Session 3-A

## Gesture Recognition

Conference
9:00 AM — 10:30 AM EDT
Local
Jul 8 Wed, 9:00 AM — 10:30 AM EDT

### Dynamic Speed Warping: Similarity-Based One-shot Learning for Device-free Gesture Signals

Xun Wang, Ke Sun, Ting Zhao, Wei Wang and Qing Gu (Nanjing University, China)

2
In this paper, we propose a Dynamic Speed Warping(DSW) algorithm to enable one-shot learning for device-free gesture signals performed by different users. The design of DSW is based on the observation that the gesture type is determined by the trajectory of hand components rather than the movement speed. By dynamically scaling the speed distribution and tracking the movement distance along the trajectory, DSW can effectively match gesture signals from different domains that have a ten-fold difference in speeds. Our experimental results show that DSW can achieve a recognition accuracy of 97% for gestures performed by unknown users, while only use one training sample of each gesture type from four training users.

### INFOCOM 2020 Best Paper: Push the Limit of Acoustic Gesture Recognition

Yanwen Wang, Jiaxing Shen and Yuanqing Zheng (The Hong Kong Polytechnic University, Hong Kong)

6
With the flourish of the smart devices and their applications, controlling devices using gestures has attracted increasing attention for ubiquitous sensing and interaction. Recent works use acoustic signals to track hand movement and recognize gestures. However, they suffer from low robustness due to frequency selective fading, interference and insufficient training data. In this work, we propose RobuCIR, a robust contact-free gesture recognition system that can work under different usage scenarios with high accuracy and robustness. RobuCIR adopts frequency-hopping mechanism to mitigate frequency selective fading and avoid signal interference. To further increase system robustness, we investigate a series of data augmentation techniques based on a small volume of collected data to emulate different usage scenarios. The augmented data is used to effectively train neural network models and cope with various influential factors (e.g., gesture speed, distance to transceiver, etc.). Our experiment results show that RobuCIR can recognize 15 gestures and outperform state-of-the-art works in terms of accuracy and robustness.

### Towards Anti-interference WiFi-based Activity Recognition System Using Interference-Independent Phase Component

Jinyang Huang, Bin Liu and Pengfei Liu (University of Science and Technology of China, China); Chao Chen (Zhejiang University, China); Ning Xiao, Yu Wu, Chi Zhang and Nenghai Yu (University of Science and Technology of China, China)

3
Human activity recognition (HAR) has become increasingly essential due to its potential to support a broad array of applications, e.g., elder care, and VR games. Recently, some pioneer WiFi-based HAR systems have been proposed due to its privacy-friendly and device-free characteristics. However, their crucial limitation lies in ignoring the inevitable impact of co-channel interference (CCI), which degrades the performance of these HAR systems significantly. To address this challenge, we propose PhaseAnti, a novel HAR system to exploit interference-independent phase component, NLPEV (Nonlinear Phase Error Variation), of Channel State Information (CSI) to cope with the impact of CCI. We provide a rigorous analysis of NLPEV data with respect to its stability and otherness. Validated by our experiments, this phase component across subcarriers is invariant to various CCI scenarios, while different for distinct motions. Based on the analysis, we use NLPEV data to perform HAR in CCI scenarios. Extensive experiments demonstrate that PhaseAnti can reliably recognize activity in various CCI scenarios. Specifically, PhaseAnti achieves 95% recognition accuracy rate (RAR) on average, which improves up to 16% RAR in the presence of CCI. Moreover, the recognition speed is 9× faster than the state-of-the-art solution.

### WiHF: Enable User Identified Gesture Recognition with Wi-Fi

Chenning Li, Manni Liu and Zhichao Cao (Michigan State University, USA)

2
In this paper, we propose WiHF, which first simultaneously enables real-time cross-domain gesture recognition and user identification with Wi-Fi, a fundamental step towards ubiquitous device-free sensing. The key innovation of WiHF is to derive a cross-domain motion change pattern of arm gestures from WiFi signals, which contains both unique gesture characteristics and personalized user performing styles. To achieve real-time processing, based on the seam carving algorithm, we develop a heuristic method to extract the motion change pattern. Taking the motion change pattern as input, a deep neural network (DNN) is adopted for gesture recognition and user identification tasks. In DNN, we apply splitting and merging schemes to optimize collaborative learning for dual tasks. We implement WiHF and extensively evaluate its performance on a public dataset which contains 6 user and 8 gestures performed across 5 locations and orientations in 3 environments. Experimental results show that WiHF achieves 97.65% and 96.74% 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$$\times$$.

###### Session Chair

Wei Gao (University of Pittsburgh)

Session 3-B

## Scheduling II

Conference
9:00 AM — 10:30 AM EDT
Local
Jul 8 Wed, 9:00 AM — 10:30 AM EDT

### Computation Scheduling for Wireless Powered Mobile Edge Computing Networks

Tongxin Zhu and Jianzhong Li (Harbin Institute of Technology, China); Zhipeng Cai and Yingshu Li (Georgia State University, USA); Hong Gao (University of Harbin Institute Technology, China)

0
Mobile Edge Computing (MEC) and Wireless Power Transfer (WPT) are envisioned as two promising techniques to satisfy the increasing energy and computation requirements of latency-sensitive and computation-intensive applications installed on mobile devices. The integration of MEC and WPT introduces a novel paradigm named Wireless Powered Mobile Edge Computing (WP-MEC). In WP-MEC networks, edge devices located at the edge of radio access networks, such as access points and base stations, transmit radio frequency signals to power mobile devices and mobile devices can offload their intensive computation workloads to edge devices. In this paper, we study the Computation Completion Ratio Maximization Scheduling problem for WP-MEC networks with multiple edge devices, which is proved to be NP-hard. We jointly optimize the WPT time allocation and computation scheduling for mobile devices in a WP-MEC network to maximize the computation completion ratio of the WP-MEC network and propose approximation algorithms. The approximation ratio and computation complexity of the proposed algorithms are theoretically analyzed. Extensive simulations are conducted to verify the performance of the proposed algorithms.

