Time-critical Applications Research Articles

Optimization of System Performance through Ant Colony Optimization: A Novel Task Scheduling and Information Management Strategy for Time-Critical Applications

Optimization of task scheduling and information storage/retrieval is crucial for managing resource utilization, which enhances system performance and ultimately impacts provider productivity and customer satisfaction. Efficient task scheduling aims to optimize computing time, while efficient information management focuses on maximizing memory usage. This paper presents a novel approach to task scheduling using Ant Colony Optimization (ACO) to improve time-critical objectives such as makespan and network latency, while maintaining balanced load distribution across systems. By enhancing makespan, we aim to maximize CPU utilization, and by optimizing information storage/retrieval, we target minimizing network latency. Performance across these multiple objectives is achieved by modifying the heuristic and visibility functions to guide ants toward specific solutions. The effectiveness of the proposed algorithm, Resource-Aware Load-Balancing for Time-Critical Applications (RALB-TCA), is demonstrated through implementation in the CloudSim simulation platform and benchmarking against existing techniques.

SP-DG: A programmable packet-level scheduling for queuing delay guarantees in time-critical networks

Time-critical applications, such as remote surgery, virtual reality, and automatic driving, impose stringent requirements for minimizing delay and jitter. The queuing delay at forwarding nodes significantly affects application traffic. To tackle this challenge, Time-Sensitive Networking (TSN) techniques have been proposed to ensure deterministic queuing delay. However, existing deterministic solutions, including the Time-Aware Shaper (TAS) and the Cyclic Queuing and Forwarding (CQF) model, encounter limitations due to their reliance on precise synchronization and dedicated forwarding plane devices. In this paper, we present a novel solution: the Strict Priority queuing model with a queuing Delay Guarantees (SP-DG), specifically designed for programmable forwarding planes. This model introduces an upper bound on the queuing delay for packets at each switch, effectively characterizing their distinct queuing delay requirements. By utilizing exponential smoothing to predict traffic intensity, packets are intelligently scheduled to appropriate queues based on their upper-bound queuing delay requirements. Our Netbench simulation and P4 software switch experiments demonstrate that the proposed SP-DG algorithm, compared to the state-of-the-art SP-based queue scheduling algorithms, achieves superior performance in delay-bounded queue scheduling.

Reinforcement Learning-Driven Decision-Making for Live Virtual Machine Migration in Fog Computing

Virtualization is an essential mechanism in fog computing that enables elasticity and isolation, which in turn helps achieve resource efficiency. To bring high flexibility in a fog environment, migration of virtual machines from one node to another is required. This can be achieved by live virtual machine migration to reduce downtime and delays. Multiple existing studies have discussed live virtual machine migration in a fog environment. However, these studies have some limitations, such as pre-migrating the virtual machines based on mobility prediction only or based on the load only, which causes an issue of late and early handover. Due to the dynamic nature of fog environments, VM migration decisions require consideration of multiple factors. Hence, there is a need to develop a system that considers multiple factors to decide to migrate a virtual machine or not to solve the issue of early and late handover. This study proposes a novel approach to live virtual machine migration that applies reinforcement learning for decision-making. Experiments show that the proposed approach significantly reduces the latency of time-critical applications. The proposed system, outperforms the existing systems in terms of total average reward. The system outperformed the mobility-only-based system by 97% when tested with two fog nodes and by 80% when tested with sixteen fog nodes in terms of average reward. Further, the proposed system outperforms the load-based system by 50% and 75% when the environment consists of two fog nodes and sixteen fog nodes, respectively. This proved that considering multiple factors in deciding virtual machine migration in a fog environment can be effectively applied in time-critical applications to reduce latency.

Edge computing solutions to support computation offloading for IoT applications

Internet of Things (IoT) devices are major stakeholders of contemporary network bandwidth. The proliferation of IoT devices and the demand for latency-free communication in time-critical applications has proven the drawback of cloud-based solutions. Edge computing is an paradigm that reduces the application’s response time by utilizing computation and storage proximate to each devices. Privacy in cloud computing is attained by system virtualization, containerization, among other evolved technologies. As privacy remains a primary concern, there is a need to test the feasibility of resource-constrained edge devices. Hence, this work aimed to examine the usability of such devices in edge computing by benchmarking on different runtime environments. The results reveal that a standard mechanism was achieved for defining the criteria to identify the suitable edge devices for computation offloading, particularly for a set of smart traffic surveillance use cases. Further, an optimization algorithm was designed to generate an optimum schedule that decides the best device to execute a particular task from the set of suitable edge devices to enhance energy and execution time in a global view. Based on the feasibility study and optimal schedule, a makespan that is nearly 11 times better than local execution for the considered traffic surveillance workflow was achieved.

