This paper studies a new but important pattern for items in data streams, called promising items. The promising items mean that the frequencies of an item in multiple continuous time windows show an upward trend overall, while a slight decrease in some of these windows is allowed. Many practical applications can benefit from the property of promising items, e.g., detecting potential hot events or news in social networks, preventing network congestion in communication channels, and monitoring latent attacks in computer networks. To accurately find promising items in data streams in real-time under limited memory space, we propose a novel structure named Scout Sketch, which consists of Filter and Finder. Filter is devised based on the Bloom filter to eliminate the ungratified items with less memory overload; Finder records some necessary information about the potential items and detects the promising items at the end of each time window, where we propose some tailor-made detection operations. We also analyze the theoretical performance of Scout Sketch. Finally, we conducted extensive experiments based on four real-world datasets. The experimental results show that the F1 Score and throughput of Scout Sketch are about 2.02 and 7.23 times that of the compared solutions, respectively.

Network fluctuations can cause unpredictable degradation of the user's quality of experience (QoE) on real-time video streaming. The intrinsic property of real-time video streaming, which generates delay-sensitive and chunk-based video frames, makes the situation even more complicated. Although previous approaches have tried to alleviate this problem by controlling the video bitrate based on the current network capacity estimate, they do not take into account the explicit queueing delay experienced by the video frame in determining the bitrate of upcoming video frames. To tackle this problem, we propose a new real-time video streaming system, Exstream, that can adapt to dynamic network conditions with the help of video bitrate control method and bandwidth estimation method designed to support real-time video streaming environments. \system explicitly estimates the queueing delay experienced by the video frame based on the transmission time budget that each frame can maximally utilize, which depends on the frame generation interval, and adjusts the bitrate of newly generated video frames to suppress the queueing delay level close to zero. Our comprehensive experiments demonstrate that Exstream achieves lower frame delay than four existing systems, Salsify, WebRTC, Skype, and Hangouts without frequent video frame skip.

In stream processing applications, an operator is often instantiated into multiple parallel execution instances, referred to as executors, to facilitate large-scale data processing. Due to unpredictable changes in executor workloads, data tuples processed by different executors may exhibit varying latency. The executor with the maximum latency significantly impacts the end-to-end latency. Existing solutions, such as load balancing and horizontal scaling, which involve workload migration, often incur substantial time overhead. In contrast, elastically scaling up/down resources of executors can offer rapid adaptability; however, prior works only considered CPU scaling.

This paper presents Emma, an elastic multi-resource manager. The core of Emma is a multi-resource provisioning plan that conducts performance analysis and resource adjustment in real-time. We explore the relationship between resources and performance experimentally and theoretically, guiding the plan to adaptively allocate the appropriate combination of resources to 1) accommodate the dynamic workload; 2) efficiently utilize resources to enhance the performance of as many executors as possible. Additionally, we propose an online learning method that makes the manager seamlessly adapt to diverse stream applications. We integrate Emma with Apache Samza, and our experiments show that compared to existing solutions, Emma can significantly reduce latency by orders of magnitude in real-world applications.

We study the optimal control of multiple video streams over a wireless downlink from a base-transceiver-station (BTS)/access point to N end-devices (EDs). The BTS sends video packets to each ED under a joint transmission energy constraint, the EDs choose when to play out the received packets, and the collective goal is to provide a high Quality-of-Experience (QoE) to the clients/end-users. All EDs send feedback about their states and actions to the BTS which reaches it after a fixed deterministic delay. We analyze this team problem with delayed feedback as a cooperative Multi-Agent Constrained Partially Observable Markov Decision Process (MA-C-POMDP).

First, using a recently established strong duality result for MA-C-POMDPs, the original problem is decomposed into N independent unconstrained transmitter-receiver (two-agent) problems---all sharing a Lagrange multiplier (that also needs to be optimized for optimal control). Thereafter, the common information (CI) approach and the formalism of approximate information states (AISs) are used to guide the design of a neural-network based architecture for learning-based multi-agent control in a single unconstrained transmitter-receiver problem. Finally, simulations on a single transmitter-receiver pair with a stylized QoE model are performed to highlight the advantage of delay-aware two-agent coordination over the transmitter choosing both transmission and play-out actions (perceiving the delayed state of the receiver as its current state).

