INFOCOM 2020 Papers

Deployment of unmanned aerial vehicles (UAVs) performing as flying aerial base stations (BSs) has a great potential of adaptively serving ground users during temporary events, such as major disasters and massive events. However, planning an efficient, dynamic, and 3D deployment of UAVs in adaptation to dynamically and spatially varying ground users is a highly complicated problem due to the complexity in air-to-ground channels and interference among UAVs. In this paper, we propose a novel distributed 3D deployment method for UAVBSs in a downlink network for on-demand coverage. Our method consists mainly of the following two parts: sensing-aided crowd density estimation and distributed push-sum algorithm. The first part estimates the ground user density from its observation through on-ground sensors, thereby allowing us to avoid the computationally intensive process of obtaining the positions of all the ground users. On the basis of the estimated user density, in the second part, each UAV dynamically updates its 3D position in collaboration with its neighboring UAVs for maximizing the total coverage. We prove the convergence of our distributed algorithm by employing a distributed push-sum algorithm framework. Simulation results demonstrate that our method can improve the overall coverage with a limited number of ground sensors. We also demonstrate that our method can be applied to a dynamic network in which the density of ground users varies temporally.

Localization and navigation play a key role in many location-based services and have attracted numerous research efforts from both academic and industrial community. In recent years, visual SLAM has been prevailing for robots and autonomous driving cars. However, the ever-growing computation resource demanded by SLAM impedes its application to resource-constrained mobile devices. In this paper we present the design, implementation, and evaluation of edgeSLAM, an edge assisted real-time semantic visual SLAM service running on mobile devices. edgeSLAM leverages the state-of-the-art semantic segmentation algorithm to enhance localization and mapping accuracy, and speeds up the computation-intensive SLAM and semantic segmentation algorithms by computation offloading. The key innovations of edgeSLAM include an efficient computation offloading strategy, an opportunistic data sharing mechanism, and an adaptive task scheduling algorithm. We fully implement edgeSLAM on an edge server and different types of mobile devices (2 types of smartphones and a development board). Extensive experiments are conducted under 3 data sets, and the results show that edgeSLAM is able to run on mobile devices at 35fps frame rate and achieves a 5cm localization accuracy, outperforming existing solutions by more than 15%. We also demonstrate the usability of edgeSLAM through 2 case studies of pedestrian localization and robot navigation. To the best of our knowledge, edgeSLAM is the first real-time semantic visual SLAM for mobile devices.

The following topics are dealt with: learning (artificial intelligence); optimisation; telecommunication traffic; Internet; cloud computing; computational complexity; mobile computing; resource allocation; security of data; and telecommunication network routing.

With the increasing demand of low-latency network services, mobile edge computing (MEC) emerges as a new paradigm, which provides server resources and processing capacities in close proximity to end users. Based on network function virtualization (NFV), network services can be flexibly provisioned as virtual network function (VNF) chains deployed at edge servers. However, due to the resource shortage at the network edge, how to efficiently deploy VNF chains with latency guarantees and resource efficiency remains as a challenging problem. In this work, we focus on jointly optimizing the resource utilization of both edge servers and physical links under the latency limitations. Specifically, we formulate the VNF chain deployment problem as a mixed integer linear programming (MILP) to minimize the total resource consumption. We design a novel two-stage latency-aware VNF deployment scheme: highlighted by a constrained depth-first search algorithm (CDFSA) for selecting paths, and a path-based greedy algorithm (PGA) for assigning VNFs by reusing as many VNFs as possible. We demonstrate that our proposed algorithm can efficiently achieve a near-optimal solution with a theoretically-proved worstcase performance bound. Extensive simulation results show that the proposed algorithm outperforms three previous heuristic algorithms.

