Improve Network Throughput Research Articles

Research and application of node fuzzy identification and localization in wireless sensor networks

SummaryIn recent years, sensor technology has attracted the attention of countries all over the world because of its potential huge application value, and it has played an important role in people's daily life and social production activities. This paper mainly studies the location algorithm based on ranging in the location algorithm and analyzes the advantages and disadvantages of the algorithm. In order to optimize the positioning of wireless sensor networks, a fuzzy controller is designed to fuzzify the input information of sensor nodes. Using Matlab network simulation tool, the congestion control algorithm proposed in this paper was simulated and compared with several typical congestion control protocols in wireless sensor networks. Experimental results show that the algorithm can improve network throughput, reduce network packet loss, and have a significant impact on network performance. The network throughput of the congestion control algorithm based on fuzzy logs is stable at 180 packets per second. Compared with CODA, the congestion control algorithm based on fuzzy logarithm increases the packet loss rate by 55–66%, and the SenTCP algorithm increases by 37–40%. It can be seen that congestion control algorithms based on fuzzy logic can play an important role in wireless sensor networks.

A Bit-Rate-Adaptive MAC Algorithm for a Vehicular Ad Hoc Network

VANET(Vehicular Ad-hoc Network), standard of IEEE 802.11p specification is based on 802.11 MAC protocol. It grants all nodes make equal opportunity to get channel, making it possible lower bit-rate nodes to get communication channel for a proper amount of time so keeping the nodes of higher bit-rate from acquiring connected channel that downward-equalize the entire network performance. Also the probability of collision occurring increases as the number of nodes grow. The proposed algorithm ensures that nodes with acquired channels for a fixed amount of time until the packet burst according to the current transmission rate have a firm priority over other nodes. This algorithm not only allows us to build a wireless network that improves network throughput and reduces the probability of collisions, but also gives each node a fair chance to gain connectivity based on channel conditions. The algorithm improves the overall performance of the network by setting the CW (contention window) width wider than the standard and modulating the continuous transmission threshold according to the channel acquisition time.

Efficient Management of Multicast Traffic in Directional mmWave Networks

Multicasting is becoming more and more important in the Internet of Things (IoT) and wearable applications (e.g., high definition video streaming, virtual reality gaming, public safety, among others) that require high bandwidth efficiency and low energy consumption. In this regard, millimeter wave (mmWave) communications can play a crucial role to efficiently disseminate large volumes of data as well as to enhance the throughput gain in fifth-generation (5G) and beyond networks. There are, however, challenges to face in view of providing multicast services with high data rates under the conditions of short propagation range caused by high path loss at mmWave frequencies. Indeed, the strong directionality required at extremely high frequency bands excludes the possibility of serving all multicast users via a single transmission. Therefore, multicasting in directional systems consists of a sequence of beamformed transmissions to serve all multicast group members, subgroup by subgroup. This paper focuses on multicast data transmission optimization in terms of throughput and, hence, of the energy efficiency of resource-constrained devices such as wearables, running their resource-hungry applications. In particular, we provide a means to perform the beam switching and propose a radio resource management (RRM) policy that can determine the number and width of the beams required to deliver the multicast content to all interested users. Achieved simulation results show that the proposed RRM policy significantly improves network throughput with respect to benchmark approaches. It also achieves a high gain in energy efficiency over unicast and multicast with fixed predefined beams.

Distributed Resource Allocation in Underlay Multicast D2D Communications

Multicast device-to-device communications operating underlay with cellular networks is a spectral efficient technique for disseminating data to nearby receivers. However, due to the critical challenge of having an intelligent interference coordination between multicast groups along with the cellular network, it is necessary to judiciously perform resource allocation for the combined network. In this work, we present a framework for a joint channel and power allocation strategy to maximize the sum rate of the combined network while guaranteeing minimum rate to individual groups and cellular users. The objective function is augmented by an austerity function that penalizes excessive assignment of low rate channels. The formulated problem is a mixed-integer-non-convex program, which requires exponential complexity to obtain the optimal solution. To tackle this, we exploit fractional programming and integer relaxation to obtain a parametric convex approximation. Based on sequential convex approximation approach, we first propose a centralized algorithm that ensures convergence to a limit point. Next, we propose a distributed algorithm in which via dual decomposition, separable sub-problems are formulated to be solved at the respective groups in cooperation with the base station. We provide convergence guarantees of the proposed solutions and demonstrate their merits by simulations, showing improvement in network throughput.