### Distributed and Optimal RDMA Resource Scheduling in Shared Data Center Networks

Dian Shen, Luo Junzhou, Fang Dong, Xiaolin Guo and Kai Wang (Southeast University, China); John Chi Shing Lui (Chinese University of Hong Kong, Hong Kong)

1
Remote Dynamic Memory Access (RDMA) suffers from unfairness issues and performance degradation when multiple applications share RDMA network resources. Hence, an efficient resource scheduling mechanism is urged to optimally allocates RDMA resources among applications. However, traditional Network Utility Maximization (NUM) based solutions are inadequate for RDMA due to three challenges: 1) The standard NUM-oriented algorithm cannot deal with coupling variables introduced by multiple dependent RDMA operations; 2) The stringently RDMA on-board resources constraint complicates the standard NUM by introducing extra optimization dimensions; 3) Naively applying traditional algorithms for NUM suffers from scalability issues in solving a large-scale RDMA resource scheduling problem.

In this paper, we present a distributed and optimal resource scheduling for RDMA networks to tackle the aforementioned challenges. First, we propose DRUM to model the RDMA resource scheduling problem as a new variation of the NUM problem. Second, we present a distributed algorithm based on the alternating directional method of multipliers (ADMM), which has the property of convergence guarantee. Third, we implement our proposed algorithm in the real-world RDMA environment, and extensively evaluate it through large scale simulations and testbed experiments. Experimental results show that our method significantly improves applications' performance under resource contention.

### Injection Time Planning: Making CQF Practical in Time-Sensitive Networking

Jinli Yan, Wei Quan and Xuyan Jiang (National University of Defense Technology, China); Zhigang Sun (National Unversity of Defense Technology, China)

0
Time-Aware Shaper (TAS) is a core mechanism to guarantee the deterministic transmission for periodic time- sensitive flows in Time-Sensitive Networking (TSN). The generic TAS requires complex configurations for the Gate Control List (GCL) attached to each queue in a switch. To simplify the design of TSN switch, a Ping-Pong queue-based model named Cyclic Queuing and Forwarding (CQF) was proposed in IEEE 802.1 Qch by assigning fixed configurations to TAS. However, IEEE 802.1 Qch only defines the queue model and workflow of CQF. A global planning mechanism which maps the time-sensitive flows onto the underlying resources both temporally and spatially is urgently needed to make CQF practical. In this paper, we propose an Injection Time Planning (ITP) mechanism to optimize the network throughput of time-sensitive flows based on the observation that the start time when the packets are injected into the network has an important influence on the utilization of CQF queue resources. ITP provides a global temporal and spatial resource abstraction to make the implementation details transparent to algorithm designers. Based on our ITP mechanism, a novel heuristic algorithm named Tabu- ITP with domain-specific optimizing strategies is designed and evaluated under three typical network topologies in industrial control scenarios.

### Preemptive All-reduce Scheduling for Expediting Distributed DNN Training

Yixin Bao, Yanghua Peng, Yangrui Chen and Chuan Wu (The University of Hong Kong, Hong Kong)

4
Data-parallel training is widely used for scaling DNN training over large datasets, using the parameter server or all-reduce architecture. Communication scheduling has been promising to accelerate distributed DNN training, which aims to overlap communication with computation by scheduling the order of communication operations. We identify two limitations of previous communication scheduling work. First, layer-wise computation graph has been a common assumption, while modern machine learning frameworks (e.g., TensorFlow) use a sophisticated directed acyclic graph (DAG) representation as the execution model. Second, the default sizes of tensors are often less than optimal for transmission scheduling and bandwidth utilization. We propose PACE, a communication scheduler that preemptively schedules (potentially fused) all-reduce tensors based on the DAG of DNN training, guaranteeing maximal overlapping of communication with computation and high bandwidth utilization. The scheduler contains two integrated modules: given a DAG, we identify the best tensor-preemptive communication schedule that minimizes the training time; exploiting the optimal communication scheduling as an oracle, a dynamic programming approach is developed for generating a good DAG, which merges small communication tensors for efficient bandwidth utilization. Experiments in a GPU testbed show that PACE accelerates training with representative system configurations, achieving up to 36% speed-up compared with state-of-the-art solutions.

