Performance Of Heterogeneous Networks Research Articles

Enhancing security offloading performance in NOMA heterogeneous MEC networks using access point selection and meta-heuristic algorithm

The research delves into the intricate domain of security offloading within the context of non-orthogonal multiple access (NOMA) heterogeneous mobile edge computing (het-MEC) networks operating over Rayleigh fading channels. The investigation centers on a system model comprising a single antenna-equipped edge user, denoted as U, which strategically offloads computational tasks to two distinct heterogeneous wireless access points (APs): the far AP (AP1) and the near one (AP2), employing NOMA techniques. Notably, the research accounts for a passive eavesdropper (E) intending to intercept the U−AP2 transmission. A four-phase protocol is proposed to ensure the security offloading process, namely SAPS, which leverages wireless access point selection (APS) and physical layer security (PLS) techniques. The focus extends to derive a closed-form expression for a novel critical system performance metric: the secrecy successful computation probability (SSCP). Furthermore, an algorithm based on Ant Colony Optimization (ACO) within the continuous domain is introduced, which aims to enhance the SSCP by intelligently determining system parameters. The impact of critical factors such as transmit power, power allocation coefficient, bandwidth, CPU frequency, and task division ratio under the SAPS scheme is explored and compared to the conventional approach using pure NOMA. Remarkably, the algorithm in the proposed scheme demonstrates up to a 3% performance improvement. The validity and accuracy of the study findings are verified through Monte-Carlo simulations. The work contributes significantly to advancing secure offloading strategies in NOMA-based MEC networks, offering valuable insights for practical deployment and optimization.

ABBR: An augmented BBR for collaborative intelligent transmission over heterogeneous networks in IIoT

In the era of Industry 5.0, with the deep convergence of Industrial Internet of Things (IIoT) and 5G technology, stable transmission of massive data in heterogeneous networks becomes crucial. This is not only the key to improving the efficiency of human–machine collaboration, but also the basis for ensuring system continuity and reliability. The arrival of 5G has brought new challenges to the communication of IIoT in heterogeneous environments. Due to the inherent characteristics of wireless networks, such as random packet loss and network jitter, traditional transmission control schemes often fail to achieve optimal performance. In this paper we propose a novel transmission control algorithm, aBBR. It is an augmented algorithm based on BBRv3. aBBR dynamically adjusts the sending window size through real-time analysis to enhance the transmission performance in heterogeneous networks. Simulation results show that, compared to traditional algorithms, aBBR demonstrates the best comprehensive performance in terms of throughput, latency, and retransmission. When random packet loss exists in the link, aBBR improves the throughput by an average of 29.3% and decreases the retransmission rate by 18.5% while keeping the transmission delay at the same level as BBRv3.

Enhancing Heterogeneous Network Performance: Advanced Content Popularity Prediction and Efficient Caching

With the popularity of smart devices and the growth of high-bandwidth applications, the wireless industry is facing an increased surge in data traffic. This challenge highlights the limitations of traditional edge-caching solutions, especially in terms of content-caching effectiveness and network-communication latency. To address this problem, we investigated efficient caching strategies in heterogeneous network environments. The caching decision process becomes more complex due to the heterogeneity of the network environment, as well as due to the diversity of user behaviors and content requests. To address the problem of increased system latency due to the dynamically changing nature of content popularity and limited cache capacity, we propose a novel content placement strategy, the long-short-term-memory–content-population-prediction model, to capture the correlation of request patterns between different contents and the periodicity in the time domain, in order to improve the accuracy of the prediction of content popularity. Then, to address the heterogeneity of heterogeneous network environments, we propose an efficient content delivery strategy: the multi-intelligent critical collaborative caching policy. This strategy models the edge-caching problem in heterogeneous scenarios as a Markov decision process using multi-base-station-environment information. In order to fully utilize the multi-intelligence information, we have improved the actor–critic approach by integrating the attention mechanism into a neural network. Whereas the actor network is responsible for making decisions based on local information, the critic network evaluates and enhances the actor’s performance. We conducted extensive simulations, and the results showed that the Long Short Term Memory content population prediction model was more advantageous, in terms of content-popularity-prediction accuracy, with a 28.61% improvement in prediction error, compared to several other existing methods. The proposed multi-intelligence actor–critic collaborative caching policy algorithm improved the cache-hit-rate metric by up to 32.3% and reduced the system latency by 1.6%, demonstrating the feasibility and effectiveness of the algorithm.

