INFOCOM 2021 论文列表

Tile-based streaming techniques have been widely used to save bandwidth in 360° video streaming. However, it is a challenge to determine the right tile size which directly affects the bandwidth usage. To address this problem, we propose to encode the video by considering the viewing popularity, where the popularly viewed areas are encoded as macrotiles to save bandwidth. We propose techniques to identify and build macrotiles, and adjust their sizes considering practical issues such as head movement randomness. In some cases, a user's viewing area may not be covered by the constructed macrotiles, and then the conventional tiling scheme is used. To support popularity-aware 360° video streaming, the client selects the right tiles (a macrotile or a set of conventional tiles) with the right quality level to maximize the QoE under bandwidth constraint. We formulate this problem as an optimization problem which is NP-hard, and then propose a heuristic algorithm to solve it. Through extensive evaluations based on real traces, we demonstrate that the proposed algorithm can significantly improve the QoE and save the bandwidth usage.

The rapid development of information and communication technologies has given rise to mobility-on-demand (MoD) systems (e.g., Uber, Didi) that have fundamentally revolutionized urban transportation. One common feature of today's MoD systems is the integration of ridesharing due to its cost-efficient and environment-friendly natures. However, a fundamental unsolved problem for such systems is how to serve people's heterogeneous transportation demands with as few vehicles as possible. Naturally, solving such minimum fleet problem is essential to reduce the vehicles on the road to improve transportation efficiency. Therefore, we investigate the fleet minimization problem in ridesharing-aware MoD systems. We use graph-theoretic methods to construct a novel order graph capturing the complicated inter-order shareability, each order's spatial-temporal features, and various other real-world factors. We then formulate the problem as a tree cover problem over the order graph, which differs from the traditional coverage problems. Theoretically, we prove the problem is NP-hard, and propose a polynomial-time algorithm with a guaranteed approximation ratio. Besides, we address the online fleet minimization problem, where orders arrive in an online manner. Finally, extensive experiments on a city-scale dataset from Shenzhen, containing 21 million orders from June 1st to 30th, 2017, validate the effectiveness of our algorithms.

We formulate and analyze a generic sequential resource access problem arising in a variety of engineering fields, where a user disposes a number of heterogeneous computing, communication, or storage resources, each characterized by the probability of successfully executing the user's task and the related access delay and cost, and seeks an optimal access strategy to maximize her utility within a given time horizon, defined as the expected reward minus the access cost. We develop an algorithmic framework on the (near-)optimal sequential resource access strategy. We first prove that the problem of finding an optimal strategy is NP-hard in general. Given the hardness result, we present a greedy strategy implementable in linear time, and establish the closed-form sufficient condition for its optimality. We then develop a series of polynomial-time approximation algorithms achieving (ϵ, δ)-optimality. The key components in our design include a pruning process eliminating dominated strategies, thus maintaining polynomial time and space overhead, and a comprehensive scheme allowing flexibly trading-off time and space overhead against performance guarantee.

Wireless Internet of Things (IoT) devices will be deployed to enable applications such as sensing and actuation. These devices are typically resource-constrained and are unable to perform resource-intensive computations. Therefore, these jobs need to be offloaded to resource-rich nodes at the edge of the IoT network for execution. However, the timeliness and correctness of edge nodes may not be trusted (such as during high network load or attack). In this paper, we look at the applicability of trust for successful offloading. Traditionally, trust is computed at the application level, with suitable mechanisms to adjust for factors such as recency. However, these do not work well in IoT networks due to resource constraints. We propose a novel device called Trust Tracker (denoted by Σ) that provides higher-level applications with up-to-date trust information of the resource-rich nodes. We prove impossibility results regarding computation offloading and show that Σ is necessary and sufficient for correct offloading. We show that, Σ cannot be implemented even in a synchronous network and we compute the probability of offloading to a bad node, which we show to be negligible when a majority of nodes are correct. We perform a small-scale deployment to demonstrate our approach.

To find a maximum number of communication requests that can be satisfied concurrently, is a fundamental network scheduling problem. In this work we investigate the problem of finding a maximum number of multicast requests that can be scheduled simultaneously in a tree network in which the edges and links have heterogeneous bandwidth limitations.This problem generalizes two problems studied in the literature: maximum k-colorable subgraph in chordal graphs, maximum multi-commodity flow in trees. The problem is NP-hard and admits a 1.585-approximation in the special case of homogeneous bandwidth limitations.We first show that the problem is harder to approximate when the bandwidth limitations are heterogeneous, i.e. vary from link to link and from node to node. We then generalize of a classical algorithm and obtain an M-approximation where M is the maximum number of leaves of the communication subtrees. Surprisingly, variants of the same algorithm, are used in the literature at least four times to solve related problems. There exists a polynomial-time algorithm for the special case of unicast requests and star topology. We generalize this result and relax the second requirement so that the set of unicast requests share a common vertex with no restriction on the tree topology.