###### Session Chair

Haipeng Dai (Nanjing University)

Session 3-C

## Security II

Conference
9:00 AM — 10:30 AM EDT
Local
Jul 8 Wed, 9:00 AM — 10:30 AM EDT

### BLESS: A BLE Application Security Scanning Framework

Yue Zhang and Jian Weng (Jinan University, China); Zhen Ling (Southeast University, China); Bryan Pearson and Xinwen Fu (University of Central Florida, USA)

2

###### Session Chair

Francesco Restuccia (Northeastern University)

Session 5-G

## SDN I

Conference
2:00 PM — 3:30 PM EDT
Local
Jul 8 Wed, 2:00 PM — 3:30 PM EDT

### A Deep Analysis on General Approximate Counters

Tong Yun and Bin Liu (Tsinghua University, China)

1
Approximate counters play an important role in many computer domains like network measurement, parallel computing, and machine learning. With the emergence of new problems in these domains like flow counting and adding approximate counters, the traditionally used simple Morris counter fails to solve them, which requires a more general Morris counter. However, there has been a lack of complete theoretical research on the statistical properties of this approximate counter so far. This paper proposes an analysis on general Morris counters and derives the minimum upper bound of the variance. To our best knowledge, this is the first work to thoroughly analyze the statistical properties of general Morris counters in theory. Besides, practical application scenarios are analyzed to show that our conclusions are practical in testing the performance of approximate counters and guiding the design of architectures. Our proof methods are also general and can be applied to analyzing other scenarios involving either simple Morris counters or their promotional versions.

### Efficient and Consistent TCAM Updates

Bohan Zhao, Rui Li and Jin Zhao (Fudan University, China); Tilman Wolf (University of Massachusetts, USA)

0
The dynamic nature of software-defined networking requires frequent updates to the flow table in the data plane of switches. Therefore, the ternary content-addressable memory (TCAM) used in switches to match packet header fields against forwarding rules needs to support high rates of updates. Existing off-the-shelf switches update rules in batches for efficiency but may suffer from forwarding inconsistencies during the batch update. In this paper, we design and evaluate a TCAM update optimization framework that can guarantee consistent forwarding during the entire update process while making use of a layered TCAM structure. Our approach is based on a modified-entry-first write-back strategy that significantly reduces the overhead from movements of TCAM entries. In addition, our approach detects reordering cases, which are handled using efficient solutions. Based on our evaluation results, we can reduce the cost of TCAM updates by 30%-88% compared to state-of-the-art techniques.

### Faster and More Accurate Measurement through Additive-Error Counters

Ran Ben Basat (Harvard University, USA); Gil Einziger (Ben-Gurion University Of The Negev, Israel); Michael Mitzenmacher (Harvard University, USA); Shay Vargaftik (VMware, Israel)

3
Network applications such as load balancing, traffic engineering, and intrusion detection often rely on timely traffic measurements, including estimating flow size and identifying the heavy hitter flows. Counter arrays, which provide approximate counts, are a fundamental building block for many measurement algorithms, and current works optimize such arrays for throughput and/or space efficiency.

We suggest a novel sampling technique that reduces the required size of counters and allows more counters to fit within the same space. We formally show that our method yields better space to accuracy guarantees for multiple flavors of measurement algorithms. We also empirically evaluate our technique against several other measurement algorithms on real Internet traces. Our evaluation shows that our method improves the throughput and the accuracy of approximate counters and corresponding measurement algorithms.

### Network Monitoring for SDN Virtual Networks

Gyeongsik Yang, Heesang Jin, Minkoo Kang, Gi Jun Moon and Chuck Yoo (Korea University, Korea (South))

3
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.

###### Session Chair

Puneet Sharma (Hewlett Packard Labs)

Session Coffee-Break-3-PM

## Virtual Coffee Break

Conference
3:30 PM — 4:00 PM EDT
Local
Jul 8 Wed, 3:30 PM — 4:00 PM EDT

### Virtual Coffee Break

N/A

0
This talk does not have an abstract.

N/A

Session 6-A

## RFID and Backscatter Systems II

Conference
4:00 PM — 5:30 PM EDT
Local
Jul 8 Wed, 4:00 PM — 5:30 PM EDT

### DeepTrack: Grouping RFID Tags Based on Spatio-temporal Proximity in Retail Spaces

Shasha Li (University of California, Riverside, USA); Mustafa Y. Arslan (NEC Laboratories America, Inc., USA); Mohammad Ali Khojastepour (NEC Laboratories America, USA); Srikanth V. Krishnamurthy (University of California, Riverside, USA); Sampath Rangarajan (NEC Labs America, USA)

0
RFID applications for taking inventory and processing transactions in point-of-sale (POS) systems improve operational efficiency but are not designed to provide insights about customers' interactions with products. We bridge this gap by solving the proximity grouping problem to identify groups of RFID tags that stay in close proximity to each other over time. We design DeepTrack, a framework that uses deep learning to automatically track groups of items carried by a customer during her shopping journey. This unearths hidden purchase behaviors helping retailers make better business decisions and paves the way for innovative shopping experiences such as seamless checkout (`a la Amazon Go). DeepTrack employs a recurrent neural network (RNN) with attention mechanisms, to solve the proximity grouping problem in noisy settings without explicitly localizing tags. We tailor DeepTrack's design to track not only mobile groups (products carried by customers) but also flexibly identify stationary tag groups (products on shelves). The key attribute of DeepTrack is that it only uses readily available tag data from commercial off-the-shelf RFID equipment. Our experiments demonstrate that, with only two hours training data, DeepTrack achieves a grouping accuracy of 98.18% (99.79%) when tracking eight mobile (stationary) groups.

