Delay In Wireless Networks Research Articles

Scheduling Approaches for Joint Optimization of Age and Delay in Industrial Wireless Networks

Scheduling Approaches for Joint Optimization of Age and Delay in Industrial Wireless Networks

Downlink Scheduler for Delay Guaranteed Services Using Deep Reinforcement Learning

In this paper, we propose a novel scheduling scheme to guarantee per-packet delay in single-hop wireless networks for delay-critical applications. We consider several classes of packets with different delay requirements, where high-class packets yield high utility after successful transmission. Considering the correla-tionship of delays among competing packets, we apply a delay-laxity concept and introduce a new output gain function for scheduling decisions. Particularly, the selection of a packet takes into account not only its output gain but also the delay-laxity of other packets. In this context, we formulate a multi-objective optimization problem aiming to minimize the average queue length while maximizing the average output gain under the constraint of guaranteeing per-packet delay. However, due to the uncertainty in the environment (e.g., time-varying channel conditions and random packet arrivals), it is difficult and often impractical to solve this problem using traditional optimization techniques. We develop a deep reinforcement learning (DRL)-based framework to solve it. Specifically, we decompose the original optimization problem into a set of scalar optimization subproblems and model each of them as a partially observable Markov Decision Process (POMDP). We then resort to a Double Deep Q Network (DDQN)-based algorithm to learn an optimal scheduling policy for each subproblem, which can overcome the large-scale state space and reduce Q-value overestimation. Simulation results show that our proposed DDQN-based algorithm outperforms the conventional Q-learning algorithm in terms of reward and learning speed. In addition, our proposed scheduling scheme can achieve significant reductions in average delay and delay outage drop rate compared to other benchmark schemes.

Multiple Access Integrated Adaptive Finite Blocklength for Ultra-Low Delay in 6G Wireless Networks

Multiple Access Integrated Adaptive Finite Blocklength for Ultra-Low Delay in 6G Wireless Networks

Investigation of virtual reality multimedia interaction technology based on wireless network

<p>In recent years, with the continuous development of wireless communication and virtual reality technology, multimedia interaction technology has received more and more attention. However, due to the limitations of bandwidth, delay, packet loss, and other problems in wireless networks, multimedia interaction technology also faces many challenges. In this paper, the virtual reality multimedia interaction technology will be studied in combination with a wireless network, in which the virtual environment is first constructed. Then, the interaction is carried out through data transmission and user interaction, and then the existing virtual reality multimedia interaction is optimized by using a data compression algorithm. In order to test the effectiveness and performance of virtual reality multimedia interaction under different network environments and determine the optimal network environment, this paper compares the multimedia interaction effect under three network environments using a wireless network, mobile network, and Bluetooth network as the research object, with download speed, loading speed, image quality, smoothness and real-time as the test variables, and 10 VR software as the constraints. The research results indicated that, under the same other conditions, taking real-time performance as an example, the delay time and feedback speed of wireless networks were between 26 ms–37 ms and 105 KB/s–115 KB/s. The delay time and feedback speed of mobile networks (3G/4G/5G, generation) were between 66 ms–75 ms and 46 KB/s–55 KB/s, while the delay time and feedback speed of Bluetooth networks were between 120 ms–130 ms and 25 KB/s–35 KB/s; this indicated that VR multimedia interaction technology in the wireless network had better performance. Virtual reality multimedia interaction technology based on wireless networks is a promising high technology that can bring users a more realistic, vivid, and intuitive communication and entertainment experience. At the same time, it will expand the application scenarios and promote the development and progress of the technology.</p>

An improved 802.15.4 unslotted CSMA/CA algorithm for reducing collision probability and delay in wireless sensor networks

An improved 802.15.4 unslotted CSMA/CA algorithm for reducing collision probability and delay in wireless sensor networks

Dragonfly Interaction Algorithm for Optimization of Queuing Delay in Industrial Wireless Networks

Dragonfly Interaction Algorithm for Optimization of Queuing Delay in Industrial Wireless Networks

Joint on-demand data gathering and recharging by multiple mobile vehicles in delay sensitive WRSN using variable length GA