Performance Analysis of Scalable User-Centric HC-RAN Over Rician Fading Channels

In this paper, we analyze the uplink performance of a scalable user-centric heterogeneous cloud-radio access network (HC-RAN) implemented by using dynamic cooperative clustering (DCC) framework over a Rician fading channel with phase shifts. This channel describes various practical aspects, such as a deterministic line of sight (LoS) component and random non-LoS (N-LoS) components. To account for the phase shifts due to user mobility, the phase of the LoS component is modeled as a uniformly distributed random variable. We assume that phase information is available at each remote radio head (RRH). We derived the phase aware-minimum mean square error (PA-MMSE) and phase unaware Linear-MMSE estimators and obtained the channel state information (CSI). We derived a closed-form expression for the achievable spectral efficiency (SE) to evaluate the system performance with both estimators. To address the effect of coherent interference in the ultra-dense networks, we developed a two-layer decoding scheme in uplink in which maximum ratio (MR) combining is performed at the RRH and large-scale fading decoding is performed at the base-band unit (BBU) pool. Based on the obtained results, the proposed method enhanced the uplink performance in an ultra-dense scenario. It is validated by comparing it with the simulation results. Moreover, the performance loss caused by the lack of phase knowledge will depend on the pilot sequence length.

Bandwidth-Aware Scheduling Repair Techniques in Erasure-Coded Clusters: Design and Analysis

Erasure codes offer a storage-efficient redundancy mechanism for maintaining data availability guarantees in storage clusters, yet also incur high network traffic consumption and recovery time in failure repair. Extensive research has been carried out to reduce the recovery time. However, previous works either target specific erasure code constructions which are not commonly used in today’s distributed storage clusters or neglect the heterogeneous bandwidth property in real network environments. Since erasure-coded clusters are typically composed of multi-node with heterogeneous bandwidth and accessed in parallel, the whole recovery time is mainly restricted by the low-bandwidth links. In this article, we propose SMFRepair, a single-node multi-level forwarding repair technique that is designed to improve the performance in heterogeneous networks based on Reed-Solomon codes for general fault tolerance. SMFRepair carefully selects the helper nodes and uses idle nodes to bypass low-bandwidth links. Idle nodes have sufficient and unused network bandwidth. It also pipelines the repair links that are optimized by idle nodes. Furthermore, a multi-node scheduling repair technique, called MSRepair, is proposed. MSRepair carefully schedules the multi-node repair link to saturate the most unoccupied bandwidth and transfers data from as large-bandwidth links as possible, with the primary objective of minimizing the recovery time. Large-scale simulation and Amazon EC2 real experiments show that compared to state-of-the-art repair techniques, SMFRepair can accelerate the single-node recovery by up to 47.69%, and MSRepair can reduce the multi-node recovery time by 33.78% <inline-formula><tex-math notation="LaTeX">$\sim$</tex-math></inline-formula> 67.53%.

