Average Network Throughput Research Articles

Imitation Learning Based Heavy-Hitter Scheduling Scheme in Software-Defined Industrial Networks

To realize flexible networking and on-demand topology reconstructing, Software-Defined Industrial Networks(SDIN) are increasingly embracing the flat structure. Similar to SDN, SDIN suffers from low traffic scheduling efficiency caused by large and imbalanced flows, known as the heavy hitters problem. Due to such heavy hitters, industrial networks may fail to satisfy application's QoS requirements, which results in more severe damages. To improve flow scheduling efficiency under heavy hitters, this article introduces a novel Imitation Learning-based Flow Scheduling (ILFS) method. ILFS utilizes P4-based In-band Network Telemetry(INT) technology to collect fine-grained, real-time traffic data from SDIN's data plane. In the control plane, it integrates the Generative Adversarial Imitation Learning (GAIL) model with a Soft Actor Critic (SAC) to preserve the experiences of flow, thereby better scheduling large flows. Our experiments thoroughly compare ILFS's performance with several state-of-the-art traffic scheduling strategies. The results indicate that ILFS successfully controls the link bandwidth utilization between 10% and 80% and significantly improves the average network throughput and link utilization rate.

Reinforcement Learning-Based Near-Optimal Load Balancing for Heterogeneous LiFi WiFi Network

Owing to the nonoverlapping spectrum, light fidelity (LiFi) and WiFi technologies can coexist and form a heterogeneous LiFi WiFi network (HLWN). The performance of HLWN significantly depends upon the load balancing strategies. Since load balancing of HLWN is a nonconvex mixed-integer nonlinear programming optimization problem, it is mathematically intractable, and therefore, the conventional optimization methods fail to provide an optimal global solution. Although an optimal solution can be obtained using the exhaustive search method, it would be computationally complex. Therefore, in this article, a reinforcement learning (RL)-based algorithm is explored for solving the load balancing problem for the downlink HLWN at reasonably low complexity and near optimal performance. We have proposed three different reward functions for RL; the first and second reward functions work toward maximizing average network throughput and user satisfaction, respectively. The third reward function is designed to maximize the long-term system throughput and ensure at least 50% user’s satisfaction for all users. In order to study the effects of link aggregation on the system performance, this article considers two different types of receiver schemes, namely, single access point (SAP) and link aggregation (LA) scheme. While the SAP allows the user to receive data only from an SAP, the LA scheme allows the user to receive data simultaneously from both LiFi and WiFi AP. This article also includes effect of random orientation of the receiver device and handover overhead. Furthermore, concepts of domain knowledge have been included in this article to reduce the computational complexity of the algorithm. The proposed system performance is compared with the the following two benchmarks: received signal strength (RSS) and exhaustive search based on the computational complexity, average system throughput, and user satisfaction. It is shown that the proposed RL scheme outperforms the RSS scheme in average system throughput and user satisfaction. The RL scheme with an appropriate reward function provides a matching performance to the exhaustive search at reasonably low complexity.

State Aware-Based Prioritized Experience Replay for Handover Decision in 5G Ultradense Networks

The traditional handover decision methods depend on the handover threshold and measurement reports, which cannot efficiently resolve the frequent handover issue and ping-pong effect in 5G (5 generation) ultradense networks. To reduce the unnecessary handover and improve the QoS (quality of service), combine with the analysis of dwell time, we propose a state aware-based prioritized experience replay (SA-PER) handover decision method. First, the cell dwell time is computed by the geometrical analysis of real-time locations of mobile users in cellular networks. The constructed state aware sequence including SINR, load coefficient, and dwell time is normalized by max-min normalization method. Then, the handover decision problem in 5G ultradense networks is formalized as a discrete Markov decision process (MDP). The random sampling and small batch sampling affect the performance of deep reinforcement learning methods. We adopt the prioritized experience replay (PER) method to resolve the learning efficiency problems. The state space, action space, and reward functions are designed. The normalized state aware decision matrix inputs the DDQN (double deep Q-network) method. The competitive and collaborative relationships between vertical handover and horizontal handover in 5G ultradense networks are mainly discussed. And the high average network throughput and long average cell dwell time make sure of the communication quality for mobile users.

