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In present paper, we put forward four types of hesitant fuzzy β covering rough sets (HFβCRSs) by uniting covering based rough sets (CBRSs) and hesitant fuzzy sets (HFSs). We firstly originate hesitant fuzzy β covering of the universe, which can induce two types of neighborhood to produce four types of HFβCRSs. We then make further efforts to probe into the properties of each type of HFβCRSs. Particularly, the relationships of each type of rough approximation operators w.r.t. two different hesitant fuzzy β coverings are groped. Moreover, the relationships between our proposed models and some other existing related models are established. Finally, we give an application model, an algorithm, and an illustrative example to elaborate the applications of HFβCRSs in multi-attribute decision making (MADM) problems. By making comparative analysis, the HFβCRSs models proposed by us are more general than the existing models of Ma and Yang and are more applicable than the existing models of Ma and Yang when handling hesitant fuzzy information.

To improve the application-level data efficiency, the scheduling of coflows, defined as a collection of parallel flows sharing the same objective, is prevailing in recent data centers. Meanwhile, optical circuit switches (OCS) are gradually applied to provide high data rate with low power consumption. However, so far few research outputs have covered the flow, let alone the coflow, scheduling in the context of OCS. In this work, we investigate coflow scheduling in OCS-based data centers. We first derive a novel operation called regularization processed respectively on the flow traffic demands and the flow start times, which can be efficiently implemented and reduce the circuit reconfiguration frequency dramatically. We then propose a 2-approximation algorithm, called Reco-Sin , for single coflow scheduling to minimize the coflow completion time (CCT). For multiple coflows, we derive Reco-Mul to minimize the total weighted CCT, which can transform any non-preemptive multi-coflow scheduling in packet switches to a scheduling scheme in OCS. Reco-Mul can achieve a constant approximation under the assumption that no tiny flows will be transmitted in OCS. To get rid of this assumption, we present another multiple coflow scheduling scheme, named Reco-Mul+ , which has an approximation ratio of $O(K)$ . Here, $K$ is the total number of coflows. Extensive simulations based on Facebook data traces show that our approaches outperform state-of-the-art schemes significantly, i.e., one single coflow can be finished up to $1.97\times $ faster with Reco-Sin , and multiple coflows can be completed up to more than $2\times $ faster with Reco-Mul and Reco-Mul+ .

Driven by a wide range of real-world applications, several ground-breaking RF-based motion-recognition systems were proposed to detect and/or recognize macro/micro human movements. These systems often suffer from various interferences caused by multiple-users moving simultaneously, resulting in extremely low recognition accuracy. Even if the repetitive motions are fairly well detectable through the wireless signals in theory, in reality they get blended into various other system noises during the motion. Moreover, irregular motion patterns among users will lead to expensive computation cost for motion recognition. To tackle these challenges, we propose a novel wireless sensing system, called Motion-Fi $\ ^+$ + , which marries battery-free wireless backscattering and device-free sensing in one clean sheet. Motion-Fi $\ ^+$ + is an accurate, interference tolerable motion-recognition system, which counts repetitive motions without using scenario-dependent templates or profiles and enables multi-user performing certain motions simultaneously because of the relatively short transmission range of backscattered signals and dedicated signal separation method. We implement a backscattering wireless platform to validate our design in various scenarios for over 6 months when different persons, distances and orientations are incorporated. In our experiments, the periodicity in motions could be recognized without any learning or training process, and the accuracy of counting such motions can be achieved within 5 percent count error. With little efforts in learning the patterns, our method could achieve 95.2 percent motion-recognition accuracy for a variety of 7 typical motions. Moreover, by leveraging the periodicity of motions, the recognition accuracy could be further improved to nearly 100 percent with only three repetitions. Our experiments also show that the motions of multiple persons separating by around $ 2$ meters cause little accuracy reduction in the counting process.

Privacy has become one of the major concerns in cloud video surveillance. Privacy protection of the surveillance videos strive to protect users’ privacy information without hampering regular security tasks of the surveillance, meanwhile retains the system’s high accuracy and efficiency. The current state of the art in protecting the video privacy is mainly realized through Privacy Region Protection, which only protects the privacy regions while keeps the non-privacy regions visually intact so that processing in the cloud is still feasible. However, the problem of determining the privacy regions has been ignored and not properly addressed. In this paper, we propose a novel notion - concept graph , and with the aid of that, we develop our system – PatronuS to determine the privacy regions subject to satisfying both privacy and security requirements. We further propose an event distilling model and a privacy inference model to assist in determining specific privacy regions. And we evaluate PatronuS in real-world settings and demonstrate its efficiency in privacy protection without degrading system’s surveillance functionality.

To prevent man-in-the-middle attack, Diffie-Hellman first proposed the concept of the session key exchange protocol, in which the author remove the long term keying material at the end of the session. In our scheme, we apply the session key concept of Diffie-Hellman for cloud computing in the presence of the Trusted Third Party (TTP). Our proposed scheme provides SEVEN security properties by using the session key exchange protocols as symmetric. In our scheme, TTP becomes free from a burden of activities, like to encrypt the requests (Ri) of the Cloud Service Users (CSUs) and to decrypt the services (Si) of the Cloud Service Providers (CSPs) again and again. By using S Performance (EP) for Elliptic Curve Point Multiplication (ECPM), Hash (H), Inversion (Inv), Exclusive oR (XoR), Encryption (Enc), and Decryption (Dec) is (87.5%, 62.5%, 57.14%), (87.5% and 75%), (75%), (97.82% and ≤ 1%), (92.85%) and (50%) respectively. The security requirements of our scheme are; data integrity, data confidentiality, authenticity, non-repudiation, forward secrecy, unforgeability, and untraceability. Our proposed scheme also outperformed performance in terms of flexibility, reliability, and efficiency as compared to existing schemes.