### Enabling RFID-Based Tracking for Multi-Objects with Visual Aids: A Calibration-Free Solution

Chunhui Duan, Wenlei Shi, Fan Dang and Xuan Ding (Tsinghua University, China)

0
Identification and tracking of multiple objects are essential in many applications. As a key enabler of automatic ID technology, RFID has got widespread adoption with item-level tagging in everyday life. However, restricted to the computation capability of passive RFID systems, tracking tags has always been a challenging task. Meanwhile, as a fundamental problem in the field of computer vision, object tracking in images has progressed to a remarkable state especially with the rapid development of deep learning in the past few years. To enable lightweight tracking of a specific target, researchers try to complement computer vision to existing RFID architecture and achieves fine granularity. However, such solution requires calibration of the camera's extrinsic parameters at each new setup, which is not convenient for usage. In this work, we propose Tagview, a pervasive identifying and tracking system that can work in various settings without repetitive calibration efforts. It addresses the challenge by skillfully deploying the RFID antenna and video camera at the identical position and devising a multi-target recognition schema with only image-level trajectory information. We have implemented Tagview with commercial RFID and camera devices and evaluated it extensively. Experimental results show that our method can archive high accuracy and robustness.

### Reliable Backscatter with Commodity BLE

Maolin Zhang (University of Science and Technology of China, China); Jia Zhao and Si Chen (Simon Fraser University, Canada); Wei Gong (University of Science and Technology of China, China)

2
Recently backscatter communication with commodity radios has received significant attention since specialized hardware is no longer needed. The state-of-the-art BLE backscatter system, FreeRider, realizes ultra-low-power BLE backscatter communication entirely using commodity devices. It, however, suffers from several key reliability issues, including unreliable two-step modulation, productive-data dependency, and lack of interference countermeasures. To address these problems, we propose RBLE, a reliable BLE backscatter system that works with a single commodity receiver. It first introduces direct frequency shift modulation with the single tone generated by an excitation BLE device, making robust single-bit modulation possible. Then it designs dynamic channel configuration that enables channel hopping to avoid interfered channels. Moreover, it presents BLE packet regeneration that uses adaptive encoding to further enhance reliability for various channel conditions. The prototype is implemented using TI BLE radios and customized tags with FPGAs. Empirical results demonstrate that RBLE achieves more than 17x uplink throughput gains over FreeRider under indoor LoS, NLoS, and outdoor environments. We also show that RBLE can realize uplink ranges of up to 25 m for indoors and 56 m for outdoors.

### Reliable Wide-Area Backscatter via Channel Polarization

Guochao Song, Hang Yang, Wei Wang and Tao Jiang (Huazhong University of Science and Technology, China)

3
A long-standing vision of backscatter communications is to provide long-range connectivity and high-speed transmissions for batteryless Internet-of-Things (IoT). Recent years have seen major innovations in designing backscatters toward this goal. Yet, they either operate at a very short range, or experience extremely low throughput. This paper takes one step further toward breaking this stalemate, by presenting PolarScatter that exploits channel polarization in long-range backscatter links. We transform backscatter channels into nearly noiseless virtual channels through channel polarization, and convey bits with extremely low error probability. Specifically, we propose a new polar code scheme that automatically adapts itself to different channel quality by continuously adding redundant bits, and design a low-cost encoder to accommodate polar codes on resource-constrained backscatter tags. We build a prototype PCB tag and test it in various outdoor and indoor environments. Our experiments show that our prototype achieves up to 10x throughput gain, or extends the range limit by 1.64x compared with the state-of-the-art long-range backscatter solution. We also simulate an IC design in TSMC 65 nm LP CMOS process. Compared with traditional encoders, our encoder reduces storage overhead by three orders of magnitude, and lowers the power consumption to tens of microwatts.

###### Session Chair

Lei Xie (Nanjing University)

Session 6-B

## Network Optimization III

Conference
4:00 PM — 5:30 PM EDT
Local
Jul 8 Wed, 4:00 PM — 5:30 PM EDT

### Clustering-preserving Network Flow Sketching

Yongquan Fu, Dongsheng Li, Siqi Shen and Yiming Zhang (National University of Defense Technology, China); Kai Chen (Hong Kong University of Science and Technology, China)

1
Network monitoring is vital in modern clouds and data center networks that need diverse traffic statistics ranging from flow size distributions to heavy hitters. To cope with increasing network rates and massive traffic volumes, sketch based approximate measurement has been extensively studied to trade the accuracy for memory and computation cost, which unfortunately, is sensitive to hash collisions.

This paper presents a clustering-preserving sketch method to be resilient to hash collisions. We provide an equivalence analysis of the sketch in terms of the K-means clustering. Based on the analysis result, we cluster similar network flows to the same bucket array to reduce the estimation variance and uses the average to obtain unbiased estimation. Testbed shows that the framework adapts to line rates and provides accurate query results. Real-world trace-driven simulations show that LSS remains stable performance under wide ranges of parameters and dramatically outperforms state-of-the-art sketching structures, with over $$10^3$$ to $$10^5$$ times reduction in relative errors for per-flow queries as the ratio of the number of buckets to the number of network flows reduces from 10% to 0.1%.

### Efficient Coflow Transmission for Distributed Stream Processing

Wenxin Li (Hong Kong University of Science & Technology, Hong Kong); Xu Yuan (University of Louisiana at Lafayette, USA); Wenyu Qu (Tianjin University, China); Heng Qi (Dalian University of Technology, China); Xiaobo Zhou, Sheng Chen and Renhai Xu (Tianjin University, China)

2
Distributed streaming applications require the underlying network flows to transmit packets continuously to keep their output results fresh. These results will become stale if no updates come, and their staleness is determined by the slowest flow. At this point, coflows can be semantically comprised. Hence, efficient coflow transmission is critical for streaming applications. However, prior coflow-based solutions have significant limitations. They use a one-shot performance metric---CCT (coflow completion time), which cannot continuously reflect the staleness of the output results for streaming applications. To this end, we propose a new performance metric---coflow age (CA), which tracks the longest time-since-last-service among all flows in a coflow. We consider a datacenter network with multiple coflows that continuously transmit packets between their source-destination pairs, and address the problem of minimizing the average long-term CA while simultaneously satisfying the throughput constraints from the coflows. We design a randomized algorithm and a drift-plus-age algorithm and show that they can make the average long-term CA to achieve nearly two times and arbitrarily close to the optimal value, respectively. Extensive simulations demonstrate that both of the proposed algorithms can significantly reduce the CA of coflows, without violating the throughput requirement of any coflow, compared to the state-of-the-art solution.