The advance of mobile sensing and edge computing has brought new opportunities for abnormal health detection (AHD) systems where edge devices such as smartphones and wearable sensors are used to collect people's health information and provide early alerts for abnormal health conditions such as stroke and depression. The recent development of federated learning (FL) allows participants to collaboratively train powerful AHD models while keeping their health data private to local devices. This paper targets at addressing a critical challenge of adapting FL to train AHD models, where the participants' health data is highly imbalanced and contains biased class distributions. Existing FL solutions fail to address the class imbalance issue due to the strict privacy requirements of participants as well as the heterogeneous resource constraints of their edge devices. In this work, we propose FedSens, a new FL framework dedicated to address the class imbalance problem in AHD applications with explicit considerations of participant privacy and device resource constraints. We evaluate FedSens using a real-world edge computing testbed on two real-world AHD applications. The results show that FedSens can significantly improve the accuracy of AHD models in the presence of severe class imbalance with low energy cost to the edge devices.

We consider an ultra-dense wireless network with N channels and M = N devices. Messages with fresh information are generated at each device according to a random process and need to be transmitted to an access point. The value of a message decreases as it ages, so each device searches for an idle channel to transmit the message as soon as it can. However, each channel probing is associated with a fixed cost (energy), so a device needs to adapt its probing rate based on the "age" of the message. At each device, the design of the optimal probing strategy can be formulated as an infinite horizon Markov Decision Process (MDP) where the devices compete with each other to find idle channels. While it is natural to view the system as a Bayesian game, it is often intractable to analyze such a system. Thus, we use the Mean Field Game (MFG) approach to analyze the system in a large-system regime, where the number of devices is very large, to understand the structure of the problem and to find efficient probing strategies. We present an analysis based on the MFG perspective. We begin by characterizing the space of valid policies and use this to show the existence of a Mean Field Nash Equilibrium (MFNE) in a constrained set for any general increasing cost functions with diminishing rewards. Further we provide an algorithm for computing the equilibrium for any given device, and the corresponding age-dependent channel probing policy.

Vehicle-to-everything (V2X) is expected to become one of the main drivers of 5G business in the near future. Dedicated network slices are envisioned to satisfy the stringent requirements of advanced V2X services, such as autonomous driving, aimed at drastically reducing road casualties. However, as V2X services become more mission-critical, new solutions need to be devised to guarantee their successful service delivery even in exceptional situations, e.g. road accidents, congestion, etc. In this context, we propose π-ROAD, a deep learning framework to automatically learn regular mobile traffic patterns along roads, detect non-recurring events and classify them by severity level. π-ROAD enables operators to proactively instantiate dedicated Emergency Network Slices (ENS) as needed while re-dimensioning the existing slices according to their service criticality level. Our framework is validated by means of real mobile network traces collected within 400 km of a highway in Europe and augmented with publicly available information on related road events. Our results show that π-ROAD successfully detects and classifies non-recurring road events and reduces up to 30% the impact of ENS on already running services.

It is critical but difficult to efficiently model re-sources in space-terrestrial integrated networks (STINs). Existing work is not applicable to STINs because they lack the joint consideration of different movement patterns and fluctuating loads. In this paper, we propose the time-varying resource graph (TVRG) to model STINs from the resource perspective. Firstly, we propose the STIN mobility model to uniformly model different movement patterns in STINs. Then, we propose a layered Resource Modeling and Abstraction (RMA) approach, where evolutions of node resources are modeled as Markov processes, by encoding predictable topologies and influences of fluctuating loads as states. Besides, we propose the low-complexity domain resource abstraction algorithm by defining two mobility-based and load-aware partial orders on resource abilities. Finally, we propose an efficient TVRG-based Resource Scheduling (TRS) algorithm for time-sensitive and bandwidth-intensive data flows, with the multi-level on-demand scheduling ability. Comprehensive simulation results demonstrate that the RMA-TRS outperforms related schemes in terms of throughput, end-to-end delay and flow completion time.

