Large-scale Wireless Networks Research Articles

An optimal cooperative relay selection strategy for delay aware image transmission in large scale multi-radio multi-channel multimedia wireless sensor network

High bit-error rates and high transmission rates are required for the Multimedia Wireless Sensor Networks (MWSN) to transmit high-quality pictures through smart devices. To fully utilize the advantages of the technology known as Multiple-Input Multiple-Output, MWSN heavily relies on cooperative communication. Large-scale wireless networks use multi-radio-multi-channel to improve performance by simultaneous broadcasts across symmetrical channels to reduce interference. Expanding cooperative communication in vast networks is subject to severe interference. And, as each node in the network is mobile, routing and transmission delay pose significant problems for cooperative multimedia wireless sensor networks. Mobility increases the MWSNs’ dynamic nature, which reflects in the overhead control traffic. To address above issues, a Cluster-based Delay Aware Cooperative Relay Selection (CDACRS) was proposed by employing mobility and distance metrics and channel assignment (CA) using dynamic Global Table (GT). To minimize the end-to-end transmission latency without compromising aggregate throughput, our approach chooses a relay-node depending on the mobility and the maximum available channel capacity. Further, to improve the end-to-end energy consumption, Power Aware Transmission (PAT) protocol is developed by calculating maximum transmission power required to meet target bit error rate (BER). The proposed method’s performance is evaluated against the Cluster-based Cooperative Multi-Hop Optimal Relay Selection (CCORS); energy efficient and quality aware multi-hop cooperative image transmission; and Energy Aware Cooperative Image Transmission (EACIT) algorithms and observed that our approach improves the transmission delay by 37.5% (approx.) and end-to-end energy consumption by 48.8%.

Sum Rate Optimization for Multi-IRS-Aided Multi-BS Communication System Based on Multi-Agent

Intelligent reflecting surface (IRS) is a revolutionizing technology for improving the spectral and energy efficiency of future wireless networks. In this paper, we consider a downlink large-scale system empowered by multi-IRS to aid communication between the multiple base stations (BSs) and multiple user equipment (UEs). We target maximizing the sum rate by jointly optimizing the UE association, the transmit powers of BSs, and the configurations of the IRS beamforming. Due to the applicability restrictions of conventional optimization methods and their high complexity with large-scale networks in dynamic environments, deep reinforcement (DRL) learning is adopted as an alternative approach to finding optimal solutions. First, we model the optimization problem as a multi-agent Markov decision problem (MAMDP). Then, because large-scale wireless networks are naturally complex and changeable, and because many entities interact and affect how the whole system works, it is important to use a multi-agent approach to understand the complex dependencies and relationships between the different parts. In order to solve the problem, we propose a cooperative multi-agent deep reinforcement learning (MADRL)-based algorithm that works for both continuous and discrete IRS phase shifts. Simulation results validate that the proposed algorithm surpasses iterative optimization benchmarks regarding both sum rate performance and convergence.

Leveraging Digital Cousins for Ensemble Q-Learning in Large-Scale Wireless Networks

Leveraging Digital Cousins for Ensemble Q-Learning in Large-Scale Wireless Networks

Optimal Measurement Geometry Directed Integrated Localization and Synchronization in Large-Scale Wireless Networks

Optimal Measurement Geometry Directed Integrated Localization and Synchronization in Large-Scale Wireless Networks

A Novel Routing Control Method Using Federated Learning in Large-Scale Wireless Mesh Networks

Currently, the volume of communication by mobile terminals are increasing owing to 5G and other technologies. A robust network and appropriate routing control methods are requied to transmit information in unstable wireless communication environments and avoid congestion. Therefore, in recent years, numerous studies have been conducted on wireless mesh networks (WMNs), which provide a fault-tolerant communication environment by securing multiple communication paths and whose topology can be freely configured and extended. Additionally, machine learning routing is attracting attention as a new routing method for wireless communication environments. However, when performing machine learning on a large WMN, the learning time increases and rapid routing control may be impossible. In this study, we apply federated learning to machine learning and propose a machine-learning-based routing method that can be applied to large-scale WMNs. Furthermore, experimental results demonstrate the effectiveness of the proposed method in various environments: congestion avoidance is achieved in a large-scale WMN by machine-learning routing using federated learning. This study is expected to serve as a basis for significant progress in the realization of large-scale WMNs as wireless communication infrastructure.

