus such as Cut, Salt, Chilling, and Drought Stresses. We make the following technical contributions to achieve this goal. First, a theoretical model to verify that sensing the Ion transmission like Ca2+ channel and K+ channel by the low-power wireless backscatter technique are feasible; Second, a hardware-based non-invasive sensing system that can obtain the physical changes in plants by using the impedance-related RSS (Received Signal Strength); Third, a low-power low-cost implementation that based on a commercial RFID tag with the hardware structure we have proposed. Experimental results demonstrate that our system can monitor the ion channel changes of the whole plant systematically, and improve the detection accuracy to the millisecond level. The detection speed is 103 times that of the traditional patch-clamp technique, and the latter is not available for whole system inspection of plants in vivo.

With the rapid development of the Internet of Things (IoT), we have seen a larger number of devices deployed with different wireless communication protocols (i.e., WiFi, ZigBee, Bluetooth). Working in the same place opens a new opportunity for these devices to communicate directly with each other, leveraging on Cross-technology Communication (CTC). However, since these devices operate in the same frequency band which results in the competition against each other for network resources, severe interfere may arise. In this paper, we explore pseudo-random sequence (PR sequence) to design a novel CTC protocol that enables low-cost direct communication between WiFi and ZigBee in noisy indoor environments. Pseudorandom sequence offers a unique statistical feature to accomplish both information transmission and synchronization between heterogeneous devices. We design a dynamic synchronous decoding strategy to handle interference coexisted among different wireless protocols. Our system does not require any modification of communication protocol and underlying hardware and firmware. We implement our system on commercial devices (Intel 5300 WiFi NIC and MicaZ CC2420), and conduct extensive experiments to evaluate the system performance in three typical scenarios. The experimental results show that the synchronization time of our approach is lower than 0.5 ms, and the accuracy is greater than 84% while the channel occupancy is as high as 50%.

Monitoring the vibration characteristics of a machine or structure provides valuable information of its health condition and this information can be used to detect problems in their incipient stage. Recently, researchers employ RFID signals for vibration sensing. However, they mainly focus on vibration frequency estimation and still face difficulties in accurately sensing the other important characteristic of vibration which is vibration amplitude in the scale of sub-millimeter. In this paper, we introduce TagSMM, a contactless RFID-based vibration sensing system which can measure vibration amplitude in sub-millimeter resolution. TagSMM employs the signal propagation theory to deeply understand how the signal phase varies with vibration and proposes a coupling-based method to amplify the vibration-induced phase change to achieve sub-millimeter level amplitude sensing for the first time. We design and implement TagSMM with commodity RFID hardware. Our experiments show that TagSMM can detect a 0.5 mm vibration, 10 times better than the state-of-the-arts. Our field studies show TagSMM can sense a drone's abnormal vibration and can also effectively detect a small 0.2 cm screw loose in a motor at a 100% accuracy.

Record-and-replay tools are important for quality assurance of Web applications by capturing user case scenarios and executing them automatically when needed. However, the tests generated by existing techniques are brittle, and often lead to test breakages as the dynamic behavior and frequent updates of modern Web applications. In this paper, we propose WebRR, a self-replay enhanced robust record-and-replay technique for Web applications testing. The novelty of WebRR is that, it introduces a new self-replay mechanism in the recording phase, which checks the captured event from the record module online, and generates multiple locators (including DOM locators, visual locator and proximity locators) automatically, to improve the robustness of generated test cases. During the replay, it combines multiple locators and new local workflow repair technique to repair test breakages, and can improve the resilience of generated tests to frequent updates of the applications. We applied our approach to 3 enterprise Web applications, which are deployed in a large power grid company of China. The experimental results show that WebRR is effective, and substantially improve the robustness of end-to-end web tests that are generated using record-and-replay technique.

