Heterogeneous Traffic Patterns Research Articles

Assessing the Compatibility of Railway Station Layouts and Mixed Heterogeneous Traffic Patterns by Optimization-Based Capacity Estimation

The operations performance of a railway station depends on the compatibility of its layout and the traffic pattern. It is necessary to determining an adaptable station layout for a railway station in accordance with its complex traffic pattern during the design phase. This paper assesses the railway station layout from a capacity perspective. In particular, this paper addresses an optimization-based capacity estimation approach for the layout variants of a railway station (i.e., the number of siding tracks and the structure of the connections in between) considering the traffic pattern variants. A mixed integer programming model for microscopic timetable compression is applied to calculate the occupation rate of the given traffic pattern with flexible route choices and train orders. A novel “schedule-and-fix” heuristic algorithm is proposed to solve large-scale instances efficiently. In the case study, we evaluate the performance of the schedule-and-fix method compared with the benchmark solvers Gurobi and CP-SAT. Applying the proposed method, we compare the capacity performances of the two station design schemes, i.e., one with a flyover and the other without. The result shows that, for the given instance, building a flyover gains capacity benefits as it reduces the potential conflict in the throat area. However, the level of benefit depends on the combination of trains. It is necessary to build the flyover when the proportion of turn-around trains is more than 70% from the perspective of station capacity.

Autonomous Flow-Based TSCH Scheduling for Heterogeneous Traffic Patterns: Challenges, Design, Simulation, and Testbed Evaluation

Autonomous Flow-Based TSCH Scheduling for Heterogeneous Traffic Patterns: Challenges, Design, Simulation, and Testbed Evaluation

Performance of WLAN in Downlink MU-MIMO Channel with the Least Cost in Terms of Increased Delay

To improve the performance of IEEE 802.11 wireless local area (WLAN) networks, different frame-aggregation algorithms are proposed by IEEE 802.11n/ac standards to improve the throughput performance of WLANs. However, this improvement will also have a related cost in terms of increasing delay. The traffic load generated by mixed types of applications in current modern networks demands different network performance requirements in terms of maintaining some form of an optimal trade-off between maximizing throughput and minimizing delay. However, the majority of existing researchers have only attempted to optimize either one (to maximize throughput or minimize the delay). Both the performance of throughput and delay can be affected by several factors such as a heterogeneous traffic pattern, target aggregate frame size, channel condition, competing stations, etc. However, under the effect of uncertain conditions of heterogeneous traffic patterns and channel conditions in a network, determining the optimal target aggregate frame size is a significant approach that can be controlled to manage both throughput and delay. The main contribution of this study was to propose an adaptive aggregation algorithm that allows an adaptive optimal trade-off between maximizing system throughput and minimizing system delay in the WLAN downlink MU-MIMO channel. The proposed approach adopted different aggregation policies to adaptively select the optimal aggregation policy that allowed for achieving maximum system throughput by minimizing delay. Both queue delay and transmission delay, which have a significant impact when frame-aggregation algorithms are adopted, were considered. Different test case scenarios were considered such as channel error, traffic pattern, and number of competing stations. Through system-level simulation, the performance of the proposed approach was validated over the FIFO aggregation algorithm and earlier adaptive aggregation approaches, which only focused on achieving maximum throughput at the expense of delay. The performance of the proposed approach was evaluated under the effects of heterogenous traffic patterns for VoIP and video traffic applications, channel conditions, and number of STAs for WLAN downlink MU-MIMO channels.

A Generative Model for Traffic Demand with Heterogeneous and Spatiotemporal Characteristics in Massive Wi-Fi Systems

A substantial amount of money and time is required to optimize resources in a massive Wi-Fi network in a real-world environment. Therefore, to reduce cost, proposed algorithms are first verified through simulations before implementing them in a real-world environment. A traffic model is essential to describe user traffic for simulations. Existing traffic models are statistical models based on a discrete-time random process and combine a spatiotemporal characteristic model with the varying parameters, such as average and variance, of a statistical model. The spatiotemporal characteristic model has a mathematically strict assumption that the access points (APs) have approximately similar traffic patterns that increase during day times and decrease at night. The mathematical assumption ensures a homogeneous representation of the network traffic. It does not include heterogeneous characteristics, such as the fact that lecture buildings on campus have a high traffic during lectures, while restaurants have a high traffic only during mealtimes. Therefore, it is difficult to represent heterogeneous traffic using this mathematical model. Deep learning can be used to represent heterogeneous patterns. This study proposes a generative model for Wi-Fi traffic that considers spatiotemporal characteristics using deep learning. The proposed model learns the heterogeneous traffic patterns from the AP-level measurement data without any assumptions and generates similar traffic patterns based on the data. The result shows that the difference between the sample generated by the proposed model and the collected data is up to 72.1% less than that reported in previous studies.