An Interface Setup Optimization Method Using a Throughput Estimation Model for Concurrently Communicating Access Points in a Wireless Local Area Network.

The IEEE 802.11 wireless local-area network (WLAN) has been deployed around the globe as a major Internet access medium due to its low cost and high flexibility and capacity. Unfortunately, dense wireless networks can suffer from poor performance due to high levels of radio interference resulting from adjoining access points (APs). To address this problem, we studied the AP transmission power optimization method, which selects the maximum or minimum power supplied to each AP so that the average signal-to-interference ratio (SIR) among the concurrently communicating APs is maximized.However, this method requires measurements of receiving signal strength (RSS) under all the possible combinations of powers. It may need intolerable loads and time as the number of APs increases. It also only considers the use of channel bonding (CB), although non-CB sometimes achieves higher performance under high levels of interference. In this paper, we present an AP interface setup optimization method using the throughput estimation model for concurrently communicating APs. The proposed method selects CB or non-CB in addition to the maximum or minimum power for each AP. This model approach avoids expensive costs of RSS measurements under a number of combinations. To estimate the RSS at an AP from another AP or a host, the model needs the distance and the obstacles between them, such as walls. Then, by calculating the estimated RSS with the model and calculating the SIR from them, the AP interface setups for a lot of APs in a large-scale wireless network can be optimized on a computer in a very short time. For evaluation, we conducted extensive experiments using Raspberry Pi for APs and Linux PCs for hosts under 12 network topologies in three buildings at Okayama University, Japan, and Jatiya Kabi Kazi Nazrul Islam University, Bangladesh. The results confirm that the proposed method selects the best AP interface setup with the highest total throughput in any topology.

DIT: A Dynamic Bandwidth Isolated Transmission System for Large-Scale Inter-DC Wireless Communication Network

Large companies are increasingly providing their services over interdomain data centers. Such large-scale wireless communication networks carry various applications, including both online time-sensitive services and offline bandwidth-sensitive services. These applications share the same physical links but have different bandwidth requirements at different time, making the coscheduling problem a key challenge in improving the overall network utility. Most existing commercial systems reserve peak value bandwidth for online time-sensitive services to ensure the quality of service and use the leftover bandwidth to transmit offline data in a best-effort manner. Such systems cannot obtain high link utilizations because of the nonstationarity of online traffic. To solve this problem, we present a dynamic isolated transmission (DIT) system in this paper, where a sliding- k algorithm is designed to predict online traffic, and a bottleneck bypass routing scheme is proposed to schedule the mixed traffic in a shared inter-DC wireless communication network. We conduct a series of experiments in an experimental inter-DC wireless communication network to analyze for efficient network operation of the DIT and compare its performance with typical existing fix-bandwidth-based solutions including Microsoft’s SWAN. The results show that DIT outperforms existing solutions, improving the interdomain link utilization by 18% and the intradomain link utilization by 65%.

Modeling and analyzing malware propagation Over wireless networks based on hypergraphs

Malware posts an increasing threat to the security of cyberspace, given the growing popularity of wireless networks. In this paper, we propose a hypergraph-based model to describe the malware propagation over a large-scale wireless network, where the hypergraph adequately describes the limited range and Internet-independent transmission over the wireless network. In the proposed model, the malware on an infected device will attack the wireless router and infect all normal devices on the wireless network. From a heterogeneous mean-field approach, we obtain the malware outbreak threshold. Through theoretical analysis and numerical simulations, we show that the malware pandemic on large-scale wireless networks depends mainly on the number of devices covered by the wireless network and the nature of the malware. Moreover, we find that isolating connections on the Internet does not completely inhibit the malware pandemic. These phenomena are also reflected in the hypergraph constructed from real data. We demonstrate that heterogeneous distributions of devices on wireless networks can lead to malware outbreaks more easily. Finally, we show that malware propagation on large-scale wireless networks is more sensitive to the number of initially infected devices compared to the propagation on the Internet.

