Maximum Network Throughput Research Articles

New Energy Efficient Routing Protocol in Wireless Sensor Networks Using Firefly Algorithm

Energy constraint has become the major challenge in designing wireless sensor networks. Network lifetime is considered the most substantial metric in these networks. The routing technique is one of the best choices for maintaining a network lifetime. This paper demonstrates the implementation of the new methodology of routing in WSN using Firefly swarm intelligence. Energy consumption is the dominant issue in wireless sensor network routing. For network cutoff avoidance while maximizing net lifetime, energy exhaustion must be balanced. Balancing energy consumption is the key feature for the rising net lifetime of WSNs. This routing technique involves the determination of the optimal route from the node toward the sink to make energy exhaustion balance in the network and at the same time maximize network throughput and lifetime. The proposed technique show that it is better than other some routing techniques like Dijkstra routing, Fuzzy routing, and ant colony (ACO) routing technique. Results demonstrate that the proposed routing technique has beat the three routing techniques in throughput and extend net lifetime.

Throughput properties and optimal locations for limited deployment of Max-pressure controls

Max-pressure (MP) control is one of the few traffic signal controllers proven to maximize network throughput or maximally stabilize the network. According to the theoretical results published so far, it can stabilize a network if all intersections are equipped with MP control for all stabilizable demands. However, budget constraints may not allow the installation of MP control on all intersections. Previous work did not consider a limited number of MP controlled intersections while proving the stability properties. Therefore, it is not clear whether a network can still be stabilized with a limited deployment of MP control. Using Lyapunov drift techniques this paper proves that even with a limited deployment, MP control can stabilize a network within feasible demand. Then we present an optimization formulation to find the optimal intersections to install MP control given a limited budget. We also present a greedy efficient algorithm to solve that optimization problem and prove that the algorithm solves the problem to optimality. Numerical results from simulations conducted on the downtown Austin network using an in-house custom simulator called AVDTA are then presented. The change in theoretical maximum servable demands for different amounts of deployments obtained from the optimization problem seemed to match with simulation results most of the times. We found that limited deployment of MP control almost always performed better than random deployment of MP control in terms of servable stable demand. Average total queue length and link density were observed to decrease as the number of MP controls increased which indicates better network performance. Average travel times per vehicle also decreased with installations of MP controls which shows how the travelers would benefit from more MP controls.

Singular Direction-Based Quantizer and Receiver Designs for User Cooperative Distributed Reception

In this article, we develop a distributed reception system to fully utilize spatial multiplexing gain by exploiting the massive connections in beyond fifth-generation networks. In a distributed reception system, assistant receiving nodes have to quantize the received signals in order to forward them to a final destination node. A signal quantizer with high-resolution codebooks can improve signal quantization performance, although it burdens the data-exchange link between devices. We propose signal quantization and reception strategies to facilitate the distributed reception system by allowing a small amount of data-exchange. First, we develop a singular direction-based signal quantizer to include more channel gain in the received signals by employing low-resolution codebooks. Furthermore, we develop a minimum mean square error type receiver for a user cooperation network to facilitate distributed reception without the aid of a fusion center. Lastly, a quantization resource allocation algorithm is developed to maximize network throughput by using limited quantization resources. Simulation results are presented to evaluate the sum-rate performance of the quantized distributed reception system.

System throughput maximization in IRS-assisted phase cooperative NOMA networks

This work investigates performance of system throughput in intelligent reflecting surfaces (IRSs)-enabled phase cooperative non-orthogonal multiple access (NOMA) framework. By exploiting heterogeneous cognitive radio networks concept the aim is to maximize the sum rate of secondary users in the proposed phase cooperative downlink network configuration via optimization solutions. However, the optimization problem comes out to be NP-hard and precludes direct solution. Hence, an alternating optimization is applied at the primary network to solve the maximization problem by exploiting the transmit beamforming (BF) at the power station (PS) and phase shift optimization at the IRS. Later, sum rate maximization for secondary network is performed by utilizing phase shifts of primary network via phase cooperation. In order to find global optimal solutions for active beamformers at both PSs, a branch-reduce-and-bound (BRnB) method is used whereas, passive phase shift optimization at the primary PS is performed via a simple iterative solution, i.e., the element-wise block coordinate descent method. For the proposed framework, Monte-Carlo simulations are performed where the optimality of the global solution is compared with heuristic BF methods including minimum-mean-square-error/regularized zero-forcing-beamforming (ZFBF) and ZFBF. The BRnB algorithm sets an upper performance bound by improving the sum rate of users in comparison with the conventional heuristic BF schemes. This work signifies the utilization of phase cooperation in IRS-assisted NOMA networks for a multi-user environment.

