Navigation paths and corresponding travel times play a key role in location-based services (LBS) of which large-scale navigation path caching constitutes a fundamental component. In view of the highly dynamic real-time traffic changes in road networks, the main challenge amounts to updating paths in the cache in a fashion that incurs minimal costs due to querying external map service providers and cache maintenance. In this paper, we propose a hybrid graph approach in which an LBS provider maintains a dynamic graph with edge weights representing travel times, and queries the external map server so as to ascertain high fidelity of the cached paths subject to stringent limitations on query costs. We further deploy our method in one of the biggest on-demand food delivery platforms and evaluate the performance against state-of-the-art methods. Our experimental results demonstrate the efficacy of our approach in terms of both substantial savings in the number of required queries and superior fidelity of the cached paths.

The Border Gateway Protocol (BGP) is a distributed protocol that manages interdomain routing without requiring a centralized record of which autonomous systems (ASes) connect to which others. Many methods have been devised to infer the AS topology from publicly available BGP data, but none provide a general way to handle the fact that the data are notoriously incomplete and subject to error. This paper describes a method for reliably inferring AS-level connectivity in the presence of measurement error using Bayesian statistical inference acting on BGP routing tables from multiple vantage points. We employ a novel approach for counting AS adjacency observations in the AS-PATH attribute data from public route collectors, along with a Bayesian algorithm to generate a statistical estimate of the AS-level network. Our approach also gives us a way to evaluate the accuracy of existing reconstruction methods and to identify advantageous locations for new route collectors or vantage points.

Nowadays, the rapid development of self-driving technology and its fusion with the current vehicle electrification process has given rise to electric self-driving taxis (es-taxis). Foreseeably, es-taxis will become a major force that serves the massive urban mobility demands not far into the future. Though promising, it is still a fundamental unsolved problem of effectively deciding when and where a city-scale fleet of es-taxis should be charged, so that enough es-taxis will be available whenever and wherever ride requests are submitted. Furthermore, charging decisions are far from isolated, but tightly coupled with the order dispatch process that matches orders with es-taxis. Therefore, in this paper, we investigate the problem of joint order dispatch and charging in es-taxi systems, with the objective of maximizing the ride-hailing platform's long-term cumulative profit. Technically, such problem is challenging in a myriad of aspects, such as long-term profit maximization, partial statistical information on future orders, etc. We address the various arising challenges by meticulously integrating a series of methods, including distributionally robust optimization, primal-dual transformation, and second order conic programming to yield far-sighted decisions. Finally, we validate the effectiveness of our proposed methods though extensive experiments based on two large-scale real-world online ride-hailing order datasets.

Vehicle-to-vehicle (V2V) communication allows vehicles to directly exchange messages, increasing their situational awareness and offering the potential to prevent hundreds of thousands vehicular crashes annually. Cellular Vehicle-to-Everything (C-V2X), with its LTE-V2X and New Radio (NR)-V2X variants in 4G/LTE- and 5G-based C-V2X, is emerging as the main V2V technology. However, despite security protocols and standards for C-V2X, we expose in this paper that its physical (PHY) and MAC layers are not resilient against intelligent, protocol-aware attacks due to the very predictable PHY-layer structure and vulnerable scheduling algorithm used in both LTE-V2X and NR-V2X. We devise two stealthy denial-of-service (DoS) exploits that dramatically degrade C-V2X availability, thereby increasing the chances of fatal vehicle collisions. We experimentally evaluate our attacks on an integrated, hybrid testbed with USRPs and state-of-the-art LTE-V2X devices as well as through extensive simulations, showing that within seconds, our attacks can reduce a target's packet delivery ratio by 90% or degrade C-V2X channel throughput by 50%. We propose, analyze, and evaluate detection approaches as well as mitigation techniques to address the vulnerabilities we expose in the C-V2X PHY/MAC layers, providing direction towards better-secured, resilient 5G C-V2X.