Numerous state-of-the-art works on joint data gathering and charging (DGAC) believe that the mobile vehicle (MV) has sufficient energy to charge the sensors and collect data from them by travelling across the path. The existing works also considered that the sensors always get full charge from MV which produces negligible dead period and minimum data gathering delay in Wireless Rechargeable Sensor Network (WRSN). However, the aforementioned considerations are not always practical in large-scale WRSN. In this article, we introduce an efficient on-demand partial DGAC scheme using battery limited multiple MVs. The proposed scheme executes the joint DGAC process such that the total dead periods of the sensors and the overall data gathering delay is reduced. We use multi objective-based genetic algorithm (GA) to optimize the entire DGAC process. The simulation is performed to demonstrate the usefulness and competitiveness of the proposed scheme. We also verify the statistical significance of the simulation results using ANalysis Of VAriance (ANOVA). The comparative analysis reveals that the proposed work improves the DGAC efficiency by reducing the dead period of sensors, data gathering delay, and energy consumption of MVs in comparison with the existing works.

Dynamic collaborative optimization of end-to-end delay and power consumption in wireless sensor networks for smart distribution grids

Wireless sensor network (WSN) technology is poised to be widely adopted in the smart distribution grid (SDG), which has strict requirements regarding communication delays. However, delays in WSNs are easily affected by dynamic interference factors (such as channel access competition, transmitting power, and node failure), and these dynamic characteristics make the traditional offline optimization methods unsuitable. Besides, the reinforcement learning (RL) based online optimization methods have dimension explosion and convergence problems. In this paper, we propose a dynamic collaborative optimization of the end-to-end delay and power consumption of the WSNs based on grouped RL. In particular, we first build an environment model for evaluating the values of the optimization objective. Those values are used to calculate the rewards for the RL algorithm. To accelerate the convergence of RL swamped by the dimensions of the action space, a novel grouped RL is proposed. Then iterative learning is performed to balance the end-to-end delay and power consumption by adjusting the transmitting power of each node. The simulation results show that the proposed algorithm is able to meet the SDG delay requirements with low power consumption when the communication is dynamically affected. The developed algorithm achieves a maximum end-to-end delay reduction of 20.3% and a computational cost reduction of 6.2% to 52.7% compared with the other two RL algorithms.

An improved 802.15.4 unslotted CSMA/CA algorithm for reducing collision probability and delay in wireless sensor networks

An improved 802.15.4 unslotted CSMA/CA algorithm for reducing collision probability and delay in wireless sensor networks

A Multimodel-Based Approach for Estimating Cause of Scanning Failure and Delay in IoT Wireless Network

The rapid growth in the IoT network comes with a huge security threat. Network scanning is considered necessary to identify vulnerable IoT devices connected to IP networks. However, most existing network scanning tools or system do not consider the burden of scan packet traffic on the network, especially in the IoT network where resources are limited. It is necessary to know the status of the communication environment and the reason why network scanning failed. Therefore, this paper proposes a multimodel-based approach which can be utilized to estimate the cause of failure/delay of network scanning over wireless networks where a scan packet or its response may sometimes be dropped or delayed. Specifically, the factors that cause network scanning failure/delay were identified and categorized. Then, using a machine learning algorithm, we introduced a multimodel linear discriminant analysis (MM-LDA) to estimate the cause of scan failure/delay based on the results of network scanning. In addition, a one-to-many model and a training data filtering technique were adopted to ensure that the estimation error was drastically reduced. The goal of our proposed method was to correctly estimate the causes of scan failure/delay in IP-connected devices. The performance of the proposed method was evaluated using computer simulation assuming a cellular (LTE) network as the targeted IoT wireless network and using LTE-connected devices as the targeted IoT devices. The proposed MM-LDA correctly estimates the cause of failure/delay of the network scan at an average probability of 98% in various scenarios. In comparison to other conventional machine learning classifiers, the proposed MM-LDA outperforms various classification methods in the estimation of the cause of scan failure/delay.

Design and Implementation of Multimedia Network Intelligent Control Robot Based on Software Definition

In order to solve the problem that the delay of wireless network and complex operating environment affects the stability and operating performance of teleoperation system, a method of intelligent control robot based on multimedia network defined by software is proposed in this paper. In the network environment established based on the software definition, the gain of the system control is increased according to the network delay to improve the operating performance of the system, and the output of parameters is dynamically adjusted to adapt to the stability of the system in complex environment. The experimental results show that the robot control system can obtain the best control stability by continuously adjusting the relevant parameters. After the simulation test, the final setting is k p = 0.8 , k i = 0.001 , k d = 0 . Conclusion. Based on the intelligence of gain scheduling control algorithm, the control effect of fuzzy control can be significantly improved when the network delay is large.