Practical Network Coding Technologies and Softwarization in Wireless Networks

Network coding is an elegant and novel technique to improve network throughput and performance. It is considered as a critical technology to facilitate ever-increasing demands of future wireless networks. It exploits the broadcast nature of wireless media and cooperatively codes packets from different senders to provide reliable, secure, and efficient transmissions. Current research focuses on either transmission delay, coding complexity, forwarding security, or end-to-end throughput. Network coding-aided solutions can recover lost packets without feedback, eliminate latency, reduce the routing cost on diverse paths, or optimize the capacity of unstable wireless networks. However, devices or smart sensors usually have limited computational capacity and some applications could not tolerate high decoding delay, which prevents network coding from being widely deployed in the real world. In recent years, many research methods consider simplifying decoding matrix or coding algorithm to alleviate the shortcoming of network coding and further satisfy the extreme demands of the future wireless network. This article summarizes complexity-optimized methods and explains the interaction effect of coding opportunities and decoding overhead. We propose a taxonomy of practical network coding methods and illustrate three practical directions on cutting computational complexity and enhancing progressive decoding. We also conclude the benefit and cost of current network coding algorithms along with the outline of future research.

An efficient hybrid model for cluster head selection to optimize wireless sensor network using simulated annealing algorithm

Objective: Energy efficiency aspect in wireless sensor networks (WSN) can be achieved by small sized rechargeable and easily replaceable batteries. The lifetime of wireless sensor network can be improved by identifying the efficient and reliable nodes as a cluster heads using Hybrid Simulated Annealing algorithm. The proposed algorithm identifies cluster head to reduce overhead and is capable of handling high volume of nodes with minimum node death rate. Methods: This study proposed initialization of population vectors using the opposite point procedure, self-adaptive control approach by node mutation rate, crossover rate, node capacity and cluster head allocation Methods. Findings: A case study in the proposed work is found to be better in throughput, accuracy, efficiency, energy utilization, batteries recharge ability and replacement procedures compared to the conventional methods. By the analysis and comparison of the proposed method with existing methods, it is identified that the reduction of the number of dead nodes gradually increases the throughput and lifetime of the nodes with respect to the number of iterations. Novelty: To overcome the limitations of conventional Low Energy Adaptive Clustering Hierarchy (LEACH), harmony search algorithm (HSA), modified HSA and differential evolution, we propose a hybrid optimal model using simulated annealing algorithm which includes a node capability function. It is used to improve the network lifetime of the cluster heads and sensor nodes. The proposed method have capability of batteries recharge ability and replacement option to improve network throughput and reliability of network. Keywords: Wireless sensor network; Differential Evolution; Low Energy Adaptive Clustering Hierarchy; HAS; modified HAS; simulated annealing algorithm

EWPS: Emergency Data Communication in the Internet of Medical Things

In this article, we explore the remote monitoring of patients in an on-demand service that can be implemented using the Internet of Medical Things (IoMT). Network congestion and delay are important issues that must be resolved to handle emergencies in IoMT. Backpressure scheduling is a well-known scheme to resolve network congestion and improve network throughput for emergency data packets. Traditional backpressure scheduling is known to categorize each packet and schedules emergency packets to be forwarded faster than regular packets. However, in large-scale networks, these scheduling algorithms find unnecessarily long paths. This leads to high end-to-end delay and decreases the performance of guaranteed delivery. To resolve these issues, this article proposes an event-aware priority scheduling algorithm for data packets. This model follows a single priority queue model to manage all packets and emergency packets have been identified by the threshold values. Also, a separate communication path has been identified as well to reduce the waiting time for each category of the packet. Meanwhile, to reduce delay in packet communication, a delay-efficient data aggregation tree is constructed, which is combined with the priority queue model. Our in-depth simulation results of the proposed model prove the novel contribution to reducing delay in emergency packet delivery and avoiding network congestion. Moreover, the proposed model is also compared with the existing state-of-the-art models to show its ability to outperform similar methodologies.

