Time-critical Tasks Research Articles

Patientensicherheit in der Kindernotfallversorgung

The treatment of critically ill children is a time-critical task that requires effective collaboration between different professional groups and specialist disciplines. Rapid recognition of the medical problem, establishment of a joint working hypothesis, coordinated implementation and regular re-evaluation of treatment are essential for patient safety. Several tools are in place to ensure that care is delivered safely. These include standardized emergency equipment that everyone can use, cognitive aids for safe medication administration and checklists for the preparation and performance of critical procedures. Furthermore, standardization of communication, a clear distribution of roles and tasks, and regular debriefings are helpful. The establishment of a safety culture and regular training are essential for the implementation of the measures.

Fairness and Utilitarianism in Allocating Energy to EVs During Power Contingencies Using Modified Division Rules

In order to maximize utilitarianism and fairness in allocating energy to electric vehicles (EVs) during outages, this study proposes an optimization method based on modified division rules. The idea of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">essential energy demand</i> is introduced in this paper to maximize the number of EVs that are served during power contingencies. For EVs carrying out the time-critical task(s), the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">essential energy demand</i> is defined as the amount of energy required by EVs to accomplish their upcoming task(s). For EVs carrying out delay-tolerant tasks, it is defined as the amount of energy required by EVs to travel to a nearby healthy charging station. To this end, an EV ranking mechanism is devised considering the full and essential energy demands along with the urgency of EVs. Subsequently, EVs with higher ranks are prioritized during energy allocation. The performance of the proposed method is compared with four existing division rules, i.e., proportional, constrained equal awards, constrained equal losses, and sequential priority rules. A utilitarianism index is proposed to analyze the performance of these methods and fairness is evaluated using existing indices, such as Jain's fairness index and cost of fairness index. The proposed method has outperformed existing division rules in both utilitarianism and fairness for the essential energy demand. Finally, a sensitivity analysis of uncertain parameters such as EV fleet size, available/required energy, and weight factors is carried out to analyze the performance of the proposed method under various conditions.

AI for Disaster Rapid Damage Assessment from Microblogs

Formal response organizations perform rapid damage assessments after natural and human-induced disasters to measure the extent of damage to infrastructures such as roads, bridges, and buildings. This time-critical task, when performed using traditional approaches such as experts surveying the disaster areas, poses serious challenges and delays response. This paper presents an AI-based system that leverages citizen science to collect damage images reported on social media and perform rapid damage assessment in real-time. Several image processing models in the system tackle non-trivial challenges posed by social media as a data source, such as high-volume of redundant and irrelevant content. The system determines the severity of damage using a state-of-the-art computer vision model. Together with a response organization in the US, we deployed the system to identify damage reports during a major real-world disaster. We observe that almost 42% of the images are unique, 28% relevant, and more importantly, only 10% of them contain either mild or severe damage. Experts from our partner organization provided feedback on the system's mistakes, which we used to perform additional experiments to retrain the models. Consequently, the retrained models based on expert feedback on the target domain data helped us achieve significant performance improvements.

Adaptive Performance Modeling Framework for QoS-Aware Offloading in MEC-Based IIoT Systems

The extensive growth in Industrial Internet of Things (IIoT) applications has tremendously increased the demands for low latency and resource-sensitive computing to accomplish critical industrial automations. This has leveraged the use of some proficient computing paradigms such as multiaccess edge computing (MEC), which facilitates a low latency and scalable solution for the execution of industrial workloads. However, continual generation of industrial data has imposed a substantial amount of stress on the resource-constrained MEC systems. In this perspective, our study proposes a consolidated stochastic computation offloading (CSCO) framework to address the increasing computational demands of MEC-based IIoT systems. The proposed framework efficiently handles industrial workloads by modeling them as stochastic processes to observe the number of data packets denied service due to the finite number of busy MEC servers. We provide an analytical solution corresponding to the loss probability of data packets denied service at the MEC servers. This leads to the development of a computation offloading mechanism for time-critical tasks. Furthermore, we provide the expression for conditional waiting time (CWT) and unconditional waiting time (UWT) of the data packets waiting to be offloaded to the remote cloud servers. Through extensive numerical simulations, it is inferred that the proposed CSCO framework provides promising results in characterizing the stochastic behavior of MEC-based IIoT systems, thereby providing a low latency, and resource sensitive solution for the considered system.

Linien: A versatile, user-friendly, open-source FPGA-based tool for frequency stabilization and spectroscopy parameter optimization.

