Buffer Size Research Articles

Integrating land use and traffic to spatial prediction of particulate matter

With degrading air quality and inefficient monitoring systems, spatial prediction of pollutants has become necessary. Air pollution exposure causes significant impacts on human health in urban areas around the world. The current study focuses on developing a deep learning model for spatial prediction of particulate matter at locations where the monitored data is unavailable. Delhi, India, is selected as the study area due to its high air pollution and fewer fixed monitoring stations throughout the city. The parameters used for the prediction modeling included PM2.5 from fixed monitoring stations, PM2.5 from mobile sensors, meteorological parameters (atmospheric temperature and relative humidity), traffic flow data, and land use parameters. The data is taken for six months, from November 2022 to April 2023, at an interval of 15 min. Four buffer sizes are used to find the spatial variability. The ANN model is used for the spatial predictions using the above-mentioned parameters. K-fold cross-validation is used to validate the robustness of the model. The SHAP summary plot is used to identify the feature importance of the features used in the model. It is observed that the integration of the land use data and traffic data increases the prediction capability of the model.

A Bayesian approach using spatiotemporal features for suitable next hop selection in opportunistic networks

SummaryOpportunistic network (OppNet) belongs to the category of Mobile Ad‐hoc Networks (MANETs), a kind of Delay Tolerant Network (DTN), where the wireless nodes are completely mobile and the data transmission routes are dynamic. The major challenge in developing a routing model for such a network is the unpredictable nature of the movement of the nodes. In this paper, a spatiotemporal prediction model based on human mobility pattern is proposed using Bayesian posterior probability (BPPR) where several clusters are identified within the network and the day and time duration of nodes visiting those clusters are recorded. The Bayesian posterior probability is then used to determine the probability of the neighbor node visiting the destination's cluster. If the calculated probability for that node is higher than a specified threshold, the packet will be forwarded. A comparison of the results obtained on simulation is made with benchmark models—Epidemic, Prophet, HBPR, EDR, NexT, and EBC, to name a few, where it is found that on average the proposed model outperforms the other models in terms of delivery probability, hop count and number of messages dropped by around 23.89%, 24.8%, 24.4%, 37%, 11%, and 42% respectively, with varying number of nodes, TTL, message generation interval, and buffer size. Similar improvements have been observed in terms of the other two metrics. In terms of overhead ratio, the proposed model outperforms Epidemic, Prophet, HBPR, NexT, and EBC. However, as the number of nodes and TTL are varied, BPPR performs better than NexT by around 9% and 12%, respectively, in terms of average latency.

Bayesian estimation of finite buffer size in single server Markovian queuing system

Bayesian estimation of finite buffer size in single server Markovian queuing system

Automated Buffer Sizing of Dataflow Applications in a High-level Synthesis Workflow

High-Level Synthesis (HLS) tools are mature enough to provide efficient code generation for computation kernels on FPGA hardware. For more complex applications, multiple kernels may be connected by a dataflow graph. Although some tools, such as Xilinx Vitis HLS, support dataflow directives, they lack efficient analysis methods to compute the buffer sizes between kernels in a dataflow graph. This article proposes an original method to safely approximate such buffer sizes. The first contribution computes an initial overestimation of buffer sizes without knowing the memory access patterns of kernels. The second contribution iteratively refines those buffer sizes, thanks to cosimulation. Moreover, the article introduces an open source framework using these methods to facilitate dataflow programming on FPGA using HLS. The proposed methods and framework have been tested on seven dataflow applications and outperform Vitis HLS cosimulation in five benchmarks, either in terms of BRAM and LUT usage, or in terms of exploration time. In the two other benchmarks, our best method gets results similar to Vitis HLS. Last but not least, our method admits directed cycles in the application graphs.

Continually adapting pre-trained language model to universal annotation of single-cell RNA-seq data.

