Low Latency Time Research Articles

Reliablity and Security for Fog Computing Systems

Fog computing (FC) is a distributed architecture in which computing resources and services are placed on edge devices closer to data sources. This enables more efficient data processing, shorter latency times, and better performance. Fog computing was shown to be a promising solution for addressing the new computing requirements. However, there are still many challenges to overcome to utilize this new computing paradigm, in particular, reliability and security. Following this need, a systematic literature review was conducted to create a list of requirements. As a result, the following four key requirements were formulated: (1) low latency and response times; (2) scalability and resource management; (3) fault tolerance and redundancy; and (4) privacy and security. Low delay and response can be achieved through edge caching, edge real-time analyses and decision making, and mobile edge computing. Scalability and resource management can be enabled by edge federation, virtualization and containerization, and edge resource discovery and orchestration. Fault tolerance and redundancy can be enabled by backup and recovery mechanisms, data replication strategies, and disaster recovery plans, with a residual number system (RNS) being a promising solution. Data security and data privacy are manifested in strong authentication and authorization mechanisms, access control and authorization management, with fully homomorphic encryption (FHE) and the secret sharing system (SSS) being of particular interest.

Adaptive Multi-Criteria-Based Load Balancing Technique for Resource Allocation in Fog-Cloud Environments

Recently, to deliver services directly to the network edge, fog computing, an emerging and developing technology, acts as a layer between the cloud and the IoT worlds. The cloud or fog computing nodes could be selected by IoTs applications to meet their resource needs. Due to the scarce resources of fog devices that are available, as well as the need to meet user demands for low latency and quick reaction times, resource allocation in the fog-cloud environment becomes a difficult problem. In this problem, the load balancing between several fog devices is the most important element in achieving resource efficiency and preventing overload on fog devices. In this paper, a new adaptive resource allocation technique for load balancing in a fog-cloud environment is proposed. The proposed technique ranks each fog device using hybrid multi-criteria decision- making approaches Fuzzy Analytic Hierarchy Process (FAHP) and Fuzzy Technique for Order Performance by Similarity to Ideal Solution (FTOPSIS), then selects the most effective fog device based on the resulting ranking set. The simulation results show that the proposed technique outperforms existing techniques in terms of load balancing, response time, resource utilization, and energy consumption. The proposed technique decreases the number of fog nodes by 11%, load balancing variance by 69% and increases resource utilization to 90% which is comparatively higher than the comparable methods.

Prospective motion correction for brain MRI using spherical navigators.

To demonstrate for the first time the feasibility of performing prospective motion correction using spherical navigators (SNAVs). SNAVs were interleaved in a 3D FLASH sequence with an additional short baseline scan (6.8 s) for fast rotation estimation. Assessment of SNAV-based prospective motion correction was performed in six volunteers. Participant motion was guided using randomly generated stepwise prompts as well as prompts derived from real motion cases. Experiments were performed on a 3 T MRI scanner using a 32-channel head coil. When optimized for real-time application, SNAV-based motion estimates were computed in 25.8 ± 1.3 ms. Phantom-based quantification of rotation and translation accuracy indicated mean absolute errors of 0.10 ± 0.09° and 0.25 ± 0.14 mm, respectively. Implementing SNAV-based motion estimates for prospective motion correction led to a clear improvement in image quality with minimal increase in scan time (<5%). Optimization of SNAV processing for real-time application enables prospective motion correction with low latency and minimal scan time requirements.

P4-HLDMC: A Novel Framework for DDoS and ARP Attack Detection and Mitigation in SD-IoT Networks Using Machine Learning, Stateful P4, and Distributed Multi-Controller Architecture

Distributed Denial of Service (DDoS) and Address Resolution Protocol (ARP) attacks pose significant threats to the security of Software-Defined Internet of Things (SD-IoT) networks. The standard Software-Defined Networking (SDN) architecture faces challenges in effectively detecting, preventing, and mitigating these attacks due to its centralized control and limited intelligence. In this paper, we present P4-HLDMC, a novel collaborative secure framework that combines machine learning (ML), stateful P4, and a hierarchical logically distributed multi-controller architecture. P4-HLDMC overcomes the limitations of the standard SDN architecture, ensuring scalability, performance, and an efficient response to attacks. It comprises four modules: the multi-controller dedicated interface (MCDI) for real-time attack detection through a distributed alert channel (DAC), the MSMPF, a P4-enabled stateful multi-state matching pipeline function for analyzing IoT network traffic using nine state tables, the modified ensemble voting (MEV) algorithm with six classifiers for enhanced detection of anomalies in P4-extracted traffic patterns, and an attack mitigation process distributed among multiple controllers to effectively handle larger-scale attacks. We validate our framework using diverse test cases and real-world IoT network traffic datasets, demonstrating high detection rates, low false-alarm rates, low latency, and short detection times compared to existing methods. Our work introduces the first integrated framework combining ML, stateful P4, and SDN-based multi-controller architecture for DDoS and ARP detection in IoT networks.