An Experimental Study on BLE 5 Mesh Applied to Public Transportation

Today, In-Vehicle Wireless Sensor Networks (IVWSNs) are being used by car manufacturers because it saves time in the assembling process; saves costs in the harness and after-sales; and makes vehicles lighter, which helps lessen fuel consumption. There is no definition for wireless solution technology for IVWSNs, because each one has its own characteristics, and probably this is one of the reasons for its smooth usage in the automotive industry. A gap identified in wireless sensor networks for the automotive domain is that the related literature focuses only on ordinary cars with a star topology and few of them with mesh topology. This article aims to cover this gap by presenting an experimental study performed on verifying the new Bluetooth 5 technology working in a mesh topology applied to public transportation systems (buses). To perform this evaluation, a setup to emulate an IVWSN was deployed in a working city bus. Measuring the network metrics, the bus was placed in a variety of conditions during its trajectory to determine the influence of the passengers and the whole environment on the data transmission. The results suggest that Bluetooth 5 in a mesh topology is a promising candidate for IVWSNs because it shows the robustness of losing only 0.16% packets in the worst test, as well as its ability to cover a wider range compared to its previous version, indeed a better RSSI and jitter, with lower transmission power, compared to a star topology. The round trip time results can support the analysis for time-critical applications.

Multi-Robot Exploration Employing Harmonic Map Transformations

Robot Exploration can be used to autonomously map an area or conduct search missions in remote or hazardous environments. Using multiple robots to perform this task can improve efficiency for time-critical applications. In this work, a distributed method for multi-robot exploration using a Harmonic Map Transformation (HMT) is presented. We employ SLAM to construct a map of the unknown area and utilize map merging to share terrain information amongst robots. Then, a frontier allocation strategy is proposed to increase efficiency. The HMT is used to safely navigate the robots to the frontiers until the exploration task is complete. We validate the efficacy of the proposed strategy via tests in simulated and real-world environments. Our method is compared to other recent schemes for multi-robot exploration and is shown to outperform them in terms of total path distance.

A new stabilized time-spectral finite element solver for fast simulation of blood flow

The increasing application of cardiorespiratory simulations for diagnosis and surgical planning necessitates the development of computational methods significantly faster than the current technology. To achieve this objective, we leverage the time-periodic nature of these flows by discretizing equations in the frequency domain instead of the time domain. This approach markedly reduces the size of the discrete problem and, consequently, the simulation cost. With this motivation, we introduce a finite element method for simulating time-periodic flows that are physically stable. The proposed time-spectral method is formulated by augmenting the baseline Galerkin’s method with a least-squares penalty term that is weighed by a positive-definite stabilization matrix. An error estimate is established for the convective–diffusive system, showing that the proposed method emulates the behavior of existing standard time methods including optimal convergence rate in diffusive regimes and stability in strong convection. This method is tested on a patient-specific Fontan model at nominal Reynolds and Womersley numbers of 500 and 10, respectively, demonstrating its ability to replicate conventional time simulation results using as few as 7 modes at 11% of the computational cost. Owing to its higher local-to-processor computation density, the proposed method also exhibits improved parallel scalability for fast simulation of time-critical applications.

Software-Defined Time-Sensitive Networking for Cross-Domain Deterministic Transmission

With the rapid development of Industrial 4.0, massive emerging time-critical applications pose new demands for industrial networks, such as strict latency boundaries, ultra-reliable transmission, and so on. Time-Sensitive Networking (TSN) is well suited for these demanding scenarios due to its support for deterministic transmission based on Ethernet. However, many use cases in real industrial scenarios involve non-TSN domains, and, thus, the network requires providing deterministic transmission across non-TSN domains to support them. To this end, this paper proposes a novel Software-Defined Time-Sensitive Networking (SD-TSN) framework that integrates the determinism of TSN and scalability of Software-Defined Networking (SDN). SD-TSN exploits the coordination between the coordinated controller and different domain controllers to bound non-deterministic queuing delays in a way that guarantees determinism. In particular, we designed a multi-domain time-aware traffic scheduling model, which harmonizes the time-aware shaper (TAS) in TSN and time-slots in non-TSN domains based on a global view, generating transmission schedules for each domain. A prototype testbed was built to conduct the experiments. The evaluation results demonstrate that the proposed method can efficiently achieve bounded delay and low delay variations in the transmission across non-TSN domains.