In this paper, we consider the scenario in which a mobile charger (MC) periodically travels within a sensor network to recharge the sensors wirelessly. We design joint charging and scheduling schemes to maximize the Quality of Monitoring (QoM) for stochastic events, which arrive and depart according to known probability distributions of time. Information is considered captured if it is sensed by at least one sensor. We focus on two closely related research issues, i.e., how to choose the sensors for charging and decide the charging time for each of them, and how to schedule the sensors’ activation schedules according to their received energy. We formulate our problem as the maximum QoM CHA rging and S ch E duling problem (CHASE). We first ignore the MC's travel time and study the resulting relaxed version of the problem, which we call CHASE-R. We show that both CHASE and CHASE-R are NP-hard. For CHASE-R, we prove that it can be formulated as a submodular function maximization problem, which allows two algorithms to achieve $1/6$ 1 / 6 - and $1/(4 + \epsilon)$ 1 / ( 4 + e ) -approximation ratios. Then, for CHASE, we propose approximation algorithms to solve it by extending the CHASE-R results. We conduct simulations to validate our algorithm design.

In edge-cloud computing, a set of servers (called edge servers) are deployed near the mobile devices to allow these devices to offload their jobs to and subsequently obtain their results from the edge servers with low latency. One fundamental problem in edge-cloud systems is how to dispatch and schedule the jobs so that the job response time (defined as the interval between the release of the job and the arrival of the computation result at the device) is minimized. In this paper, we propose a general model for this problem, where the jobs are generated in arbitrary order and at arbitrary times at the mobile devices and then offloaded to servers with both upload and download delays. Our goal is to minimize the total weighted response time of all the jobs. The weight is set based on how latency-sensitive the job is. We derive the first online job dispatching and scheduling algorithm in edge-clouds, called OnDisc , which is scalable in the speed augmentation model; that is, OnDisc is $(1 + \varepsilon )$ -speed $O(1/\varepsilon )$ -competitive for any small constant $\varepsilon >0$ . Moreover, OnDisc can be easily implemented in distributed systems. We also extend OnDisc with a fairness knob to incorporate the trade-off between the average job response time and the degree of fairness among jobs. Extensive simulations based on a real-world data-trace from Google show that OnDisc can reduce the total weighted response time dramatically compared with heuristic algorithms.

The wide adoption of smart devices with onboard cameras facilitates photo capturing and sharing, but greatly increases people's concern on privacy infringement. Here, we seek a solution to respect the privacy of persons being photographed in a smarter way that they can be automatically erased from photos captured by smart devices according to their intention. To make this work, we need to address three challenges: 1) how to enable users explicitly express their intentions without wearing any visible specialized tag, and 2) how to associate the intentions with persons in captured photos accurately and efficiently. Furthermore, 3) the association process itself should not cause portrait information leakage and should be accomplished in a privacy-preserving way. In this work, we design, develop, and evaluate a system, called COIN (Cloak Of INvisibility), that enables a user to flexibly express her privacy requirement and empowers the photo service provider (or image taker) to exert the privacy protection policy. Leveraging the visual distinguishability of people in the field-of-view and the dimension-order-independent property of vector similarity measurement, COIN achieves high accuracy and low overhead. We implement a prototype system, and our evaluation results on both the trace-driven and real-life experiments confirm the feasibility and efficiency of our system.

With the proliferation of mobile devices and various sensors ( e.g. , GPS, magnetometer, accelerometers, gyroscopes) equipped, richer services, e.g. location based services, are provided to users. A series of methods have been proposed to protect the users’ privacy, especially the trajectory privacy. Hardware fingerprinting has been demonstrated to be a surprising and effective source for identifying/authenticating devices. In this work, we show that a few data samples collected from the motion sensors are enough to uniquely identify the source mobile device, i.e. , the raw motion sensor data serves as a fingerprint of the mobile device. Specifically, we first analytically understand the fingerprinting capacity using features extracted from hardware data. To capture the essential device feature automatically, we design a multi-LSTM neural network to fingerprint mobile device sensor in real-life uses, instead of using handcrafted features by existing work. Using data collected over 6 months, for arbitrary user movements, our fingerprinting model achieves 93% F-score given one second data, while the state-of-the-art work achieves 79% F-score. Given ten seconds randomly sampled data, our model can achieve 98.8% accuracy. We also propose a novel generative model to modify the original sensor data and yield anonymized data with little fingerprint information while retain good data utility.

A coflow is a collection of related parallel flows that occur typically between two stages of a multi-stage computing task in a network, such as shuffle flows in MapReduce. The coflow abstraction allows applications to convey their semantics to the network so that application-level requirements can be better satisfied. In this paper, we study the routing and scheduling of multiple coflows to minimize the total weighted coflow completion time (CCT). We first propose a rounding-based randomized approximation algorithm, called OneCoflow , for single coflow routing and scheduling. The multiple coflow problem is more challenging as coexisting coflows will compete for the same network resources, such as link bandwidth. To minimize the total weighted CCT, we derive an online multiple coflow routing and scheduling algorithm, called OMCoflow . We then derive a competitive ratio bound of our problem and prove that the competitive ratio of OMCoflow is nearly tight. To the best of our knowledge, this is the first online algorithm with theoretical performance guarantees which considers routing and scheduling simultaneously for multi-coflows. Compared with existing methods, OMCoflow runs more efficiently and avoids frequently rerouting the flows. Extensive simulations on a Facebook data trace show that OMCoflow outperforms the state-of-the-art heuristic schemes significantly (e.g., reducing the total weighted CCT by up to 41.8% and the execution time by up to 99.2% against RAPIER).

Real-time handwriting tracking is important for many emerging applications such as Artificial Intelligence assisted education and healthcare. Existing movement tracking systems, including those based on vision, ultrasound or wireless technologies, fail to offer high tracking accuracy, no learning/training/calibration process, low tracking latency, low cost and easy to deploy at the same time. In this work, we design and evaluate a wireless backscattering based handwriting tracking system, called MilliBack, that satisfies all these requirements. At the heart of MilliBack are two Phase Differential Iterative (PDI) schemes that can infer the position of the backscatter tag (which is attached to a writing tool) from the change in the signal phase. By adopting carefully-designed differential techniques in an iterative manner, we can take the diversity of devices out of the equation. The resulting position calculation has a linear complexity with the number of samples, ensuring fast and accurate tracking. We have put together a MilliBack prototype and conducted comprehensive experiments. We show that our system can track various handwriting traces accurately, in some testings it achieve a median error of 4.9 mm. We can accurately track and reconstruct arbitrary writing/drawing trajectories such as equations, Chinese characters or just random shapes. We also show that MilliBack can support relatively high writing speed and smoothly adapt to the changes of working environment.