Wireless Communication Between Multi-FPGAs

PHY Layer implementation of the IEEE 802.11 a/g/n Wi-Fi standard using Software-Defined Radio (SDR) architecture. It addresses the challenge of implementing Wi-Fi on SDR platforms, which has been difficult due to the strict timing requirements of Wi-Fi protocols such as the Short Inter-Frame Space (SIFS) and the need for low-latency communication between the radio front-end and the processing unit. This architecture enables the implementation of the PHY (Physical layer) and low-level MAC (Media Access Control) functionalities in the FPGA, ensuring low latency and precise timing control. Key Words:

Multilevel scheduling mechanism for a stochastic fog computing environment using the HIRO model and RNN

Recent years have shown us that even when we are not physically there, we may nevertheless connect to the entire world. The enormous growth in the number of linked gadgets has made this possible. Therefore, in times of emergency, our connected era generates enormous amounts of data that require immediate analysis. These days, the majority of businesses rely on cloud computing to store and handle enormous volumes of data. Cloud computing is not the best option for handling crucial activities, nevertheless, due to its compromises of high latency and slow response time. This necessitates the processing of those activities on edge hardware, or "fog computing." Although fog computing has low latency and quick response times, scheduling with the cloud and edge has been found to be inefficient. Randomness, a lack of resources, network structure, heterogeneity, and other elements all play a role in inefficient scheduling. Although there are a number of heuristic- and reinforcement-based solutions available, research on this topic is still active due to the systems' slow generalization, inability to swiftly adapt, and inability to handle workloads that are time-specific. This work focuses on developing a schedule using recurrent neural networks for parameter updating and hierarchical reinforcement learning (HIRO) to learn with an understanding of changing contexts. The proposed model is assessed for better scheduling decisions taking into account the workload's time-specific patterns and its capacity to fine-tune its parameters with various applications. The bitbrain dataset, which simulates real-world data, is used to evaluate the experimental. The improvement found in terms of energy consumption, reaction time, service level agreement, and cost when compared to cutting-edge algorithms is 15.1%, 8.2%, 34.2%, and 5.26%, respectively.

Application-Layer Time Synchronization and Data Alignment Method for Multichannel Biosignal Sensors Using BLE Protocol.

Wearable wireless biomedical sensors have emerged as a rapidly growing research field. For many biomedical signals, multiple sensors distributed about the body without local wired connections are required. However, designing multisite systems at low cost with low latency and high precision time synchronization of acquired data is an unsolved problem. Current solutions use custom wireless protocols or extra hardware for synchronization, forming custom systems with high power consumption that prohibit migration between commercial microcontrollers. We aimed to develop a better solution. We successfully developed a low-latency, Bluetooth low energy (BLE)-based data alignment method, implemented in the BLE application layer, making it transferable between manufacturer devices. The time synchronization method was tested on two commercial BLE platforms by inputting common sinusoidal input signals (over a range of frequencies) to evaluate time alignment performance between two independent peripheral nodes. Our best time synchronization and data alignment method achieved absolute time differences of 69 ± 71 μs for a Texas Instruments (TI) platform and 477 ± 490 μs for a Nordic platform. Their 95th percentile absolute errors were more comparable-under 1.8 ms for each. Our method is transferable between commercial microcontrollers and is sufficient for many biomedical applications.

