Network Throughput Performance Research Articles

Machine-Learning-Based UAV-Assisted Agricultural Information Security Architecture and Intrusion Detection

In recent years, Unmanned Aerial Vehicle (UAV) remote sensing has developed rapidly in the field of farmland information monitoring. Real-time and accurate access to farmland information and crop growth dynamics is a prerequisite for the implementation of precision agriculture. Machine Learning identifies existing knowledge to acquire new knowledge, promotes the development of Artificial Intelligence, and brings a large number of data training sets for Machine Learning. The present work aims to ensure the safe operation of agricultural information systems and guarantee the data security of intelligent agriculture. The Machine Learning method explores the wireless network deployment of the UAV system. The geographical location deployment of agricultural information security can effectively carry out rapid security detection of agricultural information security. Firstly, the UAV-assisted information acquisition system was studied. Besides, a Double Deep Q-network (DDQN) algorithm for location deployment based on Geography Position Information (GPI) was proposed to quickly optimize the deployment location of UAVs. GPI can avoid the complicated calculation process of channel state information. The DDQN algorithm was introduced to obtain the functional relationship between GPI and the optimal UAV deployment position, forming a new GPI-Learning strategy. In addition, the Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) are integrated as the CNN-LSTM algorithm to build the intrusion detection system for agricultural IoT for agricultural. In the integrated network structure of the system, LSTM is responsible for data transmission, and CNN is capable of network model building. Combined with the influence of various parameters on the performance of the UAV deployment location algorithm, the simulation experiment set the population size as 36, the discovery probability as 0.25, the step scaling factor as 0.8, and the Levy flight index as 1.25. The network throughput performance of the GPI-Learning algorithm combined with Cuckoo Search was better than other algorithms under different numbers of UAVs. On the KDD-CUP99 data set, the accuracy and detection rate of the agricultural IoT intrusion detection system based on the CNN+LSTM algorithm reached 93.5% and 94.4%, respectively. In general, the agricultural IoT intrusion detection system reported here has crucial practical reference value for the safe operation of agricultural information systems.

Managing Energy Consumption of Devices with Multiconnectivity by Deep Learning and Software-Defined Networking.

Multiconnectivity allows user equipment/devices to connect to multiple radio access technologies simultaneously, including 5G, 4G (LTE), and WiFi. It is a necessity in meeting the increasing demand for mobile network services for the 5G and beyond wireless networks, while ensuring that mobile operators can still reap the benefits of their present investments. Multipath TCP (MPTCP) has been introduced to allow uninterrupted reliable data transmission over multiconnectivity links. However, energy consumption is a significant issue for multihomed wireless devices since most of them are battery-powered. This paper employs software-defined networking (SDN) and deep neural networks (DNNs) to manage the energy consumption of devices with multiconnectivity running MPTCP. The proposed method involves two lightweight algorithms implemented on an SDN controller, using a real hardware testbed of dual-homed wireless nodes connected to WiFi and cellular networks. The first algorithm determines whether a node should connect to a specific network or both networks. The second algorithm improves the selection made by the first by using a DNN trained on different scenarios, such as various network sizes and MPTCP congestion control algorithms. The results of our extensive experimentation show that this approach effectively reduces energy consumption while providing better network throughput performance compared to using single-path TCP or MPTCP Cubic or BALIA for all nodes.

Interference Aware Joint Power Control and Routing Optimization in Multi-UAV FANETs

In this work, we enhance the total network throughput performance of a FANET system consisting of multiple UAVs and a ground control station (GCS) by optimizing the multihop routing structure and total power budget required for communication in the presence of inter-UAV interference. To achieve this, an optimization problem is formulated to maximize the network throughput within a given power budget. However, due to the non-convex and integer nature of the problem, it is NP-hard. Hence, it is dealt with differently by solving two daughter problems iteratively, i.e., (1) power allocation (PA) and (2) optimal link selection (LS) for the given solution of PA. These subproblems are solved iteratively by utilizing alternating optimization to obtain the global optimal solution of the main problem. The study presents various numerical results, which validate the effectiveness of the proposed routing structure and provide insights into the impact of interference on system parameters.

