Time Allocation Scheme Research Articles

P.348: Split left and right liver transplantation plus domino liver transplantation: one donor liver and three recipients at the same time, a complex but feasible allocation scheme to alleviate donor liver shortage.

P.348: Split left and right liver transplantation plus domino liver transplantation: one donor liver and three recipients at the same time, a complex but feasible allocation scheme to alleviate donor liver shortage.

Energy Consumption Minimization with SNR Constraint for Wireless Powered Communication Networks.

The article addresses the energy consumption minimization problem in wireless powered communication networks (WPCNs) and proposes a time allocation scheme, named DaTA, which is based on the Different Target Simultaneous Wireless Information and Power Transfer (DT-SWIPT) scheme such that the wireless station can share the remaining energy after transmission to the Hybrid Access Point (HAP) to those who have not transmitted to the HAP to minimize the energy consumption of the WPCN. In addition to proposing a new frame structure, the article also considers the Signal-to-Noise (SNR) constraint to guarantee that the HAP can successfully receive data from wireless stations. In the article, the problem of minimization of energy consumption is formulated as a nonlinear programming model. We employ the SQP (Sequential Quadratic Programming) algorithm to figure out the optimal solution. Moreover, a heuristic method is proposed as well to obtain a near-optimal solution in a shorter time. The simulation results showed that the proposed scheme outperforms the related work in terms of energy consumption and energy efficiency.

Energy-saving time allocation strategy with uncertain dwell times in urban rail transit: Two-stage stochastic model and nested dynamic programming framework

During practical operations, the urban rail transit system suffers from various uncertainties, particularly uncertain dwell times, which significantly impact the execution of the timetable and affect its performance, regarding train energy consumption and timetable stability. Using multi-scenario dwell times to capture its uncertainty, in this study, a two-stage chance-constrained stochastic model involving a section-time allocation stage and an optimal driving strategy stage is developed to minimize the expected energy consumption and improve stability. An exact nested dynamic programming (NDP) framework was designed to solve the model. The effectiveness and performance of the proposed methodology were investigated using a series of numerical experiments based on a small-scale instance from the Beijing Yizhuang Line. The optimized section-time allocation strategy reduced the expected energy consumption by 6.6% and improved the the minimum stability ratio by 8.5% by selecting the appropriate weight ratio. Four other sensitivity analyses were conducted to provide realistic managerial insights. Finally, a large-scale study of the Beijing 4-Daxing Subway Line was conducted to validate the scalability and efficiency of the NDP framework.

Outage performance and energy efficiency optimization of wireless-powered millimeter-wave sensor networks

AbstractWith the widespread use of wireless sensor networks, one of the most pressing concerns is extending the lifetime of the sensors. By deploying directional antenna arrays, millimeter wave (mmWave) is a possible candidate for wireless energy transfer (WPT). This paper investigates a beneficial combination of WPT and data transmission in a typical mmWave sensor network with Rayleigh channels, where a transmission interval can be divided into two sub-intervals. During the first sub-interval, one hybrid access point (HAP) employs beamforming techniques to transfer energy for serving multiple sensors within the service sector. The sensors then transmit their individual signal in turn to the HAP based on time division multiple address (TDMA) strategy by using the whole harvested energy. According to stochastic geometry, the exact and approximate expressions of beam outage probability for the considered system are determined, respectively. The optimal time allocation of energy harvesting and data transmission for sensors is examined in order to maximize the energy efficiency of the system. The optimization problem can be translated into corresponding parametric form, and the resulting optimization problem can be solved using the Lagrange dual method with Karush–Kuhn–Tucker (KKT) conditions. The numerical results show the variation trend of the beam outage probability under various parameters and verify the accuracy of the theoretical analyses. Furthermore, the simulation results illustrate that the proposed optimal time allocation strategy can significantly enhance the overall energy efficiency of the system compared with a similar scheme.