Cyber attacks and malfunctions challenge the wide applications of Internet of Things (IoT). Since they are generally designed as embedded systems, typical auto-sustainable IoT devices usually have a limited capacity and a low processing power. Because of the limited computation resources, it is difficult to apply the traditional techniques designed for personal computers or super computers, like traffic analyzers and antivirus software. In this paper, we propose to leverage statistical learning methods to characterize the device behavior and flag deviations as anomalies. Because the system statistics, such as CPU usage cycles, disk usage, etc., can be obtained by IoT application program interfaces, the proposed framework is platform and deviceindependent. Considering IoT applications, we train multiple machine learning models to evaluate their feasibility and suitability. For the target auto-sustainable IoT devices, which operate well-planned processes, the normal system performances can be modeled accurately. Based on time series analysis methods, such as local outlier factor, cumulative sum, and the proposed adaptive online thresholding, the anomalous behaviors can be effectively detected. Comparing their performances on detecting anomalies as well as the computation sources required, we conclude that relatively simple machine learning models are more suitable for IoT security, and a data-driven anomaly detection method is preferred.

Following the trend of data trading and data publishing, many online social networks have enabled potentially sensitive data to be exchanged or shared on the web. As a result, users' privacy could be exposed to malicious third parties since they are extremely vulnerable to de-anonymization attacks, i.e., the attacker links the anonymous nodes in the social network to their real identities with the help of background knowledge. Previous work in social network de-anonymization mostly focuses on designing accurate and efficient de-anonymization methods. We study this topic from a different perspective and attempt to investigate the intrinsic relation between the attacker's knowledge and the expected de-anonymization gain. One common intuition is that the more auxiliary information the attacker has, the more accurate de-anonymization becomes. However, their relation is much more sophisticated than that. To simplify the problem, we attempt to quantify background knowledge and de-anonymization gain under several assumptions. Our theoretical analysis and simulations on synthetic and real network data show that more background knowledge may not necessarily lead to more de-anonymization gain in certain cases. Though our analysis is based on a few assumptions, the findings still leave intriguing implications for the attacker to make better use of the background knowledge when performing de-anonymization, and for the data owners to better measure the privacy risk when releasing their data to third parties.

In this paper, an analytical design space of power amplifiers (PAs) including the Class-A/B/J continuum for dynamic load modulation (DLM) at the current generator plane (CGP) is proposed based on the theory of load modulated (LM) continuous Class-B/J PAs. By introducing a biasing operation factor ρ, the theory of DLM PAs provides an analytical design space for all the operation modes from Class-B/J mode to Class-A mode. The analytical design space of DLM PAs shows that high efficiency is maintained at a large dynamic range of output power back-off (OPBO) with a purely resistive load modulation at the CGP when Class-B/J mode is performed. For deep Class-AB mode, the analytical design space of the DLM PAs shows that a combination of resistive and reactive load modulation at the CGP is used to maintain the high efficiency at the OPBO. The effectiveness of the proposed analytical design space is validated by load-pull measurements of a bare-chip gallium nitride (GaN) device.

Edge computing is a promising cloud computing paradigm that reduces computing latency by deploying edge servers near data sources and users, which is of great importance to implement delay-sensitive applications like AR, Cloud Gaming and Auto Driving. Due to the limited resources of edge servers, task dispatching and function configuration are the key to fully utilize edge servers. Moreover, a typical task request in edge computing (called a co-task) is consisted of a set of subtasks, where the task completion time is determined by the latest completed subtask. In this work, we propose a scheme named OnDisco, which combines reinforcement learning and heuristic methods to minimize the average completion time of co-tasks. Compared with heuristic algorithm, deep reinforcement learning can learn the inherent characteristics of the environment without any prior knowledge, and OnDisco is therefore well adapted to varying environments. Simulations on Alibaba traces shows that OnDisco reduces the average task completion time by \(58\%\) and \(76\%\) compared with the heuristic and random algorithm, respectively. Moreover, OnDisco outperforms the baselines consistently in various data environments and parameter settings.

Falls are the leading cause of fatal and non-fatal injuries for the elderly, and fall detection system is critical for reducing the aid response time. Wearable sensor-based system becomes popular for its convenience and non-invasion of privacy. In this paper, we focus on fall detection system to be integrated into low-cost COTS (Commercial Off-The-Shelf) smart band, which is expected to be worn by the elderly for a long time due to its advantages of low price and low power consumption. Aiming at the low sampling rate property inherent in such devices, we analyze the characteristic of fall signal and propose a system which achieve better accuracy by combining the features of time domain, time-frequency domain and instantaneous frequency. We further apply data augmentation mechanism to tackle the issue of fall data lack. Extensive experiments are conducted to evaluate the proposed system. The results demonstrate that our system can achieve over 98% accuracy on our dataset and 97% accuracy on open source dataset.

Acoustic motion tracking is an exciting new research area with promising progress in the last few years. Due to the inherent low propagation speed in the air, acoustic signals have the unique advantage of fine sensing granularity compared to RF signals. Speakers and microphones nowadays are pervasively available in devices surrounding us, such as smartphones and voice-controlled smart speakers. Though promising, one fundamental issue hindering the adoption of acoustic-based motion tracking is that the positions of microphones and speakers inside a device are fixed, which greatly limits the flexibility of acoustic motion tracking. In this work, we propose a new modality of acoustic motion tracking using earphones. Earphone-based tracking mitigates the constraints associated with traditional smartphone-based tracking. With novel designs and comprehensive experiments, we show earphone-based motion tracking can achieve a great flexibility and a high accuracy at the same time. We believe this is an important step towards "earable" sensing.