Cell-type annotation of single-cell RNA-sequencing (scRNA-seq) data is a hallmark of biomedical research and clinical application. Current annotation tools usually assume the simultaneous acquisition of well-annotated data, but without the ability to expand knowledge from new data. Yet, such tools are inconsistent with the continuous emergence of scRNA-seq data, calling for a continuous cell-type annotation model. In addition, by their powerful ability of information integration and model interpretability, transformer-based pre-trained language models have led to breakthroughs in single-cell biology research. Therefore, the systematic combining of continual learning and pre-trained language models for cell-type annotation tasks is inevitable. We herein propose a universal cell-type annotation tool, called CANAL, that continuously fine-tunes a pre-trained language model trained on a large amount of unlabeled scRNA-seq data, as new well-labeled data emerges. CANAL essentially alleviates the dilemma of catastrophic forgetting, both in terms of model inputs and outputs. For model inputs, we introduce an experience replay schema that repeatedly reviews previous vital examples in current training stages. This is achieved through a dynamic example bank with a fixed buffer size. The example bank is class-balanced and proficient in retaining cell-type-specific information, particularly facilitating the consolidation of patterns associated with rare cell types. For model outputs, we utilize representation knowledge distillation to regularize the divergence between previous and current models, resulting in the preservation of knowledge learned from past training stages. Moreover, our universal annotation framework considers the inclusion of new cell types throughout the fine-tuning and testing stages. We can continuously expand the cell-type annotation library by absorbing new cell types from newly arrived, well-annotated training datasets, as well as automatically identify novel cells in unlabeled datasets. Comprehensive experiments with data streams under various biological scenarios demonstrate the versatility and high model interpretability of CANAL. An implementation of CANAL is available from https://github.com/aster-ww/CANAL-torch. dengmh@pku.edu.cn. Supplementary data are available at Journal Name online.

Stalling in Queuing Systems with Heterogeneous Channels

The present paper considers a model of stalling in a queueing system (QS) with any number of different capacities of heterogeneous servers. The state graph and the corresponding linear system for steady-state probabilities are derived using the standard Markov chain technique. The obtained solution of the steady-state probabilities model is numerically stable; the complexity of the corresponding expressions does not depend on the number of QS states; thus, they enable us to analytically study the QS characteristics. Optimization of a stalling buffer is considered as well, and it is shown that stalling helps us to solve the slow server problem under an appropriate choice of stalling buffer size, making the slow servers usable under various values of system load. The asymptotic conditions of optimal query distribution in channels, when the ratio of the capacities of the fast and slow servers is increasing, are also established. Moreover, some applications of the developed model in heterogeneous server clusters and in work productivity modelling for forest harvesting applications are discussed.

A Discrete-Time Queueing Model of a Bottleneck with an Energy-Saving Mechanism Based on Setup and Shutdown Times

Producers are encouraged to reduce their energy consumption of manufacturing systems by applying less-energy-intensive modern technologies and advanced machine tools and operating methods at the system level. In the paper, organizational and analytical solutions are combined to model the sustainable production system. Managers can study the behavior of a production system organized using energy-saving rules by changing key parameters of the input model (arrival intensity, bottleneck service rate, buffer size, setup and shutdown time) to analyze the queue size of the production system and therefore performance. A discrete-time queueing model of a single-bottleneck production line with a finite input buffer capacity is proposed. Jobs occur according to a binomial process and are processed individually, one by one, according to the natural FIFO service discipline, with a general discrete-type cumulative distribution function. The total number of jobs present in the system is bounded by a non-random fixed value N. Every time the system becomes empty, an energy-saving mechanism is started: the processing machine (server) is turned off during a geometrically distributed shutdown time. Similarly, the first job arriving into the empty system initializes a geometrically distributed setup time. Identifying renewal moments in the evolution of the model, a system of difference equations is built for the transient queue-size distribution conditioned by the state of the system at the opening. The solution is obtained explicitly in terms of probability-generating functions. In addition, the Drum-Buffer-Rope concept is proposed to reduce the energy consumption of the production line. The throughput of the production system is maximized by adjusting the time between the order arrivals and the size of the input buffer to the capacity of the bottleneck. Turning off a machine under certain conditions and slowing down non-critical machines are strategies to reduce energy consumption. A detailed illustrating numerical and simulation study of the considered model is attached as well, in which the sensitivity of the queue-size behavior to changes of the key input model parameters is investigated.

An Improved Spray and Wait Routing Algorithm Based on Stable Transmission Capacity Evaluation of Nodes in Opportunistic Network

In opportunistic network, the regular connectivity of nodes and their individual conditions significantly influence message transmission. To address this, we propose an improved spray and wait routing algorithm based on stable transmission capacity evaluation of nodes (referred to as STCESW), which is derived from traditional Prophet and spray and wait algorithms but surpasses them. STCESW incorporates network models that consider node connection stability and transmission capacity, aiming to enhance the update and punishment formulas for node delivery probability. Furthermore, it combines the residual message buffer size of nodes with the angle between motion directions of nodes to determine more efficient and extensive message spraying in the network. The simulation results clearly indicate that STCESW significantly outperforms the other three enhanced algorithms in both SPMBM and RWP. It demonstrates its robust performance in terms of delivery probability, showing an increase ranging from 2% to 87% across diverse node buffer sizes or simulation time intervals. Additionally, it compromises slightly on delivery latency while maintaining relatively stable message latency and buffer time.

