Unpredictable Delays Research Articles

Firefly-Aquila optimized Deep Q network for handoff management in context aware video streaming-based heterogeneous wireless networks

Handoff management is the method in which the mobile node maintains its connection active when it shifts from location to other. The devastating success of mobile devices as well as wireless communications is emphasizing the requirement for the expansion of mobility-aware facilities. Moreover, the mobility of devices requires services adapting their behavior to abrupt context variations and being conscious of handoffs, which make an intermittent discontinuities and unpredictable delays. Thus, the heterogeneity of wireless network devices confuses the situation, since a dissimilar treatment of handoffs and context-awareness is essential for every solution. Hence, this paper introduced the Deep Q network-based Firefly Aquila Optimizer (DQN-FAO) for performing the handoff management. In order to establish the handoff management, the process of selecting network is very important. Here, the network is selected based on the devised FAO algorithm, which is the consolidation of Aquila Optimizer (AO) and Firefly algorithm (FA) that considers the metrics, such as Jitter, Handoff latency, and Received Signal Strength Indicator (RSSI) as fitness function. Moreover, the handover decision is taken by the DQN, where the hyper-parameters are tuned by the devised FAO algorithm. According to the hand over decision taken, the context aware video streaming is happened by adjusting the bit rate of the videos using network bandwidth. Besides, the devised scheme attained the superior performance based on the call drop, energy consumption, handover delay, throughput, handoff latency, and PSNR of 0.5122, 7.086 J, 10.54 ms, 13.17 Mbps, 93.80 ms and 46.89 dB.

Investigation of Efficient Backup Tecniques To Reduce Late In Cloud Systems: A Modeling Study

From the formation stages of the information society to the present day, new developments have been experienced in many areas such as data formation, processing, storage and sharing of data in electronic environments. In this context, many new technological concepts have been included in our lives. When it comes to data storage environments, the concept of "Cloud Computing", which has been developing and increasing in popularity in recent years, comes to our minds. Cloud computing, with its known definition, is an internet-based technology service that provides access to the data stored on its servers at any time through internet access and allows users to benefit from the services offered by the system to the extent they want. While the advantages of cloud computing systems such as low cost, high performance, not requiring physical materials and being flexible can be mentioned, it is also emphasized that it brings many problems in terms of system. The most important of these problems; the lack of resources in cloud computing service providers, causing unpredictable delays in server response times. Based on all this information, this research is structured on the development of solution processes on these delays. In this sense, it is possible to consider three areas of cloud infrastructure; cloud computing, distributed storage and streaming communications. One of the first solutions that comes to mind is duplicating a task on multiple machines and waiting for the earliest copy to finish can reduce service latency; but intuitively, it costs additional computing resources and increases the queue load on servers. In this context, the effect of redundancy on the tail will be analyzed in the first part of the research. As a solution proposal, in this section, it will be discussed that service delays and queue load can be reduced by using the replication method, thus making the system more efficient. In a similar way, by requesting more than one copy of a file and waiting for any of them to arrive, the cloud storage requests will be accelerated for content download. In the second part of the research, generalization will be made from replication to coding and the (n, k) fork-join model will be studied to analyze the delay in accessing a storage system with (n, k) deletion code. This analysis will provide practical information that several users can access a content at the same time and provide faster service to the relevant users. Achieving low latency in streaming communication is even more difficult, as packets must be transmitted quickly and sequentially. Based on this structure, in the third part of the research, it is aimed to develop deletion codes to transmit redundant packet combinations and ensure smooth playback. Experimental modeling method was used as a research method in the study. In general, the aim of the research is to blend various mathematical tools from queuing, theory coding and renewal processes. It is foreseen that the techniques and insights to be developed with these dimensions of the research can be applied to other systems with stochastically changing components, and integrated studies can be made and contribute to the literature in this context.

