INFOCOM 2022 论文列表

Binaural microphones, referring to two microphones with artificial human-shaped ears, are pervasively used in humanoid robots and hearing aids improving sound quality. In many applications, it is crucial for such robots to interact with humans by finding the voice direction. However, sound source localization with binaural microphones remains challenging, especially in multi-source scenarios. Prior works utilize microphone arrays to deal with the multi-source localization problem. Extra arrays yet incur higher deployment costs and take up more space. However, human brains have evolved to locate multiple sound sources with only two ears. Inspired by this fact, we propose DeepEar, a binaural microphone-based localization system that can locate multiple sounds. To this end, we develop a neural network to mimic the acoustic signal processing pipeline of the human auditory system. Different from hand-crafted features used in prior works, DeepEar can automatically extract useful features for localization. More importantly, the trained neural networks can be extended and adapted to new environments with a minimum amount of extra training data. Experiment results show that DeepEar can substantially outperform the state-of-the-art deep learning approach, with a sound detection accuracy of 93.3% and an azimuth estimation error of 7.4 degrees in multisource scenarios.

Significant advances in edge computing capabilities enable learning to occur at geographically diverse locations. In general, the training data needed in those learning tasks are not only heterogeneous but also not fully generated locally. In this paper, we propose an experimental design network paradigm, wherein learner nodes train possibly different Bayesian linear regression models via consuming data streams generated by data source nodes over a network. We formulate this problem as a social welfare optimization problem in which the global objective is defined as the sum of experimental design objectives of individual learners, and the decision variables are the data transmission strategies subject to network constraints. We first show that, assuming Poisson data streams, the global objective is a continuous DR-submodular function. We then propose a Frank-Wolfe type algorithm that outputs a solution within a 1 – 1/e factor from the optimal. Our algorithm contains a novel gradient estimation component which is carefully designed based on Poisson tail bounds and sampling. Finally, we complement our theoretical findings through extensive experiments. Our numerical evaluation shows that the proposed algorithm outperforms several baseline algorithms both in maximizing the global objective and in the quality of the trained models.

As networks and their inter-connectivity grow and become complex, failures in the networks impact society and industries more than ever. In these networks the notion of connectedness is the key to understanding and reasoning about these failures. Traditional studies in improving edge/node connectivity assume that failures occur at random. However, in many scenarios (such as earthquakes, hurricanes, and human-designed attacks on networks) failures are not random, and most traditional methods do not always work. To address this limitation, we consider region-based connectivity to capture the local nature of failures under the geographical failure model, where failures may happen only on edges in a sub-network (region) and we want to shield some edges in regions to protect the connectivity. There may be several regions and in different regions the failures occur independently. Firstly, we establish the NP-hardness of the problem for regions, answering a question proposed in previous papers. Secondly, we propose a polynomial-time algorithm for the special case of two regions based on the matroid techniques. Furthermore, we design an ILP-based algorithm to solve the problem for regions. Experimental results on random and real networks show that our algorithms are much faster than previously known algorithms.

Tor is the largest anonymous communication system, providing anonymous communication services to approximately 2.8 million users and 170,000 hidden services per day. The Tor hidden service mechanism can protect a server from exposing its real identity during the communication. However, due to a design flaw of the Tor hidden service mechanism, adversaries can deploy malicious Tor hidden service directories (HSDirs) to covertly collect all onion addresses of hidden services and further probe the hidden services. To mitigate this issue, we design customized honeypot hidden services based on one-to-one and many-to-one HSDir monitoring approaches to luring and identifying the malicious HSDirs conducting the rapid and delayed probing attacks, respectively. By analyzing the probing behaviors and payloads, we investigate a novel semantic-based probing pattern clustering approach to classify the adversaries so as to shed light on the purposes of the malicious HSDirs. Moreover, we perform theoretical analysis of the capability and accuracy of our approaches. Large-scale experiments are conducted in the real-world Tor network by deploying hundreds of thousands of honeypots during a monitoring period of more than three months. Finally, we identify 8 groups of 32 malicious HSDirs, discover 25 probing pattern clusters and reveal 3 major probing purposes.

Recently, the growing popularity of Spatial Crowd-sourcing (SC), allowing untrusted platforms to obtain a great quantity of information about workers and tasks’ locations, has raised numerous privacy concerns. In this paper, we investigate the privacy-preserving task assignment in the online scenario, where workers and tasks arrive at the platform in real time and tasks should be assigned to workers immediately. Traditional online task assignments usually make a benchmark to decide the following task assignment. However, when location privacy is considered, the benchmark does not work anymore. Hence, how to assign tasks in real time based on workers and tasks’ obfuscated locations is a challenging problem. Especially when many tasks could be assigned to one worker, path planning should be considered, making the assignment more challenging. To this end, we propose a Planar Laplace distribution based Privacy mechanism (PLP) to obfuscate real locations of workers and tasks, where the obfuscation does not change the ranking of these locations’ relative distances. Furthermore, we design a Threshold-based Online task Assignment mechanism (TOA), which could deal with the one-worker-many-tasks assignment and achieve a satisfactory competitive ratio. Simulations based on two real-world datasets show that the proposed algorithm consistently outperforms the state-of-the-art approach.