Mechanical vibration sensing/monitoring plays a critical role in today’s industrial Internet of Things (IoT) applications. Existing solutions usually involve directly attaching sensors to the target objects, which is invasive and may affect the operations of delicate devices. Non-invasive approaches such as video and laser methods have drawbacks in that, the former incurs poor performance in low light conditions, while the latter has difficulties to monitor multiple objects simultaneously.In this work, we design RF-Ear, a contactless vibration sensing system using Commercial off-the-shelf (COTS) RFID hardware. RF-Ear could accurately monitor the mechanical vibrations of multiple (up to 8) devices using a single tag: it can clearly tell which object is vibrating at what frequency without attaching tags on any device. RF-Ear can measure the vibration frequency up to 400Hz with a mean error rate of 0.2%. Our evaluation results show that RF-Ear can effectively detect 0.2cm screw loose with 90% accuracy. We further employ each device’s unique vibration fingerprint to identify and differentiate devices of exactly the same model. We also show that RF-ear can monitor not just the vibrations but also a large range of mechanical motions. Comprehensive experiments conducted in a real power plant demonstrate the effectiveness of our system with outstanding performance.

Magnetic resonant coupling wireless power transfer (MRC-WPT) enables convenient device-charging. When MIMO MRC-WPT system incorporated with multiple relay components, both relay On-Off state (i.e., power routing) and TX current (i.e., current scheduling) could be adjusted for improving charging efficiency and distance. Previous approaches need the collaboration and feedback from the energy receiver (RX), achieved using side-channels, e.g., Bluetooth, which is time/energy-consuming. In this work we propose, design, and implement a multi-relay MIMO MRC-WPT system, and design an almost optimum joint optimization of power routing and current scheduling method, without relying on any feedback from RX. We carefully decompose the joint optimization problem into two subproblems without affecting the overall optimality of the combined solution. For current scheduling subproblem, we propose an almost-optimum RX-feedback independent solution. For power routing subproblem, we first design a greedy algorithm with $\frac{1}{2}$ approximation ratio, and then design a DQN based method to further improve its effectiveness. We prototype our system and evaluate it with extensive experiments. Our results demonstrate the effectiveness of the proposed algorithms. The achieved power transfer efficiency (PTE) on average is 3.2X, 1.43X, 1.34X, and 7.3X over the other four strategies: without relay, with non-adjustable relays, greed based, and shortest-path based ones.

RFID technology has recently been exploited for not only identification but also for sensing including trajectory tracking and gesture recognition. While contact-based (an RFID tag is attached to the target of interest) sensing has achieved promising results, contactless sensing still faces severe challenges such as low accuracy and the situation gets even worse when the target is non-static, restricting its applicability in real world deployment. In this work, we present TagRay, a contactless RFID-based sensing system, which significantly improves the tracking accuracy, enabling mobile object tracking and even material identification. We design and implement our prototype on commodity RFID device. Comprehensive experiments show that TagRay achieves a high accuracy of 1.3 cm which is a 200% improvement over the-state-of-arts for trajectory tracking. For commonly seen four material types, the material identification accuracy is higher than 95% even with interference from people moving around.

To further improve the convenience and effectiveness of human computer interaction (HCI) with smart devices, human activity recognition (HAR) has been widely studied from various aspects. Unfortunately, deep learning based methods often suffer from either expensive labeling efforts or weak generalization ability. Inspired by recently developed domain adaptation strategies, we propose XHAR, a novel adversarial deep domain adaptation framework for HAR using smart devices, providing better device and user adaptation. XHAR first selects the most similar source dataset (with label), then extracts device and user independent spatial-temporal features through the combinations of Convolutional Neural Networks (CNN) and Bidirectional Gated Recurrent Units (BiGRU) feature extractors. Moreover, it removes the distribution discrepancy using multiple domain discriminators, and finally performs adaptation on the target dataset (without label) to obtain the predicted labels. We conduct extensive experiments on 50 users (i.e., of different ages, genders, and body shapes) and 4 smart devices with two kinds of datasets (i.e., gesture activities and sport activities). We compare our method with the source-only model and several state-of-theart domain adaptation models. The results show that XHAR increases the classification accuracy by at least 4.81% (to 74.50%) on the adaptation between different users, and accordingly by at least 9.25% (to 69.23%) between different devices.

With the rapid advance of smart home and office systems, it becomes possible to provide a fine-grained user activity tracking service accurately recognizing user activities and identities in a seamless and non-invasive manner. Such a system can find applications in various domains, such as elder safeguard, customized services, and simply personal activity diary. Recently, several radio frequency (RF) based sensing systems were proposed for human sensing, most of which focus on limited scenarios and suffer from interference caused by other users or wireless devices. To tackle this challenge, we propose Back-Guard, which achieves accurate and non-intrusive user activity recognition and then user identification through battery-free wireless backscattering. Back-Guard carefully examines the backscatter spectrogram data and extracts high-level features from both spatial and temporal domains that can characterize the user behaviors. Leveraging the parallel attention based deep learning models, our system can discriminate different motions and users accurately and robustly in various situations. We implement a prototype system and collect data in actual scenarios from 25 users for over 2 months. Extensive experiments demonstrate the promising performance of our system. In particular, Back-Guard achieves 93.4% activity recognition accuracy and 91.5% user identification accuracy, respectively. Our experiments also demonstrate little accuracy reduction when multiple users are separated, e.g., by around 2 meters.

This paper presents the design, implementation and evaluation of In-N-Out, a software-hardware solution for far-field wireless power transfer. In-N-Out can continuously charge a medical implant residing in deep tissues at near-optimal beamforming power, even when the implant moves around inside the human body. To accomplish this, we exploit the unique energy ball pattern of distributed antenna array and devise a backscatter-assisted beamforming algorithm that can concentrate RF energy on a tiny spot surrounding the medical implant. Meanwhile, the power levels on other body parts stay in low level, reducing the risk of overheating. We prototype In-N-Out on 21 software-defined radios and a printed circuit board (PCB). Extensive experiments demonstrate that In-N-Out achieves 0.37~mW average charging power inside a 10~cm-thick pork belly, which is sufficient to wirelessly power a range of commercial medical devices. Our head-to-head comparison with the state-of-the-art approach shows that In-N-Out achieves 5.4$\times$--18.1$\times$ power gain when the implant is stationary, and 5.3$\times$--7.4$\times$ power gain when the implant is in motion., In MobiCom 2020: The 26th Annual International Conference on Mobile Computing and Networking, London, 15 pages