A Task-Oriented Hybrid Routing Approach based on Deep Deterministic Policy Gradient

With the development of communication and transmission technologies, the applications of Internet of Things (IoT) and telemedicine are increasingly sensitive to network latency. To meet this urgent demand, learning-based routing approaches emerge, with better performance and higher flexibility. These routing approaches can be divided into single-path and multipath, which have lower transmission delay and better load balancing respectively. However, the traffic scheduling policy is not only affected by the network states but also needs to consider the requirements for traffic tasks. We propose a Task-Oriented Hybrid Routing Approach (TOHRA) based on Deep Deterministic Policy Gradient (DDPG) in Software-Defined Networking (SDN). First, the hybrid routing optimization algorithm comprehensively considers the network states and task requirements, and outputs a task-oriented hybrid routing that combines the advantages of single-path and multipath. Moreover, we design a hop-by-hop traffic segmentation model based on DDPG to output the traffic segmentation ratio on hybrid routing to adapt to the changing transmission path. Experimental results show that TOHRA achieves the best performance of load balancing and network throughput. Especially in the case of Type C tasks in Germany topology, the average network throughput of THORA is increased by 32.86%, and the average variance of link load rate is reduced by 46.62%.

Distributed Multi-Agent Deep Q-Learning for Load Balancing User Association in Dense Networks

Distributed learning can lead to effective user association with low overhead, but faces significant challenges in incorporating load balancing at all base stations (BS) because of coupling constraints. In this paper, we propose a distributed multi-agent deep Q-learning model for user association to satisfy the load balancing constraint at every BS. In particular, we design a deep Q-network (DQN) with target Q-network and experience replay buffer at each user as an agent, and propose a multi-agent matching policy to control the number of users connected to each BS for load balancing. The policy enhances network throughput performance by implementing a novel updating rule for the preference list at each BS. The proposed multi-agent DQN model operates in a fully distributed manner where each agent only uses local information and requires no information exchange between agents. Simulation results demonstrate that our proposed algorithm outperforms a conventional distributed load balancing algorithm and approaches a centralized scheme performance, while exhibiting fast convergence and high adaptability to channel changes.

Achieving Flexible and Lightweight Multipath Congestion Control Through Online Learning

The upgrade of network devices to be equipped with multiple network interfaces makes it possible to improve network throughput performance through multipath transmission protocols, especially multipath TCP (MPTCP). However, so far the mostly used MPTCP protocols have a common limitation, namely the rigid and conservative method. They have been designed with little consideration of the fact that real networks are dynamic and the network status changes frequently, thus leading to the poor performance of current MPTCP in many realistic scenarios. In this paper, we propose a lightweight multipath congestion control algorithm based on online learning, named MP-OL. MP-OL models congestion control as a multi-armed bandit problem, and adjusts the sending rate of each subflow flexibly and adaptively through online learning. Therefore, MP-OL possesses the capability of suiting various network scenarios, and can achieve fairness and high performance in dynamic network environment. It can also flexibly switch between online learning and traditional method, which reduces the computational complexity while ensuring the learning efficiency, thus making MP-OL easy to deploy and use. As the experimental results demonstrated, compared with the leading MPTCP variants, MP-OL achieves significant improvements in fairness and link utilization, and shows better resilience to non-congestion loss and better adaptability to unstable network conditions. In real networks, MP-OL also obtains better throughput performance.

Mobility-aware adaptive beam tracking for vehicles in MmWave communication networks

The millimeter wave (mmWave) is a potential solution for high data rate communication due to its availability of large bandwidth. However, it is challenging to perform beam tracking in vehicular mmWave communication systems due to high mobility and narrow beams. In this paper, an adaptive beam tracking algorithm is proposed to improve the network throughput performance while reducing the training signal overhead. In particular, based on the mobility prediction at base station (BS), a novel frame structure with dynamic bundled timeslot is designed. Moreover, an actor-critic reinforcement learning based algorithm is proposed to obtain the joint optimization of both beam width and the number of bundled timeslots, which makes the beam tracking adapt to the changing environment. Simulation results demonstrate that, compared with the traditional full scan and Kalman filter based beam tracking algorithms, our proposed algorithm can improve the time-averaged throughput by 11.34% and 24.86% respectively. With the newly designed frame structure, it also outperforms beam tracking with conventional frame structure, especially in scenarios with large range of vehicle speeds.