Machine learning models, particularly reinforcement learning (RL), have demonstrated great potential in optimizing video streaming applications. However, the state-of-the-art solutions are limited to an "offline learning" paradigm, i.e., the RL models are trained in simulators and then are operated in real networks. As a result, they inevitably suffer from the simulation-to-reality gap, showing far less satisfactory performance under real conditions compared with simulated environment. In this work, we close the gap by proposing OnRL, an online RL framework for real-time mobile video telephony. OnRL puts many individual RL agents directly into the video telephony system, which make video bitrate decisions in real-time and evolve their models over time. OnRL then aggregates these agents to form a high-level RL model that can help each individual to react to unseen network conditions. Moreover, OnRL incorporates novel mechanisms to handle the adverse impacts of inherent video traffic dynamics, and to eliminate risks of quality degradation caused by the RL model's exploration attempts. We implement OnRL on a mainstream operational video telephony system, Alibaba Taobao-live. In a month-long evaluation with 543 hours of video sessions from 151 real-world mobile users, OnRL outperforms the prior algorithms significantly, reducing video stalling rate by 14.22% while maintaining similar video quality.

Material sensing is an essential ingredient for many IoT applications. While hyperspectral camera, infrared, X-Ray, and Radar provide potential solutions for material identification, high cost is the major concern limiting their applications. In this paper, we explore the capability of employing RF signals for fine-grained material sensing with commodity RFID device. The key reason for our system to work is that the tag antenna's impedance is changed when it is close or attached to a target. The amount of impedance change is dependent on the target's material type, thus enabling us to utilize the impedance-related phase change available at commodity RFID devices for material sensing. Several key challenges are addressed before we turn the idea into a functional system: (i) the random tag-reader distance causes an additional unknown phase change on top of the phase change caused by the target material; (ii) the tag rotations cause phase shifts and (iii) for conductive liquid, there exists liquid reflection which interferes with the impedance-caused phase change. We address these challenges with novel solutions. Comprehensive experiments show high identification accuracies even for very similar materials such as Pepsi and Coke.

Contactless wireless sensing without attaching a device to the target has achieved promising progress in recent years. However, one severe limitation in this field is the limited sensing range. This paper presents WideSee to realize wide-area sensing with only one transceiver pair. WideSee utilizes the LoRa signal to achieve a larger range of sensing and further incorporates drone's mobility to broaden the sensing area. We have developed a working prototype of WideSee for human target detection and localization that are especially useful in emergency scenarios like rescue and terrorist search. We also evaluated WideSee with field study in a high-rise building, which demonstrates the great potential of WideSee for supporting wide-area contactless sensing applications with a single LoRa transceiver pair hosted on a drone.

Contactless wireless sensing without attaching a device to the target has achieved promising progress in recent years. However, one severe limitation is the small sensing range. This paper presents WideSee to realize wide-area sensing with only one transceiver pair. WideSee utilizes the LoRa signal to achieve a larger range of sensing and further incorporates drone's mobility to broaden the sensing area. WideSee presents solutions across software and hardware to overcome two aspects of challenges for wide-range contactless sensing: (i) the interference brought by the device mobility and LoRa's high sensitivity; and (ii) the ambiguous target information such as location when employing just a single pair of transceivers. We have developed a working prototype of WideSee for human target detection and localization that are especially useful in emergency scenarios such as rescue search, and evaluated WideSee with both controlled experiments and the field study in a high-rise building. Extensive experiments demonstrate the great potential of WideSee for wide-area contactless sensing with a single LoRa transceiver pair hosted on a drone.

Despite the pervasive use of real-time video telephony services, their quality of experience (QoE) remains unsatisfactory, especially over the mobile Internet. We conduct a large-scale measurement campaign on \appname, an operational mobile video telephony service. Our analysis shows that the application-layer video codec and transport-layer protocols remain highly uncoordinated, which represents one major reason for the low QoE. We thus propose \name, a machine learning based framework to resolve the issue. We train \name with the massive data traces from the measurement campaign using a custom-designed imitation learning algorithm, which enables \name to learn from past experience following an expert's iterative demonstration/supervision. We have implemented and incorporated \name into the \appname. Our experiments show that \name outperforms state-of-the-art solutions, improving video quality while reducing stalling time by multi-folds under various practical scenarios.