Probability‐based fusion rule in cooperative spectrum sensing with impact of geographic location

This paper investigates the impact of geographic locations of secondary users (SUs) on cooperative spectrum sensing (CSS) in cognitive radio networks (CRNs), where SUs send their local sensing results to a fusion center (FC) and the FC makes a global spectrum decision by a specific fusion rule. The purpose of this paper is to propose a fusion rule that takes into account the impact of geographic locations on CSS, and identify the optimal decision threshold of the proposed fusion rule in order to maximize the average achievable throughput of the secondary network subject to a collision constraint. To quantitatively capture the impact of geographic locations on CSS, a metric called absolute impact is defined, which is determined by the probability of detection and the probability of false alarm. Meanwhile, another metric is defined called relative impact to characterize the impact of SU's incorrect local sensing result on CSS. Based on the defined metrics, a probability-based fusion rule is proposed, and the core idea is that the global spectrum decision of CSS is jointly determined by the local sensing results and the absolute impacts of SUs. Then the average achievable throughput of the secondary network and the collision probability are derived, both of which are proved to decrease with the decision threshold of the proposed fusion rule. By tackling the trade-off between the accuracy of spectrum sensing and spectrum access opportunity caused by the decision threshold, an approximate algorithm based on the idea of the bisection method is proposed to obtain the optimal decision threshold that maximizes the average achievable throughput of the secondary network while protecting primary transmission to a predefined extent. Numerical results are carried out to verify the theoretical analyses and show the flexibility of the proposed fusion rule.

CovaDel: a blockchain-enabled secure and QoS-aware drone delivery framework for COVID-like pandemics

With increase in the use of contactless deliveries during the times such as COVID-19 pandemic, the emphasis was to minimize the human presence to reduce the spread of virus. In this regard, drones are one promising alternative to be used as delivery agents. However, security and Quality of Service (QoS) are major concerns while making use of drones for deliveries. In order to secure the drone communication system, we propose, CovaDel: a blockchain-based scheme to secure data transactions for the drone delivery use case that works in a phased manner. The proposed scheme make use of decoupled blockchain architecture to overcome the limited resources capabilities of drones to perform blockchain-based computations. Further, to ensure the QoS adherence, we also propose a QoS-aware communication approach that handles collisions and congestion on the basis of firefly algorithm’s attractiveness parameter (light intensity) received by the drones. Results obtained using a simulated environment verify the efficacy of the proposed scheme on the basis of gas consumed, transaction time, average network throughput, and delay.

Resource allocation of fog radio access network based on deep reinforcement learning

AbstractWith the development of energy harvesting technologies and smart grid, the future trend of radio access networks will present a multi‐source power supply. In this article, joint renewable energy cooperation and resource allocation scheme of the fog radio access networks (F‐RANs) with hybrid power supplies (including both the conventional grid and renewable energy sources) is studied. In this article, our objective is to maximize the average throughput of F‐RAN architecture with hybrid energy sources while satisfying the constraints of signal to noise ratio (SNR), available bandwidth, and energy harvesting. To solve this problem, the dynamic power allocation scheme in the network is studied by using Q‐learning and Deep Q Network respectively. Simulation results show that the proposed two algorithms have low complexity and can improve the average throughput of the whole network compared with other traditional algorithms.

A novel slot scheduling technique for duty-cycle based data transmission for wireless sensor network

The duty cycling process involves turning a radio into an active and dormant state for conserving energy. It is a promising approach for designing routing protocols for a resource-constrained Wireless Sensor Networks (WSNs). In the duty cycle-based WSNs, the network lifetime is improved and the network transmission is increased as compared to conventional routing protocols. In this study, the active period of the duty cycle is divided into slots that can minimize the idle listening problem. The slot scheduling technique helps determine the most efficient node that uses the active period. The proposed routing protocol uses the opportunistic concept to minimize the sender waiting problem. Therefore, the forwarder set will be selected according to the node's residual active time and energy. Further, the optimum routing path is selected to achieve the minimum forwarding delay from the source to the destination. Simulation analysis reveals that the proposed routing scheme outperforms existing schemes in terms of the average transmission delay, energy consumption, and network throughput.

