Low-latency Processing Research Articles

A Performance Benchmark for the PostgreSQL and MySQL Databases

This study highlights the necessity for efficient database management in continuous authentication systems, which rely on large-scale behavioral biometric data such as keystroke patterns. A benchmarking framework was developed to evaluate the PostgreSQL and MySQL databases, minimizing repetitive coding through configurable functions and variables. The methodology involved experiments assessing select and insert queries under primary and complex conditions, simulating real-world scenarios. Our quantified results show PostgreSQL’s superior performance in select operations. In primary tests, PostgreSQL’s execution time for 1 million records ranged from 0.6 ms to 0.8 ms, while MySQL’s ranged from 9 ms to 12 ms, indicating that PostgreSQL is about 13 times faster. For select queries with a where clause, PostgreSQL required 0.09 ms to 0.13 ms compared to MySQL’s 0.9 ms to 1 ms, making it roughly 9 times more efficient. Insert operations were similar, with PostgreSQL at 0.0007 ms to 0.0014 ms and MySQL at 0.0010 ms to 0.0030 ms. In complex experiments with simultaneous operations, PostgreSQL maintained stable performance (0.7 ms to 0.9 ms for select queries during inserts), while MySQL’s performance degraded significantly (7 ms to 13 ms). These findings underscore PostgreSQL’s suitability for environments requiring low data latency and robust concurrent processing capabilities, making it ideal for continuous authentication systems.

Edge Computing and Cloud Computing for Internet of Things: A Review

The rapid expansion of the Internet of Things ecosystem has created an urgent need for efficient data processing and analysis technologies. This review aims to systematically examine and compare edge computing, cloud computing, and hybrid architectures, focusing on their applications within IoT environments. The methodology involved a comprehensive search and analysis of peer-reviewed journals, conference proceedings, and industry reports, highlighting recent advancements in computing technologies for IoT. Key findings reveal that edge computing excels in reducing latency and enhancing data privacy through localized processing, while cloud computing offers superior scalability and flexibility. Hybrid approaches, such as fog and mist computing, present a promising solution by combining the strengths of both edge and cloud systems. These hybrid models optimize bandwidth use and support low-latency, privacy-sensitive applications in IoT ecosystems. Hybrid architectures are identified as particularly effective for scenarios requiring efficient bandwidth management and low-latency processing. These models represent a significant step forward in addressing the limitations of both edge and cloud computing for IoT, offering a balanced approach to data analysis and resource management.

Edge Computing: Opportunities and Challenges

Edge computing has emerged as an innovative paradigm, promising to reshape the landscape of computational processing by bringing data closer to the source of generation, namely Internet of Things (IoT) devices. As the proliferation of IoT devices surges, so does the need for efficient, low-latency processing. Edge computing addresses these demands by decentralizing computational tasks, optimizing bandwidth, and ensuring rapid data processing. This paper delves into the nuances of edge computing, highlighting its distinct advantages, particularly in latency reduction and bandwidth conservation. However, with these opportunities come challenges, notably in the domain of security. Through a systematic review and simulation-based experimentation, this research affirms the transformative potential of edge computing, while also underscoring the need for robust security frameworks. The study concludes with a forward-looking perspective, emphasizing areas of future research including security enhancements, integration with 5G technology, energy-efficient architectures, and cross-domain applications.

Video stalling identification for web live streaming under HTTP-FLV

Live broadcasts have become one of the most popular forms of entertainment. Quality of user Experience (QoE) is a vital quantitative criterion for evaluating user satisfaction while watching live broadcasts, and it is positively correlated with the increase in the income of Internet Service Providers (ISPs). Video stalling identification plays a crucial role in the evaluation of QoE. However, encrypted live streaming hides video content, which makes identifying video stalling challenging. Existing studies primarily detect video stalling in a fixed time interval and focus on high-dimensional features. However, the capacity of the client byte buffer is dynamic, resulting in the stalling and non-stalling existing in a certain and fixed stalling time. In addition, the handling time of abundant features causes further latency. We propose Truncation of Dynamic Bytes and non-linear Integrated Modification based on Double Buffers (DB2) to identify video stalling under HTTP-FLV protocol in various network conditions and live types. We pull real-time video to get client buffer parameters and build a dynamic mapping based on the double buffer between network packets and the video playing states. This allows a more objective and precise evaluation of video stalling. We design a new network feature by creating a non-linear relationship between network packets and the client buffer. This is achieved by combining non-linear convergent distribution with basic traffic features. The feature is fed into a lightweight machine learning model to train the classifier, achieving low processing latency and high identification accuracy. The experimental results show that DB2 can achieve 98.91% stalling identification accuracy with 1.256 ms operation time in a mixture of live video types, outperforming state-of-the-art techniques.

