High-rate Data Streams Research Articles

Online Support Vector Machine with a Single Pass for Streaming Data

In this paper, we focus on training a support vector machine (SVM) online with a single pass over streaming data.Traditional batch-mode SVMs require previously prepared training data; these models may be unsuitable for streaming data circumstances. Online SVMs are effective tools for solving this problem by receiving data streams consistently and updating model weights accordingly. However, most online SVMs require multiple data passes before the updated weights converge to stable solutions, and may be unable to address high-rate data streams. This paper presents OSVM_SP, a new online SVM with a single pass over streaming data, and three budgeted versions to bound the space requirement with support vector removal principles. The experimental results obtained with five public datasets show that OSVM_SP outperforms most state-of-the-art single-pass online algorithms in terms of accuracy and is comparable to batch-mode SVMs. Furthermore, the proposed budgeted algorithms achieve comparable predictive performance with only 1/3 of the space requirement.

Search for long-lived particles decaying into muon pairs in proton-proton collisions at sqrt{s} = 13 TeV collected with a dedicated high-rate data stream

A search for long-lived particles decaying into muon pairs is performed using proton-proton collisions at a center-of-mass energy of 13 TeV, collected by the CMS experiment at the LHC in 2017 and 2018, corresponding to an integrated luminosity of 101 fb−1. The data sets used in this search were collected with a dedicated dimuon trigger stream with low transverse momentum thresholds, recorded at high rate by retaining a reduced amount of information, in order to explore otherwise inaccessible phase space at low dimuon mass and nonzero displacement from the primary interaction vertex. No significant excess of events beyond the standard model expectation is found. Upper limits on branching fractions at 95% confidence level are set on a wide range of mass and lifetime hypotheses in beyond the standard model frameworks with the Higgs boson decaying into a pair of long-lived dark photons, or with a long-lived scalar resonance arising from a decay of a b hadron. The limits are the most stringent to date for substantial regions of the parameter space. These results can be also used to constrain models of displaced dimuons that are not explicitly considered in this paper.

A Probabilistic Shaping Approach for Optical Region-of-Interest Signaling

We propose a probabilistic shaping approach for region-of-interest signaling, where a low-rate signal controls the desired probabilistic ranges of a high-rate data stream using a flexible distribution controller. In addition, we introduce run-length-aware values for frozen bit indices in systematic polar code to minimize the run-length without using run-length-limited code. Our compact system can support soft-decision forward-error-correction decoding with excellent spectral efficiency compared with related work based on hybrid modulation schemes.

Mono Camera-Based Optical Vehicular Communication for an Advanced Driver Assistance System

This technical paper proposes a new waveform combining the low-rate and high-rate data streams to detect the region-of-interest signal in a high-mobility environment using optical camera communication. The proposed technique augments the bit rate of the low-rate stream; consequently, the link setup time is reduced and the requirement of low frame rate camera is eliminated. Additionally, both the low-rate and high-rate data streams in the proposed bi-level pulse position modulation are decoded with a unique adaptive thresholding mechanism with a high frame rate camera. We also propose a vehicle localization scheme to assist the drivers in maintaining a safe following distance that can significantly reduce the frequency of accidents. Moreover, two neural networks are proposed to detect the light-emitting diodes (LEDs) for localization and communication, and to estimate the road curvature from different rear LED shapes of the forwarding vehicle, respectively. The system is implemented, and its performance is analyzed in Python 3.7. The implementation results show that the proposed system is able to achieve 75% localization accuracy, a 150 bps low-rate stream, and a 600 bps high-rate stream over a range of 25 m with a commercial 30 fps camera.

Randomized error removal for online spread estimation in data streaming

Measuring flow spread in real time from large, high-rate data streams has numerous practical applications, where a data stream is modeled as a sequence of data items from different flows and the spread of a flow is the number of distinct items in the flow. Past decades have witnessed tremendous performance improvement for single-flow spread estimation. However, when dealing with numerous flows in a data stream, it remains a significant challenge to measure per-flow spread accurately while reducing memory footprint. The goal of this paper is to introduce new multi-flow spread estimation designs that incur much smaller processing overhead and query overhead than the state of the art, yet achieves significant accuracy improvement in spread estimation. We formally analyze the performance of these new designs. We implement them in both hardware and software, and use real-world data traces to evaluate their performance in comparison with the state of the art. The experimental results show that our best sketch significantly improves over the best existing work in terms of estimation accuracy, data item processing throughput, and online query throughput.