Optimizing Service Stipulation Uncertainty with Deep Reinforcement Learning for Internet Vehicle Systems

Fog computing brings computational services near the network edge to meet the latency constraints of cyber-physical System (CPS) applications. Edge devices enable limited computational capacity and energy availability that hamper end user performance. We designed a novel performance measurement index to gauge a device’s resource capacity. This examination addresses the offloading mechanism issues, where the end user (EU) offloads a part of its workload to a nearby edge server (ES). Sometimes, the ES further offloads the workload to another ES or cloud server to achieve reliable performance because of limited resources (such as storage and computation). The manuscript aims to reduce the service offloading rate by selecting a potential device or server to accomplish a low average latency and service completion time to meet the deadline constraints of sub-divided services. In this regard, an adaptive online status predictive model design is significant for prognosticating the asset requirement of arrived services to make float decisions. Consequently, the development of a reinforcement learning-based flexible x-scheduling (RFXS) approach resolves the service offloading issues, where x = service/resource for producing the low latency and high performance of the network. Our approach to the theoretical bound and computational complexity is derived by formulating the system efficiency. A quadratic restraint mechanism is employed to formulate the service optimization issue according to a set of measurements, as well as the behavioural association rate and adulation factor. Our system managed an average 0.89% of the service offloading rate, with 39 of delay over complex scenarios (using three servers with a 50% service arrival rate). The simulation outcomes confirm that the proposed scheme attained a low offloading uncertainty, and is suitable for simulating heterogeneous CPS frameworks.

Variable blank holder pressure by magneto-rheological actuators in deep drawing

Deep drawing is one of the most used processes in industry, whose reliability is closely linked to the possibility of adequate control of the blank holder force (BHF). To avoid typical failure mechanisms, such as wrinkling and tearing, it is necessary to properly adjust the BHF during the process. The paper presents the design of a new blank-holding set-up based on magneto-rheological actuators, which allow a flexible adjustment of the force during the process. A closed-loop control, based on the direct detection of the metal sheet displacement under the blank holder, is implemented to regulate in real-time the actuator's load. The obtained experimental test results show that (i) the presented set-up allows to apply blank holder pressures with an average error less than 5% with respect of the target forces; (ii) the low latency times to allow the force variations, and (iii) the possibility to vary in real-time the blank holder load response during the deep drawing.

Latency-Aware Task Scheduling for IoT Applications Based on Artificial Intelligence with Partitioning in Small-Scale Fog Computing Environments.

The Internet of Things applications have become popular because of their lightweight nature and usefulness, which require low latency and response time. Hence, Internet of Things applications are deployed with the fog management layer (software) in closely located edge servers (hardware) as per the requirements. Due to their lightweight properties, Internet of Things applications do not consume many computing resources. Therefore, it is common that a small-scale data center can accommodate thousands of Internet of Things applications. However, in small-scale fog computing environments, task scheduling of applications is limited to offering low latency and response times. In this paper, we propose a latency-aware task scheduling method for Internet of Things applications based on artificial intelligence in small-scale fog computing environments. The core concept of the proposed task scheduling is to use artificial neural networks with partitioning capabilities. With the partitioning technique for artificial neural networks, multiple edge servers are able to learn and calculate hyperparameters in parallel, which reduces scheduling times and service level objectives. Performance evaluation with state-of-the-art studies shows the effectiveness and efficiency of the proposed task scheduling in small-scale fog computing environments while introducing negligible energy consumption.

Design of fault-tolerant control system for distributed energy resources based power network using Phasor Measurement Units

Distributed Energy Resources (DERs) play a significant role in reducing carbon emissions and improving the power system, nonetheless, maintaining the stability of the system parameters under faulty conditions is a major challenge. To minimize fault duration and maximize the system’s fault tolerance capability while maintaining affordability and being environment friendly, DERs supported by storage units are used in parallel with the main grid which offers promising results. This paper proposes the utilization of DERs as the primary source while the main grid shares the peak load. The paper also discusses the application of Phasor Measurement Units (PMUs) to record current and voltage values. PMUs are used to detect the fault in its early stage and communicate to the central controller to shift the load on storage units and isolate fault locations. The operation is controlled at two levels, that is at the load end and the junction point of the grid and DER. Any anomaly detected by PMUs is tested for the fault location, where faults are then controlled and minimized using the proposed method while keeping economic factors under consideration. The system is tested in light of results from mathematical modeling and design simulation which show very low latency time against demand response and quick isolation of fault location. A comparison with existing and previous works also shows the promising performance of the proposed fault-tolerant power system using PMUs.