Enhanced-AODV Routing Protocol to Improve Route Stability of MANETs

Establishing and maintaining link stability in Mobile Ad hoc Networks (MANETs) is one of the key challenging issues. Topology changes in MANET because overhead traffic that leads to consuming extra energy of nodes as well as decreasing the performance of routing protocols. Thus, a comprise approach should be considered during the design of a routing scheme in MANETs to deal with challenges incurred by the mobility of the nodes. In this study, a simple efficient routing scheme called Enhanced_AODV (E-AODV) is proposed, aiming to enhance Ad Hoc On-Demand Distance Vector (AODV) routing protocol performance by constructing the most stable and reliable route from source to the destination node. In this routing scheme, the remaining lifetime of links and hop count are the metrics considered for calculating the Route Stability Factor (RSF) that can be utilized as a cost metric to establish the best route between source and destination node. The simulation results reveal that the proposed E-AODV routing scheme effectively outperforms the conventional AODV routing protocol and Stable and Bandwidth Aware Dynamic Routing Protocol (SBADR) in terms of packet delivery ratio, average network throughput, average end-to-end delay, and normalized routing overhead.

Performance and scalability evaluation of a permissioned Blockchain based on the Hyperledger Fabric, Sawtooth and Iroha

This paper shows the performance and scalability evaluation of different blockchain platform implementations. Hyperledger Iroha implementing YAC consensus, Sawtooth implementing PoET algorithm, and Hyperledger Fabric framework implementation. Performance evaluation and scalability assessment were done by varying different sets of parameters such as block size, transaction sending rate, network traffic distribution, and network size. Performance evaluation was done based on average transaction latency, network throughput, and transaction failure rate. Scalability was assessed based on changes in transaction latency and throughput with increasing network size. Test results let to study the impact of a particular parameter on the private blockchain network performance and show how they can be adjusted to improve performance.

Quantitative Evaluation of the Impacts of the Time Headway of Adaptive Cruise Control Systems on Congested Urban Freeways Using Different Car Following Models and Early Control Results

The impact of driving automation and adaptive cruise control (ACC) on traffic performance has been increasingly studied in recent years. This paper focuses on two widely used ACC car following models and investigates the impact of the time headway parameter on traffic operation and performance on one of the busiest freeway corridors in Ontario, Canada. Using Aimsun microsimulation, we compare two commonly used ACC car following models; the intelligent driver model (IDM) and Shladover’s model which has been recently adopted in Aimsun Next 20. Several experiments have been conducted to evaluate the freeway performance for different desired headway settings and market penetration rates of ACC-equipped vehicles. Simulations results confirm the reported IDM drawbacks of having a slow response leading to headway errors which are less pronounced with Shladover’s model thereby leading to more accurate quantification by the latter. This study further presents a simple on-off ACC-based traffic control strategy which aims to adapt in real time the driving behavior of ACC-equipped vehicles to the prevailing traffic conditions so that freeway performance is improved. The simulation results demonstrate that, even for low penetration rates of ACC vehicles, the proposed control concept improves the average network throughput, delay, and speed compared to the case of only manually driven or uncontrolled ACC vehicles.