Despite the pervasive use of real-time video telephony services, the users' quality of experience (QoE) remains unsatisfactory, especially over the mobile Internet. Previous work studied the problem via controlled experiments, while a systematic and in-depth investigation in the wild is still missing. To bridge the gap, we conduct a large-scale measurement campaign on \appname, an operational mobile video telephony service. Our measurement logs fine-grained performance metrics over 1 million video call sessions. Our analysis shows that the application-layer video codec and transport-layer protocols remain highly uncoordinated, which represents one major reason for the low QoE. We thus propose \name, a machine learning based framework to resolve the issue. Instead of blindly following the transport layer's estimation of network capacity, \name reviews historical logs of both layers, and extracts high-level features of codec/network dynamics, based on which it determines the highest bitrates for forthcoming video frames without incurring congestion. To attain the ability, we train \name with the aforementioned massive data traces using a custom-designed imitation learning algorithm, which enables \name to learn from past experience. We have implemented and incorporated \name into \appname. Our experiments show that \name outperforms state-of-the-art solutions, improving video quality while reducing stalling time by multi-folds under various practical scenarios.

Target material identification is playing an important role in our everyday life. This paper introduces a device-free target material identification system, implemented on ubiquitous and cheap commercial off-the-shelf (COTS) Wi-Fi devices. The intuition is that different materials produce different amounts of phase and amplitude changes when a target appears on the line-of-sight (LoS) of a radio frequency (RF) link. However, due to multipath and hardware imperfection, the measured phase and amplitude of the channel state information (CSI) are very noisy. We thus present novel CSI pre-processing schemes to address the multipath and hardware noise issues before they can be used for accurate material sensing. Comprehensive real-life experiments demonstrate that we can identify 10 commonly seen liquids at an overall accuracy higher than 95% with strong multipath indoors.

This paper presents PLoRa, an ambient backscatter design that enables long-range wireless connectivity for batteryless IoT devices. PLoRa takes ambient LoRa transmissions as the excitation signals, conveys data by modulating an excitation signal into a new standard LoRa "chirp" signal, and shifts this new signal to a different LoRa channel to be received at a gateway faraway. PLoRa achieves this by a holistic RF front-end hardware and software design, including a low-power packet detection circuit, a blind chirp modulation algorithm and a low-power energy management circuit. To form a complete ambient LoRa backscatter network, we integrate a light-weight backscatter signal decoding algorithm with a MAC-layer protocol that work together to make coexistence of PLoRa tags and active LoRa nodes possible in the network. We prototype PLoRa on a four-layer printed circuit board, and test it in various outdoor and indoor environments. Our experimental results demonstrate that our prototype PCB PLoRa tag can backscatter an ambient LoRa transmission sent from a nearby LoRa node (20 cm away) to a gateway up to 1.1 km away, and deliver 284 bytes data every 24 minutes indoors, or every 17 minutes outdoors. We also simulate a 28-nm low-power FPGA based prototype whose digital baseband processor achieves 220 μW power consumption.

Target material identification plays an important role in many real-life applications. This paper introduces a system that can identify the material type with cheap commercial off-the-shelf (COTS) RFID devices. The key intuition is that different materials cause different amounts of phase and RSS (Received Signal Strength) changes when radio frequency (RF) signal penetrates through the target. However, without knowing either material type, trying to obtain the information is challenging. We propose a method to address this challenge and evaluate the method's performance in real-world environment. The results show that we achieve higher than 94% material identification accuracies for 10 liquids and differentiate even very similar objects such as Coke and Pepsi.

Target material identification plays an important role in many real-life applications. This paper introduces a system that can identify the material type with cheap commercial off-the-shelf (COTS) RFID devices. The key intuition is that different materials cause different amounts of phase and RSS (Received Signal Strength) changes when radio frequency (RF) signal penetrates through the target. However, without knowing either material type, trying to obtain the information is challenging. We propose a method to address this challenge and evaluate the method's performance in real-world environment. The results show that we achieve higher than 94% material identification accuracies for 10 liquids and differentiate even very similar objects such as Coke and Pepsi.

Target imaging and material identification play an important role in many real-life applications. This paper introduces TagScan, a system that can identify the material type and image the horizontal cut of a target simultaneously with cheap commercial off the-shelf (COTS) RFID devices. The key intuition is that different materials and 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% material identification accuracies for 10 liquids and differentiate 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.