Beyond the bell: exploring the link between time allocation on extracurricular activities and academic performance in Chinese adolescents

ABSTRACT Academic performance could have an impact on job trajectories and socioeconomic status for societies in the future. However, only a few studies have focused on the impact of time allocation on extracurricular activity strategies. Based on that observation, we recruited 91,161 school students from 451 primary, middle, and high schools in China, and used a cross-sectional compositional data analysis approach to investigate the linkage between extracurricular activities and time allocation strategies and academic performance. Our study found that, in general, a suitable daily sleep routine is of primary importance in improving students’ academic performance. Additionally, daily exercise appears to be able to significantly facilitate higher academic performance. it’s important to note that exercising while sleep-restricted may have adverse effects. The most effective way to improve student academic performance proves to be reducing the time spent playing video games and watching short videos and replacing it with hours of sleep or exercise.

Optimizing Worst Case Data Freshness in RF-Powered Networked Embedded Systems

Maintaining real-time data freshness plays a critical role in ensuring system correctness and optimizing the system performance in networked embedded systems (NESs). To quantitatively measure the freshness of the collected real-time data, the concept of Age of Information (AoI) has been extensively studied in recent years. This article explores how to minimize the worst-case AoI of real-time data in RF-powered NESs. In such systems, one hybrid access point (HAP) transfers wireless power to a set of distributed sensor nodes, and in the meantime, receives the information from these sensor nodes. We utilize the metric of AoI to measure the data freshness and present a comprehensive analysis of the worst-case AoI of the real-time data in the target system. Based on the analysis, an optimal energy schedule solution is designed to judiciously determine individual sensor nodes’ energy and time allocation to minimize the worst-case AoI. Considering the varying importance of different information and sensor nodes in the target system, we further propose the optimal time and energy allocation scheme for minimizing the weighted worst-case AoI. A multi-node RF-powered NES testbed is implemented to validate the functional correctness of our solutions. The results show that our solutions significantly outperform the state-of-the-art solutions, reducing the worst-case AoI and weighted worst-case AoI by 69.3% and 75.1% on average, respectively.

Resource saving based dwell time allocation and detection threshold optimization in an asynchronous distributed phased array radar network

The resource optimization plays an important role in an asynchronous Phased Array Radar Network (PARN) tracking multiple targets with Measurement Origin Uncertainty (MOU), i.e., considering the false alarms and missed detections. A Joint Dwell Time Allocation and Detection Threshold Optimization (JDTADTO) strategy is proposed for resource saving in this case. The Predicted Conditional Cramér-Rao Lower Bound (PC-CRLB) with Bayesian Detector and Amplitude Information (BD-AI) is derived and adopted as the tracking performance metric. The optimization model is formulated as minimizing the difference between the PC-CRLBs and the tracking precision thresholds under the constraints of upper and lower bounds of dwell time and false alarm ratio. It is shown that the objective function is nonconvex due to the Information Reduction Factor (IRF) brought by the MOU. A cyclic minimizer-based solution is proposed for problem solving. Simulation results confirm the flexibility and robustness of the JDTADTO strategy in both sufficient and insufficient resource scenarios. The results also reveal the effectiveness of the proposed strategy compared with the strategies adopting the BD without detection threshold optimization and amplitude information.

Bibliometric Analysis of Internationalization Strategies in Supporting MSME Business Growth in Indonesia

Research on MSME internationalization strategies is interesting and important in increasing global scale business growth. This study aims to analyze the five dimensions of internationalization strategy including market entry, target market strategy, time strategy, allocation strategy, and coordination strategy that supports MSME business growth in Indonesia. There have been no studies that have reviewed this topic using bibliometric analysis. Our analysis includes 180 journal articles published in Scopus, Google Scholar, and Crossref between 2018 – 2023. Researchers use VOSviewer software to classify and visualize research results. Research findings show that Indonesian MSME players combine several internationalization strategies such as market entry strategies, target market strategies, allocation strategies, and coordination strategies synergistically through e-commerce development so that they can help exploit existing competitive advantages or create new competitive advantages in the international market. Research on time strategies and allocation strategies has not been explored much, therefore this can be raised as a further research topic. The implications of this research present a bibliometric analysis of the dimensions of Indonesia's MSME internationalization strategy and propose future research directions.