Throughput and Age of Information in a Cellular-Based IoT Network

This paper studies the interplay between device-to-device (D2D) communications and real-time monitoring systems in a cellular-based Internet of Things (IoT) network. In particular, besides the possibility that the IoT devices communicate directly with each other in a D2D fashion, we consider that they frequently send time-sensitive information/status updates (about some underlying physical processes observed by them) to their nearest cellular base stations (BSs). Specifically, we model the locations of the IoT devices as a bipolar Poisson Point Process (PPP) and that of the BSs as another independent PPP. For this setup, we characterize the performance of D2D communications using the average network throughput metric whereas the performance of the real-time applications is quantified by the Age of Information (AoI) metric. The IoT devices are considered to employ a distance-proportional fractional power control scheme while sending status updates to their serving BSs. Hence, depending upon the maximum transmission power available, the IoT devices located within a certain distance from the BSs can only send status updates. This association strategy, in turn, forms the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Johnson-Mehl (JM)</i> tessellation, such that the IoT devices located in the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">JM cells</i> are allowed to send status updates. The average network throughput is obtained by deriving the mean success probability for the D2D links. On the other hand, the temporal mean AoI of a given status update link can be treated as a random variable over space since its success delivery rate is a function of the interference field seen from its receiver. Thus, in order to capture the spatial disparity in the AoI performance, we characterize the spatial moments of the temporal mean AoI. In particular, we obtain these spatial moments by deriving the moments of both the conditional success probability and the conditional scheduling probability for status update links. Our results provide useful design guidelines on the efficient deployment of future massive IoT networks that will jointly support D2D communications and several cellular network-enabled real-time applications.

Group-Based Random Access and Data Transmission Scheme for Massive MTC Networks

Massive machine-type communications (mMTC) is one of the three generic services for the fifth-generation (5G) wireless communications system. To utilize fully the high rate transmission feature of the 5G system to support massive MTC devices (MTCDs), we propose a group-based random access and data transmission scheme, where the data packets of MTCDs are first aggregated by the MTC gateways (MTCGs) and then forwarded to the base station. The access process of the MTC network is divided into two phases, namely the intra-group transmission phase and MTCG forwarding phase. The entire resources are also partitioned for the two phases. We employ the discrete-time nonhomogenous Markov model to characterize the joint queue-length evolution of multiple MTCGs, which cannot be analyzed directly due to the exponential complexity and time-nonhomogeneity. To facilitate the analysis, we approximately decompose the joint nonhomogenous queue-length evolution process into multiple independent nonhomogenous queue-length evolution processes with the same state transition probabilities. Then, we establish an equivalent single queue-length evolution based homogenous Markov chain by constructing a virtual queue and determine the corresponding stationary distribution by using the Gauss-Jordan elimination method. An optimization problem is formulated to maximize the average network throughput subject to the constraints on the resource partition for the two phases and the MTCG forwarding threshold. By developing a modified differential evolution algorithm, we provide an efficient solution to the formulated problem, which can be arbitrarily close to the optimal solution. Simulation results show that the proposed scheme can efficiently improve the network performance over the existing schemes.

Jointly Adaptive Distributed Beamforming and Resource Allocation for Buffer-Aided Multiple-Relay NOMA Networks

In this paper, a novel jointly adaptive transmission strategy for buffer-aided multiple-relay non-orthogonal multiple access (NOMA) networks is proposed. By leveraging time diversity brought by the data storage buffer, and multiple relays’ beamforming gains, the source node broadcasts data in the first hop and all relays perform distributed beamforming to transmit information to users with NOMA in the second hop. Our focus is on maximizing the average network throughput (ANT) by jointly optimizing the mode selection, power and rate allocation at the source node, as well as the rate allocation and distributed beamforming coefficients at the relays along the time. As a solution to this complex problem, we propose an online scheduling scheme, which first adopts the Lyapunov optimization methodology to convert the ANT maximization problem into the drift-plus-penalty function minimization problem at each time slot. Secondly, focusing on the non-convexity of the transformed problem at each time slot, a novel two-loop algorithm is proposed as an effective solution. Extensive performance evaluation results have revealed the superiority of the proposed scheme as compared to other benchmark access schemes, namely, buffer-aided single relay NOMA, maximum ratio transmission based NOMA and buffer-aided time division multiple access schemes.