Data-Driven Mode and Group Selection for Downlink MU-MIMO With Implementation in Commodity 802.11ac Network

Multi-user MIMO (MU-MIMO) is a technique that improves spectral efficiency by allowing concurrent communication between one access point (AP) and multiple clients. In practice, the expected gain is not always achieved and is sometimes even negative. We experimentally demonstrate that the downlink MU-MIMO performance in a practical network not only depends on the client’s channel but is also influenced by factors that are not captured by conventional models, such as client motion and device type. We propose a data-driven algorithm with a low computational complexity that determines whether a client should operate in MU mode and the MU-MIMO group for clients in MU mode. Such a mode and group selection algorithm is based on a sequence of channel state information (CSI), SNR, and client device type. The algorithm can automatically adapt to the motion and characteristics of individual clients. Experimental results using implementation on a commodity 802.11ac AP show that the proposed data-driven mode and group selection algorithm can improve network throughput by up to 35% over existing algorithms based on conventional models. We also show that the proposed data-driven algorithm has limited sensitivity to environmental changes and can be deployed into new environments without retraining.

IRCTGP: A Novel Intra-node Resource Control Protocol Based on Traffic Priority and Evolutionary Games Theory for Wireless Sensor Networks

A Wireless Sensor Network (WSN) is a network which is made up of resource-constrained wireless sensors with restricted capabilities. Quality of Service in Wireless Sensor Networks is still an open area of research, due to the fact that several problems need to be solved using resource constrained WSN nodes. The probability of congestion occurrence and packet loss in Wireless Sensor Networks is high, which makes it difficult to satisfy different qualities of service requirements. Congestion in WSN needs to be controlled to improve the quality of service such as throughput and packet loss ratio along with the packet delay. In particular, we proposed the use of evolutionary game theory in the design of a QoS-aware Intra-node congestion control protocol. Intra-node congestion control protocol is a non-cooperative asymmetric game that adjusts the queue accepting rate and queue service rate such that higher priority classes have lower loss rate and lower queuing delay. Simulation results show that the proposed protocol decreases packet dropping and improves network throughput and network quality of service performance.

Intelligent Resource Allocation Method for Wireless Communication Networks Based on Deep Learning Techniques

In this paper, a deep learning approach is used to conduct an in‐depth study and analysis of intelligent resource allocation in wireless communication networks. Firstly, the concepts related to CSCN architecture are discussed and the throughput of small base stations (SBS) in CSCN architecture is analyzed; then, the long short‐term memory network (LSTM) model is used to predict the mobile location of users, and the transmission conditions of users are scored based on two conditions, namely, the mobile location of users and whether the small base stations to which users are connected have their desired cache states, and the small base stations select the transmission. The small base station selects several users with optimal transmission conditions based on the scores; then, the concept of game theory is introduced to model the problem of maximizing network throughput as a multi‐intelligent noncooperative game problem; finally, a deep augmented learning‐based wireless resource allocation algorithm is proposed to enable the small base station to learn autonomously and select channel resources based on the network environment to maximize the network throughput. Simulation results show that the algorithm proposed in this paper leads to a significant improvement in network throughput compared to the traditional random‐access algorithm and the algorithm proposed in the literature. In this paper, we apply it to the fine‐grained resource control problem of user traffic allocation and find that the resource control technique based on the AC framework can obtain a performance very close to the local optimal solution of a matching‐based proportional fair user dual connection algorithm with polynomial‐level computational complexity. The resource allocation and task unloading decision policy optimization is implemented, and at the end of the training process, each intelligent body independently performs resource allocation and task unloading according to the current system state and policy. Finally, the simulation results show that the algorithm can effectively improve the quality of user experience and reduce latency and energy consumption.