### Online Network Flow Optimization for Multi-Grade Service Chains

Victor Valls (Yale University, USA); George Iosifidis (Trinity College Dublin, Ireland); Geeth Ranmal de Mel (IBM Research, United Kingdom (Great Britain)); Leandros Tassiulas (Yale University, USA)

1
We study the problem of in-network execution of data analytic services using multi-grade VNF chains. The nodes host VNFs offering different and possibly time-varying gains for each stage of the chain, and our goal is to maximize the analytics performance while minimizing the data transfer and processing costs. The VNFs' performance is revealed only after their execution, since it is data-dependent or controlled by third-parties, while the service requests and network costs might also vary with time. We devise an operation algorithm that learns, on the fly, the optimal routing policy and the composition and length of each chain. Our algorithm combines a lightweight sampling technique and a Lagrange-based primal-dual iteration, allowing it to be scalable and attain provable optimality guarantees. We demonstrate the performance of the proposed algorithm using a video analytics service, and explore how it is affected by different system parameters. Our model and optimization framework is readily extensible to different types of networks and services.

### SketchFlow: Per-Flow Systematic Sampling Using Sketch Saturation Event

RhongHo Jang (Inha University, Korea (South) & University of Central Florida, USA); DaeHong Min and SeongKwang Moon (Inha University, Korea (South)); David Mohaisen (University of Central Florida, USA); Daehun Nyang (Ewha Womans University & TheVaulters Company, Korea (South))

4
Random sampling is a versatile tool to reduce the processing overhead in various systems. NetFlow uses a local table for counting records per flow, and sFlow sends out periodically collected packet headers to a collecting server over the network. Any measurement system falls into either one of these two models. To reduce the burden on the table or on the network, sampled packets are given to those systems. However, if the sampling rate is more than the available resource capacity, sampled packets will be dropped, which obviously degrades measurement quality. In this paper, we introduce a new concept of per-flow systematic sampling, and provide a concrete sampling method called SketchFlow using a sketch saturation event without any application-specific information to measure accurately per-flow spectral density on large volume of data in real-time. SketchFlow shows a new direction to the sampling framework of sketch saturation event-based sampling. We demonstrate SketchFlow's performance in terms of stable sampling rate, accuracy, and overhead using real-world dataset such as backbone network trace, hard disk I/O trace, and Twitter dataset.

###### Session Chair

Sergey I Nikolenko (Harbour Space University)

Session 6-C

## VR/AR

Conference
4:00 PM — 5:30 PM EDT
Local
Jul 8 Wed, 4:00 PM — 5:30 PM EDT

### Predictive Scheduling for Virtual Reality

I-Hong Hou and Narges Zarnaghinaghsh (Texas A&M University, USA); Sibendu Paul and Y. Charlie Hu (Purdue University, USA); Atilla Eryilmaz (The Ohio State University, USA)

0
A significant challenge for future virtual reality (VR) applications is to deliver high quality-of-experience, both in terms of video quality and responsiveness, over wireless networks with limited bandwidth. This paper proposes to address this challenge by leveraging the predictability of user movements in the virtual world. We consider a wireless system where an access point (AP) serves multiple VR users. We show that the VR application process consists of two distinctive phases, whereby during the first (proactive scheduling) phase the controller has uncertain predictions of the demand that will arrive at the second (deadline scheduling) phase. We then develop a predictive scheduling policy for the AP that jointly optimizes the scheduling decisions in both phases.

In addition to our theoretical study, we demonstrate the usefulness of our policy by building a prototype system. We show that our policy can be implemented under Furion, a Unity-based VR gaming software, with minor modifications. Experimental results clearly show visible difference between our policy and the default one. We also conduct extensive simulation studies, which show that our policy not only outperforms others, but also maintains excellent performance even when the prediction of future user movements is not accurate.

### PROMAR: Practical Reference Object-based Multi-user Augmented Reality

Tengpeng Li, Nam Nguyen and Xiaoqian Zhang (University of Massachusetts Boston, USA); Teng Wang (University of Massachusetts, Boston, USA); Bo Sheng (University of Massachusetts Boston, USA)

0
Augmented reality (AR) is an emerging technology that can weave virtual objects into physical environments, and enable users to interact with them through viewing devices. This paper targets on multi-user AR applications, where virtual objects (VO) 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 poor performance in terms of the accuracy. In this work, we have developed a suite of algorithms that can help the viewers accurately identify the same scene tolerating the view angle differences. 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.

### SCYLLA: QoE-aware Continuous Mobile Vision with FPGA-based Dynamic Deep Neural Network Reconfiguration

Shuang Jiang and Zhiyao Ma (Peking University, China); Xiao Zeng (Michigan State University, USA); Chenren Xu (Peking University, China); Mi Zhang (Michigan State University, USA); Chen Zhang and Yunxin Liu (Microsoft Research, China)

1
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 addresses 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.