The Influence of Agent Transparency and Complexity on Situation Awareness, Mental Workload, and Task Performance

Transparency is a design principle intended to make the inner workings of autonomous agents visible to end-users such that humans can evaluate the reasoning behind its decisions and actions. To test the effect of agent transparency on situation awareness, mental workload, and task performance, an experiment was performed where 34 nautical navigators were tasked with interpreting the information provided by an autonomous collision and grounding avoidance system. Sixteen traffic situations were created with two levels of complexity. Four levels of transparency varied the amount and type of information in terms of the system’s decisions, planned actions, reasoning, and input parameters. The results show that increased transparency improves SA without increasing mental workload. However, the time to comprehend the system’s decisions and planned actions increased when its reasoning was depicted. Traffic complexity impaired SA, mental workload, and time-to-comprehension regardless of transparency level. However, for level 2 SA, transparency was found to negate the influence of complexity, resulting in improved comprehension of the agent’s reasoning despite high traffic complexity. These outcomes demonstrate the merits of agent transparency as a design principle in supporting human supervision of autonomous agents. However, developers should take care when extending these principles to time-critical applications.

Snippet Policy Network V2: Knee-Guided Neuroevolution for Multi-Lead ECG Early Classification.

Early time series classification predicts the class label of a given time series before it is completely observed. In time-critical applications, such as arrhythmia monitoring in ICU, early treatment contributes to the patient's fast recovery, and early warning could even save lives. Hence, in these cases, it is worthy of trading, to some extent, classification accuracy in favor of earlier decisions when the time series data are collected over time. In this article, we propose a novel deep reinforcement learning-based framework, snippet policy network V2 (SPN-V2), for long and varied-length multi-lead electrocardiogram (ECG) early classification. The proposed SNP-V2 contains two main components: snippet representation learning (SRL) and early classification timing learning (ECTL). The SRL is proposed to encode inner-snippet spatial correlations and inter-snippet temporal correlations into the hidden representations of the subsegment (snippet) of the input ECG. ECTL aims to learn a decision agent to classify the time series early and accurately. To optimize the proposed framework, we design a novel knee-guided neuroevolution algorithm (KGNA) to solve cardiovascular diseases' early classification problem, automatically optimizing the proposed SPN-V2 regarding the tradeoff between accuracy and earliness. In addition, we conduct a series of experiments on two real-world ECG datasets. The experimental results show the superiority of the proposed algorithm over the state-of-the-art competing methods.

A Survey on IEEE 1588 Implementation for RISC-V Low-Power Embedded Devices

IEEE 1588, also known as the Precision Time Protocol (PTP), is a standard protocol for clock synchronization in distributed systems. While it is not architecture-specific, implementing IEEE 1588 on Reduced Instruction Set Computer-V (RISC-V) low-power embedded devices demands considering the system requirements and available resources. This paper explores various approaches and techniques to achieve accurate time synchronization in such instruments. The analysis covers software and hardware implementations, discussing each method’s challenges, benefits, and trade-offs. By examining the state-of-the-art in this field, this paper provides valuable insights and guidance for researchers and engineers working on time-critical applications in RISC-V-based embedded systems, aiding in selecting the most-suitable stack for their designs.