Трансформация цепей поставок в ситуации глобального кризиса

The article is devoted to the process of transformation of international supply chains in a crisis caused by the overlapping of three powerful negative factors: the consequences of the financial and economic crisis of 2008, of the COVID-19 pandemic and of economic sanctions against the Russian Federation. The authors point out that the destabilizing effect of the crisis primarily affected the functioning of the global supply chain system, one of the main components of the world economy. Under the current conditions, the normal functioning of supply chains is disrupted or is becoming nearly impossible due to disruptions in transport and commodity flows, denials of service, unpredictable delays, significant fluctuations in prices for goods and services, etc. The article emphasizes that the system of criteria and the basic principles of supply chain management, which have been established over decades of relatively stable development, are currently ineffective or completely inapplicable and need to be substantially adjusted. The theoretical recommendations identified by the authors are compared with the results of their survey of 240 Russian companies representing industry, distribution of goods, retail, logistics services and other segments. The survey made it possible to identify both the main negative factors affecting the supply chains and the most significant course of action for Russian business in order to reconfigure the import in crisis conditions. Based on the available theoretical and empirical data, the authors make an assumption about the new characteristics of international supply chains that they will acquire in the foreseeable future, both in terms of the general ideology of management and operation, and in terms of operational decisions.

Placement of Edge Servers in Mobile Cloud Computing using Artificial Bee Colony Algorithm

Utilizing smart mobile devices for entertainment, education, and social networking has grown recently. Even though mobile applications are getting more sophisticated and resource-intensive, the computing power of mobile devices is still constrained. Mobile phone applications can perform better by shifting parts of their functions to cloud servers. However, because the cloud is frequently positioned far from mobile phone users, there may be a significant and unpredictable delay in the data transfer between users and the cloud. It is crucial for mobile applications since customers value rapid responses greatly. Users of mobile phones can get close-up access to information technology and cloud computing services thanks to mobile edge computing. In this article, the main goal is to use an artificial bee colony meta-innovative algorithm to solve the problem of placing edge servers in mobile edge computing. Moreover, load balancing between servers is one of the challenges discussed in this article. To deal with this issue, determination the locations of the servers using considering the distribution of workload between servers as a cost function in the artificial bee colony algorithm is a focused issue in this study. The results of the proposed method are compared with the load balancing criteria. The results of K-means compared to the clustering method show the superiority of the proposed method with regard to the loading criteria compared to this clustering method.

Optimized Scheduling of Periodic Hard Real-Time Multicore Systems

Multicore systems were developed to provide a substantial performance increase over monocore systems. But shared hardware resources are a problem as they add unpredictable delays that affect the schedulability of multicore hard real-time systems. In recent years much effort has been put into modelling interference and proposing scheduling techniques to mitigate its negative effect. Using one of these models, we propose a scheduling technique, based on Integer Linear Programming (ILP) that, in combination with a task to core allocator, not only achieves a feasible schedule but also reduces the interference produced by shared hardware resources in the context of hard real-time multicore systems. The evaluation shows how both the interference is reduced (≈ 83.47%) and the schedulability is increased (≈ 12.25%) compared to traditional heuristics.

Accelerating logic-based Benders decomposition for railway rescheduling by exploiting similarities in delays

The operation of a railway system is subject to unpredictable delays or disruptions. Operators control the railway system to minimize losses in performance. Real-time rescheduling is the adaptation of a railway schedule to any unforeseen delay or disturbance and recovers an optimal system state. In this work we propose the extension of an existing Benders decomposition scheme used so far for timetabling, to the case of railway rescheduling. We show how to increase its computational speed by a factor 2, by considering libraries of Benders cuts computed for other instances, to be reused in the solution. We show how including extra cuts has to balance a speedup potential, with a general slowdown due to optimization problems of increased sizes. We show that, if delays in an instance of rescheduling are in fact unknown, but come from a known statistical distribution, we can use a similarity measure to identify a-priori the most promising libraries of Benders cuts, which lead to speedups up to 20%.

Prefrontal Cortical to Mediodorsal Thalamus Projection Neurons Regulate Posterror Adaptive Control of Behavior.