PIN inference attack leveraging keystroke-induced side-channel information poses a substantial threat to the security of people's privacy and properties. Among various PIN inference attacks, video-assisted method provide more intuitive and robust side-channel information to infer PINs. But it usually requires there is no visual occlusion between the attacker and the victims or their hand gestures, making the attackers either easy to expose themselves or inapplicable to the scenarios such as ATM or POS terminals. In this paper, we present a novel and practical video-assisted PIN inference system, ArmSpy, which infers victim's PIN by observing from behind the victims in a stealthy way. Specifically, ArmSpy explores the subtle keystroke-induced arm posture changes, including elbow bending angle changes and the spatial relationship between different arm joints, to infer the PIN entries. We develop the keystroke inference model to detect the keystroke events and pinpoint the keystroke positions, and then accurately infer the PINs with the proposed inferred PIN coordination mechanism. Extensive experimental results demonstrate that ArmSpy can achieve over 67% average accuracy on inferring the PIN with 3 attempts and even over 80% for some victims, indicating the severity of the threat posed by ArmSpy.

Deep learning has shown impressive performance in semantic segmentation, but it is still unaffordable for resource-constrained mobile devices. While offloading computation tasks is promising, the high traffic demands overwhelm the limited bandwidth. Existing compression algorithms are not fit for semantic segmentation, as the lack of obvious and concentrated regions of interest (RoIs) forces the adoption of uniform compression strategies, leading to low compression ratios or accuracy. This paper introduces STAC, a DNN-driven compression scheme tailored for edge-assisted semantic video segmentation. STAC is the first to exploit DNN's gradients as spatial sensitivity metrics for spatial adaptive compression and achieves superior compression ratio and accuracy. Yet, it is challenging to adapt this content-customized compression to videos. Practical issues include varying spatial sensitivity and huge bandwidth consumption for compression strategy feedback and offloading. We tackle these issues through a spatiotemporal adaptive scheme, which (1) takes partial strategy generation operations offline to reduce communication load, and (2) propagates compression strategies and segmentation results across frames through dense optical flow, and adaptively offloads keyframes to accommodate video content. We implement STAC on a commodity mobile device. Experiments show that STAC can save up to 20.95% of bandwidth without losing accuracy, compared to the state-of-the-art algorithm.

We present FlexPatch, a novel mobile system to enable accurate and real-time object detection over high-resolution video streams. A widely-used approach for real-time video analysis is detection-based tracking (DBT), i.e., running the heavy-but-accurate detector every few frames and applying a lightweight tracker for in-between frames. However, the approach is limited for real-time processing of high-resolution videos in that i) a lightweight tracker fails to handle occlusion, object appearance changes, and occurrences of new objects, and ii) the detection results do not effectively offset tracking errors due to the high detection latency. We propose tracking-aware patching technique to address such limitations of the DBT frameworks. It effectively identifies a set of subareas where the tracker likely fails and tightly packs them into a small-sized rectangular area where the detection can be efficiently performed at low latency. This prevents the accumulation of tracking errors and offsets the tracking errors with frequent fresh detection results. Our extensive evaluation shows that FlexPatch not only enables real-time and power-efficient analysis of high-resolution frames on mobile devices but also improves the overall accuracy by 146% compared to baseline DBT frameworks.

Crowdsourcing enables a paradigm to conduct the manual annotation and the analytics by those recruited workers, with their rewards relevant to the quality of the results. Existing dispatchers fail to capture the resource-quality trade-off for video analytics, since the configurations supported by various workers are different, and the workers' availability is essentially dynamic. To determine the most suitable configurations as well as workers for video analytics, we formulate a non-linear mixed program in a long-term scope, maximizing the profit for the crowdsourcing platform. Based on previous results under various configurations and workers, we design an algorithm via a series of subproblems to decide the configurations adaptively upon the prediction of the worker rewards. Such prediction is based on volatile multi-armed bandit to capture the workers' availability and stochastic changes on resource uses. Via rigorous proof, the regret is ensured upon the Lyapunov optimization and the bandit, measuring the gap between the online decisions and the offline optimum. Extensive trace-driven experiments show that our algorithm improves the platform profit by 37%, compared with other algorithms.

