INFOCOM 2022 论文列表

Estimating the angle-of-arrival (AoA) of an RF source by using a large-sized antenna array is a classical topic in wireless systems. However, AoA tracking systems are not yet used for Internet of Things (IoT) in real world due to their unaffordable cost. Many efforts, such as a time-sharing array, emulated array and sparse array, were recently made to cut the cost. This work introduces a log-spiral antenna belt (LSAB), a new novel sparse "planar array" that could estimate the AoA of an IoT device in 3D space by using a few antennas connected to a single timeshare channel. Unlike the conventional arrays, LSAB deploys antennas on a log-spiral-shaped belt in a non-linear manner, following the theory of minimum resolution redundancy newly discovered in this work. One physical 8×8 uniform planar array (UPA) and four logical sparse arrays, including LSAB, were prototyped to validate the theory and evaluate the performance of sparse arrays. The extensive benchmark demonstrates that the performance of LSAB was comparable to that of a UPA, with similar degree of resolution; and LSAB could provide over 40% performance improvement than existing sparse arrays.

Optical camera communication (OCC) has attracted increasing attention recently thanks to the wide usage of LED and high-resolution cameras. The lens-image sensor structure enables the camera distinguish light from various source, which is ideal for spatial MIMO. Hence, OCC can be applied to several emerging application scenarios, such as vehicle and drone communications. However, distance is a major bottleneck for OCC system, because the increase in distance makes it difficult for the camera to distinguish adjacent LEDs, which we call LED spatial mixing.In this paper, we propose a novel hierarchical coding scheme named as OnionCode to support dynamic range of channel capacity in one-to-many OCC scenario. OnionCode adopts a multi-priority receiving scheme, i.e., the receivers can dynamically discard the low-priority bit streams according to the measured channel capacity. OnionCode achieves this based on a key insight that, the luminance level of a mix-LED is distinguishable. We prototype a LED-based OCC system to evaluate the efficacy of OnionCode and the results show that OnionCode achieves a higher coding efficiency and overall throughput compared with the existing hierarchical coding.

As a key component of most machines, the status of the rotation shaft is a crucial issue in the factories, which affects both the industrial safety and the product quality. Tracking the rotation angle can efficiently monitor the status of the rotation shaft, but traditional solutions either rely on the specialized sensors, suffering from intrusive transformation, or use the computer vision-based solutions, suffering from poor light conditions. In this paper, we present a non-contacting low-cost angle tracking solution, RF-Protractor, to track the rotation shaft based on the surrounding RFID tags. Particularly, instead of directly attaching the tags to the shaft, which may lead to serious miss reading problems due to metal interference, we deploy the tags beside the shaft and leverage the polarization effect of the reflection signal from the shaft for angle tracking. To improve the polarization effect, we exploit the linear polarization feature by using the linear shaft turntable or placing a light aluminum foil on the shaft turntable, which requires no transformation of the shaft. We firstly build a polarization model to quantify the relationship between the rotation angle and the reflection signal. To extract the accurate reflection signal, we then propose to combine the signals of multiple tags to cancel the reflection effect and then estimate the environment-related parameter to calibrate the model. Finally, we propose to leverage both the power trend and the IQ signal to estimate the rotation direction and the rotation angle. We have implemented a real system and the extensive experiments in the real environment confirm the effectiveness of RF-Protractor, which achieves an average error of about 3.1° in angle tracking.

Unlike classical routing algorithms, quantum routing algorithms make use of entangled states—a type of resources that have a limited lifetime and need to be regenerated after consumption. In a nutshell, quantum routing algorithms have to use these resources efficiently, while optimizing some objectives such as the total waiting time. Current routing algorithms tend to keep a routing request waiting until all of the resources on its path are available. In this paper, we introduce a new way of managing entanglement resources in an opportunistic fashion: a request can move forward along its path as soon as possible (even if some resources on its path are not ready). We show that this opportunistic approach is fundamentally better than conventional approaches. In particular, our results indicate that this new approach achieves a 30-50% improvement in the average total waiting time and average link waiting time compared with several state-of-the-art routing algorithms. As a by-product of this work, we develop a new simulator for quantum routing, which can be used to evaluate various design choices under different scenarios.