DPLBAnt: Improved load balancing technique based on detection and rerouting of elephant flows in software-defined networks

Traffic management in software-defined networks (SDNs) is critical for efficient bandwidth utilization and resource provisioning. Recent works on SDN load balancing (LB) have focused on identifying and rerouting elephant flows (EFs) for effective bandwidth usage. These techniques have some limitations, such as using source-to-destination hop count as the primary rerouting metric and not differentiating the types of flow that result in frequent resource conflicts when handling EF with long-lived bandwidth. Besides, current EF detection techniques use predefined bandwidth-use thresholds that cannot adapt to the ever-changing traffic condition. Also, detecting EF on switches results in high controller-switch bandwidth and high EF detection time. This study presents an ant colony optimization-based technique for rerouting EFs while considering load-balancing in the SDN links. This technique, called DPLBAnt, is formulated as a shortest-path problem in SDN that can alleviate the high controller-switch load. The proposed technique first detects EF by using a pair of classifiers on both SDN controller and switches. Most EF candidates are sifted on the switches, resulting in accurate and efficient detection of EF. Then, DPLBAnt obtains the global state of the SDN from which the most optimal paths for congested links are retrieved, and EF are redirected accordingly. The performance of the proposed DPLBAnt has been extensively simulated. Results indicate its superior performance over Equal-Cost Multi-Path (ECMP) and FlowSeer techniques in terms of average end-to-end delay (54% and 7.9% better), average network throughput (3.5× and 1.5× better), and average packet loss (18% and 10% better) respectively. The overall performance indicates that the proposed LB technique based on detection and rerouting of EFs can improve SDN’s overall performance.

An energy‐efficient MAC protocol based on receiver initiation and multi‐priority backoff for wireless sensor networks

Wireless Sensor Network (WSN) has been widely used with its main characteristics of resilience, scalability, and mobility. However, the node battery capacity in WSN is limited. In addition, in scenes with emergencies such as forest ecological monitoring, data packets of wildfire emergencies have higher real-time requirements than periodic normal data. Therefore, this paper proposes an energy-efficient MAC protocol based on Receiver initiation and Multi-Priority Backoff (RMP-MAC). RMP-MAC adopts the wake-up matching mechanism initiated by the receiver. The sender adjusts its schedule according to the wake-up information of the receiver to match the receiver, which reduces the energy consumption of idle listening. In addition, RMP-MAC proposes a multi-priority factor adaptive backoff mechanism based on event priority, node remaining energy level, and the number of data packets waiting in the transmission queue, ensuring the transmission of emergency data in the actual situation of forest ecological monitoring. Simulation experiments show that compared with PB-MAC, AP-MAC, and MPQ-MAC, RMP-MAC can reduce energy consumption by 20.22%, 12.47%, and 6.62% respectively. In addition, it has improved in the average collisions, network throughput, and average delay of packets.

Multi-objective Optimization of Spectrum Sensing and Power Allocation Based on Improved Slime Mould Algorithm

In order to improve the throughput of sub-users of cognitive radio networks and reduce the interference to primary users, firstly, we consider the spectrum sensing time, decision threshold, and power allocation factors of joint sub-users, and construct the average network throughput and average interference as a multi-objective optimization problem, and secondly, propose a multi-objective optimization algorithm based on improved sticky bacterium algorithm (SMA) to model and solve this problem to obtain the best the sensing time, decision threshold and power. Finally, the proposed method is compared with the NSGA-II algorithm, and the simulation results show that the proposed method can effectively search for the optimal solutions of the spectrum sensing parameters to maximize the average network throughput and minimize the average interference.

A Framework for Mitigating DDoS and DOS Attacks in IoT Environment Using Hybrid Approach

The Internet of Things (IoT) has gained remarkable acceptance from millions of individuals. This is evident in the extensive use of intelligent devices such as smartphones, smart television, speakers, air conditioning, lighting, and high-speed networks. The general application area of IoT includes industries, hospitals, schools, homes, sports, oil and gas, automobile, and entertainment, to mention a few. However, because of the unbounded connection of IoT devices and the lack of a specific method for overseeing communication, security concerns such as distributed denial of service (DDoS), denial of service (DoS), replay, botnet, social engineering, man-in-the-middle, and brute force attacks have posed enormous challenges in the IoT environment. Regarding these enormous challenges, this study focuses on DDoS and DoS attacks. These two attacks have the most severe consequences in the IoT environment. The solution proposed in this study can also help future researchers tackle the expansion of IoT security threats. Moreover, the study conducts rigorous experiments to assess the efficiency of the proposed approach. In summary, the experimental results show that the proposed hybrid approach mitigates data exfiltration caused by DDoS and DoS attacks by 95.4%, with average network lifetime, energy consumption, and throughput improvements of 15%, 25%, and 60%, respectively.