Power and delay optimization based uplink resource allocation for wireless networks with device-to-device communications

In this paper, the resource allocation problem for all devices is investigated in a multi-sharing uplink scenario of mmWave cyclic prefix-orthogonal frequency division multiplexing (CP-OFDM) based wireless networks with device-to-device (D2D) communications. More specifically, a power and delay optimization based uplink resource allocation (PDO-URA) algorithm is proposed divided into two steps. At first, network resources are allocated to cellular user equipments (CUEs) in terms of power and transmission rate through a proposed approach that intends to maximize throughput. Next, idle resources are allocated by considering delay minimization. To this end, we propose to apply an algorithm based on delay optimization where idle resources are shared with D2D user equipments (DUEs) in the network considering the formed conflicts graphs and the delay information estimated through Network Calculus concepts such as envelope process and service curve. Computational simulations are carried out considering a communication scenario with 5G characteristics such as millimeter waves at frequencies above 6 GHz, comparing also the performance of other algorithms from the literature in terms of quality of service (QoS) parameters such as throughput and delay. The results confirm that the proposed algorithm presents superior performance in terms of throughput and delay for the mmWave CP-OFDM wireless networks with D2D communications than the other considered algorithms.

Retraction Note to: Dynamic scheduling and congestion control for minimizing delay in multihop wireless networks

Retraction Note to: Dynamic scheduling and congestion control for minimizing delay in multihop wireless networks

Adaptive Finite Blocklength for Ultra-Low Latency in Wireless Communications

With the very stringent demand for real-time transmission of wireless communication services, the requirement of sub-millisecond ultra-low end-to-end delay has been initially proposed in the sixth generation (6G) communication networks. Finite blocklength transmission is one of the potential technologies to meet such a low end-to-end delay demand for the next generation networks. However, as the finite blocklength decreases, the transmission delay decreases while the queuing delay increases, which results in the tradeoff between the transmission delay and the queuing delay. To achieve the optimal balance, in this paper we propose an adaptive blocklength transmission framework to minimize the important part of the end-to-end delay of wireless networks, where we focus on the transmission delay and queuing delay. A dynamic buffering model for variable transmission time interval (V-TTI) is introduced for the time-varying arrival of packets adaptation. Then, we propose the Flexible proximal Alternating direction method of multipliers based Blocklength Optimization (FaBo) scheme to minimize the important part of the end-to-end delay for the single user case. We also propose the Multiple deep Q-learning network based Resource Allocation (MuRa) scheme, which can efficiently balance the transmission delay and queuing delay, to minimize the important part of the end-to-end delay for the multi-user case. Numerical results show that the proposed adaptive blocklength framework can reduce the important part of the end-to-end delay compared with that of long-term evolution and the fifth generation (5G) new radio. We also show that our proposed schemes can quickly converge to the minimum end-to-end delay.

Analysis of Smart Grid Using Multimedia Sensor Networks with Effective Resource Allocation

In recent days, for smart grid network updates, video sensors are used where each node needs to be compressed before transmission. This is very much useful for developing countries as in future the smart grid communication process will play a major role in entire society. Therefore, in this article, minimization of power, energy consumption, and delay for wireless video sensor networks have been computed by providing better quality of service at a similar period. Also, the problem of optimizing transmission and delivery rate has been studied. For solving the aforementioned problems, the intuitive migrant algorithm has been implemented for providing better energy consumption. Additionally, modified larvae optimization tool has been integrated for providing better convergence rate, where all the nodes will be compressed at a better rate with necessary quality of service. The simulation results show that by applying algorithms in two folds, and each video sensor node is compressed by satisfying necessary constraints with fast convergence rate.

A Distance-Based Data-Mule Scheduling Technique for Lesser Nodal Delay in Wireless Sensor Network

Nodal delay in wireless sensor network is an indisputable factor in the medium of communication. Factor such as changeability of communication devices, network topologies, packet-sizes, and transmission rate demands to develop data-mule queue scheduling technique. Our proposed data-mule scheduling technique accomplish this through simulations using standard software written in C# by controlling data-mule schedules that collects data from all the nodes connected to the hop. The scheme identifies the hierarchical positions of static source nodes and the distance of mobile source nodes from the hop with rescheduling based on the newly acquired distances. Source nodes applied with data-mule scheduling technique resulted to lower nodal delay. Transmission of packet-data is efficiently and effectively improved.