### User Preference Based Energy-Aware Mobile AR System with Edge Computing

Haoxin Wang and Linda Jiang Xie (University of North Carolina at Charlotte, USA)

3
The advancement in deep learning and edge computing has enabled intelligent mobile augmented reality (MAR) on resource limited mobile devices. However, today very few deep learning based MAR applications are applied in mobile devices because they are significantly energy-guzzling. In this paper, we design a user preference based energy-aware edge-based MAR system that enables MAR clients to dynamically change their configuration parameters, such as CPU frequency and computation model size, based on their user preferences, camera sampling rates, and available radio resources at the edge server. Our proposed dynamic MAR configuration adaptations can minimize the per frame energy consumption of multiple MAR clients without degrading their preferred MAR performance metrics, such as service latency and detection accuracy. To thoroughly analyze the interactions among MAR configuration parameters, user preferences, camera sampling rate, and per frame energy consumption, we propose, to the best of our knowledge, the first comprehensive analytical energy model for MAR clients. Based on the proposed analytical model, we develop a LEAF optimization algorithm to guide the MAR configuration adaptation and server radio resource allocation. Extensive evaluations are conducted to validate the performance of the proposed analytical model and LEAF algorithm.

###### Session Chair

Damla Turgut (University of Central Florida)

Session 6-D

## Vehicular Networks

Conference
4:00 PM — 5:30 PM EDT
Local
Jul 8 Wed, 4:00 PM — 5:30 PM EDT

### Approximation Algorithms for the Team Orienteering Problem

Wenzheng Xu (Sichuan University, China); Zichuan Xu (Dalian University of Technology, China); Jian Peng (Sichuan University, China); Weifa Liang (The Australian National University, Australia); Tang Liu (Sichuan Normal University, China); Xiaohua Jia (City University of Hong Kong, Hong Kong); Sajal K. Das (Missouri University of Science and Technology, USA)

2
In this paper we study a team orienteering problem, which is to find service paths for multiple vehicles in a network such that the profit sum of serving the nodes in the paths is maximized, subject to the cost budget of each vehicle. This problem has many potential applications in IoT and smart cities, such as dispatching energy-constrained mobile chargers to charge as many energy-critical sensors as possible to prolong the network lifetime. In this paper, we first formulate the team orienteering problem, where different vehicles are different types, each node can be served by multiple vehicles, and the profit of serving the node is a submodular function of the number of vehicles serving it. We then propose a novel 0.32-approximation algorithm for the problem. In addition, for a special team orienteering problem with the same type of vehicles and the profits of serving a node once and multiple times being the same, we devise an improved approximation algorithm. Finally, we evaluate the proposed algorithms with simulation experiments, and the results of which are very promising. Precisely, the profit sums delivered by the proposed algorithms are approximately 12.5% to 17.5% higher than those by existing algorithms.

### Design and Optimization of Electric Autonomous Vehicles with Renewable Energy Source for Smart Cities

Pengzhan Zhou (Stony Brook University, USA); Cong Wang (Old Dominion University, USA); Yuanyuan Yang (Stony Brook University, USA)

2
Electric autonomous vehicles provide a promising solution to the traffic congestion and air pollution problems in future smart cities. Considering intensive energy consumption, charging becomes of paramount importance to sustain the operation of these systems. Motivated by the innovations in renewable energy harvesting, we leverage solar energy to power autonomous vehicles via charging stations and solar-harvesting rooftops, and design a framework that optimizes the operation of these systems from end to end. With a fixed budget, our framework first optimizes the locations of charging stations based on historical spatial-temporal solar energy distribution and usage patterns, achieving (2+\epsilon) factor to the optimal. Then a stochastic algorithm is proposed to update the locations online to adapt to any shift in the distribution. Based on the deployment, a strategy is developed to assign energy requests in order to minimize their traveling distance to stations while not depleting their energy storage. Equipped with extra harvesting capability, we also optimize route planning to achieve a reasonable balance between energy consumed and harvested en-route. Our extensive simulations demonstrate the algorithm can approach the optimal solution within 10-15% approximation error, and improve the operating range of vehicles by up to 2-3 times compared to other competitive strategies.

### Enabling Communication via Automotive Radars: An Adaptive Joint Waveform Design Approach

Ceyhun D Ozkaptan and Eylem Ekici (The Ohio State University, USA); Onur Altintas (Toyota Motor North America R&D, InfoTech Labs, USA)

0
Large scale deployment of connected vehicles with cooperative sensing technologies increases the demand on the vehicular communication spectrum band in 5.9 GHz allocated for exchange of safety messages. To support high data rates needed by such applications, the millimeter-wave (mmWave) automotive radar spectrum at 76-81 GHz spectrum can be utilized for communication. For this purpose, joint automotive radar-communication (JARC) system designs are proposed in the literature to perform both functions using the same waveform. However, employing large bandwidth at 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 that use Orthogonal Frequency-Division Multiplexing (OFDM) signal. 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.