Adaptive control is the online adjustment of behavior to guide and optimize responses after errors or conflict. The neural circuits involved in monitoring and adapting behavioral performance following error are poorly understood. The prefrontal cortex (PFC) plays a critical role in this form of control. However, these brain areas are densely connected with many other regions, and it is unknown which projections are critical for adaptive behavior. Here, we tested the involvement of four distinct dorsal and ventral prefrontal cortical projections to striatal and thalamic target areas in adaptive control. We re-analyzed data from published experiments, using trial-by-trial analyses of behavior in an operant task for attention and impulsivity. We find that male rats slow their responses and perform worse following errors. Moreover, by combining retrograde labeling and chemogenetic silencing, we find that dorsomedial prefrontal pyramidal neurons that project to the lateral nucleus of the mediodorsal thalamus (MDL) are involved in posterror performance and timing of responses, specifically with unpredictable delays until stimulus presentation. Together, these data show that dorsal medial PFC (mPFC) projection neurons targeting the lateral MDT regulate adaptive control to flexibly optimize behavioral responses in goal-directed behavior.

The Impact of Social Media Salience on the Subjective Value of Social Cues

Like face-to-face interactions, evidence shows that interacting on social media is rewarding. However, the rewards associated with social media are subject to unpredictable delays, which may shape how they are experienced. Specifically, these delays might enhance the subjective desirability of social rewards and subsequent reward-seeking behavior by sensitizing people to the presence of such rewards. Here, we ask whether thinking about a recent social media post or conversation influences the subjective value of monetary and social rewards. Across two studies, we find that individuals who are thinking about a recent social media post are more likely to sacrifice small financial gains for the chance to see a genuine smile (a social reward) compared with those who are thinking about a recent conversation. This suggests that rather than satisfying social needs, thinking about social media interactions enhances the subjective value of social rewards, potentially explaining the incentive value of social media.

Train Me If You Can: Decentralized Learning on the Deep Edge

The end of Moore’s Law aligned with data privacy concerns is forcing machine learning (ML) to shift from the cloud to the deep edge. In the next-generation ML systems, the inference and part of the training process will perform at the edge, while the cloud stays responsible for major updates. This new computing paradigm, called federated learning (FL), alleviates the cloud and network infrastructure while increasing data privacy. Recent advances empowered the inference pass of quantized artificial neural networks (ANNs) on Arm Cortex-M and RISC-V microcontroller units (MCUs). Nevertheless, the training remains confined to the cloud, imposing the transaction of high volumes of private data over a network and leading to unpredictable delays when ML applications attempt to adapt to adversarial environments. To fill this gap, we make the first attempt to evaluate the feasibility of ANN training in Arm Cortex-M MCUs. From the available optimization algorithms, stochastic gradient descent (SGD) has the best trade-off between accuracy, memory footprint, and latency. However, its original form and the variants available in the literature still do not fit the stringent requirements of Arm Cortex-M MCUs. We propose L-SGD, a lightweight implementation of SGD optimized for maximum speed and minimal memory footprint in this class of MCUs. We developed a floating-point version and another that operates over quantized weights. For a fully-connected ANN trained on the MNIST dataset, L-SGD (float-32) is 4.20× faster than the SGD while requiring only 2.80% of the memory with negligible accuracy loss. Results also show that quantized training is still unfeasible to train an ANN from the scratch but is a lightweight solution to perform minor model fixes and counteract the fairness problem in typical FL systems.

Multiple Reaction Monitoring-Mass Spectrometry Enables Robust Quantitation of Plasma Proteins Regardless of Whole Blood Processing Delays That May Occur in the Clinic