Real-Time Optimization of Dynamic Speed Scaling for Distributed Data Centers

This paper proposes a new distributed real-time optimization for MapReduce-style framework in emerging cloud platforms supporting dynamic speed scaling functions. Distinctively different from the existing MapReduce parallelism strategy with fixed specific data chuck sizes, the new approach is able to dynamically dispatch input data of adequate sizes and synthesize interim processing results according to applications and the state of data centers (DCs). The key idea is to decouple the optimizations of data dispatching, processing, and result aggregation without loss of optimality, by employing stochastic optimization techniques. Another important aspect is that we optimize the subproblem of data processing to leverage the energy- and speed-configurability of DCs, by optimally deciding the number of servers to be activated at every DC and the CPU speeds of the activated servers. Evident from extensive simulations, the proposed approach is able to increase the throughput-cost ratio by up to 94.3 $\%$ , as compared to existing initiatives, and substantially improve the throughput in the case of high-rate data streams.

Channel Characterization for Vehicle-to-Infrastructure Communications in Millimeter-Wave Band

In recent years, the intelligent transport system (ITS) has been developed rapidly because of global urbanization and industrialization, which is considered as the key enabling technology to improve road safety, traffic efficiency, and driving experience. To achieve these goals, vehicles need to be equipped with a large number of sensors to enable the generation and exchange of high-rate data streams. Recently, millimeter-wave (mmWave) technology has been introduced as a means of meeting such a high data rate requirement. In this paper, a comprehensive study on the channel characteristics for vehicle-to-infrastructure (V2I) link in mmWave band (22.1-23.1 GHz) for various road environments and deployment configurations is conducted. The self-developed ray-tracing (RT) simulator is employed with the calibrated electromagnetic (EM) parameters. The three-dimensional (3D) environment models are reconstructed from the OpenStreetMap (OSM). In the simulations, not only the vehicle user equipment (UE) moves, but also the other vehicles such as cars, delivery vans, and buses move around the vehicle UE. Moreover, the impacts of the receiver (Rx) multiple antennas and beam switching technologies at the vehicle UE are evaluated as well. The channel parameters of the V2I link in mmWave band, including received power, Rician $K$ -factor, root-mean-square delay spread, and angular spreads are explored in the target scenarios under different simulation deployments. This work aims to help the researchers understand the channel characteristics of the V2I links in mmWave band and support the link-level and system-level design for future vehicular communications.

Stochastic Optimization From Distributed Streaming Data in Rate-Limited Networks

Motivated by machine learning applications in networks of sensors, internet-of-things (IoT) devices, and autonomous agents, we propose techniques for distributed stochastic convex learning from high-rate data streams. The setup involves a network of nodes---each one of which has a stream of data arriving at a constant rate---that solve a stochastic convex optimization problem by collaborating with each other over rate-limited communication links. To this end, we present and analyze two algorithms---termed distributed stochastic approximation mirror descent (D-SAMD) and accelerated distributed stochastic approximation mirror descent (AD-SAMD)---that are based on two stochastic variants of mirror descent and in which nodes collaborate via approximate averaging of the local, noisy subgradients using distributed consensus. Our main contributions are (i) bounds on the convergence rates of D-SAMD and AD-SAMD in terms of the number of nodes, network topology, and ratio of the data streaming and communication rates, and (ii) sufficient conditions for order-optimum convergence of these algorithms. In particular, we show that for sufficiently well-connected networks, distributed learning schemes can obtain order-optimum convergence even if the communications rate is small. Further we find that the use of accelerated methods significantly enlarges the regime in which order-optimum convergence is achieved; this is in contrast to the centralized setting, where accelerated methods usually offer only a modest improvement. Finally, we demonstrate the effectiveness of the proposed algorithms using numerical experiments.

Optical-RoI-Signaling for Vehicular Communications

This paper introduces a new hybrid waveform for optical wireless communication (OWC)/optical camera communication (OCC) systems and discusses the technical considerations of these systems. In a challenged vehicular communication environment, which requires high-speed, high-mobility, and long-distance communication support, implementing a hybrid waveform guarantees high-speed data transmission while reducing the cost of the OWC/OCC systems. The application of a hybrid waveform in the OWC/OCC systems is known as the region-of-interest (RoI)-signaling technique. This technique allows the OWC/OCC systems to simultaneously transmit low-rate and high-rate data streams. The low-rate data stream is used to detect and track the RoI of light sources for setting up the communication link, whereas the high-rate data stream is used for high-speed data transmission. Selection of proper modulation schemes for two simultaneous data streams is also discussed in this paper. A new modulation scheme, such as spatial-2-phase-shift-keying (S2-PSK), is proposed for the low-rate data stream. This scheme has been used to modify the IEEE 802.15.7-2018 standard, which is the revised version of the IEEE 802.15.7-2011 standard. For the high-rate data stream, single-carrier modulation or multiple-carrier modulation, such as the proposed hybrid-spatial-phase-shift-keying (HS-PSK) or variable pulse-position modulation (VPPM), can serve as viable solutions. Technical considerations for the modulation schemes of each type of data stream are analyzed to determine the feasibility of the proposed schemes. Finally, the experimental results and numerical parameters of the intended system are presented.