Labeling data (e.g., labeling the people, objects, actions and scene in images) comprehensively and efficiently is a widely needed but challenging task. Numerous models were proposed to label various data and many approaches were designed to enhance the ability of deep learning models or accelerate them. Unfortunately, a single machine-learning model is not powerful enough to extract various semantic information from data. Given certain applications, such as image retrieval platforms and photo album management apps, it is often required to execute a collection of models to obtain sufficient labels. With limited computing resources and stringent delay, given a data stream and a collection of applicable resource-hungry deep-learning models, we design a novel approach to adaptively schedule a subset of these models to execute on each data item, aiming to maximize the value of the model output (e.g., the number of high-confidence labels). Achieving this lofty goal is nontrivial since a model's output on any data item is content-dependent and unknown until we execute it. To tackle this, we propose an Adaptive Model Scheduling framework, consisting of 1) a deep reinforcement learning-based approach to predict the value of unexecuted models by mining semantic relationship among diverse models, and 2) two heuristic algorithms to adaptively schedule the model execution order under a deadline or deadline-memory constraints respectively. The proposed framework doesn't require any prior knowledge of the data, which works as a powerful complement to existing model optimization technologies. We conduct extensive evaluations on five diverse image datasets and 30 popular image labeling models to demonstrate the effectiveness of our design: our design could save around 53\% execution time without loss of any valuable labels.

The widespread creative application and smart devices call for convenient and secure interaction with human users. We propose, design, and implement a smartwatch-based two-factor real-time identification and authentication system named ThumbUp, where smartwatch users can identify and authenticate themselves by some simple hand and finger gestures, such as thumb-up. ThumbUp leverages the signal collected from the Inertial Measurement Unit (IMU) in Commercial Off-The-Shelf (COTS) smart devices and discovers the unique fingerprint pattern produced by each user’s simple hand gestures using a carefully crafted deep learning model. We implement our system and conduct extensive experiments to evaluate its efficacy and efficiency with 65 different users over a period of more than 3 months. It reaches an accuracy of 97% for identification, and EER 0.014 for authentication using only one simple gesture. We also survey the users’ acceptance of our system and discuss how the proficiency of gestures affects authentication accuracy.

Data quality assessment is vital for many information services ranging from sensor networks to smart city systems. The current data quality assessments, however, are often derived from intrinsic data characteristics, disconnected from specific application contexts, or are not applicable or efficient for large datasets. In this work, we propose a novel task-oriented data quality assessment framework, which balances between the intrinsic and contextual quality. We carefully craft the assessment metrics, quantify them, and fuse them to rank candidate datasets by quality given specific tasks. To improve the system efficiency, two fast calculation algorithms are designed to quantify the relationship between datasets and the task, and the distribution of data items. We conduct extensive evaluations on six public image datasets (with 460, 247 images in total) and four text document datasets (with 37, 372 documents in total) to evaluate the efficacy and efficiency of our design. Experimental results show that our algorithms can save about 90% computing time with little accuracy loss which validates the feasibility and effectiveness of our framework for large datasets.

In this paper, we propose a system called "RFRecorder" based on COTS RFID system which can inspect the string vibration and recognize the tone and tempo accurately. Our system can recover the music score played by some string instruments such as ukulele. Specifically, the string vibration influences the reflection of RF signal and causes a phase change. This change can be captured and analyzed to recover the frequency of the string vibration. In addition, the RFID tag is attached on the body of instrument but not on the string, which can not affect the music playing. Compared to the recorder, our system is immune to the environmental noise. Furthermore, it also can work on NLoS scenario. For single-string vibration, we use compressive sensing to recover the frequency duo to the low sampling rate of the COTS RFID. For multiple-string vibration, we recognize the music tone using machine learning method. We build a prototype and test the performance using guitar, ukulele and zither, which achieves 90%, 89%, 85% accuracy respectively.

Desktop is a reliable and amicable object carrier that accompanies us in our daily life, while working, eating and even entertaining. In this work, we devise a contactless but accurate object tracking system on desktop with commercial RFIDs. Comparing with conventional vision or acoustic based solutions, our system needs less computational resources and could be mucheasier for deployment. Moreover, ours could record the true positions for each device instead of the relative positions delivered in most of the previous studies. To this end, recording the user's access to the object on the desktop allows the user to interact with the smart device with simple actions. We present RFdesk, a contactless object location system that accurately locates every objects on the desktop. However, compare to tracking object withcontacted tag, several challenges are tackled before we make the system work. First of all, the signal employed for contactless tracking gets reflected twice which is thus much weaker, making the signal more susceptible to environmental noise and multipath. Another well-known challenge for contactless tracking is multi-target tracking as the signals reflected from multiple targets get mixed and interact. Extensive experiments show that RFdesk canflexibly deploy devices and tags, antennas, and localization itemscan be deployed on different planes. A median error of 3cm can be achieved for target tracking without attaching tags to the targets, even if there are 4 positioning objects on the desktop. Moreover, the average localization error can still be kept within 5.6cm outperforming the state-of-the-art systems by 100%.

Nowadays, there is a growing demand for sharing multimedia data among participants in the same activity. With existing social applications, users need to conduct friending and data sharing operations manually, which is troublesome due to changing attendees and highly diverse data content of different activities. To tackle this issue, in this work we propose a novel system SHAD to achieve privacy-friendly shared activity detection and multimedia data auto-sharing based on users' historical multimodal data. Facing noisy, incomplete and asynchronous data, as well as inaccurate recognition results of machine learning models, we design an algorithm to aggregate multimodal data relevant to the same activity and propose an activity-semantic graph to comprehensively characterize each activity by fusing knowledge of multimodal data. Based on the activity-semantic graph, the privacy-preserving shared activity detection and data sharing method is designed, which protects both raw data and semantic information of data. We implemented our system and conducted comprehensive evaluations with real-life multimodal data (including photos and motion sensor data). The results show the efficacy of our system. We can achieve 94.9% precision and 91.5% recall for shared activity detection.