Analysis of a Fuzzy Based Intrusion Detection System in Wireless Ad Hoc Networks

Technology and its growth is considerably enormous. This massive growth allows the opening of new fields of application in the domain of wireless networking and mobile ad-hoc networks (MANET) is one of its kinds. Mobile ad hoc network is widely used from collaborative computing to time critical applications in indoor and outdoor environment. Mobility of ad hoc network makes very attractive in all areas of mobile applications. Nodes participating in mobile ad hoc networks are autonomous, self-configurable and act as a router as well. These types of networks are dynamic in nature and have sort life time. Dynamic nature and limitations of the wireless transmission medium make MANET unsecured and vulnerable to various attacks. It is very tough to implement security for this networks and it opens up doors for further research work on this area. There is a great scope for designing a system to identify attacks and take countermeasures to minimize it and keep the performance of the network within acceptable limits. Intrusion detection system is one of its kinds and considered as security mechanism for MANET. This thesis explores different types of intrusion detection system like misuse detection and anomaly detection for mobile ad hoc networks. Anomaly detection techniques for ad hoc networks depend on the characterization of normal behavior pattern of wireless nodes. This research work focuses on wireless node behavior based detection technique. Most of anomaly intrusion detection systems are focusing on upper layer traffic to a profile normal behavior of wireless node. This research work focus on media access control (MAC) layer and network layer of wireless node. It is inefficient to use a large feature set of MAC layer and network layer due to energy limitation in ad-hoc network. A minimal feature set from MAC layer and network layer were proposed. This research work proposed an anomaly intrusion detection system for mobile ad hoc network using fuzzy logic and weighted average method. The network performance of mobile ad hoc network with intrusion detection system was analyzed using various network parameters. Further the performance of the performance of the intrusion detection system was analyzed using detection rate and false alarms. Results show good improvement in detection rate and other performance metrics

PBRL-TChain: A performance-enhanced permissioned blockchain for time-critical applications based on reinforcement learning

Permissioned blockchains can provide high security and reliability for various Internet of Things (IoT) systems, such as smart healthcare and vehicular networks. However, the performance issues of permissioned blockchains have been a constraint for fully supporting time-critical tasks with tight requirements of low latency and high throughput. This paper proposes PBRL-TChain, a performance-enhanced permissioned blockchain for time-critical applications based on deep reinforcement learning (DRL). First, we propose a priority ordering mechanism to minimize latency and maximize reliability. Then, we design a fast retransmission mechanism to alleviate the impact of transaction conflicts on the latency performance. Finally, we propose a DRL-based dynamic adjustment method in PBRL-TChain to achieve better performance and reliability. Experiments show that our method outperforms existing methods. Compared with Fabric++ and Athena, it can reduce the latency of time-critical transactions by 10 times, achieving a level of 10 ms, significantly improving the system’s performance and reliability.

A Strictly Predefined-Time Convergent and Noise-Tolerant Neural Model for Solving Linear Equations With Robotic Applications

Nowadays, there are time-critical applications involving linear equations, such as the fault reconstruction problem, where hard response time constraints and robustness to external disturbances are expected. Zeroing neural network (ZNN) is one of the effective solutions to time-variant problems including time-variant linear equations. This paper proposes a strictly predefined-time convergent and noise-tolerant ZNN (SPTC-NT-ZNN) to solve time-variant linear equations. Differing from the existing ZNN models, the designed SPTC-NT-ZNN is enhanced to be convergent in strictly predefined time while exhibiting noise tolerance. This guarantees the desirable timely convergence and robustness for time-critical applications. In theory, the strictly predefined-time convergence and noise-tolerance properties of the proposed SPTC-NT-ZNN are mathematically proved in a rigorous manner. Comparative validations are performed to verify that the SPTC-NT-ZNN outperforms existing typical ZNNs, regarding the convergence and robustness performance. To demonstrate potential applications, the SPTC-NT-ZNN is applied to 3D stereo reconstruction and motion control of a Franka Emika Panda robot, showing the efficacy of the proposed method.

QSAM-Net: Rain Streak Removal by Quaternion Neural Network With Self-Attention Module

Real-world images captured in remote sensing, image or video retrieval, and outdoor surveillance are often degraded due to poor weather conditions, such as rain and mist. These conditions introduce artifacts that make visual analysis challenging and limit the performance of high-level computer vision methods. In time-critical applications, it is vital to develop algorithms that automatically remove rain without compromising the quality of the image contents. This article proposes a novel approach called QSAM-Net, a quaternion multi-stage multiscale neural network with a self-attention module. The algorithm requires significantly fewer parameters by a factor of 3.98 than the real-valued counterpart and state-of-the-art methods while improving the visual quality of the images. The extensive evaluation and benchmarking on synthetic and real-world rainy images demonstrate the effectiveness of QSAM-Net. This feature makes the network suitable for edge devices and applications requiring near real-time performance. Furthermore, the experiments show that the improved visual quality of images also leads to better object detection accuracy and training speed.