On the Deployment of Small Cells in 3D HetNets With Multi-Antenna Base Stations

With the dense deployment of small cells, the impact of height difference between base stations (BSs) and user equipment (UE) on the performance of heterogeneous networks (HetNets) becomes significant. The traditional two-dimensional models are no longer sufficient to capture the three-dimensional (3D) features of dense HetNets. On the other hand, deploying multiple antennas on BSs is a promising approach to improve network capacity. In this paper, we propose a 3D model for a <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$K$ </tex-math></inline-formula> -tier HetNet with multi-antenna BSs, where different tiers share the same frequency band but may differ in BS height, BS density, number of antennas per BS, BS transmit power, association bias, and path loss exponent. We analytically derive the per-tier association probability under both the strongest received signal and the closest BS cell-association strategies. Based on that, we derive the expressions for the downlink ergodic rate, area spectral efficiency (ASE) and energy efficiency. The numerical results reveal that in the presence of macrocell BSs, for low to medium small-cell BS (SBS) densities, the closest BS cell-association strategy leads to low ergodic rate, ASE and energy efficiency regardless of the SBS height; while at very high SBS densities, under both cell-association strategies, SBSs should be deployed at the same height as UE to achieve high ergodic rate, ASE and energy efficiency. Moreover, we find that for a given SBS height, there exists an optimal combination of SBS density and number of antennas per SBS that maximizes the system energy efficiency.

Cross-Regional Transmission Control for Satellite Network-Assisted Vehicular Ad Hoc Networks

Vehicular ad hoc networks (VANETs) have attracted increasing attention in cross-regional communication. The interconnection between VANETs and satellite networks expands their communication range. To improve the transmission performance of heterogeneous networks composed of VANETs and satellite networks, this paper proposes an end-to-end transmission control scheme for satellite network-assisted VANETs, analyzes the bandwidth delay product of multihop VANETs through heterogeneous networks, and establishes a transmission control model applicable to cross-regional environments. To address the long delay and high bit error rate in heterogeneous networks, the model designs the congestion window in the slow start and congestion avoidance of the transmission scheme. The data transmission volume is increased in the slow start, and various packet losses are distinguished in congestion avoidance. The experimental results show that the scheme can achieve good transmission performance in heterogeneous networks composed of VANETs and satellite networks. The results show that VANETs data can be effectively transmitted in heterogeneous networks. Compared with the traditional transmission scheme, this simple and practical end-to-end transmission control scheme can improve speed by 83.49 kbps and reduce transmission duration time of FTP server by <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$4.72\times 10^{3}$ </tex-math></inline-formula> s. The results of this study provide a reference for massive data transmission in heterogeneous networks of VANETs and satellite networks.

End-to-End Transmission Control for Cross-Regional Industrial Internet of Things in Industry 5.0

Data transmission for the industrial Internet of Things (IoT) is crucial for industrial production, especially in the Industry 5.0 era, where human–machine collaboration is increasingly intensive. To ensure the continuity and robustness of industrial IoT communications in the case of damaged infrastructure communication facilities postdisaster, the industrial IoT can be connected with satellite networks in emergencies. This article presents a cross-regional, end-to-end, transmission control scheme for satellite-supported, multihop industrial IoT. The proposed scheme adjusts the window of data transmission from two phases, slow start and congestion avoidance, to accommodate the low-transmission performance caused by a long delay and high bit error rate in converged networks. The window of data transmission is also adjusted to increase the amount of data transmission for the slow start to fill the high bandwidth-delay product of the converged network, while adjusting the threshold of data transmission based on feedback information to distinguish different data losses during congestion avoidance. The feasibility of the heterogeneous network transmission model is experimentally verified. The results show that the scheme can achieve good performance in heterogeneous networks of industrial IoT and satellite networks. The scheme is effective in ensuring the continuity and stability of intelligent machine production in Industry 5.0 in emergency communication cases.