Frequent pattern mining is an important class of knowledge discovery problems. It aims at finding out high-frequency items or structures (e.g., itemset, sequence) in a database, and plays an essential role in deriving other interesting patterns, like association rules. The traditional approach of gathering data to a central server and analyze is no longer viable due to the increasing awareness of user privacy and newly established laws on data protection. Previous privacy-preserving frequent pattern mining approaches only target a particular problem with great utility loss when handling complex structures. In this paper, we take the first initiative to propose a unified federated analytics framework (FedFPM) for a variety of frequent pattern mining problems, including item, itemset, and sequence mining. FedFPM achieves high data utility and guarantees local differential privacy without uploading raw data. Specifically, FedFPM adopts an interactive query-response approach between clients and a server. The server meticulously employs the Apriori property and the Hoeffding’s inequality to generates informed queries. The clients randomize their responses in the reduced space to realize local differential privacy. Experiments on three different frequent pattern mining tasks demonstrate that FedFPM achieves better performances than the state-of-the-art specialized benchmarks, with a much smaller computation overhead.

We formulate computation offloading as a decentralized decision-making problem with autonomous agents. We design an interaction mechanism that incentivizes agents to align private and system goals by balancing between competition and cooperation. The mechanism provably has Nash equilibria with optimal resource allocation in the static case. For a dynamic environment, we propose a novel multi-agent online learning algorithm that learns with partial, delayed and noisy state information, and a reward signal that reduces information need to a great extent. Empirical results confirm that through learning, agents significantly improve both system and individual performance, e.g., 40% offloading failure rate reduction, 32% communication overhead reduction, up to 38% computation resource savings in low contention, 18% utilization increase with reduced load variation in high contention, and improvement in fairness. Results also confirm the algorithm’s good convergence and generalization property in significantly different environments.

This paper studies an important yet overlooked applicability issue in existing American sign language (ASL) translation systems. With excessive sensing data collected for each ASL word already, current designs treat every to-be-recognized sentence as new and collect their sensing data from scratch, while the amounts of sentences and the data samples per sentence are large usually. It takes a long time to complete the data collection for each single user, e.g., hours to a half day, which brings non-trivial burden to the end users inevitably and prevents the broader adoption of the ASL systems in practice. In this paper, we figure out the reason causing this issue. We present GASLA atop the wearable sensors to instrument our design. With GASLA, the sentence-level sensing data can be generated from the word-level data automatically, which can be then applied to train ASL systems. Moreover, GASLA has a clear interface to be integrated to existing ASL systems for overhead reduction directly. With this ability, sign language translation could become highly lightweight in both initial setup and future new-sentence addition. Compared with around 10 per-sentence data samples in current systems, GASLA requires 2–3 samples to achieve a similar performance.

Programmable switches have been proposed in today’s network to enable flexible reconfiguration of devices and reduce time-to-deployment. Buffer sizing, an important factor for network performance, however, has not received enough attention in programmable network. The state-of-the-art buffer sizing solutions usually employ either fixed buffer size or adjust the buffer size heuristically. Without programmability, they suffer from either massive packet drops or large queueing delay in dynamic environment. In this paper, we propose Adaptive Buffer Sizing (ABS), a low-cost and deploy-friendly framework compatible with programmable network. By decoupling the data plane and control plane, ABS-capable switches only need to react to the actions from controller, optimizing network performance in run-time under dynamic traffic. Meanwhile, actions can be programmed by particular Machine Learning (ML) models in the controller to meet different network requirements. In this paper, we address two specific ML models for different scenarios, a reinforcement learning model for relatively stable network with user specific quality requirements, and a supervised learning model for highly dynamic network condition. We implement the ABS framework by integrating the prevalent network simulator NS-2 with ML module. The experiment shows that ABS outperforms state-of-the-art buffer sizing solutions by up to 38.23x under various network environments.