Air-gapped networks achieve security by using the physical isolation to keep the computers and network from the Internet. However, magnetic covert channels based on CPU utilization have been proposed to help secret data to escape the Faraday-cage and the air-gap. Despite the success of such cover channels, they suffer from the high risk of being detected by the transmitter computer and the challenge of installing malware into such a computer. In this paper, we propose MagView, a distributed magnetic cover channel, where sensitive information is embedded in other data such as video and can be transmitted over the air-gapped internal network. When any computer uses the data such as playing the video, the sensitive information will leak through the magnetic covert channel. The "separation" of information embedding and leaking, combined with the fact that the covert channel can be created on any computer, overcomes these limitations. We demonstrate that CPU utilization for video decoding can be effectively controlled by changing the video frame type and reducing the quantization parameter without video quality degradation. We prototype MagView and achieve up to 8.9 bps throughput with BER as low as 0.0057. Experiments under different environment are conducted to show the robustness of MagView. Limitations and possible countermeasures are also discussed.

Although time-series data collected from users can be utilized to provide services for various applications, they could reveal sensitive information about users. Recently, local differential privacy (LDP) has emerged as the state-of-art approach to protect data privacy by perturbing data locally before outsourcing. However, existing works based on LDP perturb each data point separately without considering the correlations between consecutive data points in time-series. Thus, the important patterns of each time-series might be distorted by existing LDP-based approaches, leading to severe degradation of data utility. In this paper, we focus on real-time data collection under a honest-but-curious server, and propose a novel pattern-aware privacy-preserving approach, called PatternLDP, to protect data privacy while the pattern of time-series can still be preserved. To this end, instead of providing the same level of privacy protection at each data point, each user only samples remarkable points in time-series and adaptively perturbs them according to their impacts on local patterns. In particular, we propose a pattern-aware sampling method based on Piecewise Linear Approximation (PLA) to determine whether to sample and perturb current data point. To reduce the utility loss caused by pattern change after perturbation, we propose an importance-aware randomization mechanism to adaptively perturb sampled data locally while achieving better trade-off between privacy and utility. A novel metric-based w-event privacy is introduced to measure the privacy protection degree for pattern-rich time-series. We prove that PatternLDP can provide the above privacy guarantee, and extensive experiments on real-world datasets demonstrate that PatternLDP outperforms existing mechanisms and can effectively preserve the important patterns.

Network update enables Software-Defined Networks (SDNs) to optimize the data plane performance via southbound APIs. The single update between the initial and the final network states fail to handle high-frequency changes or the burst event during the update procedure in time, leading to prolonged update time and inefficiency. On the contrary, the continuous update can respond to the network condition changes at all times. However, existing work, especially "Update Algebra" can only guarantee blackhole- and loop-free. The congestion-free property cannot be respected during the update procedure. In this paper, we propose Coeus, a continuous network update system while maintaining blackhole-, loop- and congestion-free simultaneously. Firstly, we establish an operation-based continuous update model. Based on this model, we dynamically reconstruct an operation dependency graph to capture unexecuted update operations and the link utilization variations. Subsequently, we develop an operation composition algorithm to eliminate redundant update commands and an operation node partition algorithm to speed up the update procedure. We prove that the partition algorithm is optimal and can guarantee the consistency. Finally, extensive evaluations show that Coeus can improve the makespan by at least 179% compared with state-of-the-art approaches when the arrival rate of update events equals to three times per second.

Networks of Unmanned Aerial Vehicles (UAVs), composed of hundreds, possibly thousands of highly mobile and wirelessly connected flying drones will play a vital role in future Internet of Things (IoT) and 5G networks. However, how to control UAV networks in an automated and scalable fashion in distributed, interference-prone, and potentially adversarial environments is still an open research problem. This article introduces SwarmControl, a new software-defined control framework for UAV wireless networks based on distributed optimization principles. In essence, SwarmControl provides the Network Operator (NO) with a unified centralized abstraction of the networking and flight control functionalities. High-level control directives are then automatically decomposed and converted into distributed network control actions that are executed through programmable software-radio protocol stacks. SwarmControl (i) constructs a network control problem representation of the directives of the NO; (ii) decomposes it into a set of distributed sub-problems; and (iii) automatically generates numerical solution algorithms to be executed at individual UAVs.We present a prototype of an SDR-based, fully reconfigurable UAV network platform that implements the proposed control framework, based on which we assess the effectiveness and flexibility of SwarmControl with extensive flight experiments. Results indicate that the SwarmControl framework enables swift reconfiguration of the network control functionalities, and it can achieve an average throughput gain of 159% compared to the state-of-the-art solutions.