Range aware message drop policy for delay tolerant networks.

In delay tolerant networks (DTNs) the messages are often not delivered to the destination due to a lack of end-to-end connectivity. In such cases, the messages are stored in the buffer for a long time and are transmitted when the nodes come into the range of each other. The buffer size of each node has a limited capacity, and it cannot accommodate the new incoming message when the buffer memory is full, and as a result network congestion occurs. This leads to a low delivery probability and thus increases the overhead ratio. In this research work, a new buffer management scheme called Range Aware Drop (RAD) is proposed which considers metrics such as message size and time to live (TTL). RAD utilizes TTL as an important metric and as a result, reduces the unnecessary message drop. Simulation results reveal that RAD performs significantly better than drop oldest (DOA) and size aware drop (SAD) in terms of delivery probability and overhead ratio. The obtained results also revealed that the hop-count average of SAD is 3.9 and DOA is 3.4 while the hop-count average of RAD is just 1.7. Also, the message drop ratio of the RAD is 36.2% while SAD and DOA have message drop ratios of 73.3% and 84.9% respectively.

Transient Behavior of a Two-Phase Queuing System with a Limitation on the Total Queue Size

The transient mode of a two-phase queuing system with a Poisson input flow, exponential distribution of service time in each phase, and a limitation on the total buffer size of the two phases is considered. Nonstationary probabilities of system states are found using the Laplace transform. A numerical calculation and analysis of the system performance characteristics in transient mode with parameters corresponding to new-generation optical networks were carried out.

Joint Allocation of Buffer Sizes and Service Times in Unreliable Assembly-Disassembly Systems

Joint Allocation of Buffer Sizes and Service Times in Unreliable Assembly-Disassembly Systems

Energy‐Harvesting Cooperative NOMA in IOT Networks

Buffer‐aided cooperative nonorthogonal multiple access (NOMA) improves the effectiveness of the spectral by allowing more than one user to share the same resources to realize massive connectivity. This is notably attractive in the fifth generation (5G) and beyond systems, where a massive number of connections are essential such as the Internet of Things (IoT). Wireless‐powered communication through energy harvesting is another 5G promising solution for the future massively dense networks. To sufficiently utilize energy harvesting in a buffer‐aided cooperative NOMA network, the relay selection rule must consider the energy level of the selected relay in addition to its data content to avoid an outage. This paper proposes a relay selection rule for energy‐harvesting buffer‐aided cooperative NOMA networks. The proposed relay selection scheme considers the state of both the data buffer and the energy buffer. The simulations show that the proposed selection rule has improved the network outage probability and throughput as well. This enhancement is kept with changing the number of relays. The results also show that the impact of changing the energy buffer size is crucial, and the larger, the better. Furthermore, making the source transmitting power larger than the relay transmitting power is beneficial, especially with large enough energy buffer sizes.

SOLUSI ALTERNATIF PENYAMPAIAN INFORMASI DENGAN TOLERANSI WAKTU TUNDA UNTUK PENGGUNA LAYANAN KOMUNIKASI DATA PADA PEDESTRIAN NETWORK

This research aims to evaluate the performance of routing patterns in opportunistic networks for transmitting time-tolerant information, such as product prices and store coordinates in the Kuta, Badung area by using pedestrians as intermediaries and recipients of information. Two basic routing patterns, epidemic and spray and wait, are simulated using The ONE Simulator into two scenarios including a scenario of increasing average pedestrian density and a scenario of increasing buffer size. The simulation results show that in the scenario of increasing the average pedestrian density, the epidemic routing has a higher delivery probability and lower average latency than the spray and wait routing, but the spray and wait routing has a lower overhead ratio value. In the scenario of increasing buffer size, the spray and wait routing has a higher delivery probability and lower average latency at smaller buffer sizes. Meanwhile, the epidemic routing pattern produces a higher delivery probability and lower average latency at a larger buffer size, although with a difference that is not too significant.