In this paper, we propose an indoor positioning system, Lightitude, which utilizes the already existed, uneven indoor light intensity distribution established by densely deployed indoor lights as the medium. As common indoor lights cannot act as landmarks due to lack of unique features (e.g., unique intensity or flicker frequency), systems that exploiting the received light intensity (RLI) usually impose strong constraints on user's motion and make ideal assumptions about the indoor environment. Different from these approaches, we first propose a realistic light intensity model to reconstruct the RLI distribution given any motion (position, orientation) of the receiver, thus RLI collected with every motion of the receiver could be used for positioning. Then we design a particle-filter-based positioning module, which harnesses user's natural mobility to eliminate the ambiguity of a single RLI. Experiment results show that Lightitude achieves mean accuracy 1.93m and 1.98m in an office (720m2) and a library (960m2) respectively. Lightitude is still robust against interferences like sunlight, shading of human-body and several user behaviors.

To improve the corrosion resistance of steels for grounding grids, a low-carbon Cr micro-alloyed steel was developed (C1 steel), and corrosion behavior of Q235 steel and newly developed C1 steel in simulated acidic soil was investigated. The corrosion rate was evaluated with the mass loss measurements, while the corrosion morphology of surface and cross section of rust layer was observed by scanning electron microscopy. The corrosion products were analyzed by energy-dispersive X-ray spectrometry, X-ray diffraction and X-ray photoelectron spectroscopy, and the polarization curve was measured using potentiodynamic polarization method. Results indicated that C1 steel displayed good corrosion resistance in the simulated acidic soil, of which the corrosion rate was only 30% of that of Q235 steel after corrosion for 360 h. The analysis of rust layer showed that lower carbon content in steel could reduce the tendency of micro cell corrosion and appropriate amount of chromium could improve the corrosion potential of metal matrix. Moreover, the analysis of X-ray photoelectron spectroscopy revealed that the chromium enriched in inner rust layer of C1 steel existed mainly in the form of Fe2CrO4, which facilitated the formation of Cr-goethite and improved the protection of corrosion products.

Word-Fi is a handwriting input system, driven by wireless backscattering technology and machine learning methods. It could effectively mitigate the surrounding noise and extract the weak signals incurred by tiny writing gestures accurately. Leveraging our customized wireless backscattering system, Word-Fi could be noise tolerant across relatively complex environments, especially when multiple persons are presented around, which significantly differs from status quo wireless sensing systems that suffer from multi-user presentation. For weak signal extraction, Word- Fi incorporates an efficient feature selection scheme for classification and improves the classifier by fully exploiting the physical layer information. After using the word suggestion module, it could recognize writing words with fairly high accuracy (above 90 percent) across different volunteers (7-10).

We propose a novel motion sensing/recognition system, called DF-Mose, which marries low-power wireless backscattering and device-free sensing in one clean sheet. DF-Mose is an accurate, interference tolerable motion-recognition system that counts repetitive motions without using scenario-dependent templates or profiles within 5% count error and enables multiuser to perform certain motions simultaneously based on the nature of backscattered signals and dedicated signal separation method. With little efforts in learning the patterns, our method could achieve 95.2% motion-recognition accuracy for a variety of 7 typical motions.

The introduction of wireless transmissions into the data center has shown to be promising in improving cost effectiveness of data center networks (DCNs). For high transmission flexibility and performance, a fundamental challenge is to increase the wireless availability and enable fully hybrid and seamless transmissions over both wired and wireless DCN components. Rather than limiting the number of wireless radios by the size of top-of-rack switches, we propose a novel DCN architecture, Diamond , which nests the wired DCN with radios equipped on all servers. To harvest the gain allowed by the rich reconfigurable wireless resources, we propose the low-cost deployment of scalable 3-D ring reflection spaces (RRSs) which are interconnected with streamlined wired herringbone to enable large number of concurrent wireless transmissions through high-performance multi-reflection of radio signals over metal. To increase the number of concurrent wireless transmissions within each RRS, we propose a precise reflection method to reduce the wireless interference. We build a 60-GHz-based testbed to demonstrate the function and transmission ability of our proposed architecture. We further perform extensive simulations to show the significant performance gain of diamond, in supporting up to five times higher server-to-server capacity, enabling network-wide load balancing, and ensuring high fault tolerance.

Exploiting radio frequency signals is promising for locating and tracking objects. Prior works focus on per-Tag localization, in which each object is attached with one tag. In this paper, we propose a comprehensive localization and tracking scheme by attaching two RFID tags to one object. Instead of using per-Tag localization pattern, adding one-more RFID tag to the object exhibits several benefits: 1) providing rich freedom in RFID reader's antenna spacing and placement; 2) supporting accurate calibration of the reader's antenna location and spacing, and 3) enabling fine-grained calculation on the orientation of the tags. All of these advantages ultimately improve the localization/tracking accuracy. Our extensive experimental results demonstrate that the average errors of localization and orientation of target tags are 6.415 cm and 1.330°, respectively. Our results also verify that the reader's antenna geometry does have impact on tag positioning performance.

In a software-defined network (SDN), the control plane needs to frequently collect flow statistics measured at the data plane switches for different applications, such as traffic engineering, QoS routing, and attack detection. However, existing solutions for flow statistics collection may result in large bandwidth cost in the control channel and long processing delay on switches, which significantly interfere with the basic functions, such as packet forwarding and route update. To address this challenge, we propose a cost-optimized flow statistics collection (CO-FSC) scheme and a cost-optimized partial flow statistics collection (CO-PFSC) scheme using wildcard-based requests, and prove that both the CO-FSC and CO-PFSC problems are NP-hard. For CO-FSC, we present a rounding-based algorithm with an approximation factor $f$ , where $f$ is the maximum number of switches visited by each flow. For CO-PFSC, we present an approximation algorithm based on randomized rounding for collecting statistics information of a part of flows in a network. Some practical issues are discussed to enhance our algorithms, for example, the applicability of our algorithms. Moreover, we extend CO-FSC to achieve the control link cost optimization FSC problem, and also design an algorithm with an approximation factor $f$ for this problem. We implement our designed flow statistics collection algorithms on the open virtual switch-based SDN platform. The testing and extensive simulation results show that the proposed algorithms can reduce the bandwidth overhead by over 39% and switch processing delay by over 45% compared with the existing solutions.