Fairness-driven link scheduling approach for heterogeneous gateways for digital twin enabled industry 4.0

The advent of Industry 4.0 has brought with it the integration of digital twin technology, which has enabled businesses to develop a virtual replica of their physical assets. This technology allows businesses to optimize their operations and improve their overall efficiency. However, the successful implementation of digital twin technology in Industry 4.0 heavily relies on the effective utilization of gateways. A significant challenge in gateway utilization is the fair allocation of resources, particularly in heterogeneous environments where gateways have different capabilities. Digital Twin is helping Industry 4.0 vision by connecting the authorized people to the exact data and processes to protect the data/assets from unauthorized access. It is accomplished by connecting sensing devices using a unique addressing system and transmitting their combined data to the Internet of Things (IoT) cloud. Massive volumes of heterogeneous data have resulted from the rapid growth of IoT applications and services. As a result, evaluation of data which affects the Digital Twin enabled industry is studied in this article which focuses on data traffic generated from different Industry 4.0 applications and protection of data along with the industry assets is looked by Digital Twin technology. IoT gateways are currently used to connect the devices from various technologies to the Digital Twins. In such networks, sudden increase in demand of IoT gateways will increase with the increase in IoT devices and the operational cost will also be increased. In the proposed system, low-cost specific gateways are proposed to minimize cost and maximize network performance for protecting assets of smart city through Digital Twin technology. In order to accomplish effective resource allocation in a Digital Twin based infrastructure, data transmission fairness at every gateway is accomplished in an IIoT network by considering link scheduling issues. To address these issues and provide fairness in heterogeneous networks with enhanced data transfer, two steps solution is implemented. The Long Short-Term Memory (LSTM) technique is used in the initial step of traffic prediction to assess the minimal time of prior traffic conditions before being applied to estimate dynamic traffic. In the second step, effective link scheduling and selection are made for each wireless technology, taking into account predicted load, gateway distance, link capacity, and estimated time. More data is transmitted at maximum capacity as a result of improved data transfer fairness for all gateways and then the data is protected by Digital Twin technology. Simulated results show that our suggested strategy performs better than other approaches by obtaining maximum network throughput in Industry 4.0 to provide protective solutions using Digital Twin technology.Index Terms – Internet of Things (IoT), Link Scheduling, Traffic Prediction, Machine Learning (ML), Industry 4.0.

Software-defined Wide-area Networks in the Coevolution of Network and Computational Bandwidth

Software-Defined Wide-Area Networks (SD-WAN) are a technology that utilizes programmatic configuration to maximize network throughput. The main problem to achieve efficient use of network bandwidth with SD-WAN is the network delay (latency) in the communication. A network could be further slowed down by not enough memory or CPU resources. Here we conduct experiments, investigating the capability of an SD-WAN to maximize the network throughput at increasing levels of latency. We find that to a limited extent the computational optimization at the software side could offset the network limitations. At higher network latency the SD-WAN adds little value. Further increase of the capacity can only be achieved by hardware upgrades of the network bandwidth. Such solutions may be considered drivers of a coevolutional relationship between computational and network bandwidths, whereby the network use becomes more efficient.