Plasma is an important biofluid for clinical research and diagnostics. In the clinic, unpredictable delays—from minutes to hours—between blood collection and plasma generation are often unavoidable. These delays can potentially lead to protein degradation and modification and might considerably affect intact protein measurement methods such as sandwich enzyme-linked immunosorbent assays that bind proteins on two epitopes to increase specificity, thus requiring largely intact protein structures. Here, we investigated, using multiple reaction monitoring mass spectrometry (MRM-MS), how delays in plasma processing affect peptide-centric “bottom-up” proteomics. We used validated assays for proteotypic peptide surrogates of 270 human proteins to analyze plasma generated after whole blood had been kept at room temperature from 0 to 40 h to mimic delays that occur in the clinic. Moreover, we evaluated the impact of different plasma-thawing conditions on MRM-based plasma protein quantitation. We demonstrate that >90% of protein concentration measurements were unaffected by the thawing procedure and by up to 40-h delayed plasma generation, reflected by relative standard deviations (RSDs) of <30%. Of the 159 MRM assays that yielded quantitative results in 60% of the measured time points, 139 enabled a stable protein quantitation (RSD <20%), 14 showed a slight variation (RSD 20–30%), and 6 appeared unstable/irreproducible (RSD > 30%). These results demonstrate the high robustness and thus the potential for MRM-based plasma-protein quantitation to be used in a clinical setting. In contrast to enzyme-linked immunosorbent assay, peptide-based MRM assays do not require intact three-dimensional protein structures for an accurate and precise quantitation of protein concentrations in the original sample.

Rendezvous Under Temporal Uncertainty.

Three experiments sought to understand performance limitations in controlling a ship attempting to meet another moving ship that approached from various trajectories. The influence of uncertainty, resulting from occasional unpredictable delays in one's own movement, was examined. Cognitive elements of rendezvous have been little studied. Related work such as the planning fallacy and bias toward underestimating time-to-contact imply a tendency toward late arrival at a rendezvous. In a simplified simulation, participants controlled the speed and/or heading of their own ship once per scenario to try to rendezvous with another ship. Forty-five scenarios of approximately 30 s were conducted with different starting geometries and, in two of three experiments, with different frequencies and lengths of the unexpected delays. Perfect rendezvous were hard to obtain, with a general tendency to arrive late and pass behind the target vessel, although this was dependent on the angle of approach and relative speed. When occasional delays were introduced, less frequent but longer delays disrupted performance more than shorter but more frequent delays. Where delays were possible, but no delay occurred, there was no longer evidence of a general tendency to more frequently pass behind the target ship. Additionally, people did not wait to see if the unpredictable delays would occur before executing a course of action. Different control strategies were deployed and dual axis control was preferred. The tendency to arrive late and the influence of the possibility of uncertain delays are discussed in relationship to control strategies.

Autonomous wheelchair for patient\u2019s transportation on healthcare institutions

The transport of patients from the inpatient service to the operating room is a recurrent task in a hospital routine. This task is repetitive, non-ergonomic, time consuming, and requires the labor of patient transporters. In this paper is presented a system, named Connected Driverless Wheelchair, that can receive transportation requests directly from the hospital information management system, pick up patients at their beds, navigate autonomously through different floors, avoid obstacles, communicate with elevators, and drop patients off at the designated operating room. As a result, a prototype capable of transporting patients autonomously in hospital environments was obtained. Although it was impossible to test the final developed system at the hospital as planned, due to the COVID-19 pandemic, the extensive tests conducted at the robotics laboratory facilities, and our previous experience in integrating mobile robots in hospitals, allowed to conclude that it is perfectly prepared for this integration to be carried out. The achieved results are relevant since this is a system that may be applied to support these types of tasks in the future, making the transport of patients more efficient (both from a cost and time perspective), without unpredictable delays and, in some cases, safer.

Identifying policy challenges of COVID-19 in hardly reliable data and judging the success of lockdown measures.

Identifying structural breaks in the dynamics of COVID-19 contagion is crucial to promptly assess policies and evaluate the effectiveness of lockdown measures. However, official data record infections after a critical and unpredictable delay. Moreover, people react to the health risks of the virus and also anticipate lockdowns. All of this makes it complex to quickly and accurately detect changing patterns in the virus’s infection dynamic. We propose a machine learning procedure to identify structural breaks in the time series of COVID-19 cases. We consider the case of Italy, an early-affected country that was unprepared for the situation, and detect the dates of structural breaks induced by three national lockdowns so as to evaluate their effects and identify some related policy issues. The strong but significantly delayed effect of the first lockdown suggests a relevant announcement effect. In contrast, the last lockdown had significantly less impact. The proposed methodology is robust as a real-time procedure for early detection of the structural breaks: the impact of the first two lockdowns could have been correctly identified just the day after they actually occurred.

