INFOCOM 2020 论文列表

Continuous mobile vision is becoming increasingly important as it finds compelling applications which substantially improve our everyday life. However, meeting the requirements of quality of experience (QoE) diversity, energy efficiency and multi-tenancy simultaneously represents a significant challenge. In this paper, we present SCYLLA, an FPGA-based framework that enables QoE-aware continuous mobile vision with dynamic reconfiguration to effectively address this challenge. SCYLLA pre-generates a pool of FPGA design and DNN models, and dynamically applies the optimal software-hardware configuration to achieve the maximum overall performance on QoE for concurrent tasks. We implement SCYLLA on state-of-the-art FPGA platform and evaluate SCYLLA using drone-based traffic surveillance application on three datasets. Our evaluation shows that SCYLLA provides much better design flexibility and achieves superior QoE trade-offs than status-quo CPU-based solution that existing continuous mobile vision applications are built upon.

Mobile devices are promising to apply two-factor authentication in order to improve system security and enhance user privacy-preserving. Existing solutions usually have certain limits of requiring some form of user effort, which might seriously affect user experience and delay authentication time. In this paper, we propose PPGPass, a novel mobile two-factor authentication system, which leverages Photoplethysmography (PPG) sensors in wrist-worn wearables to extract individual characteristics of PPG signals. In order to realize both nonintrusive and secure, we design a two-stage algorithm to separate clean heartbeat signals from PPG signals contaminated by motion artifacts, which allows verifying users without intentionally staying still during the process of authentication. In addition, to deal with non-cancelable issues when biometrics are compromised, we design a repeatable and non-invertible method to generate cancelable feature templates as alternative credentials, which enables to defense against man-in-the-middle attacks and replay attacks. To the best of our knowledge, PPGPass is the first nonintrusive and secure mobile two-factor authentication based on PPG sensors in wearables. We build a prototype of PPGPass and conduct the system with comprehensive experiments involving multiple participants. PPGPass can achieve an average F1 score of 95.3%, which confirms its high effectiveness, security, and usability.

Large scale deployment of connected vehicles with cooperative sensing technologies increases the demand on the vehicular communication spectrum in 5.9 GHz allocated for the exchange of safety messages. To support high data rates needed by such applications, the millimeter-wave (mmWave) automotive radar spectrum at 76-81 GHz can be utilized for communication. For this purpose, joint automotive radar-communication (JARC) system designs have been proposed in the literature to perform both functions using the same waveform. However, employing a large band in the mmWave spectrum deteriorates the performance of both radar and communication functions due to frequency-selectivity. In this paper, we address the optimal joint waveform design problem for wideband JARC systems via Orthogonal Frequency-Division Multiplexing (OFDM). We show that the problem is a non-convex quadratically constrained quadratic fractional programming (QCQFP) problem, which is known to be NP-hard. Existing approaches to solve QCQFP include Semidefinite Relaxation (SDR) and randomization approaches, which have high time complexity. Instead, we propose an approximation method to solve QCQFP more efficiently by leveraging structured matrices in the quadratic fractional objective function. Finally, we evaluate the efficacy of the proposed approach through numerical results.

The best arm identification problem in multi-armed bandit model has been widely applied into many practical applications, such as spectrum sensing, online advertising, and cloud computing. Although lots of works have been devoted into this area, most of them do not consider the cost of pulling actions, i.e., a player has to pay some cost when she pulls an arm. Motivated by this, we study a ratio-based best arm identification problem, where each arm is associated with a random reward as well as a random cost. For any δ ∈ (0, 1), with probability at least 1-δ, the player aims to find the optimal arm with the largest ratio of expected reward to expected cost using as few samplings as possible. To solve this problem, we propose three algorithms: 1) a genie-aided algorithm GA; 2) the successive elimination algorithm with unknown gaps SEUG; 3) the successive elimination algorithm with unknown gaps and variance information SEUG-V, where gaps denote the differences between the optimal arm and the suboptimal arms. We show that for all three algorithms, the sample complexities, i.e., the pulling times for all arms, grow logarithmically as 1/δ increases. Moreover, compared to existing works, the running of our elimination-type algorithms is independent of the arm-related parameters, which is more practical. In addition, we also provide a fundamental lower bound for sample complexities of any algorithms under Bernoulli distributions, and show that the sample complexities of the proposed three algorithms match that of the lower bound in the sense of log 1/δ. Finally, we validate our theoretical results through numerical experiments.