Optimal data transmission for decentralized IoT and WSN based on Type-2 Fuzzy Harris Hawks Optimization

Due to the widespread availability of services and smart devices, the decentralized internet of things and wireless sensor network-based systems (DIoT and WSN) have drawn the attention of academics and researchers and are still under constant development. Many challenges face the DIoT and WSN networks and need to be solved. The main issue among these challenges is reducing energy consumption in order to increase network lifetime. Energy-efficient routing protocols and the clustering method are the best solutions to this problem. In this paper, Type-2 Fuzzy Harris Hawks Optimization (T2FHHO) is used to choose the best path (route) from the source (cluster head (CH)) to the destination nodes (base station (BS)). Along with this, we propose a new fitness function to find the next hop based on residual energy, distance, traffic, and buffer size. Then, we apply the T2FHHO method to get the best CH. This protocol seeks to increase the long-term reward earned by each node. The best path in terms of the minimum cost obtained using the proposed method is found. The two main contributions of this study are as follows: firstly, we present a new efficient routing approach that minimizes costs and maximizes efficiency. Secondly, we apply the T2FHHO to the network parameters of energy consumption, network lifetime, and convergence curves. The results indicate that T2FHHO outperforms competing methods. The proposed method holds the promise of significantly advancing the state of the art in energy-efficient routing for DIoT and WSN networks, with significant implications in various sectors and applications, ensuring extended network lifetimes and sustainable IoT implementations.

A Meta Meeting Mountain based opportunistic message forwarding strategy

The opportunistic network is a type of ad hoc network that relies on the chance encounters between nodes to transmit messages. It also uses store-and-carry-forward techniques for data transfer between nodes. Developing effective message forwarding strategies based on opportunistic network characteristics and node behavior poses a significant challenge . In this paper, we delve into the mobility and social characteristics of nodes using historical encounter information. Furthermore, we calculate the migration degree and community relations to build the Meta Meeting Mountain, which representing node encounter characteristics. Moreover, we introduce the concept of Forwarding Degree to measure the nodes’ ability to deliver messages, enhancing the predictability of message delivery. Furthermore, a new forwarding strategy is proposed, which is equipped with Meta Meeting Mountain and Forwarding degree to improve the delivery rate. Our experimental scenarios include Infocom06, Rome taxi, and Helsinki. Therefore, the impact of buffer size and TTL is discussed in detail regarding the different opportunistic routing algorithms. Finally, extensive experimental results show that the proposed algorithm outperforms UBPR, MaxProp, PRoPHET, Spray And Wait and RAPAR in terms of delivery rate in the above scenarios by 127.905%, 93.7525%, 138.6888%, 175.3663%, and 281.4625%, respectively, while keeping the hopcount value less than 5.

Deadlock control and hybrid social spider scheduling algorithm for two-stage assembly permutation flowshop with limited buffers

This paper focuses on the deadlock control and scheduling problem of two-stage assembly permutation flowshop with limited buffers. In such a system, all parts are first processed in a permutation flowshop, then assembled into final products. The objective is to find a feasible schedule, including a manufacturing subschedule and an assembly subschedule, to minimize makespan. Owing to limited buffers, not only may the blocking phenomenon appear in the system operation, but also some manufacturing subschedules may lead to deadlock states, making them unavailable. To obtain a feasible schedule, this work first proposes a deadlock control or an availability detection and amendment (ADA) algorithm to determine whether a manufacturing subschedule is available, and convert unavailable ones to available ones. Then by embedding ADA into a social spider algorithm (SSA), a hybrid social spider algorithm (HSSA) is developed for computing feasible schedules. In HSSA, an improved heuristic search algorithm is proposed to generate a better initial population, and a simplified variable neighborhood search algorithm is designed and incorporated into HSSA to enhance its searching ability. Finally, HSSA is tested on instances, showing its superiority over SSA in performance. The effect of buffer size on the scheduling objective is discussed.

A learning-based approach for video streaming over fluctuating networks with limited playback buffers

In recent times, the surge in applications like social networking and online learning platforms has led to a substantial rise in on-demand video streaming, thus ensuring a seamless user experience holds paramount importance, particularly given the dynamic nature of network conditions. Moreover, numerous service providers are embracing smaller buffer sizes, aiming to reduce bandwidth inefficiencies due to the possibility of users ending video sessions prematurely. However, this transition presents a significant challenge for conventional adaptive bitrate (ABR) algorithms, as they grapple with the task of harmonizing low stalling time, high playback bitrate, and the constraint of a minimized buffer size. In this study, we introduce a novel ABR approach, L2-ABR, which leverages self-play reinforcement learning to address these complexities. Unlike conventional reward-engineering learning-based ABR strategies that update gradients to maximize linear reward functions, L2-ABR treats video streaming as a fundamental objective and trains neural networks (NNs) with explicit requirements tailored to video streaming with small playback buffers. Through an extensive series of trace-driven experiments, we showcase that L2-ABR outperforms existing methods, effectively striking a balance between buffer management and network scenarios without inducing excessive buffer under-runs or overflows. Compared to existing ABR schemes, our method reduces undesirable buffer events, including rebuffering events and buffer full events, while improving the average QoE by up to 71.88% and 75.25% over the HSDPA and FCC datasets, respectively.