A Novel Solution for Network Flexibility Problem in Mobile Multi-hop Tunneling Networks

Since the network throughput performance is limited by the available technology limits, there are too many attempts in the literature to improve the throughput performance of the network by modifying the routing algorithms currently in use. Although application of the tunneling on IP networks provides reserved paths for the higher priority packet streams and succeeds in providing faster communication performances, it is also determined in the literature that, the greatest up to date problem of tunneling networks is the flexibility problem, which is defined as the problem of difficulty to have minimal delay and highest throughput when congestions or node failures occur and the throughput decreases due to high traffic loads on the network. sIn this work, a high performance solution, called LB-ACN (Location Based- Avoid Congested Node) is proposed to solve this flexibility problem and to compensate the increased delay which came up due to the high traffic load and also due to inflexible tunneling network structure, considering the locations of the nodes in addition to their traffic loads. And it is shown that, it is succeeded to carry the throughput of the highly traffic loaded network back to 48.6 Mbps from 36.9 Mbps by about 31.7% performance improvement for which we could improve it from 36.9 Mbps up to 45.9 Mbps by 24.3% using the proposed ACN (Avoid Congested Node) algorithm in our previous work. As a result, we have succeeded in having about 7.4% more improvement on throughput performance in comparison with the previously proposed ACN algorithm.

Centralized Monitored Spectrum Management using Multi-resource Parallel Sensing in Cognitive Radio Networks

Spectrum sensing has a very important role in the operation of cognitive radio network. A proper sensing and utilization improve the performance of cognitive radio network (CRN). Different methodologies were developed in past to improve the sensing and utilization performance in CRN. Parallel sensing is used as an optimal approach in spectrum sensing with reduced delay of operation but restraint with multiple requests for spectrum sensing. A random generation of sensing requests results in large time delay and interference in the network. To minimize the interference effect and requesting overhead, this paper presents a new approach of spectrum sensing by centralized monitoring of spectrum engagement of Primary User (PU). The proposed approach operates on a repository monitoring of channel engagement to control the request granting for spectrum sensing in PUs. In addition, the sharing of spectrum usage is controlled by monitoring the spectral efficiency and interference in the network. Simulation results obtained illustrates an enhancement in network throughput, spectrum sensing and spectrum utilization performance where an average network throughput is observed to improve by 80% for varying number of primary channels and varying load conditions. The spectrum sensing is improved by 70% in average for varying primary channels and load conditions and Ideal channel utilization for the proposed approach is observed to improve by 90%.

A construction and inspection technology of power emergency communication system based on Beidou

In order to improve the real-time performance of information transmission of power emergency communication system, the construction and inspection technology of power emergency communication system based on Beidou is proposed in this paper. Firstly, the differences between heterogeneous networks are described based on Beidou short message. Then, the fusion probability of heterogeneous network is calculated, and the optimal communication channel is obtained. Next, communication channel link is established, and the maximum transmission range is described. In addition, the network capacity is calculated, the congestion avoidance model is established, the bandwidth is obtained by defining the interference index of the information transmission channel, the total load during the construction and inspection process is calculated, and the construction and inspection of the power emergency communication system are realized.. Experimental results show that the proposed technology has better performance in information utilization, network throughput and system response time.