TSN Switch Queue Length Prediction Based on an Improved LSTM Network

With the high‐speed development of network technology, time‐sensitive networks (TSNs) are experiencing a phase of significant traffic growth. At the same time, they have to ensure that highly critical time‐sensitive information can be transmitted in a timely and accurate manner. In the future, TSNs will have to further improve network throughput to meet the increasing traffic demand based on the guaranteed transmission delay. Therefore, an efficient route scheduling scheme is necessary to achieve network load balance and improve network throughput. A time‐sensitive software‐defined network (TSSDN) can address the highly distributed industrial Internet network infrastructure, which cannot be accomplished by traditional industrial communication technologies, and it can achieve distributed intelligent dynamic route scheduling of the network through global network monitoring. The prerequisite for intelligent dynamic scheduling is that the queue length of future switches can be accurately predicted so that dynamic route planning for flow can be performed based on the prediction results. To address the queue length prediction problem, we propose a TSN switch queue length prediction model based on the TSSDN architecture. The prediction process has three steps: network topology dimension reduction, feature selection, and training prediction. The principal component analysis (PCA) algorithm is used to reduce the dimensionality of the network topology to eliminate unnecessary redundancy and overlap of relevant information. Feature selection requires comprehensive consideration of the influencing factors that affect the switch queue length, such as time and network topology. The training prediction is performed with the help of our enhanced long short‐term memory (LSTM) network. The input‐output structure of the network is changed based on the extracted features to improve the prediction accuracy, thus predicting the network congestion caused by bursty traffic. Finally, the results of the simulation demonstrate that our proposed TSN switch queue length prediction model based on the improved LSTM network algorithm doubles the prediction accuracy compared to the original model because it considers more influencing factors as features in the neural network for training and learning.

Non-Linear Base-Station Processing Within a 3GPP Compliant Framework

MIMO mobile systems, with a large number of antennas at the base-station side, enable the concurrent transmission of multiple, spatially separated information streams, and therefore, enable improved network throughput and connectivity both in uplink and downlink transmissions. Traditionally, such MIMO transmissions adopt linear base-station processing, that translates the MIMO channel into several single-antenna channels. While such approaches are relatively easy to implement, they can leave on the table a significant amount of unexploited MIMO capacity and connectivity capabilities. Recently-proposed non-linear base-station processing methods claim this unexplored capacity and promise substantially increased network throughput and connectivity capabilities. Still, to the best of the authors’ knowledge, non-linear base-station processing methods not only have not yet been adopted by actual systems, but have not even been evaluated in a standard-compliant framework, involving all the necessary algorithmic modules required by a practical system. In this work, for the first time, we incorporate and evaluate non-linear base-station processing in a 3GPP standard environment. We outline the required research platform modifications and we verify that significant throughput gains can be achieved, both in indoor and outdoor settings, even when the number of base-station antennas is much larger than the number of transmitted information streams. Then, we identify missing algorithmic components that need to be developed to make non-linear base-station practical, and discuss future research directions towards potentially transformative next-generation mobile systems and base-stations (i.e., 6G) that explore currently unexploited non-linear processing gains.

Topology Optimization of Interactive Visual Communication Networks Based on the Non-Line-of-Sight Congestion Control Algorithm

In this paper, an in-depth study of interactive visual communication of network topology through non-line-of-sight congestion control algorithms is conducted to address the real-time routing problem of adapting to dynamic topologies, and a delay-constrained stochastic routing algorithm is proposed to enable packets to reach GB within the delay threshold in the absence of end-to-end delay information while improving network throughput and reducing network resource consumption. The algorithm requires each sending node to select an available relay set based on the location of its neighbor nodes and channel state and computes transfer probabilities for each node in the relay set combining the remaining delay of the packet with the distance from the relay node to GB. Based on the obtained transfer probability and local channel state, the sending node passes the packet to the relay node. The convergence of the algorithm is proved and its performance is verified by simulation. The first part of the algorithm is based on the greedy algorithm to deploy and locate the network flying platform nodes with the goal of efficient coverage of the network flying platform nodes, considering the ground base station services. As the delay on each link varies due to the change of channel state, the source and relay nodes asynchronously update the data generation rate and the pairwise parameters based on the received local information and use the obtained optimal values to pass the packets to GB.