How Learning Time Allocation Make Sense on Secondary School Students' Academic Performance: A Chinese Evidence Based on PISA 2018.

It is well known that the proper allocation of learning time is particularly important for promoting students' academic performance. Based on the data from PISA 2018, this research used the method of threshold regression and quantile regression to explore the optimal length of learning time to promote the students' academic performance. At the same time, this research also explored the heterogeneity of the effect of learning time on different academic levels of students. The results show that for four Chinese provinces and cities, including Beijing, Shanghai, Jiangsu province and Zhejiang province, students who study in rural areas and private schools usually have longer learning time than students in cities and public schools. Moreover, it is suggested that there is no significant association between school quality and students' learning time. The average learning time of students from the four Chinese cities and provinces is obviously longer than that in OECD countries. Moreover, it is found that the impact of learning time on academic performance across subjects is inverted U-shaped, and the optimal study time can be found in the learning of mathematics, science, and reading related subjects. As for the effect of learning time, the results showed that learning time commitment is more effective for students who are academically disadvantaged. At the same time, this study found that there is a relationship between students' excessive learning time and students' subjective well-being and attitudes toward learning activities. The non-cognitive factors can influence students' academic performance gradually. According to the results of this research, it is suggested that students need to balance their learning time allocation against the appropriate learning time standards. Moreover, schools should adopt different learning time allocation schemes for students at different academic achievement levels. The teachers also should uphold a more scientific design of students' after-school homework, and teachers and parents should also focus on improving students' learning efficiency.

Deep Reinforcement Learning for Physical Layer Security Enhancement in Energy Harvesting Based Cognitive Radio Networks

The paper studies the secrecy communication threatened by a single eavesdropper in Energy Harvesting (EH)-based cognitive radio networks, where both the Secure User (SU) and the jammer harvest, store, and utilize RF energy from the Primary Transmitter (PT). Our main goal is to optimize the time slots for energy harvesting and wireless communication for both the secure user as well as the jammer to maximize the long-term performance of secrecy communication. A multi-agent Deep Reinforcement Learning (DRL) method is proposed for solving the optimization of resource allocation and performance. Specifically, each sub-channel from the Secure Transmitter (ST) to the Secure Receiver (SR) link, along with the jammer to the eavesdropper link, is regarded as an agent, which is responsible for exploring optimal power allocation strategy while a time allocation network is established to obtain optimal EH time allocation strategy. Every agent dynamically interacts with the wireless communication environment. Simulation results demonstrate that the proposed DRL-based resource allocation method outperforms the existing schemes in terms of secrecy rate, convergence speed, and the average number of transition steps.

Throughput Maximization for the Full-Duplex Two-Way Relay System with Energy Harvesting

The full-duplex technique can improve the transmission capacity of the communication systems, and energy harvesting (EH) is a promising operation to prolong the lifespan of a wireless node by utilizing the radio-frequency signals. In this paper, the throughput performance of a full-duplex two-way energy EH capable relay system is investigated. In particular, a practical EH protocol, named the time-switching-based relaying (TSR) protocol, is used for EH and the decode-and-forward (DF) policy for information transmission. The outage probability is successfully obtained, and the corresponding system throughput for TSR protocol can be derived by it. The derived throughput is a function of different system parameters, including the time-switching (TS) ratio, power allocation ratio, and the length of the communication time slot. Meanwhile, the throughput is used to characterize a joint time and power allocation scheme for the system, and we aim to find the optimal time and power allocation to achieve the optimal throughput. Due to the existence of three variables and the integral form of throughput expression, an optimization for the throughput is difficult. However, a modified simulated annealing-based search (SABS) algorithm can be used to optimize the throughput. The modified SABS algorithm overcomes being highly impacted by the initial point, and derives the optimal solution fast. Simulation results show that the analytical throughput expression is related with the TS ratio, power allocation ratio, and the length of the communication time slot. The analytical curve of the throughput matches with the simulated one well, which shows that the obtained analytical system throughput for the TSR protocol is valid. Meanwhile, the proposed modified SABS algorithm could be used to derive accurate throughput when SNR is higher than 10 dB.