Dynamic UAV Deployment Scheme Based on Edge Computing for Forest Fire Scenarios.

This study investigates the dynamic deployment of unmanned aerial vehicles (UAVs) using edge computing in a forest fire scenario. We consider the dynamically changing characteristics of forest fires and the corresponding varying resource requirements. Based on this, this paper models a two-timescale UAV dynamic deployment scheme by considering the dynamic changes in the number and position of UAVs. In the slow timescale, we use a gate recurrent unit (GRU) to predict the number of future users and determine the number of UAVs based on the resource requirements. UAVs with low energy are replaced accordingly. In the fast timescale, a deep-reinforcement-learning-based UAV position deployment algorithm is designed to enable the low-latency processing of computational tasks by adjusting the UAV positions in real time to meet the ground devices' computational demands. The simulation results demonstrate that the proposed scheme achieves better prediction accuracy. The number and position of UAVs can be adapted to resource demand changes and reduce task execution delays.

Fog-based deep learning framework for real-time pandemic screening in smart cities from multi-site tomographies

The quick proliferation of pandemic diseases has been imposing many concerns on the international health infrastructure. To combat pandemic diseases in smart cities, Artificial Intelligence of Things (AIoT) technology, based on the integration of artificial intelligence (AI) with the Internet of Things (IoT), is commonly used to promote efficient control and diagnosis during the outbreak, thereby minimizing possible losses. However, the presence of multi-source institutional data remains one of the major challenges hindering the practical usage of AIoT solutions for pandemic disease diagnosis. This paper presents a novel framework that utilizes multi-site data fusion to boost the accurateness of pandemic disease diagnosis. In particular, we focus on a case study of COVID-19 lesion segmentation, a crucial task for understanding disease progression and optimizing treatment strategies. In this study, we propose a novel multi-decoder segmentation network for efficient segmentation of infections from cross-domain CT scans in smart cities. The multi-decoder segmentation network leverages data from heterogeneous domains and utilizes strong learning representations to accurately segment infections. Performance evaluation of the multi-decoder segmentation network was conducted on three publicly accessible datasets, demonstrating robust results with an average dice score of 89.9% and an average surface dice of 86.87%. To address scalability and latency issues associated with centralized cloud systems, fog computing (FC) emerges as a viable solution. FC brings resources closer to the operator, offering low latency and energy-efficient data management and processing. In this context, we propose a unique FC technique called PANDFOG to deploy the multi-decoder segmentation network on edge nodes for practical and clinical applications of automated COVID-19 pneumonia analysis. The results of this study highlight the efficacy of the multi-decoder segmentation network in accurately segmenting infections from cross-domain CT scans. Moreover, the proposed PANDFOG system demonstrates the practical deployment of the multi-decoder segmentation network on edge nodes, providing real-time access to COVID-19 segmentation findings for improved patient monitoring and clinical decision-making.

Modeling and Performance Analysis of a Notification-Based Method for Processing Video Queries on the Fly

With the rapid growth of video data, the search for content and events in videos is becoming increasingly relevant, and many challenges arise. Various approaches have been proposed to deal with many issues. However, many open questions are still related to computational cost and latency, especially for real-time applications. Considering the need for new and efficient solutions, the so-called NOP (Notification Oriented Paradigm) could be a suitable alternative. NOP introduced a new way of thinking and developing software in which small collaborative entities perform fact execution and logical decision processing based on precise notifications. Following these concepts and practical tools, this paper proposes a new querying processing method based on NOP, focusing on search and matching in a continuous flow context. Experiments on a labeled dataset demonstrated the suitability of the proposed method for low-latency processing with polynomial complexity. The results are better than the state of the art, which works at exponential cost.

A robust optimization approach for placement of applications in edge computing considering latency uncertainty

Edge computing brings computing and storage resources close to end-users to support new applications and services that require low network latency. It is currently used in a wide range of industries, from industrial automation and augmented reality, to smart cities and connected vehicles, where low latency, data privacy, and real-time processing are critical requirements. The latency of accessing applications in edge computing must be consistently below a threshold of a few tens of milliseconds to maintain an acceptable experience for end-users. However, the latency between users and applications can vary considerably depending on the network load and mode of wireless access. An application provider must be able to guarantee that requests are served in a timely manner by their application instances hosted in the edge despite such latency variations. This article focuses on the placement and traffic allocation problem faced by application providers in determining where to place application instances on edge nodes such that requests are served within a certain deadline. It proposes novel formulations based on robust optimization to provide optimal plans that protect against latency variations in a configurable number of network links. The robust formulations are based on two different types of polyhedral uncertainty sets that offer different levels of protection against variations in latency. Extensive simulations show that our robust models are able to keep the number of chosen edge nodes low while reducing the number of latency violations as compared to a deterministic optimization model that only considers the average latency of network links.

