Network Delay Performance Research Articles

5G Wireless Multimedia-Assisted Marketing Strategy Establishment and Effect Evaluation

5G wireless networks are attracting attention due to their high data rates and robustness to multimedia options. Based on 5G wireless multimedia technology, this paper constructs an auxiliary marketing strategy for multipath collaboration. Based on the 5G wireless network system, the model solves the optimization problem of effective service energy consumption. The solution of the problem is obtained through a two-step solution and related algorithms, and the numerical simulation proves that the multipath cooperative transmission strategy has the advantages of network delay and effective service performance. In the simulation process, a 5G NR downlink system simulation model is established according to the wireless data transmission theory; in the marketing strategy estimation, the decision feedback marketing strategy estimation algorithm is improved to improve the marketing strategy estimation performance, which can effectively reduce insertion loss of the network. The results show that with the increase of the marketing data center density, the receiving delay decreases and the energy efficiency increases; when the number of video contents in the marketing data center is configured as 264, the effective service performance under the single-path transmission strategy can be achieved. Compared with the minimum value of the effective service energy consumption under the single-path transmission strategy, the minimum value of the effective service energy consumption under the multipath cooperative transmission strategy is reduced by 11.5%, which effectively improves the network throughput of data.

Dynamic Optimization Method of Wireless Network Routing Based on Deep Learning Strategy

Wireless sensor network is a network that integrates sensor technology, computer technology, information processing technology, and communication technology. This paper aims to study how to analyze and study the routing optimization of wireless sensor network based on deep learning and describe the neural network. This paper puts forward the problem of routing optimization, which is based on the dynamic programming of wireless sensor network, and then elaborates around its concept and related algorithms and designs and analyzes the case of wireless sensor network optimization. Through the comparative analysis of the five algorithms in computer simulation, although the average network delay performance of DPER reached 0.47 s, it could effectively prolong the life cycle of the network. The DPER algorithm not only improves the network life but also improves the network energy utilization rate, shortens the average path length of the network, and reduces the standard deviation of the remaining energy of the node.

ELSA: Energy-Efficient Linear Sensor Architecture for Smart City Applications

A network of sensors and surveillance devices creates a critical infrastructure in smart cities, enabling civic authorities to render faster and more efficient services. Notably, many deployed sensor applications form linear topologies, such as road monitoring sensors (e.g., intelligent lighting, road traffic, pedestrians, etc.), railway track sensors, and pipeline sensors (e.g., sensing leaks, pressure, etc.). These linear sensors convey data to a central control station by daisy-chaining through neighbor sensors. However, it causes critical energy inefficiency and imbalance issues and creates data transmission delay, which provokes complex application and service management hassles. In this paper, we propose an Energy-Efficient Linear Sensor Architecture (ELSA) for building sensor network infrastructure of Smart Cities. We design and develop an energy-efficient Wake-up Receiving (WuRx) method on sensor nodes by using a dual-channel, an infrared (IR) based WuRx channel over a WiFi data channel to enhance the duty cycle efficiency. In addition, we build a pipelined relay algorithm to avoid any network delay performance degradation. Furthermore, we design a centrality-based algorithm to identify linear sensor networks. The experimental results show that IR WuRx ELSA performed nearly ten times better energy efficiency per node and achieved a constant network delay regardless of the number of relaying nodes in a network.

Combating Adversarial Network Topology Inference by Proactive Topology Obfuscation

The topology of a network is fundamental for building network infrastructure functionalities. In many scenarios, enterprise networks may have no desire to disclose their topology information. In this paper, we aim at preventing attacks that use adversarial, active end-to-end topology inference to obtain the topology information of a target network. To this end, we propose a Proactive Topology Obfuscation (ProTO) system that adopts a detect-then-obfuscate framework: (i) a lightweight probing behavior identification mechanism based on machine learning is designed to detect any probing behavior, and then (ii) a topology obfuscation design is developed to proactively delay all identified probe packets in a way such that the attacker will obtain a structurally accurate yet fake network topology based on the measurements of these delayed probe packets, therefore deceiving the attacker and decreasing its appetency for future inference. We evaluate ProTO under different evaluation scenarios. Experimental results show that ProTO is able to (i) achieve a detection rate of 99.9% with a false alarm of 3%, (ii) effectively disrupt adversarial topology inference and lead to the topology inferred by the attacker close to a fake topology, and (iii) result in an overall network delay performance degradation of 1.3% - 2.0%.