Modeling and Design of Millimeter-Wave Networks for Highway Vehicular Communication

Connected and autonomous vehicles will play a pivotal role in future intelligent transportation systems and smart cities, in general. High-speed and low-latency wireless communication links will allow municipalities to warn vehicles against safety hazards, as well as support cloud-driving solutions to drastically reduce traffic jams and air pollution. To achieve these goals, vehicles need to be equipped with a wide range of sensors generating and exchanging high rate data streams. Recently, millimeter wave (mmWave) techniques have been introduced as a means of fulfilling such high data rate requirements. In this paper, we model a highway communication network and characterize its fundamental link budget metrics. In particular, we specifically consider a network where vehicles are served by mmWave base stations (BSs) deployed alongside the road. To evaluate our highway network, we develop a new theoretical model that accounts for a typical scenario where heavy vehicles (such as buses and lorries) in slow lanes obstruct line-of-sight (LOS) paths of vehicles in fast lanes and, hence, act as blockages. Using tools from stochastic geometry, we derive approximations for the signal-to-interference-plus-noise Ratio (SINR) outage probability, as well as the probability that a user achieves a target communication rate (rate coverage probability). Our analysis provides new design insights for mmWave highway communication networks. In considered highway scenarios, we show that reducing the horizontal beamwidth from $90^\circ$ to $30^\circ$ determines a minimal reduction in the SINR outage probability (namely $4 \cdot 10^{-2}$ at maximum). Also, unlike bidimensional mmWave cellular networks, for small BS densities (namely one BS every $500\,\text{m}$ ) it is still possible to achieve an SINR outage probability smaller than 0.2.

A real time sorting algorithm to time sort any deterministic time disordered data stream

In new generation high intensity high energy physics experiments, millions of free streaming high rate data sources are to be readout. Free streaming data with associated time-stamp can only be controlled by thresholds as there is no trigger information available for the readout. Therefore, these readouts are prone to collect large amount of noise and unwanted data. For this reason, these experiments can have output data rate of several orders of magnitude higher than the useful signal data rate. It is therefore necessary to perform online processing of the data to extract useful information from the full data set. Without trigger information, pre-processing on the free streaming data can only be done with time based correlation among the data set. Multiple data sources have different path delays and bandwidth utilizations and therefore the unsorted merged data requires significant computational efforts for real time manifestation of sorting before analysis. Present work reports a new high speed scalable data stream sorting algorithm with its architectural design, verified through Field programmable Gate Array (FPGA) based hardware simulation. Realistic time based simulated data likely to be collected in an high energy physics experiment have been used to study the performance of the algorithm. The proposed algorithm uses parallel read-write blocks with added memory management and zero suppression features to make it efficient for high rate data-streams. This algorithm is best suited for online data streams with deterministic time disorder/unsorting on FPGA like hardware.

A high-speed transmission method for wireless personal communications

The next-generation wireless personal communications systems are expected to support a wide range of high-quality services that require high data rates. Communicating at high transmission rates over the harsh wireless environment, however, creates many difficult and challenging problems. In this paper, we describe a technique for the high-speed transmission of data in wireless personal communications which we denote as multicode modulation. In this technique, the high-rate bit transmitted data is serial to parallel converted into low-rate bit streams in a similar fashion to multicarrier or multitone modulation. However, in contrast to the multicarrier method, here we propose to modulate each low-rate bit stream using direct-sequence spread-spectrum on a single carrier. It is demonstrated that by selecting the processing gain properly the total required bandwidth will be of the same order as the original high-rate data stream; thereby, gaining the inherent benefit of multipath rejection without expanding the bandwidth of the original high-rate stream. To demonstrate the potential and merits of the proposed method as an alternative technique for high-speed transmission for wireless personal communications, various simulation results over a multipath Rayleigh fading link are presented.