INFOCOM 2020 论文列表

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.

Superspreaders (i.e., hosts with numerous distinct connections) remain severe threats to production networks. How to accurately detect superspreaders in real-time at scale remains a non-trivial yet challenging issue. We present SpreadSketch, an invertible sketch data structure for network-wide superspreader detection with the theoretical guarantees on memory space, performance, and accuracy. SpreadSketch tracks candidate super-spreaders and embeds estimated fan-outs in binary hash strings inside small and static memory space, such that multiple SpreadSketch instances can be merged to provide a network-wide measurement view for recovering superspreaders and their estimated fan-outs. We present formal theoretical analysis on SpreadSketch in terms of space and time complexities as well as error bounds. Trace-driven evaluation shows that SpreadSketch achieves higher accuracy and performance over state-of-the-art sketches. Furthermore, we prototype SpreadSketch in P4 and show its feasible deployment in commodity hardware switches.

Bluetooth Low Energy (BLE) is a widely adopted wireless communication technology in the Internet of Things (IoT). BLE offers secure communication through a set of pairing strategies. However, these pairing strategies are obsolete in the context of IoT. The security of BLE based devices relies on physical security, but a BLE enabled IoT device may be deployed in a public environment without physical security. Attackers who can physically access a BLE-based device will be able to pair with it and may control it thereafter. Therefore, manufacturers may implement extra authentication mechanisms at the application layer to address this issue. In this paper, we design and implement a BLE Security Scan (BLESS) framework to identify those BLE apps that do not implement encryption or authentication at the application layer. Taint analysis is used to track if BLE apps use nonces and cryptographic keys, which are critical to cryptographic protocols. We scan 1073 BLE apps and find that 93% of them are not secure. To mitigate this problem, we propose and implement an application-level defense with a low-cost $0.55 crypto co-processor using public key cryptography.

Network-distributed optimization has attracted sig-nificant attention in recent years due to its ever-increasing applications. However, the classic decentralized gradient descent (DGD) algorithm is communication-inefficient for large-scale and high-dimensional network-distributed optimization problems. To address this challenge, many compressed DGD-based algorithms have been proposed. However, most of the existing works have high complexity and assume compressors with bounded noise power. To overcome these limitations, in this paper, we propose a new differential-coded compressed DGD (DC-DGD) algorithm. The key features of DC-DGD include: i) DC-DGD works with general SNR-constrained compressors, relaxing the bounded noise power assumption; ii) The differential-coded design entails the same convergence rate as the original DGD algorithm; and iii) DC-DGD has the same low-complexity structure as the original DGD due to a self-noise-reduction effect. Moreover, the above features inspire us to develop a hybrid compression scheme that offers a systematic mechanism to minimize the communication cost. Finally, we conduct extensive experiments to verify the efficacy of the proposed DC-DGD and hybrid compressor.

As the emerging Internet of Things (IoT) devices and applications flourish, demand for reliable and energy-efficient low-power wireless network protocols is surging. For this purpose, IEEE 802.15.4 standardized time-slotted channel hopping (TSCH), a promising and viable link-layer solution that has shown outstanding performance achieving over 99% reliability with low duty-cycles. However, it lacks one thing, flexibility. It is not adaptable to a wide variety of applications with varying traffic load and unpredictable routing topology due to its static timeslot scheduling. To this end, we propose OST, an On-demand Scheduling scheme for TSCH with traffic-awareness. In OST, each node dynamically self-adjusts the frequency of timeslots at run time according to time-varying traffic intensity. Moreover, it features on-demand resource allocation to handle bursty/queued packets in a timely manner. By doing so, OST aims to minimize its energy consumption while guaranteeing reliable packet delivery. We evaluate OST on a large-scale 72-node testbed, demonstrating that it achieves improvement of 60% in reliability and 52% in energy-efficiency compared to the state of the art.

Cluster caching systems increasingly store structured data objects in the columnar format. However, these systems routinely face the imbalanced load that significantly impairs the I/O performance. Existing load-balancing solutions, while effective for reading unstructured data objects, fall short in handling columnar data. Unlike unstructured data that can only be read through a full-object scan, columnar data supports direct query of specific columns with two distinct access patterns: (1) columns have the heavily skewed popularity, and (2) hot columns are likely accessed together in a query job. Based on these two access patterns, we propose an effective load-balancing solution for structured data. Our solution, which we call RepBun, groups hot columns into a bundle. It then copies multiple replicas of the column bundle and stores them uniformly across servers. We show that RepBun achieves improved load balancing with reduced memory overhead, while avoiding data shuffling between cache servers. We implemented RepBun atop Alluxio, a popular in-memory distributed storage, and evaluate its performance through EC2 deployment against the TPC-H benchmark work-load. Experimental results show that RepBun outperforms the existing load-balancing solutions with significantly shorter read latency and faster query completion.