Looking away from a moving target does not disrupt the way in which the movement toward the target is guided.

People usually follow a moving object with their gaze if they intend to interact with it. What would happen if they did not? We recorded eye and finger movements while participants moved a cursor toward a moving target. An unpredictable delay in updating the position of the cursor on the basis of that of the invisible finger made it essential to use visual information to guide the finger's ongoing movement. Decreasing the contrast between the cursor and the background from trial to trial made it difficult to see the cursor without looking at it. In separate experiments, either participants were free to hit the target anywhere along its trajectory or they had to move along a specified path. In the two experiments, participants tracked the cursor rather than the target with their gaze on 13% and 32% of the trials, respectively. They hit fewer targets when the contrast was low or a path was imposed. Not looking at the target did not disrupt the visual guidance that was required to deal with the delays that we imposed. Our results suggest that peripheral vision can be used to guide one item to another, irrespective of which item one is looking at.

Robust Scheduling of Hot Rolling Production by Local Search Enhanced Ant Colony Optimization Algorithm

Scheduling of a hot strip mill is an important decision problem in the steel manufacturing industry. Previous studies on the hot strip mill scheduling problem have mostly neglected the random factors in production. However, random variations in processing times are inevitable due to unpredictable delays and disturbances. In this article, we adopt a robust optimization approach to deal with the uncertainty in processing times. The advantage is that no assumption has to be made regarding the distribution of random data, and the obtained schedule will remain strictly feasible when the variations have not exceeded a predefined uncertainty set. First, a mixed-integer linear programming model is presented to formulate the robust scheduling problem. Then, a hybrid metaheuristic algorithm, which combines ant colony system (ACS) and enhanced local search, is proposed to provide an efficient solution to the problem. Finally, extensive computational experiments involving both randomly generated and real-world instances have been conducted to verify the effectiveness of the proposed algorithm. It is shown that the algorithm achieves optimality for small instances and outperforms two state-of-the-art metaheuristics when used to solve large instances.

Towards Causal Consistent Updates in Software-Defined Networks

A network paradigm called the Software-Defined Network (SDN) has recently been introduced. The idea of SDN is to separate the control logic from forwarding devices to enable a centralized control platform. However, SDN is still a distributed and asynchronous system: events can be triggered by any network entity, while messages and packets are prone to arbitrary and unpredictable transmission delays. Moreover, the absence of a global temporal reference results in a broad combinatorial range space of event order. During network updates, an out-of-order execution of events may result in a deviation from desirable consistent network update properties, leading, for example, to forwarding loops and forwarding black holes, among others. In this paper, we introduce a study of the Transient Forwarding Loop (TFL) phenomenon during SDN updates; for this, we define a formal model of the TFL based on causal dependencies that capture the conditions under which it may occur. Based on this model, we introduce an algorithm that ensures the causal dependencies of the system oriented toward TFL-free SDN updating. We formally prove that it is sufficient to ensure the causal dependencies in order to guarantee TFL-free network updates. Finally, we analytically evaluate our algorithm and discuss how it outperforms the state-of-the-art in terms of updating overhead.