Offloading Mechanisms Based on Reinforcement Learning and Deep Learning Algorithms in the Fog Computing Environment

Fog computing has emerged as a computing paradigm for resource-restricted Internet of things (IoT) devices to support time-sensitive and computationally intensive applications. Offloading can be utilized to transfer resource-intensive tasks from resource-limited end devices to a resource-rich fog or cloud layer to reduce end-to-end latency and enhance the performance of the system. However, this advantage is still challenging to achieve in systems with a high request rate because it leads to long queues of tasks in fog nodes and reveals inefficiencies in terms of delays. In this regard, reinforcement learning (RL) is a well-known method for addressing such decision-making issues. However, in large-scale wireless networks, both action and state spaces are complex and extremely extensive. Consequently, reinforcement learning techniques may not be able to identify an efficient strategy within an acceptable time frame. Hence, deep reinforcement learning (DRL) was developed to integrate RL and deep learning (DL) to address this problem. This paper presents a systematic analysis of using RL or DRL algorithms to address offloading-related issues in fog computing. First, the taxonomy of fog computing offloading mechanisms based on RL and DRL algorithms was divided into three major categories: value-based, policy-based, and hybrid-based algorithms. These categories were then compared based on important features, including offloading problem formulation, utilized techniques, performance metrics, evaluation tools, case studies, their strengths and drawbacks, offloading directions, offloading mode, SDN-based architecture, and offloading decisions. Finally, the future research directions and open issues are discussed thoroughly.

Bandwidth partitioning in random-access Poisson networks: Stability, throughput and delay

In this paper, we consider an interference-limited multi-user large-scale wireless network with two user classes sharing a given frequency band. Each source has an infinite buffer, where packets are queued for transmission. We are interested in the scenario in which the available frequency band is partitioned among users of one class, such that users of this class can access only a fraction of the original bandwidth available to the network. The motivation for such bandwidth partitioning is to reduce the interference among users, leading to a higher network capacity. Expressions for the stability region, transmission success probability, and average delay are derived based on stochastic geometry and queueing theory. Results show that, under certain conditions, bandwidth partitioning can improve the network performance in terms of lower mean delays and higher maximum arrival rates for which queues remain stable.

Intelligent Trust Based Electrical Vehicles Using 6G

Internet of Vehicles (IoV) has been viewed as a pivotal innovation for laying out Intelligent Transportation Systems in shrewd urban communities and is perhaps of the most encouraging application later on Internet of Things. the 6th era has started to arise. With the appearance of 6G interchanges, tremendous organization frameworks, be generally utilized, and the amount of organization hubs will rise remarkable development, which brings about extremely high energy utilization. As perhaps of the most encouraging application in future Internet of Things, Internet of Vehicles (IoV) has been recognized as an essential innovation for fostering the Clever Transportation Frameworks in brilliant urban areas. With the rise of the sixth generation (6G) correspondences innovations, monstrous network frameworks will be thickly sent and the quantity of network hubs will increment dramatically, leading to very high energy utilization. There has been an upsurge important to foster the green IoV towards maintainable vehicular correspondence and networking in the 6G time. Be that as it may, as an extraordinary versatile ad-hoc network, the energy cost in an IoV framework includes the correspondence and calculation energy in addition to the fuel utilization and the power cost of moving vehicles. Besides, the energy collecting innovation, which is probably going to be adopted generally in 6G frameworks, will entangle the improvement of energy proficiency in the whole framework.A change in the 6G period, there has been an expansion in interest in fostering the green IoV for supportable vehicle correspondence and systems administration. Nonetheless, as a remarkable versatile impromptu organization, an IoV framework's energy cost incorporates energy for correspondence and figuring. Reinforcement Learning (RL) can successfully address issues with decision-making to accomplish this goal. Large-scale wireless networks have enormous and complex state and action spaces, though. As a result, RL might not be able to identify the ideal plan of action in a timely manner. To solve this problem, Deep Reinforcement Learning (DRL), a infusion of RL and DL, has been developed.

Fast and Scalable Distributed Consensus Over Wireless Large-Scale Internet of Things Network

Due to the rapid paradigm shift in Internet of Things networks from wired and centralized to flexible wireless and decentralized networks, building effective and reliable distributed consensus mechanisms over wireless is becoming essential. Especially, since the performance of consensus over communication endpoints in a large-scale wireless network is limited by their communication capability, it requires a careful co-design of communication and consensus to attain a fast and scalable distributed wireless consensus mechanism with high resiliency against faulty nodes. Within this context, this article addresses such problem by designing two wireless consensus mechanisms that well-suit in large-scale wireless networks. On the one hand, as a reinterpretation of the conventional referendum consensus (RC) in a large-scale wireless network, gossip-broadcasting-based RC (GB-RC) is proposed. On the other hand, to overcome the scalability issue of the GB-RC, cooperative-broadcast-based electoral-college consensus (CB-EC) is proposed. By mathematically analyzing the performance of both of the consensus mechanisms, in terms of consensus latency and resiliency against the faulty nodes, we show that the GB-RC outperforms the conventional RC, while the CB-EC significantly reduces the consensus latency compromising the stochastic resiliency. We further evaluate their performance numerically to show their effectiveness and feasibility under realistic large-scale wireless environments.