We present a user-friendly and versatile tool for laser frequency stabilization. Its main focus is spectroscopy locking, but the software is suitable for lock-in techniques in general as well as bare proportional-integral-derivative (PID) operation. Besides allowing for sinusoidal modulation (up to 50MHz), triangular ramp scanning, in-phase and quadrature demodulation (1-5 f), infinite impulse response, and PID filtering, Linien features two different algorithms for automatic lock point selection; one of them performs time-critical tasks completely on field-programmable gate arrays. Linien is capable of autonomously optimizing spectroscopy parameters by means of machine learning and can measure the error signal's power spectral density. The software is built in a modular way, providing both a graphical user interface as well as a Python scripting interface. It is based on the RedPitaya STEMLab platform but may be ported to different systems.

Resource Optimization in MEC-Assisted Multirobot Cooperation Systems

With prevalent utilization of multirobot cooperation (MRC) systems, people pay more attention to improve the system performance. Among them, the energy consumption and implementation latency of MRC systems are major concerns, and mobile edge computing (MEC) provides a potential way to solve these problems. Therefore, how to leverage MEC to get the balance between computing and communication consumption in MRC systems needs to be investigated urgently. In this paper, a MRC system deployed to accomplish multiple time-critical tasks by MEC technology is studied. The proposed MRC system includes a powerful master robot (MR) and several slave robots (SRs). As a scheduler, MR is responsible for allocating tasks to SRs and has more computing power. SRs are robots with sensors that interact with the environment. In this paper, we propose a strategy for task allocation and resource management in MRC systems. The results show that the proposed scheme can effectively reduce the total energy consumption in SRs.

Time-critical tasks implementation in MEC based multi-robot cooperation systems

Mobile edge computing (MEC) deployment in a multi-robot cooperation (MRC) system is an effective way to accomplish the tasks in terms of energy consumption and implementation latency. However, the computation and communication resources need to be considered jointly to fully exploit the advantages brought by the MEC technology. In this paper, the scenario where multi robots cooperate to accomplish the time-critical tasks is studied, where an intelligent master robot (MR) acts as an edge server to provide services to multiple slave robots (SRs) and the SRs are responsible for the environment sensing and data collection. To save energy and prolong the function time of the system, two schemes are proposed to optimize the computation and communication resources, respectively. In the first scheme, the energy consumption of SRs is minimized and balanced while guaranteeing that the tasks are accomplished under a time constraint. In the second scheme, not only the energy consumption, but also the remaining energies of the SRs are considered to enhance the robustness of the system. Through the analysis and numerical simulations, we demonstrate that even though the first policy may guarantee the minimization on the total SRs' energy consumption, the function time of MRC system by the second scheme is longer than that by the first one.

Simulation-Based Emergency Team Training in Pediatrics: A Systematic Review.

The rare event of handling critically ill children often challenge the emergency care team. Several studies have investigated effects of simulation-based team training to prepare for such events, but the body of evidence remains to be compiled. We performed a systematic review of the effects of simulation-based team training on clinical performance and patient outcome. From a search of MEDLINE, Embase, CINAHL, and Cochrane Library, we included studies of team training in emergency pediatric settings with reported clinical performance and patient outcomes. We extracted data using a predefined template and assessed risk of bias using the Cochrane risk-of-bias tool for randomized trials 2.0 and the Newcastle Ottawa Quality Assessment Scale. We screened 1926 abstracts and included 79 studies. We identified 15 studies reporting clinical health care professional performance or patient outcomes. Four studies reported survival data, 5 reported time-critical clinical events, 5 reported adherence to guidelines, checklists or tasks, and 2 reported on airway management. Randomized studies revealed improved team performance in simulated reevaluations 2 to 6 months after intervention. A meta-analysis was impossible because of heterogeneous interventions and outcomes. Most included studies had significant methodological limitations. Pediatric simulation-based team training improves clinical performance in time-critical tasks and adherence to guidelines. Improved survival was indicated but not concluded because of high risk of bias. Team performance and technical skills improved for at least 2 to 6 months. Future research should include longer-term measures of skill retention and patient outcomes or clinical measures of treatment quality whenever possible.