### Revealing Much While Saying Less: Predictive Wireless for Status Update

Zhiyuan Jiang, Zixu Cao, Siyu Fu, Fei Peng, Shan Cao, Shunqing Zhang and Shugong Xu (Shanghai University, China)

2
Wireless communications for status update are becoming increasingly important, especially for machine-type control applications. Existing work has been mainly focused on Age of Information (AoI) optimizations. In this paper, a status-aware predictive wireless interface design, networking and implementation are presented which aim to minimize the status recovery error of a wireless networked system by leveraging online status model predictions. Two critical issues of predictive status update are addressed: practicality and usefulness. Link-level experiments on a Software-Defined-Radio (SDR) testbed are conducted and test results show that the proposed design can significantly reduce the number of wireless transmissions while maintaining a low status recovery error. A Status-aware Multi-Agent Reinforcement learning neTworking solution (SMART) is proposed to dynamically and autonomously control the transmit decisions of devices in an ad hoc network based on their individual statuses. System-level simulations of a multi dense platooning scenario are carried out on a road traffic simulator. Results show that the proposed schemes can greatly improve the platooning control performance in terms of the minimum safe distance between successive vehicles, in comparison with the AoI-optimized status-unaware and communication latency-optimized schemes---this demonstrates the usefulness of our proposed status update schemes in a real-world application.

###### Session Chair

Onur Altintas (Toyota Motor North America, R&D InfoTech Labs)

Session 6-E

## Sprectrum Sharing

Conference
4:00 PM — 5:30 PM EDT
Local
Jul 8 Wed, 4:00 PM — 5:30 PM EDT

### CoBeam: Beamforming-based Spectrum Sharing With Zero Cross-Technology Signaling for 5G Wireless Networks

Lorenzo Bertizzolo and Emrecan Demirors (Northeastern University, USA); Zhangyu Guan (University at Buffalo, USA); Tommaso Melodia (Northeastern University, USA)

7
This article studies an essential yet challenging problem in 5G wireless networks: \emph{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 {\em CoBeam}. In CoBeam, a newly-deployed wireless network is allowed to access a spectrum band based on {\em cognitive beamforming} 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 \emph{programmable physical layer driver}, \emph{cognitive sensing engine}, \emph{beamforming engine}, and \emph{scheduling engine}. Then, we showcase the potential of the CoBeam framework by designing a practical coexistence scheme between Wi-Fi and LTE on unlicensed bands. We also present a prototype of the resulting coexisting Wi-Fi/U-LTE network built on off-the-shelf software radios. Experimental performance evaluation results indicate that CoBeam can achieve significant throughput gain while requiring \emph{no} signaling exchange between the coexisting wireless networks.

### Towards Primary User Sybil-proofness for Online Spectrum Auction in Dynamic Spectrum Access

Xuewen Dong, Qiao Kang, Qingsong Yao, Di Lu and Yang Xu (Xidian University, China); Jia Liu (National Institute of Informatics, Japan)

0
Dynamic spectrum access (DSA) is a promising platform to solve the spectrum shortage problem, in which auction based mechanisms have been extensively studied due to good spectrum allocation efficiency and fairness. Recently, Sybil attacks were introduced in DSA, and Sybil-proof spectrum auction mechanisms have been proposed, which guarantee that each single secondary user (SU) cannot obtain a higher utility under more than one fictitious identities. However, existing Sybil-poof spectrum auction mechanisms achieve only Sybil-proofness for SUs, but not for primary users (PUs), and simulations show that a cheating PU in those mechanisms can obtain a higher utility by Sybil attacks. In this paper, we propose TSUNAMI, the first Truthful and primary user Sybil-proof aUctioN mechAnisM for onlIne spectrum allocation. Specifically, we compute the opportunity cost of each SU and screen out cost-efficient SUs to participate in spectrum allocation. In addition, we present a bid-independent sorting method and a sequential matching approach to achieve primary user Sybil-proofness and 2-D truthfulness, which means that each SU or PU can gain her maximal utility by bidding with her true valuation of spectrum. We evaluate the performance and validate the desired properties of our proposed mechanism through extensive simulations.

### Online Bayesian Learning for Rate Selection in Millimeter Wave Cognitive Radio Networks

Muhammad Anjum Qureshi and Cem Tekin (Bilkent University, Turkey)

1
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: an energy harvesting CRN, in which the system solely relies on the harvested energy source, and an underlay CRN, in which a secondary user restricts its transmission power based on a dynamically changing interference temperature limit such that the primary user remains unharmed. Since the channel quality fluctuates very rapidly in mmWave networks and costly channel state information is not that useful, we consider rate adaptation over an mmWave channel as an online stochastic optimization problem, and propose a Thompson Sampling 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 performance of the proposed algorithm is superior than state-of-the-art, especially when arrivals are favorable.

### U-CIMAN: Uncover Spectrum and User Information in LTE Mobile Access Networks

Rui Zou (North Carolina State University, USA); Wenye Wang (NC State University, USA)

0
Mobile access networks dominate valuable information hardly reachable for outsiders or user devices. For instance, in Dynamic Spectrum Access (DSA) systems, Secondary Users (SUs) have to arduously infer spectrum holes well-known at network sides of Primary Users (PUs). The challenge is how to uncover the spectrum information without aid of commercial, system-wide equipment, which is critical to individual wireless devices with DSA capability. This motivates us to develop a new tool to uncover as much information used to be closed to outsiders or user devices as possible with off-the-shelf products. Given the wide-spread deployment of LTE and its continuous evolution to 5G, we design and implement U-CIMAN, a client-side system to accurately UnCover spectrum occupancy and associated user Information in Mobile Access Networks of LTE systems. Besides measuring spectrum tenancy in unit of resource blocks, U-CIMAN discovers user mobility and traffic types associated with spectrum usage through decoded control messages and user data bytes. Equipped with U-CIMAN, we conduct 4-month detailed accurate spectrum measurement on a commercial LTE cell, making observations such as the predictive power of Modulation and Coding Scheme on spectrum tenancy, and channel off-time bounded under 10 seconds, to name a few.