Traditional vibration inspection systems, equipped with separated sensing and communication modules, are either very expensive ( e.g., hundreds of dollars) and/or suffer from occlusion and narrow field of view ( e.g., laser). In this paper, we present an RFID-based solution, Tagbeat, to inspect mechanical vibration using COTS RFID tags and readers. Making sense of micro and high-frequency vibration using random and low-frequency readings of tag has been a daunting task, especially challenging for achieving sub-millisecond period accuracy. Our system achieves these three goals by discerning the change pattern of backscatter signal replied from the tag, which is attached on the vibrating surface and displaced by the vibration within a small range. This paper introduces three main innovations. First, it shows how one can utilize COTS RFID to sense mechanical vibration and accurately discover its period with a few periods of short and noisy samples. Second, a new digital microscope is designed to amplify the micro-vibration-induced weak signals. Third, Tagbeat introduces compressive reading to inspect high-frequency vibration with relatively low RFID read rate. We implement Tagbeat using a COTS RFID device and evaluate it with a commercial centrifugal machine. Empirical benchmarks with a prototype show that Tagbeat can inspect the vibration period with a mean accuracy of $0.36ms$ and a relative error rate of 0.03%. We also study three cases to demonstrate how to associate our inspection solution with the specific domain requirements.

Many cloud platforms emerge to meet urgent requirements for large-volume personal image store, sharing and search. Though most would agree that images contain rich sensitive information (e.g., people, location and event) and people’s privacy concerns hinder their participation into untrusted services, today’s cloud platforms provide little support for image privacy protection. Facing large-scale images from multiple users, it is extremely challenging for the cloud to maintain the index structure and schedule parallel computation without learning anything about the image content and indices. In this work, we introduce a novel system PIC: A Privacy-preserving Image search system on Cloud, which is a step towards feasible cloud services which provide secure content-based large-scale image search with fine-grained access control. Users can search on others’ images if they are authorized by the image owners. Majority of the computationally intensive jobs are handled by the cloud, and a querier can now simply send the query and receive the result. Specially, to deal with massive images, we design our system suitable for distributed and parallel computation and introduce several optimizations to further expedite the search process. Our security analysis and prototype system evaluation results show that PIC successfully protects the image privacy at a low cost of computation and communication.

Due to flow dynamics, a software defined network (SDN) may need to frequently update its data plane so as to optimize various performance objectives, such as load balancing. Most previous solutions first determine a new route configuration based on the current flow status, and then update the forwarding paths of existing flows. However, due to slow update operations of Ternary Content Addressable Memory-based flow tables, unacceptable update delays may occur, especially in a large or frequently changed network. According to recent studies, most flows have short duration and the workload of the entire network will vary significantly after a long duration. As a result, the new route configuration may be no longer efficient for the workload after the update, if the update duration takes too long. In this paper, we address the real-time route update, which jointly considers the optimization of flow route selection in the control plane and update scheduling in the data plane. We formulate the delay-satisfied route update problem, and prove its NP-hardness. Two algorithms with bounded approximation factors are designed to solve this problem. We implement the proposed methods on our SDN test bed. The experimental results and extensive simulation results show that our method can reduce the route update delay by about 60% compared with previous route update methods while preserving a similar routing performance (with link load ratio increased less than 3%).

To improve the performance of the governing system as well as the stability of the power system, the authors propose grey prediction extension control (GPEC) in the design of generator governing system. Comparing with the conventional control strategy such as conventional proportional–integral–derivative control and simple extension control, GPEC is more suitable for the complex, huge-dimension and time-varying modern power systems. Both a simple hydropower system and a typical multi-machine system are studied, respectively. The simulation results indicate that the proposed GPEC governor controller performs better in reducing overshoot and adjusting time with superiority in leading the system into a stable state.

Recent years have seen major innovations in cooperative wireless networks. Despite the fact that throughput gains have been achieved in packet recovery, hardly any of these technologies could effectively decode signals when the number of signal sources is greater than available antennas in each AP. Thus, conventional cooperative methods rely on adaptive scheduling for interference-free data packets. Deploying scheduling-free cooperative signal recovery requires prompt processing with low overheads. Yet potentially, the concurrent client transmissions could overwhelm any single AP, i.e., the number of concurrent transmissions are more than that of antennas. This paper presents the first step towards breaking this stalemate, by enabling symbol alignment and constellation reinforcement instead of relying on scheduling. We present POLYPHONY, a scheduling-free cooperative design, where decoding process could be coordinated without over-the-air scheduling, and coupled signals are decoded promptly after deep cooperations. We implement POLYPHONY prototype with GNURadio/USRP platform, and deploy it with a 16-node enterprise network. Particularly, we demonstrate how it manages the complex interactions with scheduling-free signal enforcement, and enables a beyond node-DoF (Degree of Freedom) decoding with AP coordinations. Furthermore, we show how the cooperative decoding process improves radio access among clients. Our results demonstrate a gain of nearly 200 percent for network throughput, which is a significant improvement for heavy contending networks.

In a software defined network (SDN), it is usually required to frequently collect state/statistics of all the flows, which may result in large overhead on control links. To reduce the flow re-routing overhead, we perform load-balanced routing using the traffic knowledge by carefully taking flow statistics collection. A key challenge for achieving effective almost-optimal load-balanced routing with less overhead relies on the quality of flow statistics collection. To address this challenge, we propose a partial flow statistics collection (PFSC) problem, in which we need to inquire statistics of flows from a subset of switches such that the flow recall ratio on every switch is at least a given value β ∈ (0, 1] while minimizing the number of queried switches. We prove that the PFSC problem is NP-Hard and present an algorithm based on primal-dual with an approximation factor f β in most situations, where f is the maximum number of switches visited by each flow. To further reduce the overhead, we design an adaptive flow statistics collection mechanism, as a complementary scheme for PFSC, based on link load similarity measurement. We implement our partial flow statistics collection algorithm and a load-balanced routing method on a testbed platform. Our extensive experimental and simulation results show that our methods can reduce the overhead by 56% compared with the previous collection method while preserving a similar routing performance (with peak-load ratio increased by ∼3%).