Skeleton Extraction and Greedy-Algorithm-Based Path Planning and its Application in UAV Trajectory Tracking

Space research is of great significance to increasingly decentralized and distributed space systems, and path planning in space systems has become a research hotspot for maintaining their safety, security, and reliability. To explore the passable path connecting the starting point and the target point, and optimize a smooth trajectory that can be tracked by unmanned aerial vehicles (UAVs) in 3-D space, a skeleton-extraction- and greedy-algorithm-based path planning has been proposed to guide the flight of UAVs. First, the rapidly exploring random tree (RRT) has been introduced for path search. To speed up the path search process, the spatial skeleton extraction method has been introduced to calculate the skeleton of free space The greedy algorithm has been utilized to increase the RRT expansion and reduce unnecessary bends in the path. The skeleton extraction and greedy-algorithm-based Lazy RRT and RRT-Connect have been proposed to build compared experiments. Second, the minimum snap has been applied to generate a smooth flight trajectory, and the flight time is allocated according to the distance between the waypoints. Third, the UAV Simulink model has been established, and the spatial position of the optimized trajectory is tracked. The experimental results prove that the skeleton extraction can significantly speed up the search process, and greedy algorithm can shorten the path length effectively. The minimum snap combined with the time allocation strategy can produce a smooth and feasible path. The UAV Simulink model also proves that the classic proportion-differentiation controller can accurately track the generated trajectory. The greedy algorithm and skeleton extraction reduce the average path length of RRT by 9.780 and 8.251%, respectively. The greedy algorithm and skeleton extraction reduce the average path length of RRT, Lazy RRT, and RRT-Connect by about 10–13%. The greedy algorithm and skeleton extraction reduce the time consumption of RRT by 62.781 and 36.276%, respectively.

Assessing Latency of Packet Delivery in the 5G 3GPP Integrated Access and Backhaul Architecture with Half-Duplex Constraints

Integrated Access and Backhaul (IAB) is an enabling technology for efficient 5G millimeter wave (mmWave) New Radio (NR) deployment. The key feature of IAB is multi-hop wireless backhauling, allowing utilizing relaying IAB-nodes to provide cost-efficient access network densification and alleviate the problem of blockages. One of the critical performance measures in such systems is the latency of packet delivery over the multi-hop paths. The paper aims at assessing the impact of multi-hop transmission on the end-to-end delay in an IAB radio access network, taking into account the half-duplex constraint. We build a detailed queuing theory model for latency assessment in time-division-multiplexing (TDM)-based IAB deployments and evaluate the delay due to queuing in the network nodes for several cell topologies and under different time allocation strategies between access and backhaul. The paper considers a practical Manhattan-style urban deployment, which is characteristically impaired by the blockage of buildings. The numerical results show that balancing the access and backhaul micro phases is crucial for reducing the end-to-end packet delay, at least in the uplink, while increasing the number of network hops yields a linear increase in the total packet delay for both the uplink and downlink. The numerical results were obtained via simulation using the open-source software OMNeT++.