With parallel decoding for backscatter communication, tags are allowed to transmit concurrently and more efficiently. Existing parallel decoding mechanisms, however, assume that signals of the tags are highly stable and, hence, may not perform optimally in the naturally dynamic backscatter systems. This paper introduces FlipTracer, a practical system that achieves highly reliable parallel decoding even in hostile channel conditions. FlipTracer is designed with a key insight; although the collided signal is time-varying and irregular, transitions between signals’ combined states follow highly stable probabilities, which offers important clues for identifying the collided signals and provides us with an opportunity to decode the collided signals without relying on stable signals. Motivated by this observation, we propose a graphical model, called one-flip-graph (OFG), to capture the transition pattern of collided signals, and design a reliable approach to construct the OFG in a manner robust to the diversity in backscatter systems. Then, FlipTracer can resolve the collided signals by tracking the OFG. We have implemented FlipTracer and evaluated its performance with extensive experiments across a wide variety of scenarios. Our experimental results have shown that FlipTracer achieves a maximum aggregated throughput that approaches 2 Mb/s, which is $6\times $ higher than the state of the art.

Device-free localization, which does not require any device attached to the target, is playing a critical role in many applications, such as intrusion detection, elderly monitoring and so on. This paper introduces D-Watch, a device-free system built on the top of low cost commodity-off-the-shelf RFID hardware. Unlike previous works which consider multipaths detrimental, D-Watch leverages the “bad” multipaths to provide a decimeter-level localization accuracy without offline training. D-Watch harnesses the angle-of-arrival information from the RFID tags’ backscatter signals. The key intuition is that whenever a target blocks a signal’s propagation path, the signal power experiences a drop which can be accurately detected by the proposed novel P-MUSIC algorithm. The proposed wireless phase calibration scheme does not interrupt the ongoing data communication and thus reduces the deployment burden. We implement and evaluate D-Watch with extensive experiments in three different environments. D-Watch achieves a median accuracy of 16.5 cm for library, 25.5 cm for laboratory, and 31.2 cm for hall environment, outperforming the state-of-the-art systems. In a table area of 2 $\text{m}\times 2$ m, D-Watch can track a user’s fist at a median accuracy of 5.8 cm. D-Watch is also capable of localizing multiple targets which is well known to be challenging in passive localization.

Device-free localization of people and objects indoors not equipped with radios is playing a critical role in many emerging applications. This paper presents an accurate model-based device-free localization system LiFS, implemented on cheap commercial off-the-shelf (COTS) Wi-Fi devices. Unlike previous COTS device-based work, LiFS is able to localize a target accurately without offline training. The basic idea is simple: channel state information (CSI) is sensitive to a target's location and by modelling the CSI measurements of multiple wireless links as a set of power fading based equations, the target location can be determined. However, due to rich multipath propagation indoors, the received signal strength (RSS) or even the fine-grained CSI can not be easily modelled. We observe that even in a rich multipath environment, not all subcarriers are affected equally by multipath reflections. Our pre-processing scheme tries to identify the subcarriers not affected by multipath. Thus, CSIs on the "clean" subcarriers can be utilized for accurate localization. We design, implement and evaluate LiFS with extensive experiments in three different environments. Without 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. Besides single target localization, LiFS is able to differentiate two sparsely-located targets and localize each of them at a high accuracy.

Device-free localization of people and objects indoors not equipped with radios is playing a critical role in many emerging applications. This paper presents a novel channel state information (CSI) pre-processing scheme that enables accurate device-free localization indoors. The basic idea is simple: CSI is sensitive to a target's location and by modelling the CSI measurements of multiple wireless links as a set of power fading based equations, the target location can be determined. However, due to rich multipaths in indoor environment, the received signal strength (RSS) or even the fine-grained CSI can not be easily modelled. We observe that even in a rich multipath environment, not all subcarriers are equally affected by multipath reflections. Our preprocessing scheme tries to identify the subcarriers not affected by multipath. Thus, CSIs on the "clean" subcarriers can be modelled and utilized for accurate localization. Extensive experiments demonstrate the effectiveness of the proposed pre-processing scheme.