Improving the Performance of Heterogeneous IoT Networks through Multi-Stage and Parallel Computing Systems

Improving the Performance of Heterogeneous IoT Networks through Multi-Stage and Parallel Computing Systems

TCP-WBQ: a backlog-queue-based congestion control mechanism for heterogeneous wireless networks

In heterogeneous wireless networks, random packet loss and high latency lead to conventional TCP variants performing unsatisfactorily in the case of competing communications. Especially on high-latency wireless links, conventional TCP variants are unable to estimate congestion degrees accurately for fine-grained congestion control because of the effects of random packet loss and delay oscillations. This paper proposes a TCP variant at the sender side to identify congestion degrees, namely TCP-WBQ, which quickly responses to the real congestion and effectively shields against random packet loss and oscillations of latency time. The proposed algorithm of congestion control firstly constructs a backlog-queue model based on the dynamics of the congestion window, and deduces the two bounds of the model which delimit oscillations of the backlog queue for non-congestion and random packet loss respectively. TCP-WBQ detects congestion degrees more accurately and thus implements the corresponding schemes of adjusting the congestion window, maintaining a tradeoff between high throughputs and congestion avoidance. The comprehensive simulations show that TCP-WBQ works efficiently in bandwidth utilization with single and multiple bottleneck scenarios, and achieves high performance and competitive fairness in heterogeneous wireless networks.

Exploring Multipath TCP Schedulers in Heterogeneous Networks

Multipath Transmission Control Protocol (MPTCP) is a transport layer protocol, which transmits TCP segments on more than one path, in multihomed devices. It was designed with the aim of Bandwidth aggregation and redundant connections. Currently, multihomed devices have wireless interfaces of heterogeneous nature. MPTCP is not able to give its optimal performance in heterogeneous networks. This paper presents an experimental performance study of four different schedulers, namely Roundrobin, Default, Blest, and Redundant. Our testbed comprises Ethernet, LTE, and Wifi networks to connect multihomed devices. We have compared the scheduler performance in terms of Throughput , Download time and path utilization rate in homogenous and heterogenous scenarios . Results showed that Round robin provides optimal throughput in homogenous networks and also performs bandwidth aggregation by utilizing both the paths but fails to perform in heterogenous networks. Blest, provides best throughput among the four schedulers but prefers fast path only.

Privacy-preserving workflow scheduling in geo-distributed data centers

Due to the increasing volume of data to be analyzed and the need for global collaborations, many scientific applications have been deployed in a geo-distributed manner. Scientific workflows provide a good model for running and managing geo-distributed scientific data analytics. However, due to the multi-level data privacy requirements in geo-distributed data centers (DCs), as well as the costly and heterogeneous inter-DC network performance, executing scientific workflows efficiently in such a geo-distributed environment is not easy. In this paper, we propose a privacy-preserving workflow scheduling algorithm named PPPS, which aims at minimizing the inter-DC data transfer time for workflows while satisfying data privacy requirements. We compare PPPS with five state-of-the-art workflow scheduling algorithms using Windows Azure cloud performance traces and real scientific workflows. Experimental results show that PPPS can greatly reduce the workflow execution time compared to the other algorithms by up to 93% while satisfying complicated data privacy constraints.

Enhancing Robustness and Transmission Performance of Heterogeneous Complex Networks via Multiobjective Optimization

Transmission networks are ubiquitous in modern society and play critical roles in facilitating the delivery and movement of information, power, and people between various locations. Robustness and transmission capacity are two pivotal and universal properties of practical networks, and much research effort has been devoted to investigating these two properties in the past decade. In this article, we consider a heterogeneous transmission network consisting of hosts and routers and aim to optimize both transmission capacity and robustness of this network simultaneously. To solve this problem, we propose a multiobjective evolutionary algorithm (MOEA) for optimizing transmission capacity and robustness. Moreover, in order to achieve optimized transmission performance and robustness at reasonable and balanced computational cost, the proposed MOEA adopts a two-phase design, i.e., a sampling phase and an optimization phase. Simulation results on scale-free and realistic transmission networks demonstrate the effectiveness of the proposed algorithm. Moreover, comprehensive analysis of the solutions from different parts of the obtained Pareto fronts shows distinct characteristics and provides various choices for optimizing transmission functionality and robustness of networks.

Communication Interface Manager for Improving Performance of Heterogeneous UAV Networks.