Mathematical Modeling of the Processes of Executing Packages of Tasks in Conveyor Systems with Intermediate Buffers of Limited Size

Modern methods of process planning in conveyor systems with buffers of a certain size between processing devices allow optimizing schedules for single tasks or fixed task packages with a limited number of them and a limited number of devices. The use of mathematical models of the processes of performing single tasks (task packages) used by these methods in optimizing the composition of packages and schedules for their execution in systems with an arbitrary number of packages and devices is impossible. At the same time, mathematical models of the processes of executing task packages in conveyor systems in the presence of buffers of limited sizes between devices are the basis for the development of methods for optimizing their (package) compositions and schedules for the implementation of actions with them on the devices of conveyor systems. In this regard, the article develops mathematical models of multi-stage processes of performing an arbitrary number of task packages in conveyor systems in the presence of intermediate buffers of limited sizes for two and three devices, as well as for an arbitrary number of devices. The use of these models makes it possible to determine the time points of the start of the execution of task packages on the devices of conveyor systems, taking into account the limited size of intermediate buffers, as well as the duration of time intervals for the use of these resources and the efficiency of their use over time. An algorithm has also been developed for mathematical modeling of the processes of executing task packages in conveyor systems in the presence of intermediate buffers of limited size, which calculates the time characteristics of these processes based on a given order of implementation of actions with task packages on the devices of conveyor systems. An application has been developed that implements synthesized mathematical models of the processes of executing task packages in conveyor systems with intermediate buffers of limited sizes and an appropriate method for modeling these processes. Versatile testing of the developed application has shown that the obtained mathematical models and the modeling method adequately describe the course of multi-stage processes of task packages in pipeline systems, set using different values of their (processes) parameters.

Urban green, blue spaces and their joint effect are associated with lower risk of emotional and behavior problem in children and adolescents, a large population-based study in Guangzhou, China

Extensive research has been conducted on the impact of urban Green and blue spaces (GBS) on mental health. However, there is a dearth of studies examining the potential influence of GBS on Emotion and behavior problems (EBP) in youth populations. This study seeks to address this research gap by examining the association between GBS exposure and EBP in youths, utilizing a large population-based survey. Exposure indicators were derived for each participant, taking into account the 1-m resolution land coverage percentage measurements at their residential and school locations, with buffer sizes of 500m and 800m. The study employed the parent-reported Strengths and Difficulties Questionnaire (SDQ) as a measurement to EBP. To investigate this relationship, a Poisson linear mixed-effects model with comprehensive adjustment was utilized. Stratified analyses were conducted, taking into account demographic variables, while sensitivity analyses were performed to evaluate the reliability of the findings. The primary analysis encompassed a sample of 172,490 individuals, with an average age of 10.2 (standard deviation = 2.99), of whom 55.8% were male. Among young individuals residing within a 500-m radius of residential green spaces (GS), those in the second and third tertiles experienced a 5.0% and 7.6% decrease in the risk of developing total difficulties respectively, compared to those in the first tertile. Exposure to blue spaces (BS) around the residence yielded comparable results, with an AOR of 0.896 (95% CI: 0.866, 0.928) for the second tertile and an AOR of 0.991 (95% CI: 0.879, 0.943) for the third tertile, compared to the first tertile. The decrease in EBPs may also be linked to the joint effect of GS and BS. Young individuals were exposed to GS or BS, as well as the higher level of GS in conjunction with comparable BS, exhibited an inverse correlation with self-reported EBPs.

Comparison and analysis of DQN performance with different hyperparameters

Deep Q-learning Network (DQN) is an algorithm that combines Q-learning and deep neural network, its model can adopt high-dimensional input and low-dimensional output. As a deep reinforcement learning algorithm proposed ten years ago, its performance on some Atari games has surpassed all previous algorithms, even some human experts, which fully reflects DQNs high research value. The tuning of hyperparameters is crucial for any algorithm, especially for those with strong performance. The same algorithm can produce completely different results when using different sets of hyperparameters, and suitable values can considerably improve the algorithm. Based on the DQN we implement, we test on number of episodes, size of replay buffer, gamma, learning rate and batch size with different values. In each round of experiments, except for the target hyperparameter, all others use default values, and we recorded the impact of these changes on training performance. The result indicates that as the number of episodes continues to increase, the performance improves steadily and degressively. The same conclusion is also applicable to the size of replay buffer, while other hyperparameters need to be given values to have optimal performance.