Network traffic classification has been widely studied to fundamentally advance network measurement and management. Machine Learning is one of the effective approaches for network traffic classification. Specifically, Deep Learning (DL) has attracted much attention from the researchers due to its effectiveness even in encrypted network traffic without compromising neither user privacy nor network security. However, most of the existing models are created from closed-world datasets, thus they can only classify those existing classes previously sampled and labeled. In this case, unknown classes cannot be correctly classified. To tackle this issue, an autonomous learning framework is proposed to effectively update DL-based traffic classification models during active operations. The core of the proposed framework consists of a DL-based classifier, a self-learned discriminator, and an autonomous self-labeling model. The discriminator and self-labeling process can generate new dataset during active operations to support classifier update. Evaluation of the proposed framework is performed on an open dataset, i.e., ISCX VPN-nonVPN, and independently collected data packets. The results demonstrate that the proposed autonomous learning framework can filter packets from unknown classes and provide accurate labels. Thus, corresponding DL-based classification models can be updated successfully with the autonomously generated dataset.

In Network Function Virtualization (NFV), to satisfy the Service Functions (SFs) requested by a customer, service providers will composite a Service Function Chain (SFC) and embed it onto the shared Substrate Network (SN). For many latency-sensitive and computing-intensive applications, the customer forwards data to the cloud/server and the cloud/server sends the results/models back, which may require different SFs to handle the forward and backward traffic. The SFC that requires different SFs in the forward and backward directions is referred to as hybrid SFC (h-SFC). In this paper, we, for the first time, comprehensively study how to optimize the latency in Hybrid SFC composition and Embedding (HSFCE). When each substrate node provides only one unique SF, we prove the NP-hardness of HSFCE and propose the first 2-approximation algorithm to jointly optimize the processes of h-SFC construction and embedding, which is called Eulerian Circuit based Hybrid SFP optimization (EC-HSFP). When a substrate node provides various SFs, we extend EC-HSFP and propose the efficient Betweenness Centrality based Hybrid SFP optimization (BC-HSFP) algorithm. Our extensive simulations and analysis show that EC-HSFP can hold the 2-approximation, while BC-HSFP outperforms the algorithms directly extended from the state-of-art techniques by an average of 20%.

This article studies an essential yet challenging problem in 5G wireless networks: Is it possible to enable spectrally-efficient spectrum sharing for heterogeneous wireless networks with different, possibly incompatible, spectrum access technologies on the same spectrum bands; without modifying the protocol stacks of existing wireless networks? To answer this question, this article explores the system challenges that need to be addressed to enable a new spectrum sharing paradigm based on beamforming, which we refer to as CoBeam. In CoBeam, a secondary wireless network is allowed to access a spectrum band based on cognitive beam-forming without mutual temporal exclusion, i.e., without interrupting the ongoing transmissions of coexisting wireless networks on the same bands; and without cross-technology communication. We first describe the main components of CoBeam, including programmable physical layer driver, cognitive sensing engine, and beamforming engine, and then we showcase the potential of the CoBeam framework by designing a practical coexistence scheme between Wi-Fi and LTE on unlicensed bands. We present a prototype of the resulting coexisting Wi-Fi/U-LTE network built on off-the-shelf software radios based on which we evaluate the performance of CoBeam through an extensive experimental campaign. Performance evaluation results indicate that CoBeam can achieve on average 169% throughput gain while requiring no signaling exchange between the coexisting wireless networks.