This paper studies reliable teleportation of quantum bits (called qubits) in a quantum data network with multiple sources (S) and destinations (D) as well as repeaters. To teleport qubits for a SD pair reliably, not only an entanglement path for the SD pair, but also appropriate purification of the links along the path is required to ensure that the end-to-end (E2E) fidelity of the established entanglement connections is high enough.This is the first work on quantifying the E2E fidelity, and also using this E2E fidelity to determine critical links to achieve the most resource efficient purification. A novel approach called E2E Fidelity aware Routing and Purification (EFiRAP) is proposed to maximize network throughput, i.e., the number of entanglement connections among multiple SD pairs, with each connection having an E2E fidelity above a given required threshold. EFiRAP accomplishes this goal by first preparing multiple candidate entanglement paths and determining optimal purification schemes, and then selecting the final set of entanglement paths that can maximize network throughput under the given quantum resource constraints. Existing works only ensured the fidelity of individual links, rather than the E2E fidelity is above a given threshold. Extensive simulations show that the proposed EFiRAP can enhance network throughput by about 50% when compared with the state-of-the-art approach.

LoRa technology promises to connect billions of battery-powered devices over a long range for years. However, recent studies and industrial deployment find that LoRa suffers severe signal attenuation because of signal blockage in smart cities and long communication ranges in smart agriculture applications. As a result, weak LoRa packets cannot be correctly demodulated or even be detected in practice. To address this problem, this paper presents the design and implementation of MALoRa: a new LoRa reception scheme which aims to improve LoRa reception performance with antenna diversities. At a high level, MALoRa improves signal strength by reliably detecting and coherently combining weak signals received by multiple antennas of a gateway. MALoRa addresses a series of practical challenges, including reliable packet detection, symbol edge extraction, and phase-aligned constructive combining of weak signals. Experiment results show that MALoRa can effectively expand communication range, increase battery life of LoRa devices, and improve packet detection and demodulation performance especially in ultra-low SNR scenarios.

Encrypted deduplication addresses both security and storage efficiency in large-scale storage systems: it ensures that each plaintext is encrypted to a ciphertext by a symmetric key derived from the content of the plaintext, so as to allow deduplication on the ciphertexts derived from duplicate plaintexts. However, the deterministic nature of encrypted deduplication leaks the frequencies of plaintexts, thereby allowing adversaries to launch frequency analysis against encrypted deduplication and infer the ciphertext-plaintext pairs in storage. In this paper, we revisit the security vulnerability of encrypted deduplication due to frequency analysis, and show that encrypted deduplication can be even more vulnerable to the sophisticated frequency analysis attack that exploits the underlying storage workload characteristics. We propose the distribution-based attack, which builds on a statistical approach to model the relative frequency distributions of plaintexts and ciphertexts, and improves the inference precision (i.e., have high confidence on the correctness of inferred ciphertext-plaintext pairs) of the previous attack. We evaluate the new attack against real-world storage workloads and provide insights into its actual damage.

The current best practice in survivable routing is to compute link or node disjoint paths in the network topology graph. It can protect single-point failures; however, several failure events may cause the interruption of multiple network elements. The set of network elements subject to potential failure events is called Shared Risk Link Group (SRLG), identified during network planning. Unfortunately, for any given list of SRLGs, finding two paths that can survive a single SRLG failure is NP-Complete. In this paper, we provide a polynomial-time SRLG-disjoint routing algorithm for planar network topologies and a large set of SRLGs. Namely, we focus on regional failures, where the failed network elements must not be far from each other. We use a flexible definition of regional failure, where the only restriction is that the topology is a planar graph, and the SRLGs form a set of connected edges in the dual of the planar graph. The proposed algorithm is based on a max-min theorem. Through extensive simulations, we show that the algorithm scales well with the network size, and one of the paths returned by the algorithm is only 4% longer than the shortest path on average.

Federated learning (FL) is a useful tool in distributed machine learning that utilizes users’ local datasets in a privacy-preserving manner. When deploying FL in a constrained wireless environment; however, training models in a time-efficient manner can be a challenging task due to intermittent connectivity of devices, heterogeneous connection quality, and non-i.i.d. data. In this paper, we provide a novel convergence analysis of non-convex loss functions using FL on both i.i.d. and non-i.i.d. datasets with arbitrary device selection probabilities for each round. Then, using the derived convergence bound, we use stochastic optimization to develop a new client selection and power allocation algorithm that minimizes a function of the convergence bound and the average communication time under a transmit power constraint. We find an analytical solution to the minimization problem. One key feature of the algorithm is that knowledge of the channel statistics is not required and only the instantaneous channel state information needs to be known. Using the FEMNIST and CIFAR-10 datasets, we show through simulations that the communication time can be significantly decreased using our algorithm, compared to uniformly random participation.