Exchanging messages with stable connections in missions composed of multiple unmanned aerial vehicles (UAV) remains a challenge. The variations in UAV distances from each other, considering their individual trajectories, and the medium dynamic factors are important points to be addressed.In this context, to increase the stability of UAV-to-UAV (U2U) communication with link quality, this paper presents an interface manager (IM) that is capable of improving communication in multi-UAV networks.Given a predefined set of available individual wireless interfaces, the proposed IM dynamically defines the best interface for sending messages based on on-flight conditions sensed and calculated dynamically from the wireless medium. Different simulation scenarios are generated using a complex and realistic experimental setup composed of traditional simulators such as NS-3, Gazebo, and GzUAV. IEEE 802.11n 2.4 GHz and 802.11p 5 GHz interfaces are used for the IM selection. The IM performance is evaluated in terms of metrics from the medium-access-control (MAC) and physical layers, which aim to improve and maintain the connectivity between the UAVs during the mission, and from the application layer, which targets the reliability in the delivery of messages. The obtained results show that compared with the cases where a single interface is used, the proposed IM is able to increase the network throughput and presents the best proportion of transmitted and received packets, reception power (−60 dBm to −75 dBm), and loss (−80 dB to −85 dB), resulting in a more efficient and stable network connections.

Flow Control in Network Media Information Transmission Based on Differential Evolution Algorithm

In order to solve the problems of transmission delay and data loss caused by network congestion and strengthen the quality of service of transmission delay and throughput, a method of flow control in network media information transmission based on differential evolution algorithm was presented. By analyzing the delay effect of buffer on network instability, the problems of flow control at the receiving end were clarified. Moreover, the jitter between packet and end-to-end time delay parameters in dynamic caching mechanism were analyzed. The maximum and minimum values were set to calculate the balance point of real-time communication and smooth network information transmission flow. In addition, the differential evolution algorithm based on adaptive quadratic mutation of population fitness variance was used to improve the adaptive global optimization structure, organize the minimum evolution and input the minimum sample. Through reasonable selection of controller parameters and adaptive calculation, the poles of the closed-loop control system were obtained. Finally, the transmission performance index could be optimized. Thus, the stable control of transmission flow was completed. Simulation results show that the proposed algorithm can effectively improve the transmission performance of heterogeneous networks. Meanwhile, it has obvious advantages when the asymmetric parameters are high.

Solving a multi-objective heterogeneous sensor network location problem with genetic algorithm

In this paper, we consider a multi-purpose two-level location problem introduced by Karatas (2020) to improve the coverage performance of heterogeneous sensor networks. The problem basically seeks to determine the best location scheme of sensors of different types and characteristics in a belt-shaped boundary area with the purpose of providing a sufficient level of field, point and barrier coverage against different types of intruders. To solve the problem, first, the Mixed Integer Linear Programming (MILP) model developed by Karatas (2020) is used together with a commercial solver and a Non-Dominated Sorting Genetic Algorithm-II (NSGA-II), adapted to solve especially large-sized instances of the problem, is applied. Next, we compare the NSGA-II heuristic with the MILP solved via a commercial exact solver on a number of test instances. The experiment results suggest that the heuristic algorithm can produce a large number of diverse and high-quality solutions in very short computation times in comparison to the exact solver.

Multi-MEC server multi-user resource allocation in heterogeneous network

Aiming at the coexistence of large and small cells in heterogeneous cellular networks, the difference of computing resources between mobile equipment and base stations, and the diversity of service quality demands of users, a resource allocation algorithm based on matching game theory was proposed. We assume that some users have computing needs and can perform MEC offloading or D2D offloading. We define cost performance as the ratio of the data rate per user to its price cost, and resource allocation as the cost performance maximization problem. Considering that the formulation problem is a mixed integer nonlinear problem and it is difficult to obtain the optimal solution, we design an improved Gale-Shapley algorithm based on matching game theory to obtain the feasible solution of the problem. Simulation results show that the proposed multi-MEC server multi-user resource allocation strategy can effectively reduce user service delay and improve system performance in heterogeneous cellular networks.