Urban traffic flow prediction techniques: A review

In recent decades, the development of transport infrastructure has had a great development, although traffic problems continue to spread due to increase due to the increase in the population in urban areas that require the use of these means of transport. This has led to increased problems related to congestion control, which has a direct impact on citizens: air pollution, fuel consumption, violation of traffic rules, noise pollution, accidents and loss of time. In Latin America, the disorderly growth of cities increases distances and routes, likewise, there is an accelerated increase in the number of cars and motorcycles, which increases the problem. In this sense, intelligent transport systems are an alternative to improve the traffic environment, they incorporate the internet of things and intelligent algorithms, for the collection of data from multiple sources and information processing, respectively, in order to improve the efficiency of the transport flow. However, the processing and modeling of traffic data is challenging due to the complexity of road networks, the space–time dependencies between them, and heterogeneous traffic patterns. In this review study, (i) the smart techniques used for the analysis of mobility data in the prediction of traffic flow in urban areas are grouped, likewise, (ii) the results of implementing said techniques are shown, in addition, (iii) The procedures performed are described and analyzed to understand the benefits and limitations of these smart techniques. Given the above, (iv) the data sets used in the literature and available for use are shown, in addition, (v) the quantifiable results of precision of the various techniques were compared, highlighting advantages and limitations, which allows us to (vi) identify the related challenges and, from there, (vii) propose a general taxonomy in which the knowledge acquired in this traffic flow review converges from a computational approach.

A distributed deep reinforcement learning method for traffic light control

This paper proposes a distributed deep reinforcement learning algorithm for the traffic light control problem, which consists of local learning and global consensus. Firstly, the reinforcement learning environment for the traffic light control problem is built by defining the three key elements of state, action, and reward. Then, the CNN-based deep Q-network is designed to process the quantized traffic state information to obtain the state-action values. After locally optimizing the deep Q-networks of multiple traffic light agents based on their experience samples, the consensus algorithm is subsequently applied to globally update these agents that are connected over a decentralized communication topology. In this way, the distributed learning agents learn from their neighbors’ experience to optimize the modeling process without actually sharing experience data samples. Lastly, homogeneous and heterogeneous traffic flow patterns on different intersections are simulated in SUMO to verify the superiority of the proposed distributed deep Q-networks, with the comparison to the fixed-time strategy, local learning and centralized learning algorithms. The simulation study demonstrates that the distributed learning algorithm without a central server shows comparable performance with centralized learning, which is much better than fixed-time strategy and the local learning method in both homogeneous and heterogeneous traffic scenarios.

Reinforcement Learning-Enabled Cross-Layer Optimization for Low-Power and Lossy Networks under Heterogeneous Traffic Patterns.

The next generation of the Internet of Things (IoT) networks is expected to handle a massive scale of sensor deployment with radically heterogeneous traffic applications, which leads to a congested network, calling for new mechanisms to improve network efficiency. Existing protocols are based on simple heuristics mechanisms, whereas the probability of collision is still one of the significant challenges of future IoT networks. The medium access control layer of IEEE 802.15.4 uses a distributed coordination function to determine the efficiency of accessing wireless channels in IoT networks. Similarly, the network layer uses a ranking mechanism to route the packets. The objective of this study was to intelligently utilize the cooperation of multiple communication layers in an IoT network. Recently, Q-learning (QL), a machine learning algorithm, has emerged to solve learning problems in energy and computational-constrained sensor devices. Therefore, we present a QL-based intelligent collision probability inference algorithm to optimize the performance of sensor nodes by utilizing channel collision probability and network layer ranking states with the help of an accumulated reward function. The simulation results showed that the proposed scheme achieved a higher packet reception ratio, produces significantly lower control overheads, and consumed less energy compared to current state-of-the-art mechanisms.

Load balance algorithm for an OPSquare datacenter network under real application traffic

To cope with the dynamic datacenter (DC) traffic matrix generated by multiple applications, load balancing algorithms can be deployed to improve the datacenter network (DCN) performance. OPSquare, as a promising optical DCN architecture, adopts fast optical switches to provide high bandwidth, low latency, large scalability, and cost and energy efficiency. Current load balancing algorithms are not optimal for improving the network performance of an OPSquare DCN. To optimize the network performance of OPSquare, a dedicated load balancing algorithm considering the peculiarity of OPSquare should be addressed. Moreover, network virtualization in today’s data DC creates new heterogeneous traffic patterns different from what so far have been reported in the literature. A synthetic virtualized DC traffic model is needed to properly evaluate the performance of the load balancing algorithms. In this paper, we provide a synthetic traffic model by capturing the traffic traces of the virtualized DC applications running in a DC. The captured traffic model is used in the simulator to quantitatively evaluate the network performance of an OPSquare DCN. Then we propose and evaluate the lowest path buffer (LPB) algorithm to optimize the OPSquare DCN performance. LPB considers the buffer occupation in the whole path (from the source to the destination) rather than the local buffer occupation. The network performance improvements of LPB are demonstrated under different central controller update periods, traffic destination distributions, and network sizes. Compared with the round-robin, DRILL, and LocalFlow load balancing algorithms, LPB could achieve 23.7%, 46%, and 32.1% less latency, respectively. Besides, LPB provides lower packet loss in comparison with round-robin, DRILL, and LocalFlow.