Acoustic tracking and positioning technologies using microphones and speakers have gained significant interest for applications like virtual reality, augmented reality, and IoT devices. However, existing methods still face challenges in real-world deployment due to multipath interference, Doppler frequency shift, and sampling frequency offset between devices. We propose a versatile Acoustic Tracking and Positioning (ATP) method to address these challenges. First, we propose an iterative sampling frequency offset calibration method. Next, We propose a Doppler frequency shift estimation and compensation model. Finally, we propose a fast adaptive algorithm to reconstruct the line-of-sight (LOS) signal under multipath. We implement ATP in Android and PC and compare it with eight different methods. Evaluation results show that ATP achieves mean accuracy of 0.66 cm, 0.56 cm, and 1.0 cm in tracking, ranging, and positioning tasks. It is 2×, 6×, and 5.8× better than the state-of-the-art methods. ATP advances acoustic sensing for practical applications by providing a robust solution for real-world environments.

Drones have demonstrated their pivotal role in various applications such as search-and-rescue, smart logistics, and industrial inspection, with accurate localization playing an indispensable part. However, in high dynamic range and rapid motion scenarios, traditional visual sensors often face challenges in pose estimation. Event cameras, with their high temporal resolution, present a fresh opportunity for perception in such challenging environments. Current efforts resort to event-visual fusion to enhance the drone's sensing capability. Yet, the lack of efficient event-visual fusion algorithms and corresponding acceleration hardware causes the potential of event cameras to remain underutilized. In this paper, we introduce EventBoost, an acceleration platform designed for drone-based applications with event-image fusion. We propose a suit of novel algorithms through software-hardware co-design on Zynq SoC, aimed at enhancing real-time localization precision and speed. EventBoost achieves enhanced visual fusion precision and markedly elevated processing efficiency. The performance comparison with two state-of-the-art systems shows EventBoost achieves a 24% im- provement in accuracy 24.33% with a 30 ms latency on resource- constrained platforms. We further substantiate EventBoost's exemplary performance through real-world application cases.

In recent years, Bluetooth Low Energy (BLE) has become one of the most wildly used wireless protocols and it is common that users carry one or more BLE devices. With the extensive deployment of BLE devices, there is a significant privacy risk if these BLE devices can be tracked. However, the common wisdom suggests that the risk of BLE location tracking is negligible. The reason is that researchers believe there are no stable BLE fingerprints that are stable across different scenarios (e.g., temperatures) for different BLE devices with the same model. In this paper, we introduce a novel physical-layer fingerprint named Transient Dynamic Fingerprint (TDF), which originated from the negative feedback control process of the frequency synthesizer. Because of the hardware imperfection, the dynamic features of the frequency synthesizer are different, making TDF unique among different devices, even with the same model. Furthermore, TDF keeps stable under different thermal conditions. Based on TDF, we propose BTrack, a practical BLE device tracking system and evaluate its tracking performance in different environments. The results show BTrack works well once BLE beacons are effectively received. The identification accuracy is 35.38%-57.41% higher than the existing method, and stable over temperatures, distances, and locations.

Crowdsensing networks for the sole purpose of performing spectrum measurements have resulted in prior initiatives that have failed primarily due to their costs for maintenance. In this paper, we take a different view and propose ORAN-Sense, a novel architecture of \ac{iot} spectrum crowd-sensing devices integrated into the Next Generation of cellular networks. We use this framework to extend the capabilities of 5G networks and localize a transmitter that does not collaborate in the process of positioning. While 5G signals can not be applied to this scenario as the transmitter does not participate in the localization process through dedicated pilot symbols and data, we show how to use Time Difference of Arrival-based positioning using low-cost spectrum sensors, minimizing hardware impairments of low-cost spectrum receivers, introducing methods to address errors caused by over-the-air signal propagation, and proposing a low-cost synchronization technique. We have deployed our localization network in two major cities in Europe. Our experimental results indicate that signal localization of non-collaborative transmitters is feasible even using low-cost radio receivers with median accuracies of tens of meters with just a few sensors spanning cities, which makes it suitable for its integration in the Next Generation of cellular networks.