Covert eavesdropping via microphones has always been a major threat to user privacy. Benefiting from the acoustic non-linearity property, the ultrasonic microphone jammer (UMJ) is effective in resisting this long-standing attack. However, prior UMJ researches underestimate adversary’s attacking capability in reality and miss critical metrics for a thorough evaluation. The strong assumptions of adversary unable to retrieve information under low word recognition rate, and adversary’s weak denoising abilities in the threat model make these works overlook the vulnerability of existing UMJs. As a result, their UMJs’ resilience is overestimated. In this paper, we refine the adversary model and completely investigate potential eavesdropping threats. Correspondingly, we define a total of 12 metrics that are necessary for evaluating UMJs’ resilience. Using these metrics, we propose a comprehensive framework to quantify UMJs’ practical resilience. It fully covers three perspectives that prior works ignored in some degree, i.e., ambient information, semantic comprehension, and collaborative recognition. Guided by this framework, we can thoroughly and quantitatively evaluate the resilience of existing UMJs towards eavesdroppers. Our extensive assessment results reveal that most existing UMJs are vulnerable to sophisticated adverse approaches. We further outline the key factors influencing jammers’ performance and present constructive suggestions for UMJs’ future designs.

Payment channel networks (PCNs) have emerged as a scalability solution for blockchains built on the concept of a payment channel: a setting that allows two parties to safely transact between themselves in high frequencies by updating pre-committed balances. Transaction requests in PCNs may be declined because of unavailability of funds due to temporary uneven distribution of the channel balances. In this paper, we investigate how to alleviate unnecessary payment blockage via proper prioritization of the transaction execution order. Specifically, we consider the scheduling problem in a payment channel: as transactions continuously arrive on both sides, nodes need to decide which ones to process and when, in order to maximize channel throughput. We introduce a stochastic model to capture the dynamics of a payment channel under discrete stochastic arrivals, with incoming transactions potentially held in buffers up until some deadline in order to enable more elaborate processing decisions. We describe a scheduling policy that maximizes the channel success rate/throughput, formally prove its optimality for fixed-amount transactions, and also show its superiority in the case of heterogeneous amounts via experiments in our discrete event simulator. Overall, our work is a step in the direction of formal research on improving PCN performance.

While Deep Learning (DL) technologies are a promising tool to solve networking problems that map to classification tasks, their computational complexity is still too high with respect to real-time traffic measurements requirements. To reduce the DL inference cost, we propose a novel caching paradigm, that we named approximate-key caching, which returns approximate results for lookups of selected input based on cached DL inference results. While approximate cache hits alleviate DL inference workload and increase the system throughput, they however introduce an approximation error. As such, we couple approximate-key caching with an error-correction principled algorithm, that we named auto-refresh. We analytically model our caching system performance for classic LRU and ideal caches, we perform a trace-driven evaluation of the expected performance, and we compare the benefits of our proposed approach with the state-of-the-art similarity caching – this testifies the practical interest of our proposal.

Wireless jammer activity from malicious or malfunctioning devices cause significant disruption to mobile network services and user QoE degradation. In practice, detection of such activity is manually intensive and costly, taking days and weeks after the jammer activation to detect it. We present a novel data-driven jammer detection framework termed JADE that leverages continually collected operator-side cell-level KPIs to automate this process. As part of this framework, we develop two deep learning based semi-supervised anomaly detection methods tailored for the jammer detection use case. JADE features further innovations, including an adaptive thresholding mechanism and transfer learning based training to efficiently scale JADE for operation in real-world mobile networks. Using a real-world 4G RAN dataset from a multinational mobile network operator, we demonstrate the efficacy of proposed jammer detection methods relative to commonly used anomaly detection methods. We also demonstrate the robustness of our proposed methods in accurately detecting jammer activity across multiple frequency bands and diverse types of jammers. We present real-world validation results from applying our methods in the operator’s network for online jammer detection. We also present promising results on pinpointing jammer locations when our methods spot jammer activity in the network along with cell site location data.