DRL-Based Sum-Rate Maximization in D2D Communication Underlaid Uplink Cellular Networks

Device-to-device (D2D) communication affords the benefits of improved network spectral efficiency, throughput, energy efficiency, and delay performance. However, D2D communication underlaid cellular uplinks are degraded by interference between D2D user equipment (DUE) and cellular user equipment (CUE) that use the same frequency band. In this context, researchers have proposed the optimization of the transmit power of the devices to mitigate interference. This power allocation problem is generally modeled as a NP-hard combinatorial optimization problem with linear constraints; therefore, traditional optimization methods are ineffective in addressing the problem. In this paper, we apply a deep reinforcement learning (DRL) algorithm to optimize the transmit power for both DUEs and CUEs in the context of D2D communication underlaid uplink cellular networks, which can solve the optimization problem via optimal decision-making together with efficient deep network training. Our simulation results show that the proposed algorithm affords a near-global-optimum solution with lower computation complexity than exhaustive search, and outperforms the fixed-power, weighted minimum mean square error (WMMSE), gradient descent method, and Newton-Raphson approaches.

Delay Performance of Two-Hop Buffer-Aided Relay Network with Wireless Assisted Links

Delay Performance of Two-Hop Buffer-Aided Relay Network with Wireless Assisted Links

Identification and Prediction of Disruptions in Airline Networks

Disruptions in the air transportation system oftentimes lead to demand-capacity imbalances, resulting in flight delays and cancellations as byproducts of traffic management and system recovery actions. In order to better predict the impact of disruptions, as well as provide more targeted and proactive system recovery actions, it is critical to unambiguously identify key characteristics such as: (1) When did the disruption occur, (2) what was the duration of the disruption and subsequent recovery, and (3) how will an ongoing disruption evolve. In particular, providing performance measures regarding the duration, intensity, and type of disruption is straightforward for individual airports; for a large, geographically disparate, and interconnected network of airports, these characteristics are less clear. To address this, we first formalize the notion of \textit{disruption-recovery trajectories} (DRTs) for the air transportation system through representing key network delay and cancellation performance measures as evolving between discrete states. These DRTs capture information regarding both the magnitude and spatial impact of air transportation system disruptions. Using this DRT framework, we report on past disruption and recovery characteristics for four major US airlines, as well as demonstrate the ability to predict short-term evolution of disruptions and recoveries without detailed schedule information, potentially enabling near-real time decision support for prescriptive management actions.

Routing Based Multi-Agent System for Network Reliability in the Smart Microgrid.

Microgrids help to achieve power balance and energy allocation optimality for the defined load networks. One of the major challenges associated with microgrids is the design and implementation of a suitable communication-control architecture that can coordinate actions with system operating conditions. In this paper, the focus is to enhance the intelligence of microgrid networks using a multi-agent system while validation is carried out using network performance metrics i.e., delay, throughput, jitter, and queuing. Network performance is analyzed for the small, medium and large scale microgrid using Institute of Electrical and Electronics Engineers (IEEE) test systems. In this paper, multi-agent-based Bellman routing (MABR) is proposed where the Bellman–Ford algorithm serves the system operating conditions to command the actions of multiple agents installed over the overlay microgrid network. The proposed agent-based routing focuses on calculating the shortest path to a given destination to improve network quality and communication reliability. The algorithm is defined for the distributed nature of the microgrid for an ideal communication network and for two cases of fault injected to the network. From this model, up to 35%–43.3% improvement was achieved in the network delay performance based on the Constant Bit Rate (CBR) traffic model for microgrids.