Outdoor Navigation Assistive System Based on Robust and Real-Time Visual-Auditory Substitution Approach.

Blindness affects millions of people worldwide, leading to difficulties in daily travel and a loss of independence due to a lack of spatial information. This article proposes a new navigation aid to help people with severe blindness reach their destination. Blind people are guided by a short 3D spatialised sound that indicates the target point to follow. This sound is combined with other sonified information on potential obstacles in the vicinity. The proposed system is based on inertial sensors, GPS data, and the cartographic knowledge of pedestrian paths to define the trajectory. In addition, visual clues are used to refine the trajectory with ground floor information and obstacle information using a camera to provide 3D spatial information. The proposed method is based on a deep learning approach. The different neural networks used in this approach are evaluated on datasets that regroup navigations from pedestrians' point-of-view. This method achieves low latency and real-time processing without relying on remote connections, instead using a low-power embedded GPU target and a multithreaded approach for video processing, sound generation, and acquisition. This system could significantly improve the quality of life and autonomy of blind people, allowing them to reliably and efficiently navigate in their environment.

Advanced Real-Time Video Dehazing and Smoke Reduction Algorithm for Indoor Fire Operations

Due to the presence of intense smoke and haze, which severely restricts visibility and situational awareness, inside fire operations provide a significant difficulty for first responders. This research study provides an innovative real-time video dehazing and smoke reduction algorithm created specifically for indoor fire scenarios in answer to this urgent demand. To overcome the difficulties presented by smoke and haze, our approach integrates cutting-edge machine learning methods with computer vision techniques. The fundamental parts of the methodology are examined in this research, including picture acquisition, dehazing methods, transmission map refinement utilizing convolutional neural networks (CNNs), and post-processing. We also go through how Cython is integrated for low-latency processing, provide experimental findings, and consider potential applications in both indoor firefighting and other fields.

Low-Latency Processing of IoT Information in the IoT Platform Named “C-NAT”

Low-Latency Processing of IoT Information in the IoT Platform Named “C-NAT”

Biosignals meet Adaptive Systems

This paper presents a perspective on Biosignal-Adaptive Systems (BAS) which automatically adapt to user needs by continuously interpreting their biosignals and by providing transparent feedback, thereby keeping the user in the loop. The major hallmark of the described BAS is the low latency with which biosignals are processed, interpreted, and applied to perform rapid system adaptation, providing the user in the loop with immediate feedback on the BAS’s understanding of his or her condition. In contrast to explicit user input or the interpretation of observable behavior, the rapid system adaptation relies on biosignals, which in context of a concrete application can be interpreted as implicit signals of user needs. Recently, great advances have been made in sensor integration into smart devices, making it possible to collect vasts amounts of multimodal biosignal data. Furthermore, powerful machine learning methods enable rapidly processing and learning from such data. We argue that the time has come to harness the full spectrum of low-latency processing of biosignals to understand user needs and to apply this information to deliver adaptive systems accordingly. However, this will just be the beginning: real-time signal processing in combination with ubiquitous devices that are always connected to huge processing and storage capacities allow systems to provide users (and bystanders) with instant and transparent feedback and adaptations for the recognized needs. In the future, such systems could run 24/7 to assist users @home, @work, and @play from the cradle to the grave. Thus, BAS must be human-centric to curate data, archive information, learn from experience, and adapt to individual users throughout their lifespan. In this position paper, we present the concept of BAS with its key building blocks, provide selected examples of BAS from our research, and articulate selected challenges for future research.

Guest Editorial Multi-Tier Computing for Next Generation Wireless Networks—Part II

Multi-tier computing effectively enables flexible computation and communication resource sharing by offloading computation-intensive tasks to nearby servers along the cloud-to-thing continuum. In essence, multi-tier computing networks can distribute computing, storage, and communication functions anywhere between the cloud and the endpoint to take full advantage of the resources available along this continuum, thus extending the traditional cloud computing architecture to the edge of the network. With multi-tier computing, some application component processing, such as delay-sensitive components, can take place at the edge of the network, while other components, such as time-tolerant and computation-intensive components, can be performed in the cloud. To best meet user requirements, centralized cloud computing with extensive resources, secure environments, and powerful algorithms is still needed, but also must be complemented by distributed fog and edge computing with shared resources, accessible environments, and simple algorithms for real-time decision-making. Given heterogeneous computing resources and collaborative service architectures, future multi-tier computing networks will be capable of supporting a full range of computing and networking services for different environments and applications. Multi-tier computing enables low-latency processing by allowing data to be processed at the network edge close to end devices. It also facilitates the distribution of fog/edge nodes to collect data from end devices. Therefore, multi-tier computing effectively complements the cloud computing architecture.