Unmanned aerial vehicles (UAVs) are being investigated to substitute for labor in many indoor applications, e.g., asset tracking and surveillance, where the global positioning system (GPS) is not available. Also, emerging autonomous UAVs are expected to land in indoor parking aprons automatically. Such GPS-denied environments require alternative non-GPS positioning methods. Although there have been some vision-based solutions for UAVs, they perform poorly in the scenes with bad illumination conditions or estimate only relative locations but not global positions. Other common indoor localization methods do not cover UAV factors, such as low power and flying behaviors. To this end, we propose a practical non-GPS positioning system for UAVs, named WBF-PS (WiGig Beam Fingerprinting based Positioning System), using low-power, off-the-shelf WiGig devices. We formulate a 3-dimensional beam fingerprint for the positioning by leveraging the diversity of available transmitter/receiver beams and the link quality. To augment the positioning accuracy, we not only use a weighted k-nearest neighbors algorithm to overcome partial fingerprint inaccuracy but also apply the particle filtering technique into considering the UAV motion. We prototype and evaluate WBF-PS on a UAV platform. The result shows that the positioning errors at the 90th percentile are below 1 m in various cases.

Many Industrial Internet of Things (IIoT) use cases are characterized by a large number of users transmitting sporadically packets to a central controller. The transmitted packets have to be conveyed within a very short time so that it is not possible to make per-packet resource reservation, and contention-based access is needed, which is conflicting with the very stringent reliability constraints. In order to achieve both reliability and latency constraints, we consider in this paper the combination of contention-based access with blind replication of packets. Knowing the limited, but large, number of potential users in the system, we propose a semi-centralized channel access scheme where each user is pre-allocated positions for its replicas in case he has a packet to convey. We show, using coding theory, how to design sequences for users so that the number of collisions is minimized. We further exploit our pre-allocation scheme to develop a successive interference cancellation method where the base station tries to decode a packet based on the knowledge of the already decoded colliding packet. We show that the proposed schemes succeed to attain very low loss rates with low resource reservation.

Shared resources facilitate stealthy communication channels, including side channels and covert channels, which greatly endanger the information security, even in cloud environments. As a commonly shared resource, DRAM memory also serves as a source of stealthy channels. Existing solutions rely on two common features of DRAM-based channels, i.e., high cache miss and high bank locality, to detect the existence of such channels. However, such solutions could be defeated. In this paper, we point out the weakness of existing detection solutions by demonstrating a new advanced DRAM-based channel, which utilizes the hardware Intel SGX to conceal cache miss and bank locality. Further, we propose a novel neural network based solution DRAMD to detect such advanced stealthy channels. DRAMD uses hardware performance counters to track not only cache miss events that are used by existing solutions, but also counts of branches and instructions executed, as well as branch misses. Then DRAMD utilizes neural networks to model the access patterns of different applications and therefore detects potential stealthy communication channels. Our evaluation shows that DRAMD achieves up to 99% precision with 100% recall. Furthermore, DRAMD introduces less than 5% performance overheads and negligible impacts on legacy applications.

We consider online downlink precoding design for multiple-input multiple-output (MIMO) wireless network virtualization (WNV) in a fading environment with imperfect channel state information (CSI). In our WNV framework, a base station owned by an infrastructure provider (InP) is shared by several service providers (SPs) that are oblivious to each other. The SPs design their virtual MIMO transmission demands to serve their own users, while the InP designs the actual downlink precoding to meet the service demands from the SPs. Therefore, the impact of imperfect CSI is two-fold, on both the InP and the SPs. We aim to minimize the long-term time-averaged expected precoding deviation, considering both long-term and short-term transmit power limits. We propose a new online MIMO WNV algorithm to provide a semi-closed-form precoding solution based only on the current imperfect CSI. We derive a performance bound for our proposed algorithm and show that it is within an O(δ) gap from the optimum over any given time horizon, where δ is a normalized measure of CSI inaccuracy. Simulation results with two popular precoding techniques validate the performance of our proposed algorithm under typical urban micro-cell Long-Term Evolution network settings.