Optimizing 5G-Advanced Networks for Time-Critical Applications: The Role of L4S

Optimizing 5G-Advanced Networks for Time-Critical Applications: The Role of L4S

Edge-AI: IoT Request Service Provisioning in Federated Edge Computing Using Actor-Critic Reinforcement Learning

Edge computing plays a critical role in the Internet of Things (IoT) environment as it potentially supports the time-critical IoT applications’ resources as well as latency requirements. In the near future, most edge service providers are envisioned to receive revenue from deploying these applications with the expenditures proportional to placing the offloaded requests from IoT devices and allocating the required resources. One way to maximize the edge profit and minimize the response latency is to integrate the edge nodes and form the edge federation. Therefore, edge service providers can have interoperability to distribute the IoT requests on the appropriate edge nodes in the light of providing satisfactory service levels to meet their objectives. Since the edge nodes are volatile and IoT time-critical applications are increasing, the edge nodes are envisioned to face massive traffic from IoT devices. Therefore, exploiting the traditional dynamic requests placement approaches cannot meet the SLA requirement of both IoT devices and edge service providers. In this article, we designed an intelligent reinforcement learning-based request service provisioning system (i.e., here, we call Edge-AI) as part of a smart edge orchestrator in the edge federation. We implement the proposed method, which is called DRL-Dispatcher, and compare it with greedy and random algorithms in edge federation. The experimental results show that the proposed DRL-Dispatcher performs better in terms of profit and low response latency as compared with the baseline approaches.

An intelligent and interpretable rule-based metaheuristic approach to task scheduling in cloud systems

Metaheuristic algorithms can be very successful in solving scheduling problems. However, these methods can be slow for time-critical applications due to the iterative stochastic processes they perform. This problem becomes even more pronounced in dynamic environments such as cloud systems. This paper proposes an interpretable rule-based solution to minimize this problem. Combining metaheuristic task scheduling solutions and machine learning techniques, this method uses a two-phase mechanism, semi-offline and online. In the semi-offline phase, we first archive metaheuristic solutions to previously randomly generated or encountered task-scheduling problems. The basic idea of the proposed method is to reuse these previously obtained successful metaheuristic solution patterns for future similar problems. Finding similar solution patterns from this extensive archive dataset is done by automatically extracting rule sets through machine learning techniques. These interpretable rule sets are used to identify the type of task scheduling problem encountered in the online phase and to find the optimal solution pattern. The performance of this method, which dramatically reduces execution time and enables the use of metaheuristics in time-critical applications, has been tested and proven for various cloud task-scheduling scenarios.

Systematic Quantification and Assessment of Digital Image Correlation Performance for Landslide Monitoring

Accurate and reliable analyses of high-alpine landslide displacement magnitudes and rates are key requirements for current and future alpine early warnings. It has been proved that high spatiotemporal-resolution remote sensing data combined with digital image correlation (DIC) algorithms can accurately monitor ground displacements. DIC algorithms still rely on significant amounts of expert input; there is neither a general mathematical description of type and spatiotemporal resolution of input data nor DIC parameters required for successful landslide detection, accurate characterisation of displacement magnitude and rate, and overall error estimation. This work provides generic formulas estimating appropriate DIC input parameters, drastically reducing the time required for manual input parameter optimisation. We employed the open-source code DIC-FFT using optical remote sensing data acquired between 2014 and 2020 for two landslides in Switzerland to qualitatively and quantitatively show which spatial resolution is required to recognise slope displacements, from satellite images to aerial orthophotos, and how the spatial resolution affects the accuracy of the calculated displacement magnitude and rate. We verified our results by manually tracing geomorphic markers in orthophotos. Here, we show a first generic approach for designing and optimising future remote sensing-based landslide monitoring campaigns to support time-critical applications like early warning systems.

Supporting logical execution time in multi-core POSIX systems

Safety-critical automotive applications require predictable and deterministic execution to not miss the timing requirements. Logical Execution Time (LET) is a paradigm already established in the automotive industry to improve the predictability and correctness of time-critical applications. Despite LET being already part of the AUTOSAR Classic standard, no prior work has addressed the design of this model on POSIX-based operating systems, which will be the base of next-generation automotive Electronic Control Units (ECUs). This paper proposes and discusses possible LET design approaches for these novel systems. Different implementations are then evaluated and compared through the WATERS Challenge automotive application running on a multi-core heterogeneous hardware platform.