MOSAL: A Subspace-Based Forecasting Algorithm for Throughput Maximization in IoT Networks

Predictive solution techniques have been developed recently to solve the massive access problem of the Internet of Things (IoT). These techniques forecast the traffic generation patterns of individual IoT devices in the coverage area of an IoT gateway and schedule the Medium Access Control (MAC)-layer resources at the gateway in advance based on these forecasts. Although predictive solutions have achieved high network performance, a key problem is that their performance depends highly on the performance of forecasters. In this article, to minimize the effects of forecasting errors on the performance of predictive networks, we develop a subspace-based forecasting algorithm called “Motion On a Subspace under Adaptive Learning rate (MOSAL).” First, our algorithm trains a forecaster by minimizing the performance loss of an IoT network based on the emulation of an Application-Specific Error Function (ASEF) by an Artificial Neural Network (ANN). Second, the algorithm moves close to a subspace of the forecasting errors while aiming to maximize network throughput. Our results show that MOSAL achieves a throughput performance that surpasses the performance of commonly used standard gradient descent training algorithms at a reasonable execution time. These results open the way to the deployment of predictive solutions at IoT gateways in practice in the near future.

Maximizing Flow Rates in Multihop Two-Tier IoT Networks With Ambient Backscattering Tags

This article considers routing and link scheduling in a two-tier wireless Internet of Things (IoT) network. The first tier consists of routers that communicate via active radio-frequency (RF) transmissions. The second tier consists of passive tags that backscatter ambient RF signals from routers. Our objective is to maximize the network throughput at both tiers. To this end, we outline a mixed-integer linear program (MILP) that jointly optimizes the active time of RF links and backscatter links, and traffic over links. We also present a heuristic called the algorithm-transmission set generator (ALGO-TSG) to compute transmission sets. Moreover, we also outline a heuristic called centralized max-flow (CMF) to maximize network throughput by jointly considering routing and link scheduling. The results show that: 1) the network throughput achieved by ALGO-TSG at both tiers is 29.80% higher as compared to the case without backscattering and 2) the throughput of CMF is on average 21.36% lower than the throughput computed by MILP.

Exploiting Hybrid SWIPT in Ambient Backscatter Communication-Enabled Relay Networks: Optimize Power Allocation and Time Scheduling

Ambient backscatter communication (ABC) has become an innovative technique for energy-efficient and short-range communication. In this article, we propose a hybrid simultaneously wireless information and power transfer (SWIPT)-assisted relay transmission scheme to extend the communication range of the ABC mode. That is, SWIPT with time-switching (TS) and power-splitting (PS) approaches is adopted into the relay for harvesting energy and processing information. Moreover, after harvesting energy, all the relays simultaneously transmit data according to power-domain nonorthogonal multiple access (NOMA) technology. Therefore, in the ABC-enabled hybrid SWIPT relay networks with power-domain NOMA, there is a problem of how to maximize the network throughput by allocating available power and time resources. We first formulate an optimization problem to achieve the maximum network throughput by finding the optimal PS ratio of relays as well as the optimal time allocation among users and relays. In order to reduce the computational complexity of the problem, we decompose it into two subproblems: 1) PS ratio optimization and 2) time allocation optimization. For PS ratio optimization, we derive the closed form of the optimal PS ratio for relays by the Lagrange dual theory and subgradient method. For time allocation optimization, the Lagrange dual theory and subgradient method are also adopted to obtain the closed form of the optimal time allocation among users and relays. Then, we propose a joint optimization of the PS ratio and time allocation-based iterative algorithm to solve the problem. Finally, simulation results show that our proposed scheme has an obvious throughput superiority over other two benchmark schemes.

Alternative Optimization for Secrecy Throughput Maximization in UAV-Aided NOMA Networks

With the rapid boost of wireless networks, it is challenging to guarantee the secure transmission and massive connectivity in the process of data collection. This letter considers an unmanned aerial vehicle (UAV)-aided non-orthogonal multiple access (NOMA) communication network, where a UAV is deployed to collect the data from sensing devices (SDs) in the NOMA manner subject to the eavesdropping attack, and a group of auxiliary devices (ADs) are deployed to provide the cooperative jamming to the eavesdropper. We aim to maximize the total secrecy throughput by jointly optimizing SDs’ transmit power and ADs’ scheduling decisions. We first formulate this problem as a mixed-integer non-convex optimization problem, which turns out to be mathematically intractable due to the non-convexity. Then, an alternative optimization algorithm is developed to find a suboptimal solution. Specifically, the successive convex approximation approach and the cross-entropy technique are used to solve the two sub-problems decomposed from the optimization problem, respectively. Numerical results demonstrate that the proposed algorithm can achieve considerable performance compared with the benchmark schemes.

