INFOCOM 2023 论文列表

Exploiting a constellation of small satellites to realize continuous earth observations (EO) is gaining popularity. Large-volume EO data acquired from space needs to be transferred to the ground. However, existing EO delivery approaches are either: (a) efficiency-limited, suffering from long delivery completion time due to the intermittent ground-space communication, or (b) scalability-limited since they fail to support concurrent delivery for multiple satellites in an EO constellation.To make big data delivery for emerging EO constellations fast and scalable, we propose Falcon, a multi-path EO delivery framework that wisely exploits diverse paths in broadband constellations to collaboratively deliver EO data effectively. In particular, we formulate the constellation-wide EO data multi-path download (CEOMD) problem, which aims at minimizing the delivery completion time of requested data for all EO sources. We prove the hardness of solving CEOMD, and further present a heuristic multipath routing and bandwidth allocation mechanism to tackle the technical challenges caused by time-varying satellite dynamics and flow contention, and solve the CEOMD problem efficiently. Evaluation results based on public orbital data of real EO constellations show that as compared to other state-of-the-art approaches, Falcon can reduce at least 51% delivery completion time for various data requests in large EO constellations.

Bootstrapping security among wireless devices without prior-shared secrets is frequently demanded in emerging wireless and mobile applications. One promising approach for this problem is to utilize in-band physical-layer radio-frequency (RF) signals for authenticated key establishment because of the efficiency and high usability. However, existing in-band authenticated key agreement (AKA) protocols are mostly vulnerable to Man-in-the-Middle (MitM) attacks, which can be launched by modifying the transmitted wireless signals over the air. By annihilating legitimate signals and injecting malicious signals, signal modification attackers are able to completely control the communication channels and spoof victim wireless devices. State-of-the-art (SOTA) techniques addressing such attacks require additional auxiliary hardware or are limited to single attackers. This paper proposes a novel in-band security bootstrapping technique that can thwart colluding signal modification attackers. Different from SOTA solutions, our design is compatible with commodity devices without requiring additional hardware. We achieve this based on the internal randomness of each device that is unpredictable to attackers. Any modification to RF signals will be detected with high probabilities. Extensive security analysis and experimentation on the USRP platform demonstrate the effectiveness of our design under various attack strategies.

Fifth Generation (5G) has been acknowledged as a significant shift in cellular networks, expected to run significantly different classes of services and do so with outstanding performance in terms of low latency, high capacity, and extreme reliability. Managing the resulting complexity of mobile network architectures will depend on making efficient decisions at all network levels based on end-user requirements. However, to achieve this, it is critical to first understand the current mobile ecosystem and capture the device heterogeneity, which is one of the major challenges for ensuring the successful exploitation of 5G technologies.In this paper, we conduct a large-scale measurement study of a commercial mobile operator in the UK, focusing on bringing forward a real-world view on the available network resources, as well as how more than 30M end-user devices utilize the mobile network. We focus on the current status of the 5G Non-Standalone (NSA) deployment and the network-level performance and show how it caters to the prominent use cases that 5G promises to support. Finally, we demonstrate that a fine-granular set of requirements is, in fact, necessary to orchestrate the service to the diverse groups of 5G devices, some of which operate in permanent roaming.

The two-factor authentication (2FA) has become pervasive as the mobile devices become prevalent. Existing 2FA solutions usually require some form of user involvement, which could severely affect user experience and bring extra burdens to users. In this work, we propose a secure 2FA that utilizes the individual acoustic fingerprint of the speaker/microphone on enrolled device as the second proof. The main idea behind our system is to use both magnitude and phase fingerprints derived from the frequency response of the enrolled device by emitting acoustic beep signals alternately from both enrolled and login devices and receiving their direct arrivals for 2FA. Given the input microphone samplings, our system designs an arrival time detection scheme to accurately identify the beginning point of the beep signal from the received signal. To achieve a robust authentication, we develop a new distance mitigation scheme to eliminate the impact of transmission distances from the sound propagation model for extracting stable fingerprint in both magnitude and phase domain. Our device authentication component then calculates a weighted correlation value between the device profile and fingerprints extracted from run-time measurements to conduct the device authentication for 2FA. Our experimental results show that our proposed system is accurate and robust to both random impersonation and Man-in-the-middle (MiM) attack across different scenarios and device models.