Cooperative Multirelay Network Design With Hybrid Backscatter and Wireless-Powered Relaying

In this article, a wireless multirelay network in which the relays are energy constrained is studied. Especially, in order to consume the harvested energy efficiently at the relays so as to improve the network throughput, a new hybrid relaying protocol is first proposed. In the proposed protocol, each relay can flexibly switch its operation among energy harvesting (EH), information receiving (IR), active information transmission (IT), and two passive backscatter communication (BC) modes according to the channel states as well as its data buffer states and energy states in each transmission block, by which the harvested energy can be efficiently utilized and superior throughput performance can be achieved. However, under the hybrid relaying protocol, it is challenging to achieve a strategy to optimally determine the operation mode for each relay, and the energy and information scheduling at the relays that operate in the IR, IT, and BC modes. To address this issue, the involved optimization problem is formulated as a stochastic optimization problem, which cannot be tackled directly. To make it tractable, the stochastic optimization problem is transformed into a Markov decision process (MDP) with finite state and action spaces. By solving the MDP via a dynamic programming (DP) algorithm, the optimal strategy for the multirelay network is achieved. Furthermore, to reduce the computational complexity in the DP algorithm, an efficient algorithm with low complexity is developed by using a Lyapunov optimization framework. Numerical simulations show that our proposed hybrid relaying strategy can achieve superior throughput performance in wireless multirelay networks.

The ratio model between throughput and delay based on payload transmission time in wireless blockchain network

In wireless blockchain network (WBN), it is necessary to study the impact of delay performance on blockchain technology with different data rates, because the throughput performance is not the only goal. We propose an analysis model to evaluate network performance based on both delay performance and network performance. Firstly, we separate the communication process from business process in WBN, and package the business data into the payload in a frame. Secondly, we define throughput-time S_V as a ratio of throughput to data transmission rate and propose a ratio model of S_V to network delay. Then, we analyze the ratio model with the parameter of payload transmission time, which is defined as a ratio of payload in a frame to data transmission rate. Using the DATA/ACK type in IEEE 802.11, we learn that the optimal payload transmission time is independent of node number and only determined by some interframe space of access mechanism and propagation time. Finally, we analyze the performance of network delay and throughput between DATA/ACK type and RTS/CTS/DATA/ACK type based on payload transmission time. The simulation data verify the validity and accuracy of the proposed model.

Influence of Density on Throughput Performance in Cognitive Ultra-dense Networks

Current advancements of Fifth Generation (5G) of mobile communications and beyond, have envisioned future networks as highly dense and coexisting in various bandwidths, providing seamless connectivity to users at any location. Thus, it is important to describe the effects and limits of densification and spectrum sharing. This article examines a less explored system model of a terrestrial cognitive radio (CR) based ultra-dense network (UDN) that operates within the range of a cellular macro base station (BS) and its users. It shares the incumbent spectrum in the interweave mode to avoid interference to the primary network, by implementing two common methods for energy detection (ED) spectrum sharing – Gaussian ED and Fading ED (FED). Through extensive simulations, the critical density of the UDN’s cognitive access points (CAPs), the ED efficiency, as well as the throughput gains, are determined through the measured signal-to-noise-ratio (SNR) at the CAPs and SUs. Additionally, the influence of different SU densification on the throughput is analyzed for the critical CAP density. It has been assessed that due to the high path loss in UDNs, the spectrum utilization gain (SUG) is small, but it may be improved through appropriate SU densification.

SDN-Driven Internet of Health Things: A Novel Adaptive Switching Technique for Hospital Healthcare Monitoring System

In the last decent, the number of Internet of Things (IoT) health-based paradigm reached to a huge number of users, services, and applications across different disciplines. Thus, hundreds of wireless devices seem to be distrusted over a limited or small area. To provide a more efficient network, the software-defined network (SDN) thought to be a good candidate to deal with these huge number of wireless users. In this work, after a novel SDN algorithm is proposed for the hospital environment, it is also designed and integrated into an Internet of Health Things (IoHT) paradigm. The novel algorithm called adaptive switching (AS) is proposed as a novel adaptive access strategy based on adaptively hoping among existing Go-Back-N and Selective Repeat techniques. Finally, the throughput performance of the proposed AS method is compared with the performances of traditional Go-Back-N and Selective Repeat ARQ methods using the developed MATLAB simulation. For this, an optimal P error rate that the network should prefer to switch either from Go-Back-N to Selective Repeat or from Selective Repeat to Go-Back-N method to maximize the network throughput performance is determined. The evaluated results are also confirmed by theoretical calculation results using well-known Mathis throughput formula. It is observed from the simulation results that the best throughput performance can be evaluated, when AS switches to Go-Back-N if the P error is less than 3.5% and it switches back to Selective Repeat when the P error is greater than 3.5%. By this way, it is also observed that the throughput always has its best possible results for all P error rates and up to 37.52% throughput improvement is provided by the use of novel proposed adaptive switching (AS) algorithm.