Enhancing Earth Observation Throughput Using Inter-Satellite Communication

Earth observation systems play important roles in many critical applications. The rapid increase of the number of satellites and their sensing capability, however, makes it challenging to send the massive amount of observed data back to the Earth. One promising direction to enhance the earth observation throughput is to use inter-satellite communication. Towards this, we identify two key design factors: 1) the capability to support on-demand scheduling of inter-satellite communication; and 2) the capability to co-optimize the scheduling of observation and transmission missions. For both, rigorous study is needed to determine whether they provide sufficient throughput gain to justify their additional complexity. Our work formulates a generic earth observation and transmission problem to study the maximum network throughput under different settings. By succinctly modeling the different constraints using a generalized time-varying graph representation, we can efficiently find the optimal scheduling solutions. We conduct an extensive study, which shows that using 40 relay satellites from the “starlink” constellation can increase the throughput of 10 sensing satellites from the “Gaofen” constellation by more than 400%. In particular, on-demand scheduling under heavy load and co-optimization of observation/transmission under light but time-critical load can improve the throughput by more than 180% and 100%, respectively.

IRSML: An intelligent routing algorithm based on machine learning in software defined wireless networking

In software-defined wireless networking (SDWN), the optimal routing technique is one of the effective solutions to improve its performance. This routing technique is done by many different methods, with the most common using integer linear programming problem (ILP), building optimal routing metrics. These methods often only focus on one routing objective, such as minimizing the packet blocking probability, minimizing end-to-end delay (EED), and maximizing network throughput. It is difficult to consider multiple objectives concurrently in a routing algorithm. In this paper, we investigate the application of machine learning to control routing in the SDWN. An intelligent routing algorithm is then proposed based on the machine learning to improve the network performance. The proposed algorithm can optimize multiple routing objectives. Our idea is to combine supervised learning (SL) and reinforcement learning (RL) methods to discover new routes. The SL is used to predict the performance metrics of the links, including EED quality of transmission (QoT), and packet blocking probability (PBP). The routing is done by the RL method. We use the Q-value in the fundamental equation of the RL to store the PBP, which is used for the aim of route selection. Concurrently, the learning rate coefficient is flexibly changed to determine the constraints of routing during learning. These constraints include QoT and EED. Our performance evaluations based on OMNeT++ have shown that the proposed algorithm has significantly improved the network performance in terms of the QoT, EED, packet delivery ratio, and network throughput compared with other well-known routing algorithms.

Graph-Based Resource Allocation for Integrated Space and Terrestrial Communications.

Resource allocation (RA) has always had a prominent place in wireless communications research due to its significance for network throughput maximization, and its inherent complexity. Concurrently, graph-based solutions for RA have also grown in importance, providing opportunities for higher throughput and efficiency due to their representational capabilities, as well as challenges for realizing scalable algorithms. This article presents a comprehensive review and analysis of graph-based RA methods in three major wireless network types: cellular homogeneous and heterogeneous, device-to-device, and cognitive radio networks. The main design characteristics, as well as directions for future research, are provided for each of these categories. On the basis of this review, the concept of Graph-based Resource allocation for Integrated Space and Terrestrial communications (GRIST) is proposed. It describes the inter-connectivity and coexistence of various terrestrial and non-terrestrial networks via a hypergraph and its attributes. In addition, the implementation challenges of GRIST are explained in detail. Finally, to complement GRIST, a scheme for determining the appropriate balance between different design considerations is introduced. It is described via a simplified complete graph-based design process for resource management algorithms.