Traffic aware routing in 3D NoC using interleaved asymmetric edge routers

Network-on-Chip (NoC) has emerged as a cost-effective on-chip interconnects solution for Tiled Chip Multi Processors (TCMP) where many computational cores occupy a single chip. The performance of NoC network can be greatly enhanced by incorporating 3D IC technology formed by stacking several active NoC layers using Through Silicon Via (TSV) vertical interconnections. 3D NoC routers improve network throughput and have minimal latency at the cost of increased router area and power dissipation. Performance degradation can occur in 3D structures due to unequal traffic distribution across the chip leading to larger power density and larger on-chip temperature that affect system reliability. In this paper, we come up with an interleaved vertical edge routing design approach in 3D NoC that employs asymmetrical routing algorithm and uses a unique flit prioritization unit for improving performance of bufferless mesh NoC. Experimental results indicate that our proposed router has better network performance with minimal hardware overhead when compared with conventional bufferless networks, engaging same number of routers.

An Implementation of Trusted Key Management Protocol (TKMP) in Wireless Network

The Trusted Key Management Protocol (TKMP) provides one of the most secure communication technologies in MANET cluster-based data protection. For security reasons, TKMP is a trusted key that can be sent to all nodes in the communication cluster. This document introduces the Trusted Key Management Protocol (TKMP) feature to improve the quality of secure communications over a cluster-based wireless network. The proposed TKMP execution process includes CBPPRS (Cluster Based Privacy Preserving Routing Selection), LBCPR (Load Balancing Cluster Based Privacy Routing) and DLBPS (Dynamic Load Balancing Privacy Path Selection) procedure. To lock the data from the malicious node, the Paillier Cryptosystem (PC) encrypts packets with homomorphic encryption. The trust score makes it easier to update routing information and improves network throughput. The experimental results show that the proposed TKMP method works better than the other Trust-ECC method.

Concurrent transmission scheduling algorithm based on Stackelberg game to enhance time reuse for D2D communications in mmWave networks

Due to the tremendous propagation loss of millimeter wave (mmWave) communication, device-to-device (D2D) communications are widely used over directional mmWave networks to improve network throughput. Most of existing works are mainly focused on concurrent transmission among D2D pairs that have no mutual interference. However, considering the propagation loss and directional interference features, the D2D pairs that interfere with each other can be used to improve network throughput. In this paper, we propose a time resource sharing scheme based on Stackelberg game for D2D pairs to further enhance network throughput. The interference D2D pairs can access to the time resource through paying the price for the interference caused by them. At the same time, the non-interference D2D pairs can be scheduled. Concurrent transmission scheduling among interference D2D pairs is formulated as a non-cooperative game, which enables a distributed transmission power control for interference D2D pairs. Moreover, the price strategy can be adjusted by setting interference threshold such that the transmission quality can be guaranteed. Simulation results show that the proposed scheme can achieve significant performance gains compared with classical concurrent transmission in terms of network throughput and energy efficiency. In addition, the convergence and utility of the game are also verified.

Design and implementation of beamformed physical downlink control channel for 4G massive MIMO systems

The Full Dimension-MIMO (FD-MIMO) technology is capable of achieving huge improvements in network throughput with simultaneous connectivity of a large number of mobile wireless devices, unmanned aerial vehicles, and the Internet of Things (IoT). In the Long Term Evolution (LTE), with a large number of antennae at the base station and the ability to perform beamforming, the capacity of the physical downlink shared channel (PDSCH) has increased a lot. However, the current 3rd Generation Partnership Project (3GPP) specifications of the LTE do not allow the base station to perform any beamforming techniques for the physical downlink control channel (PDCCH). Hence, PDCCH has neither the capacity nor the coverage of PDSCH. Therefore, PDCCH capacity still limits the performance of a network, as it dictates the number of users that a base station can schedule at a given time instant. In Release 11, 3GPP introduced enhanced PDCCH (EPDCCH) to increase the PDCCH capacity at the cost of sacrificing the PDSCH resources. The problem of enhancing the PDCCH capacity within the available control channel resources has not been addressed yet in the literature. Hence, in this paper, we propose a novel beamformed PDCCH (BF-PDCCH) design which is aligned to the 3GPP specifications and requires simple software changes at the base station. For the evaluation of the proposed BF-PDCCH, we model various link abstractions. We evaluate them through link-level simulations and then use all these abstractions in the system-level simulations. We show that the proposed BF-PDCCH achieves significant improvement in network throughput and outperforms the current state of the art algorithms, PDCCH, and EPDCCH schemes.