Dual-task interference while receiving information on a head mounted display and manual tracking with and without auditory warnings

BackgroundDual-task interference is a concern when users attempt to use head mounted displays (HMD) while performing seated manual control tasks. Auditory warnings of information displayed on the HMD may or may not assist users while multi-tasking. ObjectiveIn this study, we investigated the dual-task interference of words, to be remembered later, presented on a HMD and motor control in a manual tracking task. The word presentations were warned or not warned with auditory signals and the impact of audio warnings were examined. MethodsParticipants were required to perform five tasks: (1) a compensatory tracking task performed alone, (2) a word memory task performed alone, (3) a tracking and word memory task, (4) a word memory task with words preceded by audial signals, and (5) a tracking and word memory task with words preceded by audial signals. In addition, in the dual-tasks, half of the word presentations were paired with directions changes in the tracking task to test for immediate perceptual interference. ResultsThere were significant dual task costs for both mean tracking error and later word recall. Additionally, participants took significantly longer to respond to motion changes paired with word presentations than motion changes not paired with word presentations. However, the impact of auditory warnings on tracking performance was nuanced. ConclusionEven with an in field of view transparent HMD momentary and sustained cognitive dual-task interference remains. Reaction times are affected most in the worst case scenario, when task critical events occur at the same time as a text message. ApplicationThe use of HMDs in time critical manual control tasks (such as operating machinery) should be limited. The use of audial warnings to alert operators to information displayed on an HMD requires further research; it may disrupt appropriate or natural task ordering.

Online scheduling of time-critical tasks to minimize the number of calibrations

We study the online scheduling problem where the machines need to be calibrated before processing any jobs. To calibrate a machine, it will take λ time steps as the activation time, and then the machine will remain calibrated status for T time steps. The job can only be processed by the machine that is in calibrated status. Given a set of jobs arriving online, each of the jobs is characterized by a release time, a processing time, and a deadline. We assume that there is an infinite number of machines for usage. The objective is to minimize the total number of calibrations while feasibly scheduling all jobs.For the case that all jobs have unit processing times, we propose an O(λ)-competitive algorithm, which is asymptotically optimal. When λ=0, the problem is degraded to rent minimization, where our algorithm achieves a competitive ratio of 3e+7(≈15.16) which improves upon the previous results for such problems.

Design and FPGA based Implementation of IEEE 1588 Precision Time Protocol for Synchronisation in Distributed IoT Applications

ABSTRACT In distributed network applications such as the Internet of Things (IoT), a master clock is used to provide a frame of reference among all constituent nodes. Any drift in the clock on a single node can cause serious cascading effects in a system that largely involves ordered events and time-critical tasks. To provide a stable reference clock, time synchronisation protocols are utilised in distributed systems where client nodes can synchronise their clock with a master node. IEEE 1588 Precision Time Protocol (PTP) is increasingly being used to provide high time synchronisation accuracy in comparison with other synchronisation protocols. In this paper, we have proposed a hardware design and FPGA-based implementation of the IEEE 1588 protocol to be used in wired LAN communication. Experimental results show synchronisation accuracy in the range of few nanoseconds, which is significantly better than the existing implementations. The FPGA implementation was carried out on a Xilinx Artix-7 evaluation board.

Load - aware intelligent multiprocessor scheduler for time-critical cyber-physical system applications

The cyber-physical system involves domains like physical, communication, and cyber system. The time-critical tasks in the physical domain demand a quick computational response from the cyber system. To meet such critical response times, the cyber system uses a multiprocessor computing system to execute the task. But the performance of such a computing system depends on the multiprocessor task scheduler and load-state of the multiprocessor. If the scheduler is not aware of the load-state of the multiprocessor, the scheduling overloads the particular set of processors, and the time-critical task-sets are not able to meet their deadline. To overcome this issue, this paper presents a load-aware multiprocessor scheduling mechanism. The proposed scheduler uses a machine-learning algorithm to efficiently predict the load to make prior awareness of the load and schedule. The simulation results prove that the algorithm outperforms in comparison with the classical multiprocessor scheduler.

Hybrid Multiagent Collaboration for Time-Critical Tasks: A Mathematical Model and Heuristic Approach

Principal–assistant agent teams are often employed to solve tasks in multiagent collaboration systems. Assistant agents attached to the principal agents are more flexible for task execution and can assist them to complete tasks with complex constraints. However, how to employ principal–assistant agent teams to execute time-critical tasks considering the dependency between agents and the constraints among tasks is still a challenge so far. In this paper, we investigate the principal–assistant collaboration problem with deadlines, which is to allocate tasks to suitable principal–assistant teams and construct routes satisfying the temporal constraints. Two cases are considered in this paper, including single principal–assistant teams and multiple principal–assistant teams. The former is formally formulated in an arc-based integer linear programming model. We develop a hybrid combination algorithm for adapting larger scales, the idea of which is to find an optimal combination of partial routes generated by heuristic methods. The latter is defined in a path-based integer linear programming model, and a branch-and-price-based (BP-based) algorithm is proposed that introduces the number of assistant-accessible tasks surrounding a task to guide the route construction. Experimental results validate that the hybrid combination algorithm and the BP-based algorithm are superior to the benchmarks in terms of the number of served tasks and the running time.