Curriculum Reinforcement Learning for Cohesive Team in Mobile Ad Hoc Networks

In emergency scenarios, such as disaster or military situations, ad hoc networks should be deployed as no central coordination is available. In this letter, we propose a distributed solution for building mobile ad hoc networks, where the mobile nodes determine their positions as a team autonomously based on reinforcement learning. We propose a special design of a decentralized partially observable Markov decision process to build a cohesive team of mobile nodes in a distributed manner. Each mobile node in the team learns an individual policy that determines movement under partial observation, with the common goal of maximizing network throughput. In the learning process, each node indirectly negotiates the role in the team while explicitly considering the locations of other neighboring nodes and network throughput. To improve learning efficiency, we design a curriculum that encourages nodes to disperse initially but reside in specific regions eventually. Such a curriculum enables each node to be placed in its best location, thereby expediting the collective convergence of all nodes as a cohesive team. Simulation results confirm that the proposed solution can successfully build a cohesive team that maintains high network throughput with low power consumption.

Energy Aware Talented Clustering with Compressive Sensing (TCCS) for Wireless Sensor Networks

Wireless sensor networks (WSNs) are networks of sensor nodes that interact wirelessly to gather information about the surrounding environment. Nodes are often low-powered and dispersed in an ad hoc, decentralized manner. Although WSNs have gained in popularity, they still have several serious shortcomings, like limited battery life and bandwidth. In this paper, the cluster head (CH) selection, the Compressive Sensing (CS) theory, the Connection-based Decentralized Clustering (CDC), the relay node selection, and the Multi Objective Genetic Algorithm (MOGA)are all taken into account The initial stage provided a theoretical revision to the concepts of network construction, compressive sensing, and MOGA, which impacted the improvement of network lifetime. In the second stage developed a novel model such as Energy Aware Talented Clustering with Compressive Sensing (TCCS) for the sensor network. This approach considers increasing longevity but also raises the network's overall quality of service (QoS). In the analysis, the TCCS model is applied to both the centralized and distributed networks and compared with the existing methods. When compared to the previous methods, the simulation results show that the proposed work performs better in terms of the calculation of maximum packet delivery ratio of 93.93 percent, minimum energy consumption of 8.04J, maximum energy efficiency of 91.04 percent, maximum network throughput of 465.51kbps, minimum packet loss of 282 packets, and minimum delay of 63.82 msec.

Microsimulation Study Evaluating the Benefits of Cyclic and Non-Cyclic Max-Pressure Control of Signalized Intersections

Max-pressure control is a decentralized method of traffic intersection control, making computations at individual intersections simple. In addition, this method of control has been proven to maximize network throughput if any traffic signal control can stabilize the demand. This paper tests max-pressure controllers in a large-scale microsimulation of the downtown Austin network using the microscopic traffic simulation package SUMO. Nine combinations of weight function and method of defining green time are studied to see how different variations on the max-pressure controller compare. It is shown that the way green time is assigned (cyclic or non-cyclic) has a larger impact on performance than the weight function used by the max-pressure controller. Based on these results a new way of assigning green time is devised. This novel controller mirrors the performance of either the cyclic or the non-cyclic controller depending on the geometry and demand. Large-scale simulation shows that this controller compares favorably with existing controllers using metrics of number of waiting vehicles and average travel time. Common problems with non-cyclic control include the higher likelihood of gridlock and the potential for very long waiting times when demand at a single intersection is asymmetric. On the other hand, the cyclic controller is required to allocate green time to every phase even if the demand is low, increasing the loss time. The novel semi-cyclic controller solves these inherent problems with the cyclic and non-cyclic controllers, making it more likely to be implemented by traffic engineers.