Learning heterogeneous traffic patterns for travel time prediction of bus journeys

In this paper, we address the problem of travel time prediction of bus journeys which consist of bus riding times (may involve multiple bus services) and also the waiting times at transfer points. We propose a novel method called Traffic Pattern centric Segment Coalescing Framework (TP-SCF) that relies on learned disparate patterns of traffic conditions across different bus line segments for bus journey travel time prediction. Specifically, the proposed method consists of a training and a prediction stage. In the training stage, the bus lines are partitioned into bus line segments and the common travel time patterns of segments from different bus lines are explored using Non-negative Matrix Factorization (NMF). Bus line segments with similar patterns are classified into the same cluster. The clusters are then coalesced in order to extract data records for model training and bus journey time prediction. A separate Long Short Term Memory (LSTM) based model is trained for each cluster to predict the bus travel time under various traffic conditions. During prediction, a given bus journey is partitioned into the riding time components and waiting time components. The riding time components are predicted using the corresponding LSTM models of the clusters while the waiting time components are estimated based on historical bus arrival time records. We evaluated our method on large scale real-world bus travel data involving 30 bus services, and the results show that the proposed method notably outperforms the state-of-the-art approaches for all the scenarios considered.

Pktgen: Measuring performance on high speed networks

Pktgen is a tool for high-speed packet generation and testing. It runs in the Linux kernel, and is designed to accommodate a wide range of network performance tests. Pktgen consists of a packet generator, a receiver, and a protocol that defines the format for test packets. This paper describes the design of pktgen, and discusses its usage as a capable performance testing tool. The design is focused on performance, and in order to generate packets at high packet rate, pktgen takes advantage of multicore systems and multi-queue features on modern network cards. To support realistic heterogeneous traffic patterns, pktgen can generate multiple simultaneous UDP flows. Pktgen supports measurements of throughput, latency, packet inter-arrival time, and packet delay variation with a wide-range of fine-grained user specified rates.We evaluate and compare pktgen to other tools, and show that it is more than an order of magnitude faster than current Linux applications such as Iperf and Netperf, and has the same upper limit on performance as special-purpose high-speed tools, such as DPDK and Netmap, when it comes to throughput measurements with user-specified rate.

A Framework for Handling Heterogeneous M2M Traffic

Abstract Sensors, actuators and devices that compose the Internet of Things (IoT) world are becoming more diverse every day in terms of capabilities and amount of generated traffic. Current Machine-to-Machine (M2 M) communication standardization efforts try to formalize the interfaces between M2 M nodes based on the perspective of exchanging uniform small data size with low sampling rate only. However, many devices will require support for more heterogeneous traffic patterns, with different network capacity. This paper introduces a communication concept for supporting gracefully a heterogeneous set of devices. This paper analyses the effect of traffic size in M2 M transactions and propose a concept to adapt gracefully to support heterogeneous traffic patterns in M2 M systems. To prove its feasibility, the concept is exemplified on top of oneM2 M architecture and implemented as part of the Fraunhofer FOKUS OpenMTC toolkit. Additionally, the concept was applied to a deployment in an E-Health pilot and practical measurements during functional evaluation are reported.

Analysis of Queuing Delay and Medium Access Distribution over Wireless Multihop PANs

This paper proposes a novel analytical approach to the analysis of the queuing delay and medium access distribution of carrier sense multiple access with collision avoidance (CSMA-CA) protocols over multihop personal area networks (MPANs). The proposed approach considers a general case without restrictions on the topology, traffic generation patterns, or multipath routing policy, and it accounts for heterogeneous traffic patterns, all possible packet loss scenarios, and the effect of the length of the backoff on the channel assessment. This paper uses statistical theorems to derive the moment generating function (mgf) of the time required to service a packet and then uses this function as a service distribution in a GI/G/1 queuing model to derive the interdeparture and total queuing delays. From these functions, the end-to-end delay, jitter, throughput, and failure transmission probability over a path are derived. In addition to determining these metrics, this paper exploits the statistical features of the mgf to determine the rare-event probability of the service function. The proposed approach is exemplified using the state-of-the-art IEEE 802.15.4 CSMA-CA protocol, and its accuracy is demonstrated through comparison with simulations.

Transmit beamforming and interference shaping in cellular cognitive radio networks

Optimising the quality-of-service for secondary users and minimising interference to the primary users are two important objectives of cognitive radio networks. In this study, the authors address these issues by introducing interference shaping criteria into orthogonal transmit beamforming (OTBF) strategies. Then, the authors formulate this problem using semi-definite programming (SDP) and find the optimal beamforming weight vectors at the secondary base station. The authors also introduce a resource allocation strategy which enables the base station to ensure a weighted fairness among the secondary users, hence, optimising the network for heterogeneous traffic patterns. The authors show that in terms of satisfying the signal-to-interference-plus-noise-ratio (SINR) requirements at the secondary users, their modified maximin SINR and modified minimum transmit power strategies produce fairly close results at the same transmit powers. However, in terms of minimising interference seen by the primary users, the former outperforms the latter. Finally, the authors study the impact of various interference shaping margins (ISMs) on the OTBF strategies. The authors show that the allocated power by the modified minimum transmit power strategy and the SINRs achieved by the modified maximin SINR strategy are inversely and directly proportional with the ISMs, respectively.