Despite that Function-as-a-Service (FaaS) has settled down as one of the fundamental cloud programming models, it is still evolving quickly. Recently, Amazon has introduced S3 Object Lambda, which allows a user-defined function to be automatically invoked to process an object as it is being downloaded from S3. As with any new feature, careful study thereof is the key to elucidate if S3 Object Lambda, or more generally, if inline serverless data processing, is a valuable addition to the cloud. For this reason, we conduct an extensive measurement study of this novel service, in order to characterize its architecture and performance (in terms of coldstart latency, TTFB times, and more). We particularly put an eye on the streaming capabilities of this new form of function, as it may open the door to empower existing serverless systems with stream processing capacities. We discuss the pros and cons of this new capability through several workloads, concluding that S3 Object Lambda can go far beyond its original purpose and be leveraged as a building block for more complex abstractions.

Edge computing is increasingly proposed as a solution for reducing resource consumption of mobile devices running simultaneous localization and mapping (SLAM) algorithms, with most edge-assisted SLAM systems assuming the communication resources between the mobile device and the edge server to be unlimited, or relying on heuristics to choose the information to be transmitted to the edge. This paper presents AdaptSLAM, an edge-assisted visual (V) and visual-inertial (VI) SLAM system that adapts to the available communication and computation resources, based on a theoretically grounded method we developed to select the subset of keyframes (the representative frames) for constructing the best local and global maps in the mobile device and the edge server under resource constraints. We implemented AdaptSLAM to work with the state-of-the-art open-source V-and VI-SLAM ORB-SLAM3 framework, and demonstrated that, under constrained network bandwidth, AdaptSLAM reduces the tracking error by 62% compared to the best baseline method.

Recent advances in web technologies make it more difficult than ever to detect and block web tracking systems. In this work, we propose ASTrack, a novel approach to web tracking detection and removal. ASTrack uses an abstraction of the code structure based on Abstract Syntax Trees to selectively identify web tracking functionality shared across multiple web services. This new methodology allows us to: (i) effectively detect web tracking code even when using evasion techniques (e.g., obfuscation, minification, or webpackaging); and (ii) safely remove those portions of code related to tracking purposes without affecting the legitimate functionality of the website. Our evaluation with the top 10k most popular Internet domains shows that ASTrack can detect web tracking with high precision (98%), while discovering about 50k tracking code pieces and more than 3,400 new tracking URLs not previously recognized by most popular privacy-preserving tools (e.g., uBlock Origin). Moreover, ASTrack achieved a 36% reduction in functionality loss in comparison with the filter lists, one of the safest options available. Using a novel methodology that combines computer vision and manual inspection, we estimate that full functionality is preserved in more than 97% of the websites.

Software-Defined Networking (SDN) enables the deployment of diversified networking applications by providing global visibility and open programmability on a centralized controller. As SDN enters its second decade, several well-developed open source controllers have been widely adopted in industry, and various commercial SDN applications are built to meet the surging demand of network innovation. This complex ecosystem inevitably introduces new security threats, as malicious applications can significantly disrupt network operations. In this paper, we introduce a new vulnerability in existing SDN controllers that enable adversaries to create a backdoor and further deploy malicious applications to disrupt network service via a series of topology poisoning attacks. The root cause of this vulnerability is that SDN systems simply process received Packet-In messages without checking the integrity, and thus can be misguided by manipulated messages. We discover that five popular SDN controllers (i.e., Floodlight, ONOS, OpenDaylight, POX and Ryu) are potentially vulnerable to the disclosed attack, and further propose six new attacks exploiting this vulnerability to disrupt SDN services from different layers. We evaluate the effectiveness of these attacks with experiments in real SDN testbeds, and discuss feasible countermeasures.

Constrained combinatorial contextual bandits have emerged as trending tools in intelligent systems and networks to model reward and cost signals under combinatorial decision-making. On one hand, both signals are complex functions of the context, e.g., in federated learning, training loss (negative reward) and energy consumption (cost) are nonlinear functions of edge devices’ system conditions (context). On the other hand, there are cumulative constraints on costs, e.g., the accumulated energy consumption should be budgeted by energy resources. Besides, real-time systems often require such constraints to be guaranteed anytime or in each round, e.g., ensuring anytime fairness for task assignment to maintain the credibility of crowdsourcing platforms for workers. This setting imposes a challenge on how to simultaneously achieve reward maximization while subjecting to anytime cumulative constraints. To address such challenge, we propose a primal-dual algorithm (Neural-PD) whose primal component adopts multi-layer perceptrons to estimate reward and cost functions, and its dual component estimates the Lagrange multiplier with the virtual queue. By integrating neural tangent kernel theory and Lyapunov-drift techniques, we prove Neural-PD achieves a sharp regret bound and a zero constraint violation. We also show Neural-PD outperforms existing algorithms with extensive experiments on both synthetic and real-world datasets.