RDRL: A Recurrent Deep Reinforcement Learning Scheme for Dynamic Spectrum Access in Reconfigurable Wireless Networks

Reconfigurable wireless network can flexibly provide efficient spectrum access service and keep stable operation in highly dynamic environment. In this paper, a primary-prioritized recurrent deep reinforcement learning algorithm for dynamic spectrum access based on cognitive radio (CR) technology is proposed. The spectrum Markov state is modeled to capture the evolution behavior to achieve the priority queuing of the primary users and the secondary users. According to the spectrum access strategies of the secondary users under different optimal criteria, we can obtain the best tradeoff benefits of spectrum access fairness and throughput. Furthermore, we proposed a learning-based algorithm for dynamic spectrum access, which allows the secondary users to modify their parameters to select the optimal access policy to maximize network throughput utilization. The Dueling Deep <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$Q$</tex-math></inline-formula> -Network (Dueling DQN) with prioritized experience replay combined with recurrent neural network is used to improve the convergence speed. Extensive experimental results demonstrate that the proposed RDRL scheme outperforms the existing Dueling DQN and DQN schemes in terms of convergence speed and channel throughput.

SR-WMN: Online Network Throughput Optimization in Wireless Mesh Networks With Segment Routing

In this letter, we for the first time introduce segment routing (SR) to wireless mesh networks (WMNs) under the primary interference model, and try to obtain the upper bound of maximum network throughput from an online routing perspective. We first propose an SR-enabled WMN (SR-WMN) architecture. We then develop an online primal-dual algorithm with guaranteed worst case performance bound. We finally show via simulation that the proposed solution tends to be close to the optimum by configuring just over half of the wireless mesh routers (WMRs) to support SR.

Service-Oriented LSTM Multi-Criteria RAT Selection Scheme for Vehicle-to-Infrastructure Communication

The evolution towards future Intelligent Transportation Systems (ITS) is significantly influenced by enabling telematics and safety services for vehicular use-cases, especially for autonomous driving. These vehicular use-cases have their own network requirements, with a variety of instantaneous requirements and dynamic characterizations that make delivering data via a single Radio Access Technology (RAT) severely challenging. By utilizing current and future network context and vehicular use-case requirements, the RAT selection scheme must select the optimum network that guarantees the continuity and high throughput data transfer. Moreover, the consideration of on-time stochastic queue backlog of all use-cases in each RATs, while maximizing throughput is important for a reliable vehicular use-case deployment. In this paper, we developed a Service-Oriented Joint LSTM Multi-Criteria (SOLMC) RAT selection scheme for Vehicle-to-Infrastructure (V2I) networks. The objective of this scheme is to maximize the overall network throughput while reducing the current stochastic queue backlog in deployed RATs. LSTM prediction technique is used as a network filter process that rejects the worst channel quality before the initialization RAT selection scheme. Transmission capacity, connectivity time, data delivery cost, and queue backlog are the decision criteria in our proposed SOLMC RAT scheme. In particular, to characterize the importance of these criteria factors, we construct Analytical Hierarchy Process (AHP) for each use-case. Extensive simulations are carried out under different network context conditions. The results demonstrate that the SOLMC scheme can significantly achieve up to 47.5% network throughput maximization and 20.42% packet delivery ratio improvement, using lower queue length compared to nearest-RAT-based selection. In addition, implementing LSTM in SOLMC improves the total average throughput by up to 3.1% and 6% packet delivery ratio.

Towards Efficient and Delay-Aware NFV-Enabled Unicasting With Adjustable Service Function Chains

Network Function Virtualization (NFV) has becoming an emerging technology for ensuring the reliability, security and scalability of data flows. The Virtual Network Function (VNF) embedding problem, which tries to minimize the embedding cost and link connection cost toward customers or maximize network throughput for a given set of NFV-enabled requests, has attracted extensive interests recently. However, the existing works always assume the fixed execution order of VNFs, which limits their application. Thus, we investigate the VNF embedding problem without such limitations in this paper. Firstly, we propose a general transformation framework for the NFV-enabled unicast routing problem with arbitrary order of service function chains, and an optimal algorithm is proposed for the unicast VNF embedding problem without delay constraint. Secondly, an efficient algorithm with theoretical guarantee is also proposed for such a problem with delay constraint. Thirdly, the throughput maximization problem where there exists a set of unicast requests with delay constraints is also investigated, and an efficient algorithm is also proposed to maximize the number of admitted requests while the total traffic delivery cost is minimized. Finally, we evaluate the proposed algorithms via extensive simulations, which demonstrates the high efficiency of the proposed algorithms.