###### Session Chair

Mariya Zheleva (UAlbany SUNY)

Session 6-F

## mmWave

Conference
4:00 PM — 5:30 PM EDT
Local
Jul 8 Wed, 4:00 PM — 5:30 PM EDT

### MAMBA: A Multi-armed Bandit Framework for Beam Tracking in Millimeter-wave Systems

Irmak Aykin, Berk Akgun, Mingjie Feng and Marwan Krunz (University of Arizona, USA)

2
Millimeter-wave (mmW) spectrum is a major candidate to support the high data rates of 5G systems. However, due to directionality of mmW communication systems, misalignments between the transmit and receive beams occur frequently, making link maintenance particularly challenging and motivating the need for fast and efficient beam tracking. In this paper, we propose a multi-armed bandit framework, called MAMBA, for beam tracking in mmW systems. We develop a reinforcement learning algorithm, called adaptive Thompson sampling (ATS), that MAMBA embodies for the selection of appropriate beams and transmission rates along these beams. ATS uses prior beam-quality information collected through the initial access and updates it whenever an ACK/NAK feedback is obtained from the user. The beam and the rate to be used during next downlink transmission are then selected based on the updated posterior distributions. Due to its model-free nature, ATS can accurately estimate the best beam/rate pair, without making assumptions regarding the temporal channel and/or user mobility. We conduct extensive experiments over the 28 GHz band using a 4 x 8 phased-array antenna to validate the efficiency of ATS, and show that it improves the link throughput by up to 182%, compared to the beam management scheme proposed for 5G.

### PASID: Exploiting Indoor mmWave Deployments for Passive Intrusion Detection

Francesco Devoti (Politecnico di Milano, Italy); Vincenzo Sciancalepore (NEC Laboratories Europe GmbH, Germany); Ilario Filippini (Politecnico di Milano, Italy); Xavier Costa-Perez (NEC Laboratories Europe, Germany)

3
As 5G deployments start to roll-out, indoor solutions are increasingly pressed towards delivering a similar user experience. Wi-Fi is the predominant technology of choice indoors and major vendors started addressing this need by incorporating the mmWave band to their products. In the near future, mmWave devices are expected to become pervasive, opening up new business opportunities to exploit their unique properties.

In this paper, we present a novel PASsive Intrusion Detection system, namely PASID, leveraging on already deployed indoor mmWave communication systems. PASID is a software module that runs in off-the-shelf mmWave devices. It automatically models indoor environments in a passive manner by exploiting regular beamforming alignment procedures and detects intruders with a high accuracy. We model this problem analytically and show that for dynamic environments machine learning techniques are a cost-efficient solution to avoid false positives. PASID has been implemented in commercial off-the-shelf devices and deployed in an office environment for validation purposes. Our results show its intruder detection effectiveness (~ 99% accuracy) and localization potential (~ 2 meters range) together with its negligible energy increase cost (~ 2%).

### Turbo-HB: A Novel Design and Implementation to Achieve Ultra-Fast Hybrid Beamforming

Yongce Chen, Yan Huang, Chengzhang Li, Thomas Hou and Wenjing Lou (Virginia Tech, USA)

3
Hybrid beamforming (HB) architecture has been widely recognized as the most promising solution to mmWave MIMO systems. A major practical challenge for HB is to obtain a solution in $$\sim$$1 ms -- an extremely stringent time requirement considering the complexities involved in HB. In this paper, we present the design and implementation of Turbo-HB -- a novel beamforming design under the HB architecture that can obtain the beamforming matrices in about 1 ms. The key ideas in our design include (i) reducing the complexity of SVD techniques by exploiting the limited number of channel paths at mmWave frequencies, and (ii) achieving large-scale parallel computation. To validate our design, we implement Turbo-HB on an off-the-shelf Nvidia GPU and conduct extensive experiments. We show that Turbo-HB can meet $$\sim$$1 ms timing requirement while delivering competitive throughput performance compared to state-of-the-art algorithms.

### SIMBA: Single RF Chain Multi-User Beamforming in 60 GHz WLANs

Keerthi Priya Dasala (Rice University, USA); Josep M Jornet (Northeastern University, USA); Edward W. Knightly (Rice University, USA)

1
Multi-user transmission in 60 GHz Wi-Fi can achieve data rates up to 100 Gb/sec by multiplexing multiple user data streams. However, a fundamental limit in the approach is that each RF chain is limited to supporting one stream or one user. To overcome this limit, we propose $$\textit{\(\textbf{SI}$$ngle RF chain $$\textbf{M}$$ulti-user $$\textbf{B}$$e$$\textbf{A}$$mforming (SIMBA)}\), a novel framework for multi-stream multi-user downlink transmission via a single RF chain. We build on single beamformed transmission via overlayed constellations to multiplex multiple users' modulated symbols such that grouped users at different locations can share the same transmit beam from the AP. For this, we introduce user grouping and beam selection policies that span tradeoffs in data rate, training, and computation overhead. We implement a programmable WLAN testbed using software-defined radios and commercial 60-GHz transceivers and collect over-the-air measurements using phased array antennas and horn antennas with varying beamwidth. We find that in comparison to single user transmissions, $$\textit{SIMBA}$$ achieves $$2\times$$ improvement in aggregate rate and two-fold delay reduction for simultaneous transmission to four users.

###### Session Chair

Anna Maria Vegni (Roma Tre University)

Session 6-G

## SDN II

Conference
4:00 PM — 5:30 PM EDT