Copa is a delay-based congestion control algorithm proposed in NSDI recently. It can achieve consistent high performance under various network environments and has already been deployed in Facebook. In this paper, we theoretically analyze Copa and reveal its large queuing delay and poor fairness issue under certain conditions. The root cause is that Copa fails to clear the bottleneck buffer occupancy periodically as expected. Accordingly, Copa may get a wrong base RTT estimation and enter its competitive mode by mistake, leading to large delay and unfairness. To address these issues, we propose Copa+, which enhances Copa with a parameter adaptation mechanism and an optimized competitive mode entrance criterion. Designed based on our theoretical analysis, Copa+ can adaptively clear the bottleneck buffer occupancy for correct estimation of base RTT. Consequently, Copa+ inherits the advantages of Copa but achieves lower queuing delay and better fairness under different environments, as confirmed by the real-world experiments and simulations. Specifically, Copa+ has the highest throughput similar to Copa but 11.9% lower queuing delay over different Internet links among different cloud nodes, and achieves 39.4% lower queuing delay and 8.9% higher throughput compared to Sprout over emulated cellular links.

Reconfigurable Intelligent Surfaces (RISs) are considered one of the key disruptive technologies towards future 6G networks. RISs revolutionize the traditional wireless communication paradigm by controlling the wave propagation properties of the impinging signals at will. A major roadblock for RIS is though the need for a fast and complex control channel to continuously adapt to the ever-changing wireless channel conditions. In this paper, we ask ourselves the question: Would it be feasible to remove the need for control channels for RISs? We analyze the feasibility of devising Self-Configuring Smart Surfaces that can be easily and seamlessly installed throughout the environment, following the new Internet-of-Surfaces (IoS) paradigm, without requiring modifications of the deployed mobile network. To this aim we design MARISA, a self-configuring metasurfaces absorption and reflection solution. Our results show that MARISA achieves outstanding performance, rivaling with state-of-the-art control channel-driven RISs solutions.

To train modern large DNN models, pipeline parallelism has recently emerged, which distributes the model across GPUs and enables different devices to process different microbatches in pipeline. Earlier pipeline designs allow multiple versions of model parameters to co-exist (similar to asynchronous training), and cannot ensure the same model convergence and accuracy performance as without pipelining. Synchronous pipelining has recently been proposed which ensures model performance by enforcing a synchronization barrier between training iterations. Nonetheless, the synchronization barrier requires waiting for gradient aggregation from all microbatches and thus delays the training progress. Optimized pipeline planning is needed to minimize such wait and hence the training time, which has not been well studied in the literature. This paper designs efficient, near-optimal algorithms for expediting synchronous pipeline-parallel training of modern large DNNs over arbitrary inter-GPU connectivity. Our algorithm framework comprises two components: a pipeline partition and device mapping algorithm, and a pipeline scheduler that decides processing order of microbatches over the partitions, which together minimize the per-iteration training time. We conduct thorough theoretical analysis, extensive testbed experiments and trace-driven simulation, and demonstrate our scheme can accelerate training up to 157% compared with state-of-the-art designs.