LoRa has been widely applied in various vertical areas such as smart grids, smart cities, etc. Packet collisions caused by concurrent transmissions have become one of the major limitations of LoRa networks due to the ALOHA MAC protocol and dense deployment. The existing studies on packet recovery usually assume that the collided packet signals are above the noise floor. However, considering the large-scale deployment and low-power nature of LoRa communications, many collided packets are below the noise floor. Consequently, the existing schemes will suffer from significant performance degradation in practical LoRa networks. To address this issue, we propose CPR, a Cooperative Packet Recovery mechanism aiming at recovering the collided packets below the noise floor. CPR firstly employs the incoherence of signals and noises at multiple gateways to detect and extract the frequency features of the collided packets hidden in the noise. Then, CPR adopts a novel gateway selection strategy to select the most appropriate gateways based on their packet power domain features extracted from collision detection, such that the interference can be eliminated and the original packets can be recovered. Extensive experimental results demonstrate that CPR can significantly increase the symbol recovery ratio in low-SNR scenarios.

Demand-aware reconfigurable datacenter networks can be modeled as a ToR-Matching-ToR (TMT) two-layer architecture, in which each top-of-rack (ToR) is represented by a static switch, and n ToRs are connected by a set of reconfigurable optical circuit switches (OCS). Each OCS internally connects a set of in-out ports via a matching that may be updated at runtime. The matching model is a formalization of such networks, where the datacenter topology is defined by the union of matchings over the set of nodes, each of which can be reconfigured at unit cost.In this work we propose a scalable matching model for scenarios where OCS have a constant number of ports. Furthermore, we present OpticNet, a framework that maps a set of n static ToR switches to a set of p-port OCS to form any constant-degree topology. We prove that OpticNet uses a minimal number of reconfigurable switches to realize any desired network topology and allows to apply any existing self-adjusting network (SAN) algorithm on top of it, also preserving amortized performance guarantees. Our experimental results based on real workloads show that OpticNet is a flexible and efficient framework to design efficient SANs.

Data generated at the network edge can be processed locally by leveraging the paradigm of edge computing (EC). Aided by EC, decentralized federated learning (DFL), which overcomes the single-point-of-failure problem in the parameter server (PS) based federated learning, is becoming a practical and popular approach for machine learning over distributed data. However, DFL faces two critical challenges, i.e., system heterogeneity and statistical heterogeneity introduced by edge devices. To ensure fast convergence with the existence of slow edge devices, we present an efficient DFL method, termed FedHP, which integrates adaptive control of both local updating frequency and network topology to better support the heterogeneous participants. We establish a theoretical relationship between local updating frequency and network topology regarding model training performance and obtain a convergence upper bound. Upon this, we propose an optimization algorithm, that adaptively determines local updating frequencies and constructs the network topology, so as to speed up convergence and improve the model accuracy. Evaluation results show that the proposed FedHP can reduce the completion time by about 51% and improve model accuracy by at least 5% in heterogeneous scenarios, compared with the baselines.

Learning-based Internet congestion control algorithms have attracted much attention due to their potential performance improvement over traditional algorithms. However, such performance improvement is usually at the expense of black-box design and high computational overhead, which prevent them from large-scale deployment over production networks. To address this problem, we propose a novel Internet congestion control algorithm called Gemini. It contains a parameterized congestion control module, which is white-box designed with low computational overhead, and an online parameter optimization module, which serves to adapt the parameterized congestion control module to different networks for higher transmission performance. Extensive trace-driven emulations reveal Gemini achieves better balances between delay and throughput than state-of-the-art algorithms. Moreover, we successfully deploy Gemini over production networks. The evaluation results show that the average throughput of Gemini is 5% higher than that of Cubic (4% higher than that of BBR) over a mobile application downloading service and 61% higher than that of Cubic (33% higher than that of BBR) over a commercial network speed-test benchmarking service.