Study on the Solutions to Heterogeneous ONU Propagation Delays for Energy-Efficient and Low-Latency EPONs

The scattered Optical Network Units (ONUs) in an optical access network have different propagation delays. In an Ethernet passive optical network (EPON) adopting the traditional Interleaved Polling with Adaptive Cycle Time (IPACT) scheme, such ONU propagation delay difference will waste network resources and will degrade both network delay performance and network energy efficiency in delivering low-latency services. In this article, to deal with the heterogeneous ONU propagation delays problem, two solutions are proposed and investigated. The first solution, namely the Upstream Postponing with ONU Dozing (UP-OD) scheme, is to properly postpone the upstream transmissions of those ONUs having relatively short propagation delays to improve channel utilization efficiency, and ONU doze mode is incorporated to enhance network energy efficiency. The second solution, namely the Identical Fiber Length with ONU Sleeping (IFL-OS) scheme, is to adopt an identical distribution fiber length for ONUs to enhance channel utilization, and ONU sleep mode is incorporated for energy consumption reduction. Simulation results show that both the UP-OD scheme and the IFL-OS scheme reduce network delay and improve network energy efficiency in delivering low-latency (<; 1 ms) data, and the IFL-OS scheme shows lower energy consumption in transmitting per bit of low-latency (<; 1 ms) data compared with the UP-OD scheme. Further practical value discussion shows that for the case of serving services requiring 1 ms delay, the UP-OD scheme is suitable for applying in the 1G-EPONs, whereas the IFL-OS scheme is considerable for the 10G-EPONs.

Retroactive Packet Sampling for Traffic Receipts

Is it possible to design a packet-sampling algorithm that prevents the network node that performs the sampling from treating the sampled packets preferentially? We study this problem in the context of designing a "network-transparency" system. In this system, networks emit receipts for a small sample of the packets they observe, and a monitor collects these receipts to estimate each network's loss and delay performance. Sampling is a good building block for this system, because it enables a solution that is flexible and combines low resource cost with quantifiable accuracy. The challenge is cheating resistance: when a network's performance is assessed based on the conditions experienced by a small traffic sample, the network has a strong incentive to treat the sampled packets better than the rest. We contribute a sampling algorithm that is provably robust to such prioritization attacks, enables network performance estimation with quantifiable accuracy, and requires minimal resources. We confirm our analysis using real traffic traces.

Dynamic bandwidth allocation algorithms for improved throughput and latency performance in CDMA-based next-generation ethernet passive optical networks

In this paper, we propose new scheduling algorithms to improve the performance of the code division multiple access (CDMA) enabled next generation ethernet passive optical networks (NG-EPON). The original dynamic bandwidth allocation (DBA) scheme based on a round robin scheduling algorithm was originally proposed for the first generation CDMA based EPON where the network throughput and delay requirements per optical network unit (ONU) are relatively moderate. On the other hand, in the NG-EPON standard, the network capacity is increased up to 100 Gb/s and stringent requirements are applied to the packet delay. For this reason, in this work, we propose new scheduling algorithms for NG-EPON to assign a grant size on each upstream CDMA code in order to guarantee the newly required network throughput and delay performance. The first DBA algorithm is the first in first out (FIFO) algorithm in which the ONU requests are ordered in terms of their request arrival time. The second scheme is the reservation pattern (RP) algorithm in which each ONU starts transmitting its queue content in its reserved pattern of subcycles following a pre-defined pattern. The performances of the newly proposed algorithms are evaluated and compared. Results show that both newly proposed scheduling schemes can significantly improve the network performance in terms of the packet delay, jitter, and throughput. In addition, the results are compared to many dynamic wavelength and bandwidth allocation (DWBA) algorithms. The results show that the performances are comparable and that CDMA can be a good alternative for WDM for multi-channels based NG-EPON.

Retroactive Packet Sampling for Traffic Receipts

Is it possible to design a packet-sampling algorithm that prevents the network node that performs the sampling from treating the sampled packets preferentially? We study this problem in the context of designing a "network transparency'' system. In this system, networks emit receipts for a small sample of the packets they observe, and a monitor collects these receipts to estimate each network's loss and delay performance. Sampling is a good building block for this system, because it enables a solution that is flexible and combines low resource cost with quantifiable accuracy. The challenge is cheating resistance: when a network's performance is assessed based on the conditions experienced by a small traffic sample, the network has a strong incentive to treat the sampled packets better than the rest. We contribute a sampling algorithm that is provably robust to such prioritization attacks, enables network performance estimation with quantifiable accuracy, and requires minimal resources. We confirm our analysis using real traffic traces.