With the growth of participating clients, the centralized parameter server (PS) will seriously limit the scale and efficiency of Federated Learning (FL). A straightforward approach to scale up the FL system is to construct a Parallel FL (PFL) system with multiple PSes. However, it is unclear whether PFL can really achieve a faster convergence rate or not. Even if the answer is yes, it is non-trivial to design a highly efficient parameter average algorithm for a PFL system. In this paper, we propose a completely parallelizable FL algorithm called P-FedAvg under the PFL architecture. P-FedAvg extends the well-known FedAvg algorithm by allowing multiple PSes to cooperate and train a learning model together. In P-FedAvg, each PS is only responsible for a fraction of total clients, but PSes can mix model parameters in a dedicatedly designed way so that the FL model can well converge. Different from heuristic-based algorithms, P-FedAvg is with theoretical guarantees. To be rigorous, we conduct theoretical analysis on the convergence rate of P-FedAvg, and derive the optimal weights for each PS to mix parameters with its neighbors. We also examine how the overlay topology formed by PSes affects the convergence rate and robustness of a PFL system. Lastly, we perform extensive experiments with real datasets to verify our analysis and demonstrate that P-FedAvg can significantly improve convergence rates than traditional FedAvg and other competitive baselines. We believe that our work can help to lay a theoretical foundation for building more efficient PFL systems.

Hand hygiene has a significant impact on human health. Proper handwashing, having a crucial effect on reducing bacteria, serves as the cornerstone of hand hygiene. For the elder with dementia, they suffer from a gradual loss of memory and difficulty in coordinating steps in the execution of handwashing. Proper assistance should be provided to them to ensure their hand hygiene adherence. Toward this end, we propose AWash, leveraging only commodity IMU sensor mounted on most wrist-worn devices (e.g., smartwatches) to characterize hand motions and provide assistance accordingly. To handle particular interference of senile dementia patients in IMU sensor readings, we design a number of effective techniques to segment handwashing actions, transform sensory input to body coordinate system, and extract sensor-body inclination angles. A hybrid neural network model is used to enable AWash to generalize to new users without retraining or adaptation, avoiding the trouble of collecting behavior information of every user. To meet the diverse needs of users with various executive functioning, we use a state machine to make prompt decisions, which supports customized assistance. Extensive experiments on a prototype with eight older participants demonstrate that AWash can increase the user's independence in the execution of handwashing.

Cache advertisements reduce the access cost by allowing users to skip the cache when it does not contain their datum. Such advertisements are used in multiple networked domains such as 5G networks, wide area networks, and information-centric networking. The selection of an advertisement strategy exposes a trade-off between the access cost and bandwidth consumption. Still, existing works mostly apply a trial-and-error approach for selecting the best strategy, as the rigorous foundations required for optimizing such decisions is lacking.Our work shows that the desired advertisement policy depends on numerous parameters such as the cache policy, the workload, the cache size, and the available bandwidth. In particular, we show that there is no ideal single configuration. Therefore, we design an adaptive, self-adjusting algorithm that periodically selects an advertisement policy. Our algorithm does not require any prior information about the cache policy, cache size, or work-load, and does not require any apriori configuration. Through extensive simulations, using several state-of-the-art cache policies, and real workloads, we show that our approach attains a similar cost to that of the best static configuration (which is only identified in retrospect) in each case.

Communication networks are evolving towards a more adaptive and reconfigurable nature due to the evergrowing demands they face. A framework for measuring network flexibility has been proposed recently, but the cost of rendering communication networks more flexible has not yet been mathematically modeled. As new technologies such as software-defined networking (SDN), network function virtualization (NFV), or network virtualization (NV) emerge to provide network flexibility, a way to estimate and compare the cost of different implementation options is needed. In this paper, we present a comprehensive model of the cost of a flexible network that takes into account its transient and stationary phases. This allows network researchers and operators to not only qualitatively argue about their new flexible network solutions, but also to analyze their cost for the first time in a quantitative way.

Mobile RFID robots have been increasingly used in warehousing and intelligent manufacturing scenarios to pinpoint the locations of tagged objects. The accuracy of state-of-the-art RFID robot localization systems depends much on the stability of robot moving speed. However, in reality this assumption can hardly be guaranteed because a Commercial-Off-The-Shelf (COTS) robot typically has an inconsistent moving speed, and a small speed inconsistency will cause a large localization error. To this end, we propose a Speed Inconsistency-Immune approach to mobile RFID robot Localization (SILoc) system, which can accurately locate RFID tagged targets when the robot moving speed varies or is even unknown. SILoc employs multiple antennas fixed on the mobile robot to collect the phase data of target tags. We propose an optimized unwrapping method to maximize the use of the phase data, and a lightweight algorithm to calculate the locations in both 2D and 3D spaces based on the unwrapped phase profile. By utilizing the characteristics of tag-antenna distance and combining the phase data from multiple antennas, SILoc can effectively eliminate the side effects of moving speed inconsistency. Extensive experimental results demonstrate that SILoc can achieve a centimeter-level localization accuracy in the scenario with an inconsistent or unknown robot moving speed.