This paper presents an efficient topology and route management approach in Software-Defined Wide Area Networks (SD-WAN). Traditional WANs suffer from low utilization and lack of global view of the network. Therefore, during failures, topology/service/traffic changes, or new policy requirements, the system does not always converge to the global optimal state. Using Software Defined Networking architectures in WANs provides the opportunity to design WANs with higher fault tolerance, scalability, and manageability. We exploit the correlation matrix derived from monitoring system between the virtual links to infer the underlying route topology and propose a route update approach that minimizes the total route update cost on all flows. We formulate the problem as an integer linear programming optimization problem and provide a centralized control approach that minimizes the total cost while satisfying the quality of service (QoS) on all flows. Experimental results on real network topologies demonstrate the effectiveness of the proposed approach in terms of disruption cost and average disrupted flows.

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.

The cloud computing market has a wide variety of customers that deploy various applications from deep learning to classical web services. Each application may have different computing, memory and networking requirements, and each customer may be willing to pay a different price for the service. When a request for a VM arrives, the cloud provider decides online whether to serve it or not and which resources to allocate for this purpose. The goal is to maximize the revenue while obeying the constraints imposed by the limited physical infrastructure and its layout.Although requests arrive online, cloud providers are not entirely in the dark; historical data is readily available and may contain strong indications regarding future requests. Thus, standard theoretical models that assume the online player has no prior knowledge are inadequate. In this paper, we adopt a recent theoretical model for the design and analysis of online algorithms that allows taking such historical data into account. We develop new competitive online algorithms for multidimensional resource allocation and analyze their guaranteed performance. Moreover, using extensive simulation over real data from Google and AWS, we show that our new approach yields much higher revenue to cloud providers than currently used heuristics.

Augmented reality (AR) is an emerging technology that weaves virtual objects into physical environments, and enables users to interact with them through viewing devices. This paper targets on multi-user AR applications, where virtual objects (VOs) placed by a user can be viewed by other users. We develop a practical framework that supports the basic multi-user AR functions of placing and viewing VOs, and our system can be deployed on off-the-shelf smartphones without special hardware. The main technical challenge we address is that when facing the exact same scene, the user who places the VO and the user who views the VO may have different view angles and distances to the scene. This setting is realistic and the traditional solutions yield a poor performance in terms of the accuracy. In this work, we have developed a suite of algorithms that help the viewers accurately identify the same scene and restore the VO under a moderate range of view angle difference. We have prototyped our system, and the experimental results have shown significant performance improvements. Our source codes and demos can be accessed at https://github.com/PROMAR2019.

Access networks are continuously going through many reformations to make them better suited for various demanding applications and meet new challenging requirements. On one hand, incorporating fog and edge computing has become a necessity for alleviating network congestions and supporting numerous applications that can no longer rely on the resources of a remote cloud. On the other hand, energy-efficiency has grown to be essential for these networks to reduce both their operational costs and carbon footprint but often leads to degradation in their network performance. In this paper, we study the challenges posed by these two imperatives by examining the integration of fog computing with passive optical networks (PONs) under power-conserving frameworks. As most power-conserving frameworks in the literature are centralized-based, we also propose a decentralized-based framework and compare its performance with its centralized counterpart. We study the possible cloudlet placements and the offloading performance in each allocation paradigm to determine which paradigm is able to meet the requirements of next-generation access networks by having better network performance with less energy consumption.

This paper proposes V-Sight, a network monitoring framework for software-defined networking (SDN)-based virtual networks. Network virtualization with SDN (SDN-NV) makes it possible to realize programmable virtual networks; so, the technology can be beneficial to cloud services for tenants. However, to the best of our knowledge, although network monitoring is a vital prerequisite for managing and optimizing virtual networks, it has not been investigated in the context of SDN-NV. Thus, virtual networks suffer from non-isolated statistics between virtual networks, high monitoring delays, and excessive control channel consumption for gathering statistics, which critically hinders the benefits of SDN-NV. To solve these problems, V-Sight presents three key mechanisms: 1) statistics virtualization for isolated statistics, 2) transmission disaggregation for reduced transmission delay, and 3) pCollector aggregation for efficient control channel consumption. V-Sight is implemented on top of OpenVirteX, and the evaluation results demonstrate that V-Sight successfully reduces monitoring delay and control channel consumption up to 454 times.