Off-the-shelf buffer-based approaches leverage a simple yet effective buffer-bound to control the adaptive bitrate (ABR) streaming system. Nevertheless, such approaches in standard parameters fail to always provide high quality of experience (QoE) video streaming services under all considered network conditions. Meanwhile, state-of-the-art learning-based ABR approach Pensieve outperforms existing schemes but is impractical to deploy. Therefore, how to harmoniously fuse the buffer-based and learning-based approach has become a key challenge for further enhancing ABR methods. In this paper, we propose Stick, an ABR algorithm that fuses the deep learning method and traditional buffer-based method. Stick utilizes the deep reinforcement learning (DRL) method to train the neural network, which outputs the buffer-bound to control the buffer-based approach for maximizing the QoE metric with different parameters. Trace-driven emulation illustrates that Stick betters Pensieve by 3.5% - 9.41% with an overhead reduction of 88%. Moreover, aiming to further reduce the computational costs while preserving the performances, we propose Trigger, a light-weighted neural network that determines whether the buffer-bound should be adjusted. Experimental results show that Stick+Trigger rivals or outperforms existing schemes in average QoE by 1.7%-28%, and significantly reduces the Stick's computational overhead by 24%-61%. Meanwhile, we show that Trigger also helps other ABR schemes mitigate the overhead. Extensive results on real-world evaluation demonstrate the superiority of Stick over existing state-of-the-art approaches.

Radio fingerprinting uniquely identifies wireless devices by leveraging tiny hardware-level imperfections inevitably present in off-the-shelf radio circuitry. This way, devices can be directly identified at the physical layer by analyzing the unprocessed received waveform - thus avoiding energy-expensive upper-layer cryptography that resource-challenged embedded devices may not be able to afford. Recent advances have proven that convolutional neural networks (CNNs) - thanks to their multidimensional mappings - can achieve fingerprinting accuracy levels impossible to achieve by traditional low-dimensional algorithms. The same research, however, has also suggested that the wireless channel may negatively impact the accuracy of CNN-based radio fingerprinting algorithms by making device-unique hardware imperfections much harder to recognize.In spite of the growing interest in radio fingerprinting research by academia and DARPA, the wireless research community still lacks (i) a large-scale open dataset for radio fingerprinting collected in diverse environments and rich, diverse, channel conditions; and (ii) a full-fledged, systematic, quantitative investigation of the impact of the wireless channel on the accuracy of CNN-based radio fingerprinting algorithms. The key contribution of this paper is to bridge this gap by (i) collecting and sharing with the community more than 7TB of wireless data obtained from 20 wireless devices with identical RF circuitry (and thus, worst-case scenario for fingerprinting) over the course of several days in (a) an anechoic chamber, (b) in-the-wild testbed, and (c) with cable connections; and (ii) providing a first-of-its-kind evaluation of the impact of the wireless channel on CNN-based fingerprinting algorithms through (a) the 7TB experimental dataset and (b) a 400GB dataset provided by DARPA containing hundreds of thousands of transmissions from thousands of WiFi and ADS-B devices with different SNR conditions. Experimental results conclude that (i) the wireless channel impacts the classification accuracy significantly, i.e., from 85% to 9% and from 30% to 17% in the experimental and DARPA dataset, respectively; and that (ii) equalizing I/Q data can increase the accuracy to a significant extent (i.e., by up to 23%) when the number of devices increases significantly.

Multi-user transmission in 60 GHz Wi-Fi can achieve data rates up to 100 Gbps by multiplexing multiple user data streams. However, a fundamental limit in the approach is that each RF chain is limited to supporting one stream or one user. To overcome this limit, we propose SIngle RF chain Multiuser BeAmforming (SIMBA), a novel framework for multi-stream multi-user downlink transmission via a single RF chain. We build on single beamformed transmission via overlayed constellations to multiplex multiple users' modulated symbols such that grouped users at different locations can share the same transmit beam from the AP. For this, we introduce user grouping and beam selection policies that span tradeoffs in data rate, training and computation overhead. We implement a programmable WLAN testbed using software-defined radios and commercial 60-GHz transceivers and collect over-the-air measurements using phased array antennas and horn antennas with varying beamwidth. We find that in comparison to single user transmissions, SIMBA achieves 2× improvement in aggregate rate and two-fold delay reduction for simultaneous transmission to four users.