Head-Up Display Graphic Warning to Support Collision Avoidance: Effect of Graphic Animation and Border on Driving Behavior and Eye Movement Pattern

Several studies have determined the positive effect of head-up display (HUD) graphic warning to support collision avoidance. However, the visual complexity of the graphic warning may overburden the driver. Design principles and empirical evidence suggest that animation and border designs may increase visual saliency and reduce the visual complexity of graphic warnings. This study aimed to examine the effects of animation (within-subject factors: animated/non-animated) and border (within-subject factors: with/without border) on drivers’ attention and driving performance. Thirty drivers (19 females) experienced five warning conditions (the no-warning condition was the baseline condition) in simulated driving conditions. The results showed the positive effects of animation and border designs on the visual detection time (animation: 25.64% reduction; border: 6.73% reduction) and attention allocation strategies (animation: 9.53% less dwell time on the warning; border: 6.62% less dwell time on the warning) when compared with the original HUD warning. We only observed a positive effect of borders on brake reaction time (17.14% reduction). However, the positive effect of the border design on brake reaction time was only observed in the non-animated warning condition, and not in the animated warning condition. A limitation of the study is that the results were generated from young and inexperienced drivers, and they should be generalized to other driver groups only with caution. Overall, we recommend using non-animated warnings with borders in conditions that require fast reactions, and animated warnings with borders in conditions that require a superior attention strategy.

Intelligent Time Allocation for Wireless Power Transfer in Wireless-Powered Mobile Edge Computing

Wireless-powered mobile edge computing is a new network computing paradigm that combines with the advantages of wireless power transfer and mobile edge computing. When the harvest-then-offload protocol is adopted in this network, the time of wireless power transfer has a significant impact on system performance. If the time is too short, the user cannot harvest enough energy. If it is too long, the user will not have enough time to complete the task offloading. Both result in many of user tasks being discarded. To address this problem, DEWPT, a differential evolution-based optimization scheme for wireless power transfer time, is proposed in this paper. DEWPT is designed with a hybrid mutation operator and a perturbation-based binomial crossover operator. The hybrid mutation operator combines the benefits of two mutation operators with distinct characteristics, so that DEWPT not only has a strong exploration ability but also can quickly converge. Meanwhile, the perturbation-based binomial crossover operator improves DEWPT’s ability to exploit local space. These two improvements effectively enhance DEWPT’s optimization performance, which is beneficial to find the optimal time for wireless power transfer. Furthermore, to improve the optimization efficiency, micro-population is introduced into DEWPT. Finally, the computation completion ratio maximization model is used to validate the performance of DEWPT in the wireless-powered mobile edge computing network with multiple edge servers. Numerical results show that the computation offloading scheme integrating with DEWPT can achieve a higher computation completion rate than three benchmark schemes, and is competitive in complexity. This demonstrates that DEWPT is an effective time allocation scheme for wireless power transfer.

Relay Selection-Based Cooperative Backscatter Transmission With Energy Harvesting: Throughput Maximization

In this letter, an optimal time allocation scheme is proposed to improve the throughput of energy harvesting based backscatter communication. The base station (BS) serving as the power supply is transmitting continuously with constant power. All the relays can harvest radio frequency (RF) energy from the transmission of BS while the user performs backscatter transmission, and then the selected relay decodes and forwards the information from user with harvested RF energy. We propose an optimal time allocation scheme that considers the adaptive backscatter coefficient and relay selection to maximize throughput. Simulation results demonstrate that the proposed scheme outperforms the benchmark strategies in system throughput improvement.

FASTER: Fast and Safe Trajectory Planner for Navigation in Unknown Environments

Planning high-speed trajectories for UAVs in unknown environments requires algorithmic techniques that enable fast reaction times to guarantee safety as more information about the environment becomes available. The standard approaches that ensure safety by enforcing a “stop” condition in the free-known space can severely limit the speed of the vehicle, especially in situations where much of the world is unknown. Moreover, the ad-hoc time and interval allocation scheme usually imposed on the trajectory also leads to conservative and slower trajectories. This work proposes FASTER (Fast and Safe Trajectory Planner) to ensure safety without sacrificing speed. FASTER obtains high-speed trajectories by enabling the local planner to optimize in both the free-known and unknown spaces. Safety is ensured by always having a safe back-up trajectory in the free-known space. The MIQP formulation proposed also allows the solver to choose the trajectory interval allocation. FASTER is tested extensively in simulation and in real hardware, showing flights in unknown cluttered environments with velocities up to 7.8 m/s, and experiments at the maximum speed of a skid-steer ground robot (2 m/s).