Benchmarking feature selection methods with different prediction models on large-scale healthcare event data

With the development of the Electronic Health Record (EHR) technique, vast volumes of digital clinical data are generated. Based on the data, many methods are developed to improve the performance of clinical predictions. Among those methods, Deep Neural Networks (DNN) have been proven outstanding with respect to accuracy by employing many patient instances and events (features). However, each patient-specific event requires time and money. Collecting too many features before making a decision is insufferable, especially for time-critical tasks such as mortality prediction. So it is essential to predict with high accuracy using as minimal clinical events as possible, which makes feature selection a critical question. This paper presents detailed benchmarking results of various feature selection methods, applying different classification and regression algorithms for clinical prediction tasks, including mortality prediction, length of stay prediction, and ICD-9 code group prediction. We use the publicly available dataset, Medical Information Mart for Intensive Care III (MIMIC-III), in our experiments. Our results show that Genetic Algorithm (GA) based methods perform well with only a few features and outperform others. Besides, for the mortality prediction task, the feature subset selected by GA for one classifier can also be used to others while achieving good performance.

A Secure Key Aggregate Searchable Encryption with Multi Delegation in Cloud Data Sharing Service

As the amount of data generated in various distributed environments is rapidly increasing, cloud servers and computing technologies are attracting considerable attention. However, the cloud server has privacy issues, including personal information and requires the help of a Trusted Third Party (TTP) for data sharing. However, because the amount of data generated and value increases, the data owner who produces data must become the subject of data sharing. In this study, we use key aggregate searchable encryption (KASE) technology, which enables keyword search, to efficiently share data without using TTP. The traditional KASE scheme approach only discusses delegation of authority from the data owner to another user. However, if the delegated entity cannot perform time-critical tasks because the shared data are unavailable, the delegate must further delegate the rights given to other users. Consequently, this paper proposes a new KASE scheme that enables multi-delegation without TTP and includes an authentication technique between the user and the server. After that, we perform informal and formal analysis using BAN logic and AVISPA for security evaluation, and compare the security and performance aspects with existing schemes.

Threats and Trade-Offs in Resource Critical Crowdsourcing Tasks Over Networks

In recent times, crowdsourcing over social networks has emerged as an active tool for complex task execution. In this paper, we address the problem faced by a planner to incentivize agents in the network to execute a task and also help in recruiting other agents for this purpose. We study this mechanism design problem under two natural resource optimization settings: (1) cost critical tasks, where the planner's goal is to minimize the total cost, and (2) time critical tasks, where the goal is to minimize the total time elapsed before the task is executed. We define a set of fairness properties that should be ideally satisfied by a crowdsourcing mechanism. We prove that no mechanism can satisfy all these properties simultaneously. We relax some of these properties and define their approximate counterparts. Under appropriate approximate fairness criteria, we obtain a non-trivial family of payment mechanisms. Moreover, we provide precise characterizations of cost critical and time critical mechanisms.

On GPS spoofing of aerial platforms: a review of threats, challenges, methodologies, and future research directions.