Scalable On-Chain and Off-Chain Blockchain for Sharing Economy in Large-Scale Wireless Networks

In the future sharing economy, billions of underutilized IoT devices will be deployed to enable a powerful and large-scale sharing market that produces economic, environmental, and social benefits. Given the fact that communications in numerous IoT devices through wireless links are unreliable, blockchain technology, as a promising solution, has emerged to achieve reliable and secure sharing services in a decentralized manner. However, applying blockchain in large-scale wireless networks confronts scalability challenges. This motivates us to propose a real-time, trusted data interactive, and fine-grained transaction supportable sharing framework, the core of which is a novel two-layer scaling blockchain design. In the on-chain layer, sharing-oriented sharding is employed to enable secure and efficient processing of macro-transactions on the chain. In the off-chain layer, cross-zone off-chain channels are set up to provide real-time sharing transactions with high-frequency micro-trading scenarios. Finally, a proof-of-concept case study of electric vehicle sharing data is implemented with experimental results to demonstrate the feasibility of our framework.

An Unsupervised Detection Method for Multiple Abnormal Wi-Fi Access Points in Large-Scale Wireless Network

ABSTRACT The probability of a single access point (AP) failure is very small. In addition, APs communicate with each other; therefore, it is considered that these failures have little impact on the wireless network. Only when a large number of APs are abnormal offline, do we consider that the wireless network is faulty and needs to be recovered immediately. Network breakdown, network congestion, and AP management software shutdown may cause numerous APs in aborted status. In this article, we utilize DBSCAN algorithm to detect abnormal Wi-Fi APs. Compared with other research works, our proposed unsupervised method can distinguish between normal and abnormal offline APs. This study proposes a new date dimension to calculate the number of online APs together with the time dimension, and it provides new insights to set up thresholds of online APs automatically. Experimental results show that this 3-D model based on date and time is more accurate than the traditional 2-D model only based on time. With regard to the sampling method of random forest, this paper carries out repetitive random sampling to form small sample sets and finally to obtain the mean feature plane, which can reduce the interference of abnormal points to our algorithm.

Cooperative Localization in Massive Networks

Network localization is capable of providing accurate and ubiquitous position information for numerous wireless applications. This paper studies the accuracy of cooperative network localization in large-scale wireless networks. Based on a decomposition of the equivalent Fisher information matrix (EFIM), we develop a random-walk-inspired approach for the analysis of EFIM, and propose a position information routing interpretation of cooperative network localization. Using this approach, we show that in large lattice and stochastic geometric networks, when anchors are uniformly distributed, the average localization error of agents grows logarithmically with the reciprocal of anchor density in an asymptotic regime. The results are further illustrated using numerical examples.

Performance Analysis of Cooperative Multiple Access With Minimum Interference Range for Large Scale Wireless Networks

Cooperative communication techniques are widely used in large-scale wireless networks for improving network throughput. This is achieved through the creation of transmission diversity by forming multiple cooperative transmission links which involve several relaying nodes as helpers for an end-to-end data transmission. Consequently, link interference range will inevitably be enlarged, which will adversely hamper the diversity created and offset the network throughput improvement as a result. This problem has not been properly addressed by most cooperative communication protocols reported so far. In this paper, a novel protocol that performs cooperative medium access control as well as minimizes the interference range, namely MIR-CMAC, is proposed to directly address the enlarged link interference range problem introduced by cooperative communication. Specifically, functions like channel reservation, cooperative priority differentiation and contention for relay selection are collectively enabled in MIR-CMAC to reduce channel access collision and select the suitable helper node that can maximize the integrated link data rate and minimize the link interference concerned. In addition, a full interference model is established to evaluate the effect of interference across the whole process from channel access to packet transmission and derive a new network throughput analysis method for MIR-CMAC. Both numerical and simulation results demonstrate the effectiveness of MIR-CMAC and its performance improvements over other protocols.