New battery-free sensor tags that interoperate with unmodified standard IoT devices and protocols can extend a sensor network's capabilities in a scalable and cost-effective manner. The tags achieve battery-free operation through backscatterrelated techniques, while the standard IoT devices avoid additional dedicated infrastructure by providing the unmodulated carrier that tags need to communicate. However, this approach requires coordination between devices transmitting, receiving and generating carrier, adds extra latency and energy consumption to already constrained devices, and increases interference and contention in the shared spectrum. We present a scheduling mechanism that optimizes the use of carrier generators, minimizing any disruptions to the regular nodes. We employ time slots to coordinate the unmodulated carrier while minimizing latency, energy consumption and overhead radio emissions. We propose an efficient scheduling algorithm that parallelizes communications with battery-free tags when possible and shares carriers among multiple tags concurrently. In our evaluation we demonstrate the feasibility and reliability of our approach in testbed experiments. We find that we can significantly reduce the excess latency and energy consumption caused by the addition of sensor tags when compared to sequential interrogation. We show that the gains tend to improve with the network size and that our solution is close to optimal on average.

This paper proposes a self-sustaining broadband long-range maritime communication as an alternative to the expensive and slow satellite communications in offshore areas. The proposed system, named Marinet, consists of many buoys. Each of the buoys has two units: an energy harvesting unit and a wireless communication unit. The energy harvesting unit generates electrical energy from ocean waves to support the operation of the wireless communication unit. The wireless communication unit on each buoy operates in a TV white space frequency band and connects to each other and wired high-speed gateways on land or islands to form a mesh network. The resulting mesh network provides wireless access services to marine users in their range. A prototype of the energy harvesting unit and the wireless communication unit are built and tested in the field. In addition, to ensure Marinet will maintain stable communications in rough sea states, an ocean-link-state prediction algorithm is designed. The algorithm predicts ocean link-states based on ocean wave movements. A realistic ocean simulator is designed and used to evaluate how such a link-state prediction algorithm can improve routing algorithm performance.

In this paper, we introduce technology-independent ambient backscatter systems where a backscatter tag utilizes all single-stream ambient signals transmitted by nearby devices. Specifically, we design a phase demodulation algorithm that detects a backscatter signal from the phase difference between the two antennas, no matter which signal the tag reflects. We then develop a parallelized backscatter receiver that mitigates the dead spot problem by leveraging antenna diversity. To show the feasibility of our design, we implement a backscatter receiver on the software-defined radio platform and analyze 50 MHz RF bandwidth in real-time. Our evaluation shows that the receiver can decode backscatter bits carried by any single stream ambient signal such as a continuous wave, a QAM signal, and even a noise signal. We also demonstrate backscatter transmissions with commodity Wi-Fi and Bluetooth devices to prove that our design can be combined with existing wireless networks.

With the increasing popularity of geo-positioning technologies and mobile Internet, spatial keyword data services have attracted growing interest from both the industrial and academic communities in recent years. Meanwhile, a massive amount of data is increasingly being outsourced to cloud in the encrypted form for enjoying the advantages of cloud computing while without compromising data privacy. Most existing works primarily focus on the privacy-preserving schemes for either spatial or keyword queries, and they cannot be directly applied to solve the spatial keyword query problem over encrypted data. In this paper, we study the challenging problem of Privacy-preserving Boolean Range Query (PBRQ) over encrypted spatial databases. In particular, we propose two novel PBRQ schemes. Firstly, we present a scheme with linear search complexity based on the space-filling curve code and Symmetric-key Hidden Vector Encryption (SHVE). Then, we use tree structures to achieve faster-than-linear search complexity. Thorough security analysis shows that data security and query privacy can be guaranteed during the query process. Experimental results using real-world datasets show that the proposed schemes are efficient and feasible for practical applications, which is at least ×70 faster than existing techniques in the literature.

Network function virtualization has introduced a high degree of flexibility for orchestrating service functions. The provisioning of chains of service functions requires making decisions on both (1) placement of service functions and (2) scheduling of traffic through them. The placement problem (1) can be tackled during the planning phase, by exploiting coarse-grained traffic information, and has been studied extensively. However, runtime traffic scheduling (2) for optimizing system utilization and service quality, as required for future edge cloud and mobile carrier scenarios, has not been addressed so far.We fill this gap by presenting a queuing-based system model to characterize the runtime traffic scheduling problem for service function chaining. We propose a throughput-optimal scheduling policy, called integer allocation maximum pressure policy (IA-MPP). To ensure practicality in large distributed settings, we propose multi-site cooperative IA-MPP (STEAM), fulfilling runtime requirements while achieving near-optimal performance. We examine our policies in various settings representing real-world scenarios. STEAM closely matches IA-MPP in terms of throughput, and significantly outperforms (possible adaptations of) existing static or coarse-grained dynamic solutions, requiring 30%-60% less server capacity for similar service quality. Our STEAM prototype shows feasibility running on a standard server.