The proliferation of Internet of Things (IoT) has transformed the way people interact with the world. Various kinds of wireless protocols have been developed to support diverse types of IoT communications. Unfortunately, the lack of spectrum resources puts a hard limit on managing the large-scale heterogeneous IoT system. Although previous works alleviate this strain by coordinating transmission power, time slots, and sub-channels, they may not be feasible in future IoT applications with dense deployments. In this paper, we explore a physical-level parallel inclusive communication paradigm for the coexistence of Wi-Fi and ZigBee, which leverages novel bits embedding approaches on the OQPSK protocol to enable both Wi-Fi and ZigBee IoT devices to decode the same inclusive signals at the same time but with each one’s different data. By carefully crafting the inclusive signals using legacy Wi-Fi protocol, the overlapping spectrum can be simultaneously re-used by both protocols, expecting a maximum data rate (250kbps) for ZigBee devices and up to 3.75Mbps for a Wi-Fi pair over only a 2MHz bandwidth. The achieved spectrum efficiency outperforms a majority of CTC schemes and parallel communication designs. Compared with existing works on parallel communication, our proposed system is the first one that achieves an entire software-level design, which can be readily implemented on Commercial Off-The-Shelf (COTS) devices without any hardware modification. Based on extensive real-world experiments on both USRP and COTS device platforms, we demonstrate the feasibility, generality, and efficiency of the proposed new paradigm.

Radio frequency identification (RFID) based localization has attracted increasing attentions due to competitive advantages of RFID tags: unique identification, low-cost, and battery-free. Although many advanced phase-based localization methods are proposed, few of them take fully the unknown phase center (PC) and the phase offset (PO) into account, which however are the key factors in fine-grained localization. In this paper, we propose a novel localization algorithm called Segment Aligned Hologram (SAH) that jointly calibrates the PC and the PO. SAH first builds a phase matrix and then designs a phase alignment algorithm based on the phase matrix for reducing the multi-path effect. Afterwards, SAH constructs a hologram for calibration and localization, which greatly reduces the system errors. We implement SAH through commercial RFID devices. Extensive experiments show that SAH can achieve a fine-grained localization accuracy in both the lateral and the radial directions with only a single antenna.

Mobile Live Streaming (MLS) services are now one of the most popular types of mobile apps. They involve a (often amateur) user broadcasting content to a potentially large online audience via unreliable networks (e.g., LTE). Although prior work has focused on viewer-side behavior, it is equally important to study and improve broadcaster operations. Using detailed logs obtained from a major MLS provider, we first conduct an in-depth measurement study of uploading behavior. Our key findings include large wasteful uploads, strong viewing locality, and traffic dominance of loyal viewers. Specifically, 33.3% of uploads go unwatched, and the viewership of broadcasters tends to be localized to a small set of broadcaster-specific network regions. Inspired by our findings, we propose two system innovations to streamline MLS systems: adaptive uploading and edge server pre-fetching. These optimizations leverage machine learning for reduced waste and improved QoE. Trace-driven experiments show that the adaptive uploading reduces the resources wastage by 63%, and the pre-fetching boosts the startup by 29.5%.

With the advent of standards for deterministic network behavior, synthesizing network designs under delay constraints becomes the natural next task to tackle. Network Calculus (NC) has become a key method for validating industrial networks, as it computes formally verified end-to-end delay bounds. However, analyses from the NC framework were thus far designed to bound one flow’s delay at a time. Attempts to use classical analyses for derivation of a network configuration revealed this approach to be poorly fitted for practical use cases. Take finding a delay-optimal routing configuration: One model for each routing alternative had to be created, then each flow delay had to be bounded, then the bounds were compared to the given constraints. To overcome this three-step procedure, we introduce Differential Network Calculus. We extend NC to allow for differentiation of delay bounds w.r.t. to a wide range of network parameters – such as flow routes. This opens up NC to a class of efficient nonlinear optimization techniques taking advantage of the delay bound computation’s gradient. Our numerical evaluation on the routing problem shows that our novel method can synthesize flow path in a matter of seconds, outperforming existing methods by multiple orders of magnitude.