This paper introduces a wireless phase calibration algorithm that can be applied on cheap commercial off-the-shelf (COTS) radio frequency identification (RFID) system and auto acquire an accurate radio frequency (RF) phase information without any offline training. The key observation is that the raw phase measurements even measured form different RFID tags contain a same set of unknown phase errors. With enough tags' phase measurements, we can determine all the unknown phase errors, since the number of known phase measurements is much larger than the number of unknown phase errors. Real-world experimental results demonstrate the effectiveness of the proposed method.

Recent advances in unmanned aerial vehicle (UAV) and wireless charging technologies have made it practical to introduce a mobile node into Wireless Sensor Networks (WSN). This mobile node with sink ability or charging equipment on it can travel around the entire network to gather data from isolate sensor nodes or charge the nodes wirelessly. This can help with the large scale WSN to be connected or keep working for a long time without replacing batteries. For the mobile node, routing design is an important problem. We can regard this problem as a special case of traveling salesman problem with neighborhoods (TSPN), which is NP-hard. In this paper, we propose a novel routing design algorithm base on Variable Dimensions Particle Swarm Optimization (VD-PSO). In this algorithm, every particle is a feasible solution of TSPN. Every dimension of the particle is the coordinates of a rendezvous point (the point where mobile node stay to gather data or charge the nodes). And the dimensionality is right the number of the rendezvous point. With the evolutionary method of the particles, we can derive the best route of the mobile node. Simulation results show that our scheme has fast convergence speed, and the result is quite approximate to the optimal solution.

Moving target detection serves as a key enabler for various applications such as campus security, military battlefield, and wildlife protection. Conventional detection systems usually adopt the multimedia or RF based technology (e.g. the Doppler shift). The performance of the detection system based on multimedia is usually limited by light and monitoring perspective. In the detection system based on RF, the detection rate is affected by false noise, because the signal mutation (maybe caused by fast motion) and weak change (maybe caused by slow motion) are deemed to the false noise which is easy ignored. In this paper, we present the moving target detection system MovLoc based on Doppler detecting, that is robust to velocity different motion velocity. In order to the minute Doppler shifts extracted accurately, MovLoc uses the Sliding Cumulative Difference Window (SCDW) under the Filtered Multi-tone (FMT) modulation. Then, the arrival angle estimated by the Accumulated Ring (AR) algorithm, can used to localize the target. Our experimental results show that MovLoc achieves an average false positive of 9% and an average false negative of 6% in detecting target with 3 different velocities.

Wireless localization technology has become more and more important in our life. Many applications would benefit from such device-free localization, e.g. intruder detection, privacy-enhanced monitoring, etc. This paper presents a system, named Pilm, for localizing a moving target based on the detection probability model. It aims to enable a device-free localization technology in the outdoors that is low-cost, compact and strong theoretical supported. In traditional devicefree methods, the transceiver collects a large number of the received signal strength (RSS) changing distorted by target, which is high cost for resource-constraint networks, such as wireless sensor networks (WSNs). Unlink past works, which need to process the RSS values from each communication link of networks during the localization, our design exploits the minimum number of links to detect and localize a moving target. We evaluate its performance through real-world deployment and simulation experiments. Empirical measurements with a prototype show that Pilm can use the minimum number of links to localize a moving target with low energy consumption.

Device-free localization(DFL) is an emerging technology to detect whether there exists any target(s) in the area of interests without carrying any device to them. It is an essential primitive for a large-scale range of application including intrusion localization of warehouse, elder care at home, and action about hostage rescue, etc. Most DFL relying on received signal strength (RSS) have proposed to be able to provide acceptable accuracy and distance error. Although RSS can be easily measured with commercial equipments, it is suspectable to measurement itself due to multipath effect in indoor environment. In this paper, we present the Ailot approach to overcome the preceding RSS-based limitation. Ailot explores properties of Channel State Information(CSI) from PHY layer and uses the novel feature extracted from CSI to leverages frequency and spatial diversity. The target localization of Ailot approach is conducted with maximum likelihood method. We implement Ailot approach with commercial IEEE 802.11n NICs and evaluate its performance in a real-world indoor environment. Experimental results show that Ailot can achieve high localization accuracy and distance error. Moreover, comparing to RSS-based scheme, our CSI-based approach enables better localization performance in accuracy and distance error.