Evaluating the Mobility Impact on the Performance of Heterogeneous Wireless Networks Over η – μ Fading Channels

The 5G and beyond networks will intrinsically accommodate a wide range of use-case scenarios and expand the limit of legacy mobile systems. The 5G network architecture can handle the seamless operation of various wireless channels in a heterogeneous environment. The $\eta $ - $\mu $ fading model is well-suited for versatile channels as it adapts to different fading behaviors in a broad-range propagation for non-line-of-sight (NLOS) circumstances. This paper evaluates the performance of heterogeneous wireless networks using $\eta $ - $\mu $ fading channel under mobility conditions. We incorporated the random waypoint (RWP) model with $\eta $ - $\mu $ distribution to model the dynamic behavior of non-homogeneous fading. The derivation of expressions for the probability density function (PDF) and cumulative distribution function (CDF) of the received signal power for a mobile network in all three-dimensional topologies is extracted. Consequently, the outage probability (OP) and average bit error rate (ABER) are analyzed to quantify the performance of the mobile system. The effect of co-channel interference (CCI) is investigated based on a desired and interfering signal transmitted in mobile networks. The proposed novel-form can characterize the performance of a mobile user, and the derivation is useful for measuring the effect of noise and interference on the signal. Finally, the novel-form applicability analyzes the impact of mobility incorporated in different fading channels such as Nakagami-m, Nakagami-q (Hoyt), Rayleigh, and one-sided Gaussian distributions.

Effects of Femtocell Ultradense Deployment on Downlink Performance in LTE Heterogeneous Networks

With huge number of smart gadgets and wireless devices being interconnected to each other, the demand for very high data bandwidth is becoming critically challenging. With such density of nodes inside wireless networks, providing high‐quality service as well as wide coverage in indoor environment is a real challenge, which is due to the limited radio frequency and intense interference between nodes. As a one way to solve such problem and improve indoor service quality, femtocells have been introduced as an extension to the existing macrocell stations. Although femtocell is a promising technology, the pervasive deployment of huge number of femtocells without very tight network planning as well as coverage strategy may worsen the problem and degrade the service quality. One important problem that needs to be addressed when deploying femtocell technology in heterogenous networks (HetNets) is mitigating the various types of cross‐tier and cotier interferences in between wireless cells. This study investigates the effect of unplanned ultradensity femtocell deployment in the downlink performance of two‐tier heterogeneous networks in urban area based on LTE system. Instead of deploying femtocells one by one, grids of size either (3 × 3) or (5 × 5) of neighboring femtocell will be deployed inside each macrocell sector area. The simulation results show that femtocell deployment improves overall average user throughput in case of low and medium density scenarios. However, for ultradensity scenario, there is no enhancement in terms of fairness and throughput. The results confirm that this leads to high degradation for macrocell and femtocell user performance due to the severe interference between macrocells and femtocells, as well as among neighboring femtocells in each grid.

Reinforcement Learning Based Vertical Handoff Decision Algorithm for Next Generation Wireless Network

Next-generation wireless network systems and technologies provide a new paradigm for achieving the fastest access to any network. But one of the significant design concerns is the support of handoff, irrespective of the services. The key objective of this work is to enable a node to make appropriate decisions for performing handoff through Reinforcement learning. The work concentrates on the handoff decision phase for choosing the best network with a minimum delay during the handoff process. The reduction in decision delay has been achieved by minimizing the number of handoffs. The environment is modeled as a Markov decision process with the aim of increasing the total anticipated reward per link. The network resources that are used by the link is taken by a reward function and network switching cost that is utilized to model the signaling and processing load incurred on the network during handoff. It has been shown that the total number of unnecessary handoffs can be decreased enhancing the performance of heterogeneous networks. Also, an assessment of the proposed scheme with the existing Vertical handoff decision algorithm like the Simple Additive Weighting method (SAW) has been made and the results show an improved performance over SAW.