While both Internet Service Providers (ISPs) and third-party Security Service Providers (SSPs) offer Distributed Denial-of-Service (DDoS) mitigation services through cloud-based scrubbing centers, it is often beneficial for ISPs to outsource part of the traffic scrubbing to SSPs to achieve less economic cost and better network performance. To explore this potential, we design an online auction mechanism, featured by the challenge of the switching cost of using different winning bids over time. Formulating the social cost minimization as a nonconvex integer program, we firstly relax it and design an online algorithm that breaks it into a series of modified single-shot problems and solves each of them in polynomial time, without requiring knowledge of future inputs; then, we design a randomized rounding algorithm to convert the fractional decisions into integers without violating any constraints; and finally, we design the payment for each bid based on its winning probability. We rigorously prove that our mechanism achieves a parameterized-constant competitive ratio for the long-term social cost, with truthfulness and individual rationality in expectation. We also exhibit its superior practical performance via evaluations driven by real-world data traces.

User identified gesture recognition is a fundamental step towards ubiquitous device-free sensing. We propose WiHF, which first simultaneously enables cross-domain gesture recognition and user identification using WiFi in a real-time manner. The basic idea of WiHF is to derive a cross-domain motion change pattern of arm gestures from WiFi signals, rendering both unique gesture characteristics and the personalized user performing styles. To extract the motion change pattern in realtime, we develop an efficient method based on the seam carving algorithm. Moreover, taking as input the motion change pattern, a Deep Neural Network (DNN) is adopted for both gesture recognition and user identification tasks. In DNN, we apply splitting and splicing schemes to optimize collaborative learning for dual tasks. We implement WiHF and extensively evaluate its performance on a public dataset including 6 users and 6 gestures performed across 5 locations and 5 orientations in 3 environments. Experimental results show that WiHF achieves 97.65% and 96.74% for in-domain gesture recognition and user identification accuracy, respectively. The cross-domain gesture recognition accuracy is comparable with the state-of-the-art methods, but the processing time is reduced by 30×.

Spoofing attacks pose a serious threat to wireless communications. Exploiting physical-layer features to counter spoofing attacks is a promising solution. Although various physical-layer spoofing attack detection (PL-SAD) techniques have been proposed for conventional 802.11 networks in the sub-6GHz band, the study of PL-SAD for 802.11ad networks in 5G millimeter wave (mmWave) 60GHz band is largely open. In this paper, we propose a unique physical layer feature in IEEE 802.11ad networks, i.e., the signal-to-noise-ratio (SNR) trace obtained at the receiver in the sector level sweep (SLS) process, to achieve efficient PL-SAD. The SNR trace is readily extractable from the off-the-shelf device, and it is dependent on both transmitter location and intrinsic hardware impairment. Therefore, it can be used to achieve an efficient detection no matter the attacker is co-located with the legitimate transmitter or not. The detection problem is formulated as a machine learning classification problem. To tackle the small sample learning and fast model construction challenges, we propose a novel neural network framework consisting of a backpropation network, a forward propagation network, and generative adversarial networks (GANs). It can tackle small sample learning and allow for quick model construction. We conduct experiments using off-the-shelf 802.11ad devices, Talon AD7200s and MG360, to evaluate the performance of the proposed PL-SAD scheme. Experimental results confirm the effectiveness of the proposed PL-SAD scheme, and the detection accuracy can reach 98% using small sample sizes under different scenarios.

Fine-grained flow management is useful in many practical applications, e.g., resource allocation, anomaly detection and traffic engineering. However, it is difficult to provide fine-grained management for a large number of flows in SDNs due to switches' limited flow table capacity. While using wildcard rules can reduce the number of flow entries needed, it cannot fully ensure fine-grained management for all the flows without degrading application performance. In this paper, we design and implement HiFi, a system that achieves fine-grained management with a minimal number of flow entries. To this end, HiFi takes a two-step approach: wildcard entry installment and application-specific exact-match entry installment. How to optimally install wildcard and exact-match flow entries, however, is intractable. Therefore, we design approximation algorithms with bounded factors to solve these problems. We consider how to achieve network-wide load balancing via fine-grained flow management as a case study. Both experimental and simulation results show that HiFi can reduce the number of required flow entries by about 45%-69% and reduce the control overhead by 28%-50% compared with the state-of-the-art approaches for achieving fine-grained flow management.