LoRa (Long Range) is an emerging wireless technology that enables long-distance communication and keeps low power consumption. Therefore, LoRa plays a more and more important role in Low-Power Wide-Area Networks (LPWANs), which easily extend many large-scale Internet of Things (IoT) applications in diverse scenarios (e.g., industry, agriculture, city). In lots of environments where various types of land-covers usually exist, it is challenging to precisely predict a LoRa link's path loss. As a result, how to deploy LoRa gateways to ensure reliable coverage and develop precise fingerprint-based localization becomes a difficult issue in practice. In this paper, we propose DeepLoRa, a deep learning-based approach to accurately estimate the path loss of long-distance links in complex environments. Specifically, DeepLoRa relies on remote sensing to automatically recognize land-cover types along a LoRa link. Then, DeepLoRa utilizes Bi-LSTM (Bidirectional Long Short Term Memory) to develop a land-cover aware path loss model. We implement DeepLoRa and use the data gathered from a real LoRaWAN deployment on campus to evaluate its performance extensively in terms of estimation accuracy and model transferability. The results show that DeepLoRa reduces the estimation error to less than 4 dB, which is 2× smaller than state-of-the-art models.

Current facial authentication (FA) systems are mostly based on the images of human faces, thus suffering from privacy leakage and spoofing attacks. Mainstream systems utilize facial geometry features for spoofing mitigation, which are still easy to deceive with the feature manipulation, e.g., 3D-printed human faces. In this paper, we propose a novel privacy-preserving anti-spoofing FA system, named RFace, which extracts both the 3D geometry and inner biomaterial features of faces using a COTS RFID tag array. These features are difficult to obtain and forge, hence are resistant to spoofing attacks. RFace only requires users to pose their faces in front of a tag array for a few seconds, without leaking their visual facial information. We build a theoretical model to rigorously prove the feasibility of feature acquisition and the correlation between the facial features and RF signals. For practicality, we design an effective algorithm to mitigate the impact of unstable distance and angle deflection from the face to the array. Extensive experiments with 30 participants and three types of spoofing attacks show that RFace achieves an average authentication success rate of over 95.7% and an EER of 4.4%. More importantly, no spoofing attack succeeds in deceiving RFace in the experiments.

We study a crowdsourcing problem where the platform aims to incentivize distributed workers to provide high-quality and truthful solutions without the ability to verify the solutions. While most prior work assumes that the platform and workers have symmetric information, we study an asymmetric information scenario where the platform has informational advantages. Specifically, the platform knows more information regarding workers' average solution accuracy, and can strategically reveal such information to workers. Workers will utilize the announced information to determine the likelihood that they obtain a reward if exerting effort on the task. We study two types of workers: (1) naive workers who fully trust the announcement, and (2) strategic workers who update prior belief based on the announcement. For naive workers, we show that the platform should always announce a high average accuracy to maximize its payoff. However, this is not always optimal for strategic workers, as it may reduce the credibility of the platform's announcement and hence reduce the platform's payoff. Interestingly, the platform may have an incentive to even announce an average accuracy lower than the actual value when facing strategic workers. Another counter-intuitive result is that the platform's payoff may decrease in the number of high-accuracy workers.

With the concern of privacy, a user usually encrypts the images before they were uploaded to the cloud service providers. Classification over encrypted images is essential for the service providers to collect coarse-grained statistical information about the images, therefore offering better services without sacrificing users' privacy. In this paper, we propose CryptoEyes to address the challenges of privacy-preserving classification over encrypted images. We present a two-stream convolutional network architecture for classification over encrypted images to capture the contour of encrypted images, therefore significantly boosting the classification accuracy. By sharing a secret sequence between the service provider and the image owner, CryptoEyes allows the service provider to obtain category information of encrypted images while preventing the unauthorized users from learning it. We implemented and evaluated CryptoEyes on popular datasets and the experimental results demonstrate the superiority of CryptoEyes over existing state of the arts in terms of classification accuracy over encrypted images and better privacy preservation performance.

Deploying accurate machine learning algorithms into a high-throughput networking environment is a challenging task. On the one hand, machine learning has proved itself useful for traffic classification in many contexts (e.g., intrusion detection, application classification, and early heavy hitter identification). On the other hand, most of the work in the area is related to post-processing (i.e., training and testing are performed offline on previously collected samples) or to scenarios where the traffic has to leave the data plane to be classified (i.e., high latency). In this work, we tackle the problem of creating simple and reasonably accurate machine learning models that can be deployed into the data plane in a way that performance degradation is acceptable. To that purpose, we introduce a framework and discuss issues related to the translation of simple models, for handling individual packets or flows, into the P4 language. We validate our framework with an intrusion detection use case and by deploying a single decision tree into a Netronome SmartNIC (Agilio CX 2x10GbE). Our results show that high-accuracy is achievable (above 95%) with minor performance degradation, even for a large number of flows.