Video streaming plays a critical role in the video analytics pipeline and thus its adaptation scheme has been a focus of optimization. As machine learning algorithms have become main consumers of video contents, the streaming adaptation decision should be made to optimize their inference performance. Existing video streaming adaptation schemes for video analytics are usually designed to adapt to bandwidth and content variations separately, which fail to consider the coordination between transmission and computation. Given the nature of batch transmission in video streaming and batch processing in deep learning-based inference, we observe that the choices of the batch sizes directly affects the bandwidth efficiency, the response delay and the accuracy of the deep learning inference in video analytics. In this work, we investigate the effect of the batch size in transmission and processing, formulate the optimal batch size adaptation problem, and further develop the deep reinforcement learning-based solution. Practical issues are further addressed for Implementation. Extensive simulations are conducted for performance evaluation, whose results demonstrate the superiority of our proposed batch adaptive streaming approach over the baseline streaming approaches.

The advent of high-accuracy and resource-intensive deep neural networks (DNNs) has fulled the development of live video analytics, where camera videos need to be streamed over the network to edge or cloud servers with sufficient computational resources. Although it is promising to strike a balance between available bandwidth and server-side DNN inference accuracy by adjusting video encoding configurations, the influences of fine-grained network and video content dynamics on configuration performance should be addressed. In this paper, we propose CASVA, a Configuration-Adaptive Streaming framework designed for live Video Analytics. The design of CASVA is motivated by our extensive measurements on how video configuration affects its bandwidth requirement and inference accuracy. To handle the complicated dynamics in live analytics streaming, CASVA trains a deep reinforcement learning model which does not make any assumptions about the environment but learns to make configuration choices through its experiences. A variety of real-world network traces are used to drive the evaluation of CASVA. The results on a multitude of video types and video analytics tasks show the advantages of CASVA over state-of-the-art solutions.

Interactive applications have deadline requirements, e.g. video conferencing and online gaming. Compared with a single path, which may be less stable or bandwidth insufficient, using multiple network paths simultaneously (e.g., WiFi and cellular network) can leverage the ability of multiple paths to service for the deadline. However, existing multipath schedulers usually ignore the deadline and the influence from subsequent blocks to the current scheduling decision when multiple blocks exist at the sender. In this paper, we propose DAMS, a Deadline-Aware Multipath Scheduler aiming to deliver more blocks with heterogeneous attributes before their deadlines. DAMS carefully schedules the sending order of blocks and balances its allocation on multiple paths to reduce the waste of bandwidth resources with the consideration of the block's deadline. We implement DAMS with the inspiration of MPQUIC in user space. The extensive experimental results show that DAMS brings 41%-63% performance improvement on average compared with existing multipath solutions.

The widespread of smart devices and the development of mobile networks brings the growing popularity of live streaming services worldwide. In addition to the video and audio transmission, a lot more media content is sent to the audiences as well, including player statistics for a sports stream, subtitles for living news, etc. However, due to the diverse transmission process between live streams and other media content, the synchronization of them has grown to be a great challenge. Unfortunately, the existing commercial solutions are not universal, which require specific server cloud services or CDN and limit the users' free choices of web infrastructures. To address the issue, we propose a lightweight universal event-synchronizing solution for live streaming, called LSync, which inserts a series of audio signals containing metadata into the original audio stream. It brings no modification to the original live broadcast process and thus fits prevalent live broadcast infrastructure. Evaluations on real system show that the proposed solution reduces the signal processing delay by at most 5.62% of an audio buffer length in mobile phones and ensures real-time signal processing. It also achieves a data rate of 156.25 bps in a specific configuration and greatly outperforms recent works.