Many emerging applications and the ubiquitous wireless signals have accelerated the development of WiFi-based indoor localization techniques. However, there is one primary issue, that is the environmental dynamics will greatly undermine the fingerprint stability over time, hindering these techniques to be fully practical. Although many researchers dedicated to deal with the fingerprint adaptation problem, it still remains an open problem without a wholesome approach. In this study, we propose TaLc, a time adaptive and low cost indoor localization approach that fortifies the indoor localization to be a fully practical service. TaLc can automatically identify the time to update the fingerprint without the need of any extra devices. Then by collecting the fresh RSS measurements of only a few of reference locations, which are at most equal to the number of APs and much less than the number of locations, to update the fingerprint and thus reduce the human effort for fingerprint calibrating. Finally TaLc utilizes the Compressive sensing (CS) based localization to reduce the data volume and energy consumption, which can achieve an fine-grained localization accuracy with only a few of RSS measurements. Extensive real-word experiments over 3 months illustrate that TaLc can effectively update the fingerprint with the average error of less than 3.6dBm and improves the localization accuracy of about 50%.

The device-free localization (DFL), i.e., localizing target without requiring target attached any devices, is attractive. Current localization methods, however, query a significant degree of pre-deployment effort, such as the transceivers' locations, the transmission power, which cost huge human effort. In this paper, we present Alico, an accurate and low human cost DFL method that does not require any pre-deployment effort, such as building the detailed fingerprints or requiring the prior knowledge of deployment. The key intuition is that (i) the distorted wireless links caused by the target, even the many from unknown locations, are constrained the presence of the target; (ii) with the increase of the number of unknown targets and transceivers, the constraints grows in a quadratic fashion, while the unknown locations of targets and transceivers grows linearly. This suggests that given enough measurements, there will be eventually enough constraints to make the every target uniquely localizable. Alico leverages these constraints and model them as a set of equations. By using a hybrid gradient descent and genetic algorithms, Alico can solve the equations and estimate the target locations accurately based just on the Received Signal Strength (RSS) measurements. Despite the absence of any explicit pre-deployment calibration effort, Alico achieves the 60th and 80th percentile errors of 1m and 1.4m in real-world experiments, respectively, which is better than the three state-of-the-art algorithms.

This poster introduces JRD, a novel device-free localization system which can achieve high accuracy with low cost and little human effort, and is even robust to different scenarios. Unlike the previous Radio Signal Strength (RSS)-based systems which depend on the dense deployment to provide high accuracy, JRD extracts the fine-grained RSS distributions of a single link and presents a voting algorithm based on multi-link to identify the object location accurately while maintaining a low-cost deployment. Furthermore, JRD is flexible to different scenarios by using the transferring technique with less time-consuming and human effort. Experimental results show that JRD can improve the localization accuracy by up to 50% with less cost as compared with the existing RSS approaches.

For a long history, stampede is one of the high potential disaster when thousands of people gathered. Current monitoring systems, however, can only detect the presence of a small number of sparsely located targets, rather than to monitor the change of people flow where there are large number of dense crowd in the environment. This paper presents DanSen, a low-cost people flow monitoring system for sensing the potential danger using the existing wifi infrastructures. Inspired by the dynamic light scattering (DLS) theory, the designed DanSen calculates the correlations between the initial channel state information (CSI) data and all the history CSI data to monitor the changes of people flow and also estimates the sharpness of the changes. By doing so, DanSen can be utilised to perceive the potential danger. Real-world experimental results illustrate the advantage and effectiveness of DanSen.

Intermittent connection of wireless links, caused by low duty-cycle radio operation, harsh working environment, movement of sensor nodes, etc., makes clock synchronization a challenging task. Prior synchronization approaches in wireless sensor networks (WSNs) typically require that nodes exchange time messages frequently with the reference clock, which is difficult in networks with low or intermittent connectivity. This poster presents RobSync, a robust design for clock synchronization in intermittent-connected wireless networks. Having recognized that clock skew is highly correlated to the voltage supply, we use the local voltage information as a reference for clock self-calibration, which helps reduce the frequency of time-stamp exchanges. To prevent a misuse of the voltage information, leading to error accumulation, a re-synchronization interval adjustment design is developed to make a trade-off between accuracy and energy consumption. We present the theory behind RobSync, and provide preliminary results by experiments to compare our approach and the recent approach.