Realizing multioperations and multiprefixes in Thick Control Flow processors

Multioperations are primitives of parallel computation by which threads perform reductions, e.g., additions, on values provided by multiple threads into a single value in a constant number of steps. Multiprefixes resemble multioperations, but return to each participating thread a cumulative ordered reduction of all preceding values. Algorithmically, multioperations and multiprefixes can speed up parallel programs by a logarithmic factor over their single operation counterparts. In this paper, we introduce architectural techniques for realizing multioperations and multiprefixes in so-called Thick Control Flow (TCF) processors. A thick control flow is a computational construct that bundles homogeneous threads following the same control path into a data parallel entity. Our proposed processors optimized for executing TCFs feature a frontend-backend structure with low-latency processing of TCF-common computations and high-throughput execution of data parallel parts. Our solution relies on step caches and equally sized multioperation scratchpads, while on the memory side, we make use of active memory modules. The idea is to compute partial results in backend units to reduce the traffic to the referred shared memory location. The final multioperation result is then computed in the active memory unit of the target memory module. Multiprefixes use an additional phase where the final results are computed with a help of backend-wise prefixes. According to our evaluation, the proposed techniques indeed speed up certain N data element algorithms by a logN factor with reasonable hardware costs.

Guest Editorial Multi-Tier Computing for Next Generation Wireless Networks—Part I

Multi-tier computing effectively enables flexible computation and communication resource sharing by offloading computation-intensive tasks to nearby servers along the cloud-to-thing continuum. In essence, multi-tier computing networks can distribute computing, storage, and communication functions anywhere between the cloud and the endpoint to take full advantage of the resources available along this continuum, thus extending the traditional cloud computing architecture to the edge of the network. With multi-tier computing, some application component processing, such as delay-sensitive components, can take place at the edge of the network, while other components, such as time-tolerant and computation-intensive components, can be performed in the cloud. To best meet user requirements, centralized cloud computing with extensive resources, secure environments, and powerful algorithms is still needed, but also must be complemented by distributed fog and edge computing with shared resources, accessible environments, and simple algorithms for real-time decision-making. Given heterogeneous computing resources and collaborative service architectures, future multi-tier computing networks will be capable of supporting a full range of computing and networking services for different environments and applications. This Special Issue aims to provide a forum for the latest advances in multi-tier computing for next-generation wireless network research, innovations, and applications. Multi-tier computing enables low-latency processing by allowing data to be processed at the network edge close to end devices. It also facilitates the distribution of fog/edge nodes to collect data from end devices. Therefore, multi-tier computing effectively complements the cloud computing architecture.

Sparse Vector Coding for Short-Packet Transmission on Industrial Communications: Reference Architecture and Design Challenges

Reliable, fast, and deterministic communications are fundamental for future industrial wireless systems. This goal requires multiple cooperating technologies that pertain to different areas of the communication arena. The weight of the waveform and coding technique choices are critical to match industrial use cases' reliability and latency requirements. Specifically, this article contributes to the field of channel coding in wireless fieldbus links. Classical block coding schemes were designed to maximize information bit rates without stringent latency requirements. Their performance in applications that use short messages degrades significantly. Recently, sparse vector coding (SVC) has been proposed as a coding approach suitable for short-packet communications with moderate complexity and low processing latency. This article presents a comprehensive analysis of SVC and a reference communication framework for its implementation on a wireless system. A set of simulations is also designed to establish the cross correlation between the different parameters involved in the code design. As a result, the coding matrix has been identified as a critical parameter that can improve coding performance. In addition, this article includes two new procedures for determining the matrix that could lead to a gain of up to 2 dB with respect to the current state of the art.