Coefficient of Restitution based Cross Layer Interference Aware Routing Protocol in Wireless Mesh Networks

In Multi-Radio Multi-Channel (MRMC) Wireless Mesh Networks (WMN), Partially Overlapped Channels (POC) has been used to increase the parallel transmission. But adjacent channel interference is very severe in MRMC environment; it decreases the network throughput very badly. In this paper, we propose a Coefficient of Restitution based cross layer interference aware routing protocol (CoRCiaR) to improve TCP performance in Wireless Mesh Networks. This approach comprises of two-steps: Initially, the interference detection algorithm is developed at MAC layer by enhancing the RTS/CTS method. Based on the channel interference, congestion is identified by Round Trip Time (RTT) measurements, and subsequently the route discovery module selects the alternative path to send the data packet. The packets are transmitted to the congestion free path seamlessly by the source. The performance of the proposed CoRCiaR protocol is measured by Coefficient of Restitution (COR) parameter. The impact of the rerouting is experienced on the network throughput performance. The simulation results show that the proposed cross layer interference aware dynamic routing enhances the TCP performance on WMN.

Transmission Delay-Based Uplink Multi-User Scheduling in IEEE 802.11ax Networks

As the demands for uplink traffic increase, improving the uplink throughput has attracted research attention in IEEE 802.11 networks. To avoid excessive competition among stations and enhance the uplink throughput performance, the IEEE 802.11ax standard supports uplink multi-user transmission scenarios, in which AP triggers certain stations in a network to transmit uplink data simultaneously. The performance of uplink multi-user transmissions highly depends on the scheduler, and station scheduling is still an open research area in IEEE-802.11ax-based networks. In this paper, we propose a transmission delay-based uplink multi-user scheduling method. The proposed method consists of two steps. In the first step, the proposed method makcreateses station clusters so that stations in each cluster have similar expected transmission delays. The transmission delay-based station clustering increases the ues of uplink data channels during the uplink multi-user transmission scenario specified in IEEE 802.11ax. In the second step, the proposed method selects cluster for uplink multi-user transmissions. The cluster selection can be performed with a proportional fair-based approach. With the highly channel-efficient station cluster, the proposed scheduling method increases network throughput performance. Through the IEEE 802.11ax standard compliant simulations, we verify the network throughput performance of the proposed uplink scheduling method.

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.

Enhancement of network coding in wireless sensor network using improved lion algorithm: intention toward maximizing network throughput and lifetime