Delay-Aware Optimization Framework for Proportional Flow Delay Differentiation in Millimeter-Wave Backhaul Cellular Networks

The next generation of cellular networks (5G) will provide dense millimeter-wave backhaul architectures to wirelessly forward heterogeneous data traffic in a multihop fashion. In this paper, we present a general optimization framework for the design of delay-aware (DA) policies in multihop wireless networks, providing proportional prioritization of traffic. We develop three throughput-optimal DA algorithms (BP-DA, BPE-DA, and HD-DA) for joint dynamic routing and dynamic link-scheduling problems with good to optimal average network delay performance. Our DA framework considers both the classical back-pressure (BP) and the recent heat-diffusion (HD) algorithms, since queue back-pressure algorithms are being considered for mmWave backhauling management. We provide analytical results for the throughput-optimality of the proposed policies and average delay minimization of HD-DA within the class of DA policies. These are policies that make decisions at each timeslot based only on current channel state, current network queue sizes, and flow priorities. We discuss the applications of our proposals to backhaul management of mmWave cellular networks in light of recent works in the literature. Finally, we present extensive simulations of our proposed algorithms, which confirm the theoretical results and show how the algorithms effectively differentiate data traffic in terms of delay while satisfying flow rate requirements.

Burst Stream Resource Reservation Algorithm of Satellite E-OBS Network Based on Preemption Window

An onboard optical burst network resource reservation algorithm named PWBR is proposed for satellite optical switching network. The algorithm uses burst stream as reservation unit and is based on preemption window which can guarantee the QoS of HP burst; network delay performance and packet loss ration are improved through big granularity burst stream. Computer simulations show that the packet loss ratio of HP BDP is lowered 79%, the packet loss ratio of LP BDP is lowered 73% in E-OBS structure than C-OBS when network load is 0.7.and its end-to- end delay is improved 2.1% than RRBS, 3.5% than JET, 7.7% than DRR, 11.5% than JIT.

The Modeling and Cross-Layer Optimization of 802.11p VANET Unicast

Vehicular ad-hoc networks (VANETs) play an important role in enabling ubiquitous communications and connectivity among vehicles in intelligent transportation systems. Various messages can be transmitted in a VANET to improve road safety and furnish multiple types of application services. Therefore, the evaluation of VANET performance and its optimization should be considered. Previous conventional considerations regarding VANET modeling merely incorporated a general homogeneous road traffic scenario. Furthermore, prior research works primarily focused on the broadcasting performance in VANETs, since the safety beacon packets are transmitted in periodic broadcast. However, the exchange of some important data between vehicles is better accomplished by using unicast instead of broadcast with the retransmission mechanism. On the other hand, in the context of VANET optimization, most conventional schemes required continuous monitoring of the network by measuring the number of neighboring nodes to configure the transmission power or adjusting the transmission rate accordingly. Such constant tracking leads to large transmission overheads and measurement delay. In this paper, we propose a set of 802.11p unicast modeling and optimization methodologies to determine the optimal network parameters without continuously monitoring the vehicles in vicinity. This is accomplished by integrating a stochastic urban traffic model in the analysis then performing a cross-layer optimization for each network node to reduce packet collisions. The optimal transmission range and contention window size at different locations are derived based on the spatio-temporal velocity profile and are made known to entering vehicles. These aid the vehicles to configure their transmission power and rate accordingly upon entering a road segment. We evaluate the proposed system in terms of the network delay and throughput performance. Considerable simulation runs to verify the feasibility of the proposed model and optimization methods. Our results indicate that delay (throughput) is improved by about 53% (120%) on average for homogeneous traffic and 45% (104%) on average for heterogeneous traffic.

Analysis of the Vlan Network Delay Performance to Improve Quality of Services (QOS)

Technological revolution that has conquered the world and the In the modern life, the internet network become important part in the life because it provides many effective services in many fields that leading the responsible Categories to interested in improve quality of services (QOS) for the network. There are increasing in using internet networks and many new modern services access in the life fields that leading to emergence of many problems and challenges in network. The delay is the most effected in the network because it caused reducing in QOS. The real time application is modern services such as voice and video which needing more time and effort to transfer data in the network because it have large data that making it to be the most effect applications by delay. A large number from network studding interested in create new technical to improve QOS in local area networks and wide area networks that using different technical methods. This study interested in showing the importance in using VLAN technology to improve QOS and it refers to its role in reducing the delay in the network. The structures of network when building it or improving it that is more important and sensitive thing that effected in ability it in providing the services in effective way. That referred to manage the networks and divided it into parts helping to transfer data in direct way to selected partition not all. Vlan technology mean divided the local area network in to many departments to give the security for each departments and acceleration of the arrival of the data, which sent to it. That in clearly referring to overcome the problem of delay when using VLAN technology and improve QOS for the networks.