A widely adopted approach to guarantee high-quality services on on-demand service platforms is to introduce a reputation system, where good reputation workers will receive a bonus for providing high-quality services. In this paper, we propose a general reputation framework motivated by various practical examples. Our model captures the evolution of a reputation system, jointly considering worker's strategic behaviors and imperfect customer reviews that are usually studied separately before. We characterize the stationary equilibrium of the market, in particular, the existence and uniqueness of a non-trivial equilibrium that ensures high-quality services. Furthermore, we propose an efficient subsidization mechanism that helps induce high-quality services on the platform, and show the market convergence to the high service quality equilibrium under such a mechanism.

Multi-user identification could facilitate various large-scale identity-based services such as access control, automatic surveillance system, and personalized services, etc. Although existing solutions can identify multiple users using cameras, such vision-based approaches usually raise serious privacy concerns and require the presence of line-of-sight. Differently, in this paper, we propose MU-ID, a gait-based multi-user identification system leveraging a single commercial off-the-shelf (COTS) millimeter-wave (mmWave) radar. Particularly, MU-ID takes as input frequency-modulated continuous-wave (FMCW) signals from the radar sensor. Through analyzing the mmWave signals in the range-Doppler domain, MU-ID examines the users' lower limb movements and captures their distinct gait patterns varying in terms of step length, duration, instantaneous lower limb velocity, and inter-lower limb distance, etc. Additionally, an effective spatial-temporal silhouette analysis is proposed to segment each user's walking steps. Then, the system identifies steps using a Convolutional Neural Network (CNN) classifier and further identifies the users in the area of interest. We implement MU-ID with the TI AWR1642BOOST mmWave sensor and conduct extensive experiments involving 10 people. The results show that MU-ID achieves up to 97% single-person identification accuracy, and over 92% identification accuracy for up to four people, while maintaining a low false positive rate.

Voice interaction, as an emerging human-computer interaction method, has gained great popularity, especially on smart devices. However, due to the open nature of voice signals, voice interaction may cause privacy leakage. In this paper, we propose a novel scheme, called SeVI, to protect voice interaction from being deliberately or unintentionally eavesdropped. SeVI actively generates jamming noise of superior characteristics, while a user is performing voice interaction with his/her device, so that attackers cannot obtain the voice contents of the user. Mean-while, the device leverages the prior knowledge of the generated noise to adaptively cancel received noise, even when the device usage environment is changing due to movement, so that the user voice interactions are unaffected. SeVI relies on only normal microphone and speakers and can be implemented as light-weight software. We have implemented SeVI on a commercial off-the- shelf (COTS) smartphone and conducted extensive real-world experiments. The results demonstrate that SeVI can defend both online eavesdropping attacks and offline digital signal processing (DSP) analysis attacks.

Networks can now process data as well as transporting it; it follows that they can support multiple services, each requiring different key performance indicators (KPIs). Because of the former, it is critical to efficiently allocate network and computing resources to provide the required services, and, because of the latter, such decisions must jointly consider all KPIs targeted by a service. Accounting for newly introduced KPIs (e.g., availability and reliability) requires tailored models and solution strategies, and has been conspicuously neglected by existing works, which are instead built around traditional metrics like throughput and latency. We fill this gap by presenting a novel methodology and resource allocation scheme, named OKpi, which enables high-quality selection of radio points of access as well as VNF (Virtual Network Function) placement and data routing, with polynomial computational complexity. OKpi accounts for all relevant KPIs required by each service, and for any available resource from the fog to the cloud. We prove several important properties of OKpi and evaluate its performance in two real-world scenarios, finding it to closely match the optimum.

Understanding the nature of user-device interactions (e.g., who is using the device and what he/she is doing with it) is critical to many applications including time management, user profiles, and privacy protection. However, in scenarios where mobile devices are shared among family members or multiple employees in a company, conventional account-based statistics are not meaningful. This poses an even bigger problem when dealing with sensitive data. Moreover, fingerprint readers and front-facing cameras were not designed to continuously identify users. In this study, we developed MagPrint, a novel approach to fingerprint users based on unique patterns in the electromagnetic (EM) signals associated with the specific use patterns of users. Initial experiments showed that time-varying EM patterns are unique to individual users. They are also temporally and spatially consistent, which makes them suitable for fingerprinting. MagPrint has a number of advantages over existing schemes: i) Non-intrusive fingerprinting, ii) implementation using a small and easy-to-deploy device, and iii) high accuracy thanks to the proposed classification algorithm. In experiments involving 30 users, MagPrint achieves 94.3% accuracy in classifying users from these traces, which represents an 10.9% improvement over the state-of-the-art classification method.