The widespread of smart devices and the development of mobile networks brings the growing popularity of live streaming services worldwide. In addition to the video and audio transmission, a lot more media content is sent to the audiences as well, including player statistics for a sports stream, subtitles for living news, etc. However, due to the diverse transmission process between live streams and other media content, the synchronization of them has grown to be a great challenge. Unfortunately, the existing commercial solutions are not universal, which require specific server cloud services or CDN and limit the users’ free choices of web infrastructures. To address the issue, we propose a lightweight universal event-synchronizing solution for live streaming, called LSync, which inserts a series of audio signals containing metadata into the original audio stream. It brings no modification to the original live broadcast process and thus fits prevalent live broadcast infrastructure. Evaluations on real system show that the proposed solution reduces the signal processing delay by at most 5.62% of an audio buffer length in mobile phones and ensures real-time signal processing. It also achieves a data rate of 156.25 bps in a specific configuration and greatly outperforms recent works.

Network modeling is a fundamental tool in network research, design, and operation. Arguably the most popular method for modeling is Queuing Theory (QT). Its main limitation is that it imposes strong assumptions on the packet arrival process, which typically do not hold in real networks. In the field of Deep Learning, Graph Neural Networks (GNN) have emerged as a new technique to build data-driven models that can learn complex and non-linear behavior. In this paper, we present RouteNet-Erlang, a pioneering GNN architecture designed to model computer networks. RouteNet-Erlang supports complex traffic models, multi-queue scheduling policies, routing policies and can provide accurate estimates in networks not seen in the training phase. We benchmark RouteNet-Erlang against a state-of-the-art QT model, and our results show that it outperforms QT in all the network scenarios.

Recent advances in cyber-physical systems, artificial intelligence, and cloud computing have driven the wide deployment of Internet-of-things (IoT) in smart homes. As IoT devices often directly interact with the users and environments, this paper studies if and how we could explore the collective insights from multiple heterogeneous IoT devices to infer user activities for home safety monitoring and assisted living. Specifically, we develop a new system, namely IoTMosaic, to first profile diverse user activities with distinct IoT device event sequences, which are extracted from smart home network traffic based on their TCP/IP data packet signatures. Given the challenges of missing and out-of-order IoT device events due to device malfunctions or varying network and system latencies, IoTMosaic further develops simple yet effective approximate matching algorithms to identify user activities from real-world IoT network traffic. Our experimental results on thousands of user activities in the smart home environment over two months show that our proposed algorithms can infer different user activities from IoT network traffic in smart homes with the overall accuracy, precision, and recall of 0.99, 0.99, and 1.00, respectively.

Unmanned aerial vehicles (a.k.a. drones) with high-resolution video cameras are useful for applications in e.g., public safety and smart farming. Inefficient configurations in drone video analytics applications due to edge network miscon-figurations can result in degraded video quality and inefficient resource utilization. In this paper, we present a novel scheme for offline/online learning-based network edge orchestration to achieve pertinent selection of both network protocols and video properties in multi-drone based video analytics. Our approach features both supervised and unsupervised machine learning algorithms to enable decision making for selection of both network protocols and video properties in the drones’ pre-takeoff stage i.e., offline stage. In addition, our approach facilitates drone trajectory optimization during drone flights through an online reinforcement learning-based multi-agent deep Q-network algorithm. Evaluation results show how our offline orchestration can suitably choose network protocols (i.e., amongst TCP/HTTP, UDP/RTP, QUIC). We also demonstrate how our unsupervised learning approach outperforms existing learning approaches, and achieves efficient offloading while also improving the network performance (i.e., throughput and round-trip time) by least 25% with satisfactory video quality. Lastly, we show via trace-based simulations, how our online orchestration achieves 91% of oracle baseline network throughput performance with comparable video quality.

Surveillance drones are unmanned aerial vehicles (UAVs) that are utilized to collect video recordings of targets. In this paper, we propose a novel design framework for aerial video surveillance in urban areas, where a cellular-connected UAV captures and transmits videos to the cellular network that services users. Fundamental challenges arise due to the limited onboard energy and quality of service (QoS) requirements over environment-dependent air-to-ground cellular links, where UAVs are usually served by the sidelobes of base stations (BSs). We aim to minimize the energy consumption of the UAV by jointly optimizing the mission completion time and UAV trajectory as well as transmission scheduling and association, subject to QoS constraints. The problem is formulated as a mixed-integer nonlinear programming (MINLP) problem by taking into account building blockage and BS antenna patterns. We first consider the average performance for uncertain local environments, and obtain an efficient sub-optimal solution by employing graph theory and convex optimization techniques. Next, we investigate the site-specific performance for specific urban local environments. By reformulating the problem as a Markov decision process (MDP), a deep reinforcement learning (DRL) algorithm is proposed by employing a dueling deep Q-network (DQN) neural network model with only local observations of sampled rate measurements. Simulation results show that the proposed solutions achieve significant performance gains over baseline schemes.