RDMA Congestion Control: It Is Only for the Compliant

Remote direct memory access (RDMA) networks enable low latency and low central processing unit utilization, and their widespread adoption in datacenters enables improved application performance. However, there are performance isolation concerns for RDMA deployed in a shared cloud environment. In particular, congestion control enforcement and congestion control algorithms in RDMA make the network susceptible to performance hacking attacks, which give the attacker extra bandwidth and cause severe congestion in the network. These attacks can increase short flow completion times by several orders of magnitude. We surface a fundamental tradeoff in congestion control between short flow completion time and performance isolation. We discuss this tradeoff and how existing approaches do not provide a robust solution. We also advocate that researchers incorporate performance isolation concerns into the design and evaluation of congestion control.

Healthcare Monitoring Using Ensemble Classifiers in Fog Computing Framework

Nowadays, the cloud environment faces numerous issues like synchronizing information before the switch over the data migration. The requirement for a centralized internet of things (IoT)-based system has been restricted to some extent. Due to low scalability on security considerations, the cloud seems uninteresting. Since healthcare networks demand computer operations on large amounts of data, the sensitivity of device latency evolved among health networks is a challenging issue. In comparison to cloud domains, the new paradigms of fog computing give fresh alternatives by bringing resources closer to users by providing low latency and energy-efficient data processing solutions. Previous fog computing frameworks have various flaws, such as overvaluing response time or ignoring the accuracy of the result yet handling both at the same time compromises the network community. In this proposed work, Health Fog is integrated with the Optimized Cascaded Convolution Neural Network framework for diagnosing heart disease. Initially, the data is collected, and then pre-processing is done by Linear Discriminant Analysis. Then the features are extracted and optimized using Galactic Swarm Optimization. The optimized features are given into the Health Fog framework for diagnosing heart disease patients. It uses ensemble-based deep learning in edge computing devices, which automatically monitors real-life health networks such as heart disease analysis. Finally, the classifiers such as bagging, boosting, XGBoost, Multi-Layer Perceptron (MLP), and Partitions (PART) are used for classifying the data. Then the majority voting classifier predicts the result. This work uses FogBus architecture and evaluates the execution of power usage, bandwidth of the network, latency, execution time, and accuracy.

Cloud Computing and Edge Computing For Scalability and Performance

Building effective and scalable systems that can handle enormous volumes of data processing and provide low-latency processing and real-time analytics may be accomplished by combining cloud and edge computing. Edge and cloud computing are both used in hybrid cloud computing. We investigated the various ways that cloud and edge computing may be coupled in this research report. The names "cloud," "edge," "cloud and edge," "edge as a service," "mobile edge computing," and "fog computing" were all used to characterise these techniques. When choosing a strategy, organisations should carefully consider their needs because each strategy has benefits and drawbacks of its own. We also reviewed a range of academic articles that discuss the advantages and disadvantages of using a hybrid cloud/edge computing architecture. The papers show how cloud and edge computing may be applied to support special use cases in a variety of industries, including manufacturing, transportation, and healthcare. The issues that need to be resolved include those related to connectivity, security, complexity, cost, and scalability, to name just a few. If organisations wish to guarantee the success of their cloud and edge computing solutions, they must carefully tackle the aforementioned concerns. Looking ahead, we can see that the field of cloud and edge computing has a lot of potential for advancement and innovation. This can only happen with the development of intelligent edge devices, new security and privacy technologies, and the optimisation of cloud and edge computing for 5G networks. We could anticipate even more substantial developments in the integration of cloud and edge computing as time goes on. These innovations will make it possible to introduce creative and original use cases in a variety of business situations.

Japanese Neural Incremental Text-to-Speech Synthesis Framework With an Accent Phrase Input

Work in the development of neural incremental text-to-speech (iTTS), which is attracting increasing attention, has recently pursued low-latency processing by generating speech on the fly before reading complete sentences. Most current state-of-the-art iTTS systems use a prefix-to-prefix neural iTTS framework with look-ahead of 1-2 unit segments (i.e., phonemes or words). However, since the Japanese language is based on accent phrase units that are longer than words, using a prefix-to-prefix neural iTTS with a look-ahead approach increases latency. Here, we propose an alternative to the end-to-end neural iTTS architecture that does not apply look-ahead input when synthesizing speech chunks. We further propose a method to use information from the previous time step by connecting the synthesized vector and the model’s internal state to the current time step. We experimentally investigated the latency of various iTTS systems with different modeling and synthesis chunks. The experimental results show that, for Japanese, the proposed iTTS is able to synthesize better speech quality, with a similar latency range, than the conventional baseline prefix-to-prefix neural iTTS with word units. Moreover, we found that our proposed approach improved the prosodic naturalness among synthesized units in the Japanese language. Subjective evaluations also revealed that the proposed approach with an incremental unit of two accent phrases achieved the best scores in Japanese iTTS systems.