Modeling time delay, external noise and multiple malware infections in wireless sensor networks

The essentiality of wireless sensor networks (WSNs) in military and health applications cannot be overemphasized, and this has made these tiny sensors soft targets for malware attacks. However, with the ubiquity of single-group infection models, few researchers have studied the effects of many concurrent infection types on WSNs. Therefore, we proposed the differential Susceptible–Exposed (virus)–Exposed (worm)–Infectious (virus)–Infectious (worm)–Recovered–Susceptible with Vaccination (SE1E2I1I2RV) epidemic model in order to study the dynamics of malicious-code dissemination in WSNs. Using the multi-group model, which represents multiple infections due to worms and viruses, first, delay analyses were performed and, through the Routh-Hurwitz criteria, sufficient conditions for stability were established. Secondly, the SE1E2I1I2RV model was extended to incorporate external noise, thereby changing the deterministic nature of the original model and allowing stochastic analyses for random factors such as temperature, physical obstructions, etc. The role that delay plays in the model is shown when it surpasses the critical value, thus the system loses stability and allows the occurrence of a Hopf bifurcation. Finally, numerical simulations were performed using Matlab in order to account for theoretical analyses.

Performance evaluation of SDWNs in cloud systems

The bursty traffic conditions and fast mobility of consumers induce significant delays in wireless networks. Taking the aforementioned factors into account, designing cost-effective wireless solutions with efficient resource usage and manageability is significantly challenging. Although cloud providers undeniably offer high-performance scalable wireless networks, delays may occur due to frequent packet losses as a result of weak signal strength, high mobility, and congestion. There is a slight difference between a wireless networking environment and typical wired networks, however, in practice, various variables exist, making it impossible to fully implement the core advantages of SDN such as rapid data processing, among others. To cope with such situations, we propose software-defined wireless networking (SDWN), a new means for wireless network virtualisation and programming capabilities. Moreover, to evaluate the proposed system, Mininet-WiFi in SDWN is used and conduct an experimental study in terms of packet transmission time and mobility visualisation over distance.

Communication-Efficient Federated Learning With Binary Neural Networks

Federated learning (FL) is a privacy-preserving machine learning setting that enables many devices to jointly train a shared global model without the need to reveal their data to a central server. However, FL involves a frequent exchange of the parameters between all the clients and the server that coordinates the training. This introduces extensive communication overhead, which can be a major bottleneck in FL with limited communication links. In this paper, we consider training the binary neural networks (BNNs) in the FL setting instead of the typical real-valued neural networks to fulfill the stringent delay and efficiency requirement in wireless edge networks. We introduce a novel FL framework of training BNNs, where the clients only upload the binary parameters to the server. We also propose a novel parameter updating scheme based on the Maximum Likelihood (ML) estimation that preserves the performance of the BNN even without the availability of aggregated real-valued auxiliary parameters that are usually needed during the training of the BNN. Moreover, for the first time in the literature, we theoretically derive the conditions under which the training of BNN is converging. Numerical results show that the proposed FL framework significantly reduces the communication cost compared to the conventional neural networks with typical real-valued parameters, and the performance loss incurred by the binarization can be further compensated by a hybrid method.

Data-Driven Beam Tracking for Mobile Millimeter-Wave Communication Systems Without Channel Estimation

In order to guarantee the reliability of millimeter-wave communication under user mobility, fast beam tracking is essential to adapt the beamforming vectors in time-varying beamspace channels. To find the best beam alignment, traditional exhaustive search scans all possible beam directions, thus introducing up to seconds of delay for wireless networks to accommodate mobile clients. In this letter, we propose a data-driven beam tracking approach to find the beamforming/combining vectors that achieve the target quality of service based on a series of equivalent dynamic linearization data models with a time-varying pseudo-gradient parameter estimation procedure. Unlike the model-based approach, which requires the prior knowledge about the channel and user mobility in beamforming design, the proposed data-driven approach depends only on the real-time measurement data. Numerical analyses show that the proposed data-driven beam tracking algorithm can achieve reliable tracking performance with much shorter alignment time compared to traditional schemes.