Importance sampling (IS) is widely used in rare event simulation, but it is costly to deal with many rare events simultaneously. For example, a rare event can be the failure to provide the quality-of-service guarantee for a critical network flow. Since network providers often need to deal with many critical flows (i.e., rare events) simultaneously, if using IS, providers have to simulate each rare event with its customized importance distribution individually. To reduce such cost, we propose an efficient mixture importance distribution for multiple rare events, and formulate the mixture importance sampling optimization problem (MISOP) to select the optimal mixture. We first show that the "search direction" of mixture is computationally expensive to evaluate, making it challenging to locate the optimal mixture. We then formulate a " zero learning cost " online learning framework to estimate the "search direction", and learn the optimal mixture from simulation samples of events. We develop two multi-armed bandit online learning algorithms to: (1) Minimize the sum of estimation variances with a regret of (ln T)2/T; (2) Minimize the simulation cost with a regret of √ln T/T , where T denotes the number of simulation samples. We demonstrate our method on a realistic network and show that it can reduce the cost measures (i.e., sum of estimation variances and simulation cost) by as high as 61.6% compared with the uniform mixture IS.

In the era of edge computing and Artificial Intelligence (AI), securing billions of edge devices within a network against intelligent attacks is crucial. We propose PUFGAN, an innovative machine learning attack-proof security architecture, by embedding a self-adversarial agent within a device fingerprint- based security primitive, public PUF (PPUF) known for its strong fingerprint-driven cryptography. The self-adversarial agent is implemented using Generative Adversarial Networks (GANs). The agent attempts to self-attack the system based on two GAN variants, vanilla GAN and conditional GAN. By turning the attacking quality through generating realistic secret keys used in the PPUF primitive into system vulnerability, the security architecture is able to monitor its internal vulnerability. If the vulnerability level reaches at a specific value, PUFGAN allows the system to restructure its underlying security primitive via feedback to the PPUF hardware, maintaining security entropy at as high a level as possible. We evaluated PUFGAN on three different machine environments: Google Colab, a desktop PC, and a Raspberry Pi 2, using a real-world PPUF dataset. Extensive experiments demonstrated that even a strong device fingerprint security primitive can become vulnerable, necessitating active restructuring of the current primitive, making the system resilient against extreme attacking environments.

The rapid growth of Ethereum blockchain has brought extremely heavy overhead for coin owners or developers to bootstrap and access transactions on Ethereum. To address this, light client is enabled, which only stores a small fraction of blockchain data and relies on bootstrapped full nodes for transaction retrievals. However, because the retrieval requests are outsourced, it raises several severe concerns about the integrity of returned results and the leakage of sensitive blockchain access histories, largely hindering the wider adoption of this important lightweight design. In addition to security issues, the continuously increasing blockchain storage also urges for more effective query functionalities for the Ethereum blockchain, so as to enable more flexible and precise transaction retrievals.In this paper, we propose EncELC, a new Ethereum light client design that enforces full-fledged protections for clients and enables rich queries over the Ethereum blockchain. EncELC leverages trusted hardware (e.g., Intel SGX) as a starting point for building efficient yet secure processing, and further crafts several crucial performance and security refinement designs to boost query efficiency and conceal leakages inside and outside SGX enclave. We implement a prototype of EncELC and test its performance in several real settings, and the results have confirmed the practicality of EncELC.

Straggler mitigation can be achieved by redundant computation. In MDS redundancy method, a task is divided into k sub-tasks which are encoded to n coded sub-tasks, such that a task is completed if any k coded sub-tasks are completed. Two important metrics of interest are task completion time, and server utilization cost which is the aggregate completed work by all servers in this duration. We consider a proactive straggler mitigation strategy where n0out of n coded sub-tasks are started at time 0 while the remaining n - n0coded sub-tasks are launched when ℓ0≤ min(n0, k) of the initial ones finish. The coded sub-tasks are halted when k of them finish. For this flexible forking strategy with multiple parameters, we analyze the mean of two performance metrics for the proposed forking strategy when the random service completion time at each server is independent and distributed identically to a shifted exponential. Our analysis demonstrates that the regime of n00= n), and is thus not a regime of interest. For n0≥ k, we find that there is a tradeoff between the two performance metrics and leads to decrease in mean server utilization cost at the expense of mean service completion time and an efficient choice of the parameters is helpful.