Unmanned Aerial Systems (UAVs, Drones), initially known only for their military applications, are getting increasingly popular in the civil sector as well. Over the military canvas, drones have already proven themselves as a potent force multiplier through unmanned, round-the-clock, long-range and high-endurance missions for surveillance, reconnaissance, search and rescue, and even armed combat applications. With the emergence of the Internet of Things (IoT), commercial deployments of drones are also growing exponentially, ranging from cargo and taxi services to agriculture, disaster relief, risk assessment and monitoring of critical infrastructures. Irrespective of the deployment sector, drones are often entrusted to conduct safety, time and liability critical tasks, thus requiring secure, robust and trustworthy operations. In contrast, the rise in UAVs’ demand, coupled with market pressure to reduce size, weight, power and cost (SwaP-C) parameters, has caused vendors to often ignore security aspects, thus inducing serious safety and security threats. As UAVs rely on Global Positioning System (GPS) for positioning and navigation, they can fall prey to GPS jamming and spoofing attacks. The vulnerability of GPS to spoofing has serious implications for UAVs, as victim drones using civil GPS can be misdirected or even completely hijacked for malicious intents, as already demonstrated in several academic research efforts using commercially available GPS spoofing hardware. Beside UAVs, GPS spoofing attacks are equally applicable to other GPS-dependent platforms, including manned aircraft, ground vehicles, and cellular systems. This paper conducts a comprehensive review of GPS spoofing threats, with a special focus on their applicability over UAVs and other GPS-dependent mobile platforms. It presents a novel taxonomy of GPS spoofing attacks and critically analyzes different spoofing techniques based upon placement of spoofing device, attack stealthiness, attack methodologies, and objectives of the attacker. We also discuss some of the recent experiments from open literature which utilized commercially available hardware for successfully conducting spoofing attacks.

Query the trajectory based on the precise track: a Bloom filter-based approach.

Fast and precise querying in a given set of trajectory points is an important issue of trajectory query. Typically, there are massive trajectory data in the database, yet the query sets only have a few points, which is a challenge for the superior performance of trajectory querying. The current trajectory query methods commonly use the tree-based index structure and the signature-based method to classify, simplify, and filter the trajectory to improve the performance. However, the unstructured essence and the spatiotemporal heterogeneity of the trajectory-sequence lead these methods to a high degree of spatial overlap, frequent I/O, and high memory occupation. Thus, they are not suitable for the time-critical tasks of trajectory big data. In this paper, a query method of trajectory is developed on the Bloom Filter. Based on the gridded space and geocoding, the spatial trajectory sequences (tracks) query is transformed into the query of the text string. The geospace was regularly divided by the geographic grid, and each cell was assigned an independent geocode, converting the high-dimensional irregular space trajectory query into a one-dimensional string query. The point in each cell is regarded as a signature, which forms a mapping to the bit-array of the Bloom Filter. This conversion effectively eliminates the high degree of overlap and instability of query performance. Meanwhile, the independent coding ensures the uniqueness of the whole tracks. In this method, there is no need for additional I/O on the raw trajectory data when the track is queried. Compared to the original data, the memory occupied by this method is negligible. Based on Beijing Taxi and Shenzhen bus trajectory data, an experiment using this method was constructed, and random queries under a variety of conditions boundaries were constructed. The results verified that the performance and stability of our method, compared to R*tree index, have been improved by 2000 to 4000 times, based on one million to tens of millions of trajectory data. And the Bloom Filter-based query method is hardly affected by grid size, original data size, and length of tracks. With such a time advantage, our method is suitable for time-critical spatial computation tasks, such as anti-terrorism, public safety, epidemic prevention, and control, etc.

A deferring strategy to improve schedulability for the imprecise convergence on-line tasks

The imprecise real-time scheduling can be used for minimizing the bad effects of timing faults by leaving less important tasks unfinished if necessary when a transient overload occured. In the imprecise scheduling, every time-critical task can be logically decomposed into two tasks : a mandatory task and an optional task. Recently, some studies in this field showed good schedulability performance and minimum total error by deferring the optional tasks. But the schedulability performance of the studies can be shown only when the execution time of each optional task was less than or equal to the execution time of its corresponding mandatory task. Therefore, in this paper, a new deferring strategy is proposed under the reverse execution time restriction to the previous studies. Nevertheless, the strategy produces comparable or superior schedulability performance to the previous studies and can minimize the total error also.

A Hybrid Framework for Effective Prediction of Online Streaming Data

In this paper, we present a hybrid model to perform the training and testing of prediction model with online streaming data. Prediction of online streaming data is a time critical task. Huge volume of data that is being generated online need to be ingested to a prediction model and to be used to train and test the prediction model dynamically which improves the learning rate. The existing approaches for dynamic training and testing use the local infrastructure or virtual machines from the cloud infrastructure to increase the learning rate of the prediction model with streaming data. Recently many applications prefer serverless cloud infrastructure than virtual machines. However, using the serverless infrastructure for the entire prediction process will have time and space tradeoffs due to its autonomic feature. Hence in this paper we propose a hybrid approach that uses the three different environments such as the local infrastructure, virtual machine and serverless cloud for different stages. A novel approach to select the suitable environment to train and test the LSTM based air quality prediction model with stream data is proposed with increased learning rate and reduced resource utilization.