Optimal path strategy for the web computing under deep reinforcement learning

Purpose With the advent of the web computing era, the transmission mode of the Internet of Everything has caused an explosion in data volume, which has brought severe challenges to traditional routing protocols. The limitations of the existing routing protocols under the condition of rapid data growth are elaborated, and the routing problem is remodeled as a Markov decision process. this paper aims to solve the problem of high blocking probability due to the increase in data volume by combining deep reinforcement learning. Finally, the correctness of the proposed algorithm in this paper is verified by simulation. Design/methodology/approach The limitations of the existing routing protocols under the condition of rapid data growth are elaborated and the routing problem is remodeled as a Markov decision process. Based on this, a deep reinforcement learning method is used to select the next-hop router for each data transmission task, thereby minimizing the length of the data transmission path while avoiding data congestion. Findings Simulation results show that the proposed method can significantly reduce the probability of data congestion and increase network throughput. Originality/value This paper proposes an intelligent routing algorithm for the network congestion caused by the explosive growth of data volume in the future of the big data era. With the help of deep reinforcement learning, it is possible to dynamically select the transmission jump router according to the current network state, thereby reducing the probability of congestion and improving network throughput.

Device Association for RAN Slicing Based on Hybrid Federated Deep Reinforcement Learning

Network slicing (NS) has been widely identified as a key architectural technology for 5G-and-beyond systems by supporting divergent requirements in a sustainable way. In radio access network (RAN) slicing, due to the device-base station (BS)-NS three layer association relationship, device association (including access control and handoff management) becomes an essential yet challenging issue. With the increasing concerns on stringent data security and device privacy, exploiting local resources to solve device association problem while enforcing data security and device privacy becomes attractive. Fortunately, recently emerging federated learning (FL), a distributed learning paradigm with data protection, provides an effective tool to address this type of issues in mobile networks. In this paper, we propose an efficient device association scheme for RAN slicing by exploiting a hybrid FL reinforcement learning (HDRL) framework, with the aim to improve network throughput while reducing handoff cost. In our proposed framework, individual smart devices train a local machine learning model based on local data and then send the model features to the serving BS/encrypted party for aggregation, so as to efficiently reduce bandwidth consumption for learning while enforcing data privacy. Specifically, we use deep reinforcement learning to train the local model on smart devices under a hybrid FL framework, where horizontal FL is employed for parameter aggregation on BS, while vertical FL is employed for NS/BS pair selection aggregation on the encrypted party. Numerical results show that the proposed HDRL scheme can achieve significant performance gain in terms of network throughput and communication efficiency in comparison with some state-of-the-art solutions.

Improving Network Throughput by Hardware Realization of a Dynamic Content Caching Scheme for Information-Centric Networking (ICN)

Caching popular content at storage enabled routers or small-cell base stations (SBSs) is a key technology for future communication systems. Primarily limited to wired backbone systems, content-caching techniques are useful to offload mobile data traffic too. Several protocols have been developed towards realization of content-caching, namely, information-centric networking (ICN), content-centric networking, and named data networking. Studies have revealed that incorporating ICN caching schemes at SBSs could significantly enhance the user level performance, i.e., the lesser download delay and the higher throughput. However, smaller coverage area of a SBS would make the communication prone to high handover probability. Therefore, the implementation of ICN caching schemes at SBS requires extremely low processing delay, i.e., delay to search if the requested content is available at the cache, and accordingly make decision to serve the content request or forward it to the content server. In this article, a configurable hardware add-on has been developed to incorporate content caching features in a traditional SBS that can serve the user with extremely low processing delay while optimizing a combination of various network and hardware metrics. Using extensive results being generated from simulations, the usefulness of content caching has been demonstrated, and the effect of several hardware parameters over the latency performance has been studied too. In addition, the caching hardware accelerator has the ability to adapt dynamically to the optimal caching policy required by the prevailing network conditions as well as hardware requirements. An improvement upto 53% in network delay and 15% in handover probability due to the support of hardware-based caching is observed that incurs a negligible $$0.07\,{\hbox {mm}}^2$$ and up to only 11.1 mW energy overhead while operating in the range of 690–910 MHz.