We consider the problem of dynamic rate selection in a cognitive radio network (CRN) over the millimeter wave (mmWave) spectrum. Specifically, we focus on the scenario when the transmit power is time varying as motivated by the following applications: i) an energy harvesting CRN, in which the system solely relies on the harvested energy source, and ii) an underlay CRN, in which a secondary user (SU) restricts its transmission power based on a dynamically changing interference temperature limit (ITL) such that the primary user (PU) remains unharmed. Since the channel quality fluctuates very rapidly in mmWave networks and costly channel state information (CSI) is not that useful, we consider rate adaptation over an mmWave channel as an online stochastic optimization problem, and propose a Thompson Sampling (TS) based Bayesian method. Our method utilizes the unimodality and monotonicity of the throughput with respect to rates and transmit powers and achieves logarithmic in time regret with a leading term that is independent of the number of available rates. Our regret bound holds for any sequence of transmits powers and captures the dependence of the regret on the arrival pattern. We also show via simulations that the performance of the proposed algorithm is superior than the stateof-the-art algorithms, especially when the arrivals are favorable.

Recently, a new class of "arbitrator-based" membership protocols have been proposed. These claim to provide time bounds on how long membership lists can stay inconsistent-this property is critical in many distributed applications which need to take timely recovery actions. In this paper, we: 1) present the first fully decentralized and stabilizing version of membership protocols in this class; 2) formally prove properties and claims about both our decentralized version and the original protocol; and 3) present experimental results from both a simulation and a real cluster implementation.

Traditional congestion control algorithms were designed with a hardwired heuristic mapping between packet- level events and predefined control actions in response to these events, and may fail to satisfy all the desirable performance goals as a result. In this paper, we seek to reconsider these fundamental goals in congestion control, and propose Eagle, a new congestion control algorithm to refine existing heuristics. Eagle takes advantage of expert knowledge from an existing algorithm, and uses deep reinforcement learning (DRL) to train a generalized model with the hope of learning from an expert. Learning by trial-and-error may not be as efficient as imitating a teacher; by the same token, DRL alone is not enough to guarantee good performance. In Eagle, we seek help from an expert congestion control algorithm, BBR, to help us train a long-short term memory (LSTM) neural network in the DRL agent, with the hope of making decisions that can be as good as or even better than the expert. With an extensive array of experiments, we discovered that Eagle is able to match and even outperform the performance of its teacher, and outperformed a large number of recent congestion control algorithms by a considerable margin.

Auction is an effective mechanism to distribute spectrum resources. Although many privacy-preserving auction schemes for spectrum allocation have been proposed, none of them is able to perform practical spectrum auctions while ensuring enough security for bidders' private information, such as geo-locations, bid values, and data access patterns. To address this problem, we propose SLISA, a lightweight auction framework which enables an efficient spectrum allocation without revealing anything but the auction outcome, i.e., the winning bidders and their clearing prices. We present contributions on two fronts. First, as a foundation of our design, we adopt a Shuffle-then-Compute strategy to build a series of secure sub-protocols based on lightweight cryptographic primitives (e.g., additive secret sharing and basic garbled circuits). Second, we improve an advanced spectrum auction mechanism to make it data-oblivious, such that data access patterns can be hidden. Meanwhile, the modified protocols adapt to our elaborate building blocks without affecting its validity and security. We formally prove the security of all protocols under a semi-honest adversary model, and demonstrate performance improvements compared with state-of-the-art works through extensive experiments.

This paper proves an impossibility result for stochastic network utility maximization for multi-user wireless systems, including multi-access and broadcast systems. Every time slot an access point observes the current channel states and opportunistically selects a vector of transmission rates. Channel state vectors are assumed to be independent and identically distributed with an unknown probability distribution. The goal is to learn to make decisions over time that maximize a concave utility function of the running time average transmission rate of each user. Recently it was shown that a stochastic Frank-Wolfe algorithm converges to utility-optimality with an error of O(log(T)/T), where T is the time the algorithm has been running. An existing Ω(1/T) converse is known. The current paper improves the converse to Ω(log(T)/T), which matches the known achievability result. The proof uses a reduction from the opportunistic scheduling problem to a Bernoulli estimation problem. Along the way, it refines a result on Bernoulli estimation.