PurposeSince communication usually accounts as the foremost problem for power consumption, there are some approaches, such as topology control and network coding (NC), for diminishing the activity of sensors’ transceivers. If such approaches are employed simultaneously, then the overall performance does raise as expected. In a wireless sensor network (WSN), the linear NC has been shown to enhance the performance of network throughput and reduce delay. However, the NC condition of existing NC-aware routings may experience the issue of false-coding effect in some scenarios and usually neglect node energy, which highly affects the energy efficiency performance. The purpose of this paper is to propose a new NC scheduling in a WSN with the intention of maximizing the throughput and minimizing the energy consumption of the network.Design/methodology/approachThe improved meta-heuristic algorithm called the improved mutation-based lion algorithm (IM-LA) is used to solve the problem of NC scheduling in a WSN. The main intention of implementing improved optimization is to maximize the throughput and minimize the energy consumption of the network during the transmission from the source to the destination node. The parameters like topology and time slots are taken for optimizing in order to obtain the concerned objective function. While solving the current optimization problem, it has considered a few constraints like timeshare constraint, data-flow constraint and domain constraint. Thus, the network performance is proved to be enhanced by the proposed model when compared to the conventional model.FindingsWhen 20 nodes are fixed for the convergence analysis, performed in terms of multi-objective function, it is noted that during the 400th iteration, the proposed IM-LA was 10.34, 13.91 and 50% better than gray wolf algorithm (GWO), firefly algorithm (FF) and particle swarm optimization (PSO), respectively, and same as LA. Therefore, it is concluded that the proposed IM-LA performs extremely better than other conventional methods in minimizing the cost function, and hence, the optimal scheduling of nodes in a WSN in terms of the multi-objective function, i.e. minimizing energy consumption and maximizing throughput using NC has been successfully done.Originality/valueThis paper adopts the latest optimization algorithm called IM-LA, which is used to solve the problem of network coding scheduling in a WSN. This is the first work that utilizes IM-LA for optimal network coding in a WSN.

Joint Statistical and Machine Learning Approach for Practical Data-Driven Assessment of User Throughput Quality in Microcellular Radio Networks

The provision of enhanced user experience with higher-data transmission speed over the radio frequency channels was one of the core objectives of evolving Long Term Evolution (LTE), which is a 4th generation of cellular broadband communication standard. LTE was developed to supports data transmission speed up to 300 Mbps and 75 Mbps in downlink and uplink, respectively, utilizing a robust multiplexing technique. For mobile radio networks operators to maintain high operability around the ever increasing and demanding subscribers, periodic evaluation, quantitative estimation and analysis of network performance is pivotal. There exist a quantum of previous research works on LTE system network performance that have been conducted both at spatial domain and temporal domain, but the authors concentrated their studies either on practical LTE radio coverage issues only or on general LTE network performance issues using analytical/simulation techniques. In this contribution, a combined statistical and machine learning approach is proposed and applied to provide an in-depth user data throughput performance of operational LTE networks in relation to signal coverage and signal quality parameters, using in typical microcellular built-up terrains as case study. Specifically, the impact of transmitter- receiver communication distance, received signal reference power, received Signal reference quality, Signal to Noise Ratio, Received Signal Strength and Channel Quality Indicator on user throughput over Radio Link Control, Physical Downlink Shared Channel and Packet Data Convergence Protocol layers have been shown using professional TEMS investigation tools. Particularly, for the purpose of effective LTE system network planning and management, the most influencing signal coverage and signal quality parameters on user throughput performance are also examined and shown in the framework of Self-organsing map (SOM) and statistical correlation stratagems.

Achieving High Throughput in Wireless Networks With Hybrid Backscatter and Wireless-Powered Communications

This article studies a network where a transmitter communicates with a receiver by hybrid communications that consist of passive information transmission (IT) via backscatter communication (BC) and active IT via wireless-powered communication (WPC). Because the circuit energy consumption in the passive IT of BC is much lower than that in the active IT of WPC, BC usually achieves a higher data transmission rate than WPC. Thus, it was suggested in the literature that the network throughput performance could not be improved by hybrid communications. However, our work in this article demonstrates that the throughput can be enhanced by a newly designed hybrid communication strategy. To demonstrate this, we develop a novel protocol that enables the transmitter to adaptively switch its operation between BC, active IT, and energy harvesting in one time block while scheduling energy consumption flexibly among multiple time blocks. Under the developed protocol, we formulate an optimization problem to jointly optimize the operation mode and resource allocation at the transmitter. The formulated problem is difficult to solve because the energy scheduling at the transmitter is coupled across multiple time blocks, and noncausal channel state information (CSI) is required. To address this problem, we first solve a simplified optimization problem via dynamic programming (DP) and a layered optimization method by assuming that the noncausal CSI is known. Then, we employ an approximate DP approach to solve the original problem with causal CSI. Finally, we verify by simulations that the proposed scheme can achieve superior throughput performance.