Satisfiability Modulo Theories (SMT) based network configuration verification tools are powerful tools in preventing network configuration errors. However, their fundamental limitation is efficiency, because they rely on generic SMT solvers to solve SMT problems, which are in general NP-complete. In this paper, we show that by leveraging network domain knowledge, we can substantially accelerate SMT-based network configuration verification. Our key insights are: given a network configuration verification formula, network domain knowledge can (1) guide the search of solutions to the formula by avoiding unnecessary search spaces; and (2) help simplify the formula, reducing the problem scale. We leverage these insights to design a new SMTbased network configuration verification tool called NetSMT. Extensive evaluation using real-world topologies and synthetic network configurations shows that NetSMT achieves orders of magnitude improvements compared to state-of-the-art methods.

P4 is widely adopted for programming data planes in software-defined networking. Formal verification of P4 programs is essential to ensure network reliability and security. However, existing P4 verifiers overlook the stateful nature of packet processing, rendering them inadequate for verifying complex stateful P4 programs.

In this paper, we introduce a novel concept called packet invariants to address the stateful aspects of P4 programs. We present an automated verification tool specifically designed for stateful P4 programs. This algorithm efficiently discovers and validates packet invariants in a data-driven manner, offering a novel and effective verification approach for stateful P4 programs. To the best of our knowledge, this approach represents the first attempt to generate and leverage domain-specific invariants for P4 program verification. We implement our approach in a prototype tool called P4Inv. Experimental results demonstrate its effectiveness in verifying stateful P4 programs.

Given the high cost associated with directly measuring the traffic matrix (TM), researchers have dedicated decades to devising methods for estimating the complete TM from low-cost link loads by solving a set of heavily ill-posed linear equations. Today's increasingly intricate networks present an even greater challenge: the routing matrix within these equations can no longer be deemed reliable. To address this challenge, we, for the first time, employ a flow-based generative model for TM estimation problem by establishing an invertible correlation between TM and link loads, oblivious of the routing matrix. We demonstrate that the lost information within the ill-posed equations can be independently segregated from the TM. Our model collaboratively learns the invertible correlations between TM and link loads as well as the distribution of the lost information. As a result, our model can unbiasedly reverse-transform the link loads to the true TM. Our model has undergone extensive experiments on two real-world datasets. Surprisingly, even without knowledge of the routing matrix, it significantly outperforms six representative baselines in deterministic and noisy routing scenarios regarding estimation accuracy and distribution similarity. Particularly, if the actual routing matrix is absent, our model can improve the performance of the best baseline by 41%～58%.

Edge computing gained popularity for its promises of low latency and high-quality computing services to users. However, it has also introduced the challenge of mutual untrust between user and edge devices for service level agreement (SLA) compliance. This obstacle hampers wide adoption of edge computing, especially in pervasive edge computing (PEC) where edge devices can freely enter or exit the market, which makes verifying and enforcing SLAs significantly more challenging. In this paper, we propose a framework for verifying and enforcing SLAs in PEC, allowing a user to assess SLA compliance of an edge service and ensure correctness of the service results. Our solution, called VeriEdge, employs a verifiable delayed sampling approach to sample a small number of computation steps, and relies on randomly selected verifiers to verify correctness of the computation results. To make sure the verification process is non-manipulable, we employ verifiable random functions to post-select the verifier(s). A dispute protocol is designed to resolve disputes for potential misbehavior. Rigorous security analysis demonstrates that VeriEdge achieves a high probability of detecting SLA violation with a minimal overhead. Experimental results indicate that VeriEdge is lightweight, practical, and efficient.