We are interested in throughput gains over wireless networks that employ network coding schemes (such as COPE [1] and BFLY [2]). However, very few studies have focused on the effect of information content, neighbor state learning model and buffer occupancy on these schemes. Sometimes they could consume coding gains we acquired laboriously. In this paper, we explain the phenomenon that pursuing high information content blindly will reduce network performance, and we prove the constant incorrect ratio in neighbor state learning model mathematically. Then, we analyze the relationship between traffic load and buffer occupancy of COPE through simulations. And finally, we develop FSNC, a novel network coding scheme, which can be used in complex environments and low-cost equipments.

Motivated by the needs of WSNs with ubiquitous structure such as the strip networks deployed in the Great Wall or pipelines, we present a lightweight geographic opportunistic routing approach to cope with the routing problem in complicated strip network. By carefully taking into account both the geographic information and the transmission direction, we propose the Virtual Plane Mirror (VPM) algorithm, which is inspired by the light propagation to select forwarding candidate with a short path. Two implementation problems of VPM, transmission spread angle and the communication range, are thoroughly analyzed based on the percolation theory. Based on VPM, we propose a geographic opportunistic routing strategy (LIPS). Experimental results show that the transmission successful rate of LIPS is 26.37% higher than the state-of-the-art approach, and the communication overhead and energy consumption rate are 33.11% and 40.23% lower, respectively. Furthermore, the path length in LIPS is close to the shortest path.

This poster introduces MoveLoc, a system that locates a moving object without carrying any devices from detecting Doppler shifts reflected off the moving object. It works even if the object walking or running in different directions without any training. MoveLoc does not require the user to carry any communication devices, yet its accuracy exceeds current moving object localization systems using Radio Signal Strength(RSS). We implement the system and evaluate the performance on moving person by experiments. Experimental result shows that MoveLoc achieves an average location accuracy of 0.69 meters and reduces the equipment deployment density compared with other known wireless moving object localization approaches using RSS.

In this paper, we propose a novel clock calibration approach, which addresses two key challenges for clock calibration in Wireless Sensor Networks: excessive communication overhead and the trade-off between accuracy and cost. To achieve this, our approach leverages the fact that the clock skew is highly correlated to temperature, which can serve as both an assistant for clock skew estimation and a regulatory factor for the duty-cycled design. Our approach is one order of magnitude more power-efficient than communication based approaches since the calibration largely relies on local temperature information. In addition, our approach provides a nice feature of self-adaptive period, which can substantially promote the system flexibility. We present the theory behind our approach, and provide preliminary results of a simulated comparison of our approach and some recent approaches.

The VM (Virtual effective solution to protect software, making it extremely a Machine)-based software protection technique provides difficulty to analyze and crack. In this paper, we improve it from two aspects. Firstly, the time diversity is to fight against cumulative attack by making software executing along variant paths in different running time. Secondly, transform instructions in an execution path with reducing performance penalty through controlling deformation strategy. At last, we design and develop a VM-based protection with time diversity system, named TDVMP, and carry out some experiments with it. The results show that the improvements are effective.

In this demo, we design a monitoring system based on sensor network for zoologists to research the activity budget of Rhinopithecus roxellana. We designed the hardware platform of the system for wild deployment and presented an analysis of the relationship between environmental factors and survival conditions of Rhinopithecus roxellana. The system can locate and track Rhinopithecus roxellana by individual identification. The prototype has been deployed in Wildlife Reserve of Qiling Mountain, China.

Earthen site is an important part of our cultural heritage, so monitoring and protecting its living condition is vital. Like other country, in china many sites also face collapse even destruction danger. In this paper, we present a novel framework and a site survival model to monitor Danfeng Gate of Daming Palace, analyze the data collected by wireless sensor networks (WSNs) for early warning. In order to forecast the health trend, we also adopt the Neural networks and Bayesian model processing internal and external environment data to predict the health status.