Towards Design of an Efficient Sensing Data Acquisition Scheme for UAVs-Assisted Wireless Sensor Networks

This paper investigates sensing data acquisition issues from large-scale hazardous environments using UAVs-assisted WSNs. Most of the existing schemes suffer from low scalability, high latency, low throughput, and low service time of the deployed network. To overcome these issues, we considered a clustered WSN architecture in which multiple UAVs are dispatched with assigned path knowledge for sensing data acquisition from each cluster heads (CHs) of the network. This paper first presents a non-cooperative Game Theory (GT)-based CHs selection algorithm and load balanced cluster formation scheme. Next, to provide timely delivery of sensing information using UAVs, hybrid meta-heuristic based optimal path planning algorithm is proposed by combing the best features of Dolphin Echolocation and Crow Search meta-heuristic techniques. In this research work, a novel objective function is formulated for both load-balanced CHs selection and for optimal the path planning problem. Results analyses demonstrate that the proposed scheme significantly performs better than the state-of-art schemes.

VeriFly: On-the-fly Assertion Checking via Incrementality

AbstractAssertion checking is an invaluable programmer’s tool for finding many classes of errors or verifying their absence in dynamic languages such as Prolog. For Prolog programmers, this means being able to have relevant properties, such as modes, types, determinacy, nonfailure, sharing, constraints, and cost, checked and errors flagged without having to actually run the program. Such global static analysis tools are arguably most useful the earlier they are used in the software development cycle, and fast response times are essential for interactive use. Triggering a full and precise semantic analysis of a software project every time a change is made can be prohibitively expensive. This is specially the case when complex properties need to be inferred for large, realistic code bases. In our static analysis and verification framework, this challenge is addressed through a combination of modular and incremental (context- and path-sensitive) analysis that is responsive to program edits, at different levels of granularity. In this tool paper, we present how the combination of this framework within an integrated development environment (IDE) takes advantage of such incrementality to achieve a high level of reactivity when reflecting analysis and verification results back as colorings and tooltips directly on the program text – the tool’s VeriFly mode. The concrete implementation that we describe is Emacs-based and reuses in part off-the-shelf “on-the-fly” syntax checking facilities (flycheck). We believe that similar extensions are also reproducible with low effort in other mature development environments. Our initial experience with the tool shows quite promising results, with low latency times that provide early, continuous, and precise assertion checking and other semantic feedback to programmers during the development process. The tool supports Prolog natively, as well as other languages by semantic transformation into Horn clauses.

Anti-Interference Design Based on Prime Interleaving for a Combined FH and DS Spreading Spectrum System

A combined prime interleaving design is proposed for a spreading spectrum system with frequency hopping (FH), direct sequence (DS), and coherent detection. Obtaining a lower bit error rate (BER) is the object of classical anti-jamming communication systems. By employing combined prime interleaving, we get lower BER results compared with conventional block interleaving under the same signal-to-noise ratio (SNR) level by merit of superior storage and interleaving effects. Prime interleaving requires only one row to interleave while both block and random interleaving require the whole interleaving matrix, which results in lower latency time. Simulations reveal that the proposed combined interleaving design has comparative performance with random interleaving, and has advantages in transmission reliability and system delay. Moreover, the prime interleaving outputs slightly worse BER compared with random interleaving over the same SNR. Compared with random interleaving, the proposed combined interleaving has 0.25 dB worse SNR over the same average BER.

Using 3D Convolutional Neural Networks for Real-time Detection of Soccer Events

Developing systems for the automatic detection of events in video is a task which has gained attention in many areas including sports. More specifically, event detection for soccer videos has been studied widely in the literature. However, there are still a number of shortcomings in the state-of-the-art such as high latency, making it challenging to operate at the live edge. In this paper, we present an algorithm to detect events in soccer videos in real time, using 3D convolutional neural networks. We test our algorithm on three different datasets from SoccerNet, the Swedish Allsvenskan, and the Norwegian Eliteserien. Overall, the results show that we can detect events with high recall, low latency, and accurate time estimation. The trade-off is a slightly lower precision compared to the current state-of-the-art, which has higher latency and performs better when a less accurate time estimation can be accepted. In addition to the presented algorithm, we perform an extensive ablation study on how the different parts of the training pipeline affect the final results.

Intelligent Computation Offloading for IoT Applications in Scalable Edge Computing Using Artificial Bee Colony Optimization

Most of the IoT-based smart systems require low latency and crisp response time for their applications. Achieving the demand of this high Quality of Service (QoS) becomes quite challenging when computationally intensive tasks are offloaded to the cloud for execution. Edge computing therein plays an important role by introducing low network latency, quick response, and high bandwidth. However, offloading computations at a large scale overwhelms the edge server with many requests and the scalability issue originates. To address the above issues, an efficient resource management technique is required to maintain the workload over the edge and ensure the reduction of response time for IoT applications. Therefore, in this paper, we introduce a metaheuristic and nature-inspired Artificial Bee Colony (ABC) optimization technique that effectively manages the workload over the edge server under the strict constraints of low network latency and quick response time. The numerical results show that the proposed ABC algorithm has outperformed Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), and Round-Robin (RR) Scheduling algorithms by producing low response time and effectively managing the workload over the edge server. Furthermore, the proposed technique scales the edge server to meet the demand of high QoS for IoT applications.