To explore the advantages of software defined network (SDN), while preserving the legacy networking systems, a natural deployment strategy is to deploy a hybrid SDN incrementally to improve the network performance. In this paper, we address two technical challenges: an incremental deployment strategy and a throughput-maximization routing, for deploying a hybrid network incrementally. For incremental deployment, we propose a heuristic algorithm for deploying a hybrid SDN under the budget constraint, and prove the approximate factor of $ 1- \frac {1}{e} $ . For throughput-maximization routing, we apply a depth-first-search method and a randomized rounding mechanism to solve the multi-commodity $h$ -splittable flow routing problem in a hybrid SDN, where $h\ge 1$ . We also prove that our method has approximation ratio $O\left({\frac {1}{\log N}}\right)$ , where $ N $ is the number of links in a hybrid SDN. We then show, by both analysis and simulations, that our algorithms can obtain significant performance gains and perform better than the theoretical worst-case bound. For example, our incremental deployment scheme helps to enhance the throughout about 40% compared with the previous deployment scheme by deploying a small number of SDN devices, and the proposed routing algorithm can improve the throughput about 31% compared with ECMP in hybrid networks.

Visible light communication (VLC) has been shown to have several advantages over traditional wireless communication. In this paper, we envision an LED-light-to-smartphone VLC protocol for delivering messages to a group of randomly arriving smartphone receivers. Our goal is to increase the throughput for large message delivery, as well as to reduce the delay of message broadcast. Key challenges for implementing such a VLC message broadcast protocol are: 1) the imperfect synchronization among receivers and the transmitter; 2) the receivers’ arbitrary arrival times; and 3) the diversity of receivers’ smartphones (e.g., location, capability, and frame-rates). In this paper, we propose a new modulation scheme and design link-layer protocols for improving the network data rate. We carefully design and implement our protocol, Martian, which allows smooth communication from the LED lights to a group of smartphone embedded cameras. Across several phone models, Martian can achieve data rate of about 1.6 kb/s even with NLOS -light. It also has a stable and small delay for broadcasting messages to the randomly arriving receivers.

We propose and implement a novel positioning system, WalkieLokie, which directly calculates the relative position from a smart device to a target. The requirement of the target is simple: it is attached with a “dummy” acoustic speaker, which does not have any other rich capabilities, such as audio recording, communication, or computation. Hence, the proliferation of smart devices, together with the cheap accessory (e.g., dummy speaker) embedded in daily used items (e.g., smart clothes), paves the way for WalkieLokie applications. WalkieLokie leverages the walking motion for locating an acoustic speaker. The key insight is that the distance between the user and the speaker varies in real time when the user walks, and the pattern of the variance implies the relative position. We design a novel algorithm to estimate the position and signal processing methods to support accurate positioning. The experiment results show that the mean errors of ranging and direction estimation are 0.63 m and 2.46°, respectively. Extensive experiments conducted in noisy environments validate the robustness of WalkieLokie.

In this paper, we address a critical task of dynamically detecting the simultaneous behavior of driving and texting using smartphone as the sensor. We propose, design, and implement TEXIVE which achieves the goal of detecting texting operations during driving utilizing irregularities and rich micro-movements of users. Without relying on any external infrastructures and additional devices, and no need to bring any modification to vehicles, TEXIVE is able to successfully detect dangerous operations with good sensitivity, specificity, and accuracy by leveraging the inertial sensors integrated in regular smartphones. To validate our approach, we conduct extensive experiments involving in a number of volunteers on various of vehicles and smartphones. Our evaluation results show that TEXIVE has a classification accuracy of 87.18%, and precision of 96.67%.

In this work, we design and develop Montage for real-time multi-user formation tracking and localization by off-the-shelf smartphones. Montage achieves submeter-level tracking accuracy by integrating temporal and spatial constraints from user movement vector estimation and distance measuring. In Montage , we designed a suite of novel techniques to surmount a variety of challenges in real-time tracking, without infrastructure and fingerprints, and without any a priori user-specific (e.g., stride-length and phone-placement) or site-specific (e.g., digitalized map) knowledge: (1) a coded audio tone to support multi-user tracking with minimal latency, in the presence of high noise, multi-path effect, and Doppler Shift, (2) an innovative stride-length and walking direction estimation method without a priori knowledge of user and site, and (3) a vector-based multi-user tracking scheme which connects successive localization snapshots to refine users’ locations and generate continuous moving traces. We implemented, deployed, and evaluated Montage in both outdoor and indoor environment. Our experimental results (847 traces from 15 users) show that the stride-length estimated by Montage over all users has error within $9\text{\,cm}$ , and the moving-direction estimated by Montage is within $20$ degrees. For real-time tracking, Montage provides meter-second-level formation tracking accuracy with off-the-shelf mobile phones.

Nowadays, metro systems play an important role in meeting the urban transportation demand in large cities. The understanding of passenger route choice is critical for public transit management. The wide deployment of Automated Fare Collection(AFC) systems opens up a new opportunity. However, only each trip's tap-in and tap-out timestamp and stations can be directly obtained from AFC system records; the train and route chosen by a passenger are unknown, which are necessary to solve our problem. While existing methods work well in some specific situations, they don't work for complicated situations. In this paper, we propose a solution that needs no additional equipment or human involvement than the AFC systems. We develop a probabilistic model that can estimate from empirical analysis how the passenger flows are dispatched to different routes and trains. We validate our approach using a large scale data set collected from the Shenzhen metro system. The measured results provide us with useful inputs when building the passenger path choice model., Comment: 12 pages, 12 figures

Device authentication is a critical and challenging issue for the emerging Internet of Things (IoT). One promising solution to authenticate IoT devices is to extract a fingerprint to perform device authentication by exploiting variations in the transmitted signal caused by hardware and manufacturing inconsistencies. In this paper, we propose a lightweight device authentication protocol [named speaker-to-microphone (S2M)] by leveraging the frequency response of a speaker and a microphone from two wireless IoT devices as the acoustic hardware fingerprint. S2M authenticates the legitimate user by matching the fingerprint extracted in the learning process and the verification process, respectively. To validate and evaluate the performance of S2M, we design and implement it in both mobile phones and PCs and the extensive experimental results show that S2M achieves both low false negative rate and low false positive rate in various scenarios under different attacks.