Modern communication networks support local fast re-routing (FRR) to quickly react to link failures. However, configuring such FRR mechanisms is challenging as the rules have to be defined ahead of time, without knowledge of the failures, and can depend only on local decisions made by the nodes incident to a failed link. Designing failover protection against multiple link failures is particularly difficult. We present a novel synthesis approach which addresses this challenge by generating FRR rules in an automated and provably correct manner. Our network model assumes that each node maintains a prioritised list of backup links (a.k.a. skipping forwarding) - an FRR method that allows for a memory-efficient deployment. We study the theoretical properties of the model and implement a synthesis method in our tool SyPer that aims to provide perfect resilience: if there are up to k link failures, we can always route traffic between any two nodes as long as they are still connected in the underlying physical network. To this end, SyPer focuses on the synthesis of efficient forwarding rules using the BDD (binary decision diagram) methodology and our empirical evaluation shows that SyPer is feasible, and can synthesize robust network configuration in realistic settings.

Industrial protocols are widely used in Industrial Control Systems (ICSs) to network physical devices, thus playing a crucial role in securing ICSs. However, most commercial industrial protocols are proprietary and owned by their vendors, which impedes the implementation of protections against cyber threats. In this paper, we design REInPro to Reverse Engineer Industrial Protocols. REInPro is inspired by the fact that the structure of industrial protocols can be determined by a particular field referred to control field. By applying a probabilistic model of network traffic behavior, REInPro automatically identifies the control field and groups the associated network traffic into clusters. REInPro then infers critical semantics of industrial protocols by differentiating the features of corresponding protocol fields. We have experimentally implemented and evaluated REInPro using 8 different industrial protocols across 6 Programmable Logic Controllers (PLCs) belonging to 5 original equipment manufacturers. The experimental results show REInPro to reverse engineer the formats and semantics of industrial protocols with an average correctness/perfection of 0.70/0.58 and 0.96/0.39.

Recently, sharding has become a popular direction to scale out blockchain systems by dividing the network into shards that process transactions in parallel. However, secure and efficient cross-shard transaction processing remains a vital and unaddressed challenge. Existing work handles a cross-shard transaction via transaction division: dividing it into sub-transactions, processing them separately, and combing the processing results. Such an approach is unfavorable for decentralized blockchain due to its reliance on trustworthy parties, e.g., the client or a reference node, to perform the transaction division and result combination. Furthermore, the processing result of one transaction can affect another, violating the important property of transaction isolation. In this work, we propose Sharon, a novel sharding protocol that processes cross-shard transactions via shard rotation rather than transaction division. In Sharon, shards rotate to merge pairwisely and process cross-shard transactions when merged. Sharon eliminates reliance on trustworthy parties and provides transaction isolation in nature because transactions are no longer divided. Nevertheless, it poses a scientific question of when and how to merge the shards to improve system performance. To answer the question, we formally define the shard scheduling problem to minimize transaction confirmation latency and propose a novel construction algorithm.

Fiber cuts reduce network capacity and take a long time to fix in optical wide-area networks. It is important to select the best restoration plan that minimizes throughput loss by reconfiguring wavelengths on remaining healthy fibers for affected IP links. Recent work studies optimal restoration plan or ticket selection problem in traffic engineering (TE) in a one-shot setting of only one TE interval (5 minutes). Since fiber repair often takes hours, in this work, we extend to consider restoration ticket selection with traffic dynamics over multiple intervals.

To balance restoration performance with reconfiguration overhead, we perform dynamic ticket selection every T time steps. We propose an end-to-end learning approach to solve this T-step ticket selection problem as a classification task, combining traffic trend extraction and ticket selection in the same learning model. It uses convolution LSTM network to extract temporal and spatial features from past demand matrices to determine the ticket most likely to perform well T steps down the road, without predicting future traffic or solving any TE optimization. Trace-driven simulation shows that our new TE system, Archie, reduces over 25% throughput loss and is over 3500x faster than conventional demand prediction approach, which requires solving TE many times.