A Large-Scale Wireless Cell Long-Term Daily-Granularity Forecasting Method

Optimizing and managing wireless communication network, including improving the utilization of network resources, energy efficiency, automatically carrying out wireless network planning and network construction, is very important to the communication service providers (CSPs). Key performance indicators (KPIs) forecasting for wireless cells, especially the long-term forecasting task, plays a key role in wireless network planning and construction. A new adaptive combination forecasting method is proposed in this paper. The adaptive combination forecasting method has been verified by a real large-scale wireless network dataset which contains thousands of wireless cells and corresponding daily KPIs. After a series steps such as dataset analysis, and Auto-encoder algorithm, K-means algorithm and time series forecasting algorithms, we can obtain the prediction model, then compare its symmetric mean absolute percentage error (SAMPE) value with Holt exponential smoothing, Comb method and Theta method.Experimental results have demonstrated that the proposed method has a better performance, especially in the medium and long term forecasting scenario in terms of symmetric mean absolute percentage error (SMAPE) when compared with some existing methods. It proved that our method can be more suitable for complex wireless communication network environment.

UAV-Enabled Wireless Power Transfer: A Tutorial Overview

Unmanned aerial vehicle (UAV)-enabled wireless power transfer (WPT) has recently emerged as a promising technique to provide sustainable energy supply for widely distributed low-power ground devices (GDs) in large-scale wireless networks. Compared with the energy transmitters (ETs) in conventional WPT systems which are deployed at fixed locations, UAV-mounted aerial ETs can fly flexibly in the three-dimensional (3D) space to charge nearby GDs more efficiently. This paper provides a tutorial overview on UAV-enabled WPT and its appealing applications, in particular focusing on how to exploit UAVs’ controllable mobility via their 3D trajectory design to maximize the amounts of energy transferred to all GDs in a wireless network with fairness. First, we consider the single-UAV-enabled WPT scenario with one UAV wirelessly charging multiple GDs at known locations. To solve the energy maximization problem in this case, we present a general trajectory design framework consisting of three innovative approaches to optimize the UAV trajectory, which are multi-location hovering, successive-hover-and-fly, and time-quantization-based optimization, respectively. Next, we consider the multi-UAV-enabled WPT scenario where multiple UAVs cooperatively charge many GDs in a large area. Building upon the single-UAV trajectory design, we propose two efficient schemes to jointly optimize multiple UAVs’ trajectories, based on the principles of UAV swarming and GD clustering, respectively. Furthermore, we consider two important extensions of UAV-enabled WPT, namely UAV-enabled wireless powered communication networks (WPCN) and UAV-enabled wireless powered mobile edge computing (MEC), by integrating the emerging WPCN and MEC techniques, respectively. For both cases, we investigate the UAV trajectory design jointly with communication/computation resource allocations to optimize the system performance, subject to the energy availability constraints at GDs. Finally, open problems in UAV-enabled WPT and promising directions for its future research are discussed.

A Private Strategy for Workload Forecasting on Large-Scale Wireless Networks

The growing convergence of various services characterizes wireless access networks. Therefore, there is a high demand for provisioning the spectrum to serve simultaneous users demanding high throughput rates. The load prediction at each access point is mandatory to allocate resources and to assist sophisticated network designs. However, the load at each access point varies according to the number of connected devices and traffic characteristics. In this paper, we propose a load estimation strategy based on a Markov’s Chain to predict the number of devices connected to each access point on the wireless network, and we apply an unsupervised machine learning model to identify traffic profiles. The main goals are to determine traffic patterns and overload projections in the wireless network, efficiently scale the network, and provide a knowledge base for security tools. We evaluate the proposal in a large-scale university network, with 670 access points spread over a wide area. The collected data is de-identified, and data processing occurs in the cloud. The evaluation results show that the proposal predicts the number of connected devices with 90% accuracy and discriminates five different user-traffic profiles on the load of the wireless network.

Stochastic Geometry and Performance Analysis of Large Scale Wireless Networks

Stochastic Geometry has attained massive growth in modelling and analysing of wireless network. This suits well for analysing the performance of large scale wireless network with random topologies. Analytical framework is established to evaluate the performance of the network. Here we have created a mathematical model for uplink analysis and the gain of uplink and downlink is obtained. Then ad-hoc network architecture is designed and the performance of the network is compared with the traditional method. Finally, a new scheduling algorithm is developed for cellular network and the gain parameter is quantified with the help of Stochastic Geometry tool. The accuracy is acquired from extensive Monte Carlo simulator.