Availability and Determinacy Evaluation of ITER-PPEN Communication Network

A highly reliable and deterministic communication network is required to guarantee the protection switch control and data acquisition of International Thermonuclear Experimental Reactor (ITER) pulsed power electrical network (PPEN) substation. This study aims at quantitatively assessing the availability indices and evaluating the end to end (ETE) time delay performance of PPEN communication network. The overall architecture of PPEN communication network is first introduced. Theoretical analysis of PPEN communication network under unrepairable and repairable condition are then examined and sensitivity analysis are carried out to find out the best way to improve the availability. Subsequently, OPNET modeler is used to simulate the message communication in PPEN communication network and time delay test was taken to prove the correctness of simulation model. The results indicate that PPEN communication network’s availability indices meet ITER’s requirement and the ETE time delay conforms to IEC 61850.

On MAC optimization for large-scale wireless sensor network

The media access control (MAC) performance of a large-scale wireless sensor network (L-WSN) determines the efficiency of the wireless communication channel. A good MAC protocol could reduce network energy consumption and network delay, which are two problems to be solved urgently in L-WSN. In this paper, we proposed a multi-level integrated MAC protocol (MI-MAC) to solve the overall performance optimization problem of L-WSN. Compared with other protocols, MI-MAC has two mainly improved performances: (1) It improved binary exponential backoff algorithm by twice back off strategy; (2) It designed a sending and receiving algorithm based on the threshold value to recognize control frames (small frames), which effectively avoids the collision probability of data frame. The simulation results show that the MI-MAC protocol improves network throughput and delay performance, significantly reduces energy consumption, and obtains overall network optimization.

Cooperation Improves Delay in Cognitive Networks With Hybrid Random Walk

In this paper, we study the capacity and delay scaling laws of cognitive radio networks (CRN) with static primary nodes (PNs) and mobile secondary nodes (SNs). The primary network consists of randomly distributed primary nodes of density n, which have a higher priority to access the spectrum. The secondary network consists of randomly distributed secondary nodes of density m = nβ, where β represents the density relationship in CRN. Secondary nodes move according to hybrid random walk models with parameter α (0 ≤ α <; 1/2), and the step size of the mobility model is Θ(m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-2α</sup> ). Motivated by observation that the performance of CRN can benefit from the cooperation among primary nodes and secondary nodes, we propose a novel cooperative scheduling mechanism to fully utilize the mobility and geographic information of secondary nodes to enhance the performance of the primary network. For both networks, the delay performance varies with α. We show that the delay performance of primary network can be significantly improved from O(n/log n) [16] to Θ(n <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">β/3</sup> log n) when β <; 3 for an optimal value of α, while a near-optimal throughput of Θ(1/log n) is obtained. Furthermore, the secondary network can still achieve the same throughput and delay scaling laws as a stand-alone network simultaneously.

From Instantly Decodable to Random Linear Network Coded Broadcast

Our primary goal in this paper is to better under- stand and extend the achievable tradeoffs between the through- put and decoding delay performance of network coded wireless broadcast. To this end, we traverse the performance gap between two linear network coding schemes: random linear network cod- ing (RLNC) and instantly decodable network coding (IDNC). Our approach is to appropriately partition a block of partially received data packets into subgenerations and broadcast them separately using RLNC. Through analyzing the factors that af- fect the performance of a generic partitioning scheme, we are led to develop a coding framework in which subgenerations are created from IDNC coding sets in an IDNC solution. This coding framework consists of a series of coding schemes, with classic RLNC and IDNC identified as two extreme schemes. We develop two basic partitioning guidelines, including disjoint partitioning and even partitioning. We design various implementations of this coding framework, such as partitioning algorithms and gener- ation scheduling strategies, to further improve its throughput and decoding delay, to manage feedback frequency and coding complexity, or to achieve in-block performance adaption. Their effectiveness is verified through extensive simulations, and their performance is compared with an existing work in the literature.