A decision model for selecting best reliable relay queue for cooperative relaying in cooperative cognitive radio networks: the extent analysis based fuzzy AHP solution

Nowadays, the selection of relay in cooperation-based communication is a very demanding topic in research. The choice of the relay majorly depends on various criteria; hence, multiple criteria decision-making techniques are required to find out the best relay as relay-based transmission performs better than without relay transmission. Existing research has not considered the transmission failure of the relay node. In this paper, the authors also suggested a relay queue instead of a single BEST relay, which increases the network's efficiency for preventing unsuccessful transmission and guarantees relay-based connectivity without fail. In this paper, the authors have discussed different AHP based multiple criteria decision-making techniques to select reliable relay nodes as AHP based techniques require low latency time in decision making. Again, in this paper, authors have selected relays using extent analysis based fuzzy AHP technique over other AHP techniques as this technique not only registers the ambiguity and fuzziness of the judgment creators but also provides better time complexity than other fuzzy AHP methods. An algorithm, numerical examples, and simulation study are all carried out in this correspondence to find the best relays.

Assisted-Fog-Based Framework for IoT-Based Healthcare Data Preservation

Healthcare has witnessed a technological advancement in improving the quality of care and speeding the process of diagnosing patients due to its intervention with the internet of medical things. IoT in healthcare (H-IoT) plays a significant role in facilitating the process of diagnosing and detecting diseases. Different IoT-based medical sensors are used to measure biometrics and send them to the cloud for more analysis. However, the sensed data are massive and vary in their criticality level in which some sensed data are more critical (health-related data) than others. Moreover, computing such critical data in the cloud encounters some delay which is not preferable in real-time monitoring applications. Thus, this work proposes an IoT-fog-based framework to classify the streamed data according to their criticality level and compute the critical data in the fog to detect abnormalities with low latency and high response time. Before designing the proposed work, an analysis was conducted to explore the real data collected by IoT-based medical apps. The exploration of the data involved downloading and manually analyzing up-to-date privacy policies of eight IoT-based medical apps that provide details about data collection practices. The study showed that the streamed data in H-IoT include medical sensors data, apps registration data (personal information), device information, and other information related to cookies. The proposed work introduced the design of fog-based data classification and the algorithm for such classification. The implementation and evaluation of the proposed framework is future work.

ADC-DSP-Based 10-to-112-Gb/s Multi-Standard Receiver in 7-nm FinFET

This article describes a 4 × 112 Gb/s digital receiver targeting long-reach (LR) channels. An SNR optimized approach is presented, which relaxes the ADC resolution requirement and the number of FFE taps without sacrificing BER. The discrete-time front end overcomes gain-BW limitations to provide 10+ dB gain at 28 GHz. A 56-GS/s ADC then converts the signal to 6-b digital consuming only 195 mW. The following DFE-FFE-based digital equalizer is capable of compensating 36-dB loss achieving a BER of 2e-5. Furthermore, TDC and ISI filter-based low-latency timing recovery meet jitter tolerance specs over a wide range of data rates from 10 to 112 Gb/s, including 28-Gb/s NRZ. The overall receiver consumes 338 mW with 3.18-pJ/bit energy efficiency.

More on Pipelined Dynamic Scheduling of Big Data Streams

An important as well as challenging task in modern applications is the management and processing with very short delays of large data volumes. It is quite often, that the capabilities of individual machines are exceeded when trying to manage such large data volumes. In this regard, it is important to develop efficient task scheduling algorithms, which reduce the stream processing costs. What makes the situation more difficult is the fact that the applications as well as the processing systems are prone to changes during runtime: processing nodes may be down, temporarily or permanently, more resources may be needed by an application, and so on. Therefore, it is necessary to develop dynamic schedulers, which can effectively deal with these changes during runtime. In this work, we provide a fast and fair task migration policy while maintaining load balancing and low latency times. The experimental results have shown that our scheme offers better load balancing and reduces the overall latency compared to the state of the art strategies, due to the stepwise communication and the pipeline based processing it employs.