ByteScheduler partitions and rearranges tensor transmissions to improve the communication efficiency of distributed Deep Neural Network (DNN) training. The configuration of hyper-parameters (i.e., the partition size and the credit size) is critical to the effectiveness of partitioning and rearrangement. Currently, ByteScheduler adopts Bayesian Optimization (BO) to find the optimal configuration for the hyper-parameters beforehand. In practice, however, various runtime factors (e.g., worker node status and network conditions) change over time, making the statically-determined one-shot configuration result suboptimal for real-world DNN training.To address this problem, we present a real-time configuration method (called AutoByte) that automatically and timely searches the optimal hyper-parameters as the training systems dynamically change. AutoByte extends the ByteScheduler framework with a meta-network, which takes the system’s runtime statistics as its input and outputs predictions for speedups under specific configurations. Evaluation results on various DNN models show that AutoByte can dynamically tune the hyper-parameters with low resource usage, and deliver up to 33.2% higher performance than the best static configuration in ByteScheduler.

Network Function Virtualization (NFV) has the potential of cost-efficiency, manage-convenience, and flexibility but meanwhile poses challenges for the service function chain (SFC) deployment problem, which is NP-hard. It is so complicated that existing work conspicuously neglects the flow changes along the chains and only gives heuristic algorithms without a performance guarantee. In this paper, we fill this gap by formulating a traffic-sensitive online joint SFC placement and flow routing (TO-JPR) model and proposing a novel two-stage scheme to solve it. Moreover, we design a dynamic segmental packing (DSP) algorithm for the first stage, which not only maintains the minimal traffic burden for the network but also achieves an approximation ratio of 2 on the resource cost. Such a two-stage scheme and DSP can pave the way for efficiently solving TO-JPR. For example, simply applying the nearest neighbor (NN) algorithm for the second stage can guarantee a global approximation ratio of O(log(M)) on the network latency, where M is the number of servers. More future work can be done based on our scheme to get better performance on the network latency. Finally, we perform extensive simulations to demonstrate the outstanding performance of DSP+NN compared with the optimal solutions and benchmarks.

Cross-Technology Communication (CTC) is an emerging technique that enables direct interconnection among incompatible wireless technologies. Recent work proposes CTC from IEEE 802.11b to LoRa but has a low efficiency due to their extremely asymmetric data rates. In this paper, we propose WiRa that emulates LoRa waveforms with IEEE 802.11ax to achieve an efficient CTC from WiFi to LoRa. By taking advantage of the OFDMA in 802.11ax, WiRa can use only a small Resource Unit (RU) to emulate LoRa chirps and set other RUs free for high-rate WiFi users. WiRa carefully selects the RU to avoid emulation failures and adopts WiFi frame aggregation to emulate the long LoRa frame. We propose a subframe header mapping method to identify and remove invalid symbols caused by irremovable subframe headers in the aggregated frame. We also propose a mode flipping method to solve Cyclic Prefix errors, based on our finding that different CP modes have different and even opposite impacts on the emulation of a specific LoRa symbol. We implement a prototype of WiRa on the USRP platform and commodity LoRa device. The extensive experiments demonstrate WiRa can efficiently transmit complete LoRa frames with the throughput of 40.037kbps and the symbol error rate (SER) lower than 0.1.

Fueled by advances in distributed deep learning (DDL), recent years have witnessed a rapidly growing demand for resource-intensive distributed/parallel computing to process DDL computing jobs. To resolve network communication bottleneck and load balancing issues in distributed computing, the so-called "ring-all-reduce" decentralized architecture has been increasingly adopted to remove the need for dedicated parameter servers. To date, however, there remains a lack of theoretical understanding on how to design resource optimization algorithms for efficiently scheduling ring-all-reduce DDL jobs in computing clusters. This motivates us to fill this gap by proposing a series of new resource scheduling designs for ring-all-reduce DDL jobs. Our contributions in this paper are threefold: i) We propose a new resource scheduling analytical model for ring-all-reduce deep learning, which covers a wide range of objectives in DDL performance optimization (e.g., excessive training avoidance, energy efficiency, fairness); ii) Based on the proposed performance analytical model, we develop an efficient resource scheduling algorithm called GADGET (greedy ring-all-reduce distributed graph embedding technique), which enjoys a provable strong performance guarantee; iii) We conduct extensive trace-driven experiments to demonstrate the effectiveness of the GADGET approach and its superiority over the state of the art.