Deciding Fast and Slow: The Role of Cognitive Biases in AI-assisted Decision-making

Several strands of research have aimed to bridge the gap between artificial intelligence (AI) and human decision-makers in AI-assisted decision-making, where humans are the consumers of AI model predictions and the ultimate decision-makers in high-stakes applications. However, people's perception and understanding are often distorted by their cognitive biases, such as confirmation bias, anchoring bias, availability bias, to name a few. In this work, we use knowledge from the field of cognitive science to account for cognitive biases in the human-AI collaborative decision-making setting, and mitigate their negative effects on collaborative performance. To this end, we mathematically model cognitive biases and provide a general framework through which researchers and practitioners can understand the interplay between cognitive biases and human-AI accuracy. We then focus specifically on anchoring bias, a bias commonly encountered in human-AI collaboration. We implement a time-based de-anchoring strategy and conduct our first user experiment that validates its effectiveness in human-AI collaborative decision-making. With this result, we design a time allocation strategy for a resource-constrained setting that achieves optimal human-AI collaboration under some assumptions. We, then, conduct a second user experiment which shows that our time allocation strategy with explanation can effectively de-anchor the human and improve collaborative performance when the AI model has low confidence and is incorrect.

Long-Term Energy Consumption and Transmission Delay Tradeoff in Wireless-Powered Body Area Networks

In this article, we investigate the long-term energy consumption and transmission delay (EC-TD) tradeoff in a wireless-powered body area network that consists of a multiantenna hybrid access point and a number of single-antenna sensor nodes (SNs). The beamforming technique and the simultaneous wireless information and power transfer (SWIPT) technique are adopted. Each SN is equipped with a battery and data buffer for storing harvested energy and sensory data. The long-term energy consumption minimization problem is addressed subject to the constraint of transmission delay. Meanwhile, the residual energy constraints of SNs are considered, which enable the setting up of the available energy of the SNs according to requirements. By employing the Lyapunov optimization theory, the original stochastic optimization problem is transformed into an equivalent instantaneous nonconvex problem in which the long-term EC-TD tradeoff can be adjusted using a system control parameter <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$V$ </tex-math></inline-formula> . A joint power and time allocation scheme is then proposed to solve this instantaneous problem. Moreover, based on the derived upper bounds of the long-term energy consumption and data buffer length, we reveal that the proposed resource allocation scheme achieves an EC-TD tradeoff as <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$[\mathcal {O}(1/V),\mathcal {O}(V)]$ </tex-math></inline-formula> . Since the value of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$V$ </tex-math></inline-formula> can be adjusted to achieve different energy consumption and transmission delay, the flexibility and applicability of the proposed scheme are enhanced. The simulation results validate the theoretical analysis and verify the effectiveness of the proposed scheme.

Throughput Optimization of Backscatter-Assisted Wireless Relay Networks in Obstacle Environment

Internet of Things nodes need new charging methods, and RF energy is not well utilized. Backscatter can harvest RF energy from the environment and use the idle spectrum to realize wireless power supply communication. In this article, each enhanced backscatter device (eBD) can select from a variety of cooperative receivers (CR) to provide different transmission services in three modes: energy harvesting (EH), relay communication (RelayCom), and backscatter communication (BackCom). To analyze the throughput of the secondary network, we model the three working modes, respectively. We describe the problem as the objective function of three variables: the period α of EH when there are no obstacles, period μ of RelayCom with obstacles, and period γ of BackCom with obstacles. The ideal time allocation strategy is then investigated. Then, we apply the particle swarm optimization algorithm to obtain a solution that maximizes the throughput. The simulation results show that compared to the communication network using backscatter or relay protocol alone, the proposed backscatter-assisted wireless relay communication model can greatly improve the throughput and coverage of the secondary system.