A Proposed Adaptive Least Load Ratio Algorithm to Improve Resources Management in Software Defined Network OpenFlow Environment

So far, the existing load-balancing schemes (static and dynamic) have not taken over a concise and reasonable mechanism for systematic isolation of the resource counters monitored in the OpenFlow switch periodically. To address the aforementioned issue, the adaptive least load ratio algorithm was proposed and followed a straightforward procedure to generate the best possible decisions based on significant motivation characteristics identified in dynamic load balancing schemes like Least Connection-Based and Least Bandwidth-Based. the study was carried out using the HTTPerf tool as it provided a flexible facility for the generation of various HTTP workloads to evaluate server performance. Results revealed that, while Software defined network was under the influence of client requests load ranging from 0 up to 180 (req/sec), the proposed algorithm confirmed a faster server reply time up to 13.37%, server connection time up to 16.3% and average network throughput was improved up to 8%. Moreover, server CPU utilization time and average queue length were reduced to 2.5% and 14%. respectively. accordingly, and based on what the proposed algorithm has achieved in terms of quantitative performance parameters, it could be adopted in SDN-based data centers.

Design of Minimizing Expected Energy of Multisource Wireless Cooperative Network Based on Multiobjective Optimization

In order to solve the problems of high average power consumption, low average throughput, high average energy consumption per unit of data, and short network life cycle in traditional multisource wireless cooperation methods, this paper proposes a multisource wireless cooperative network design method based on multiple goals. We analyze the characteristics of heterogeneous deployment of multisource wireless cooperative networks and the energy consumption of nodes and control the energy consumption of network data transmission through distributed opportunistic transmission scheduling methods according to the analysis results. We use the optimal strategy of minimizing expected energy consumption, transform the problem of data transmission energy consumption, establish a mathematical model, and obtain the optimal solution for minimizing expected energy consumption. According to the optimal stop rule, the minimum expected energy consumption threshold is obtained, and then the optimal solution is obtained on the constraint set of the multiobjective optimization problem through the multiobjective optimization method, so as to achieve the goal of minimizing the expected energy of the network. Experimental results show that this method prolongs the network life cycle, reduces the average power consumption of network data transmission, and improves the average network throughput.

A Comparative Performance Analysis for Static and Dynamic Load Balancing Techniques in Software Defined Network Environment

Traditional load balancer suffers from inflexibility, high cost and the difficulty to manage the network. Software-Defined Network (SDN) gives a promising solution for traditional load balancer limitations through enabling programmability and centralized control over the network, which offers inexpensive and scalable solutions. This paper investigates the impact of increasing the workload requests from 0 up to 180 requests per second (req/sec) in order to explore average network throughput under the deployment of static as well as dynamic load balancing algorithms by the POX controller. The study was based on the utilization of HTTPerf based on the fact that it provides a flexible facility for the generation of various HTTP workloads as well for the measurement of server performance. Our experiments revealed that as the number of requests increases the throughput increases as well. Dynamic least bandwidth-based load balance scheme has shown a remarkable improvement in terms of average network throughput up to 8%, 3.3% and 2.56 %, as compared with static load balancing schemes like random, round-robin and weighted round-robin. However, Dynamic least bandwidth recorded a slight ineffective improvement less than 1 % was recorded when a comparison was carried out dynamic least connections, this directed us to the fact that their performance was almost the same. Based on what dynamic least bandwidth scheme has achieved in terms of average network throughput, more efforts should be driven by the researchers on algorithm development like introducing static weight for each server as well as the application of the algorithm on several POX controllers to avoid a single point of failure.