The impacts of connected vehicle technology on network-wide traffic operation and fuel consumption under various incident scenarios

ABSTRACTIncidents are a major source of traffic congestion and can lead to long and unpredictable delays, deteriorating traffic operations and adverse environmental impacts. The emergence of connected vehicles and communication technologies has enabled travelers to use real-time traffic information. The ability to exchange traffic information among vehicles has tremendous potential impacts on network performance especially in the case of non-recurrent congestion. To this end, this paper utilizes a microscopic simulation model of traffic in El Paso, Texas to investigate the impacts of incidents on traffic operation and fuel consumption at different market penetration rates (MPR) of connected vehicles. Several scenarios are implemented and tested to determine the impacts of incidents on network performance in an urban area. The scenarios are defined by changing the duration of incidents and the number of lanes closed. This study also shows how communication technology affects network performance in response to congestion. The results of the study demonstrate the potential effectiveness of connected vehicle technology in improving network performance. For an incident with a duration of 900 s and MPR of 80%, total fuel consumption and total travel time decreased by approximately 20%; 26% was observed in network-wide travel time and fuel consumption at 100% MPR.

VersaVIS-An Open Versatile Multi-Camera Visual-Inertial Sensor Suite.

Robust and accurate pose estimation is crucial for many applications in mobile robotics. Extending visual Simultaneous Localization and Mapping (SLAM) with other modalities such as an inertial measurement unit (IMU) can boost robustness and accuracy. However, for a tight sensor fusion, accurate time synchronization of the sensors is often crucial. Changing exposure times, internal sensor filtering, multiple clock sources and unpredictable delays from operation system scheduling and data transfer can make sensor synchronization challenging. In this paper, we present VersaVIS, an Open Versatile Multi-Camera Visual-Inertial Sensor Suite aimed to be an efficient research platform for easy deployment, integration and extension for many mobile robotic applications. VersaVIS provides a complete, open-source hardware, firmware and software bundle to perform time synchronization of multiple cameras with an IMU featuring exposure compensation, host clock translation and independent and stereo camera triggering. The sensor suite supports a wide range of cameras and IMUs to match the requirements of the application. The synchronization accuracy of the framework is evaluated on multiple experiments achieving timing accuracy of less than . Furthermore, the applicability and versatility of the sensor suite is demonstrated in multiple applications including visual-inertial SLAM, multi-camera applications, multi-modal mapping, reconstruction and object based mapping.

Public Key Certificate Privacy in VoNDN: Voice Over Named Data Networks

Named Data Network (NDN) is a network paradigm that attempts to answer today's needs for distribution. One of the NDN key features is in-network caching to increase content distribution and network efficiency. However, this feature may increase the privacy concerns, as the adversary may identify the call history, and the callee/caller location through side-channel timing responses from the cache of trusted Voice over NDN (VoNDN) application routers. The side-channel timing attack can be mitigated by countermeasures, such as additional unpredictable delay, random caching, group signatures, and no-caching configurations. However, the content distribution may be affected by pre-configured countermeasures, which may be against the original purpose of NDN. In this work, the detection and defense (DaD) approach is proposed to mitigate the attack efficiently and effectively. With the DaD usage, an attack can be detected by a multi-level detection mechanism, in order to apply the countermeasures against the adversarial faces. Also, the detections can be used to determine the severity of the attack. In order to detect the behavior of an adversary, a brute-force timing attack was implemented and simulated of the VoNDN application on NDN-testbed. A trusted application that mimics the VoNDN and identifies the cached certificate on a worldwide NDN-testbed. In simulation primary results showed that the multi-level detection based on DaD mitigated the attack about 39.1% in best-route, and 36.5% in multicast communications. Additionally, the results showed that DaD preserves privacy without compromising the efficiency benefits of in-network caching in the VoNDN application.

A stochastic programming approach for offshore flight scheduling

In this paper, we propose a stochastic programming approach to perform optimal and robust offshore flight scheduling from a service level perspective, reducing flight delays. The two-stage stochastic programming is reduced to a deterministic equivalent linear program and, considering the combinatorial characteristic of scheduling problems, we use Sample Average Approximation to generate scenarios. A Discrete Event Simulation model is developed to compare the stochastic and deterministic approaches. Numerical results indicate that a stochastic approach to offshore flight scheduling can reduce unpredictable delays, which have a major impact on passengers, without significantly increasing aircraft idle time. In addition, the stochastic approach allows dealing with operational downtime windows with uncertainties in duration and occurrence.