Wireless Network Topology Research Articles

Intelligent Efficient Routing and Localization in the Underwater Wireless Sensor Network to Improve Network Lifetime

The inefficiency of the localization process mostly causes delays and operational difficulties in wireless sensor network (WSN) topologies. When localization is suboptimal, the time required for path creation significantly increases, leading to reduced network lifetime and diminished routing efficiency. To address these critical issues, our research focuses on developing a novel procedure that optimizes routing and localization. In this context, localization is the initial step for reaching sensor terminals, followed by activating the routing mechanism. To tackle these issues in Underwater Wireless Sensor Networks (UWSN), the proposed work presents the Elman Bat-based Hello Routing (EBbHR). The primary objective of our research is to locate wireless sensor nodes within the designed UWSN effectively. The process is initiated to achieve this by creating the necessary wireless sensor hubs within the MATLAB Simulink environment. Subsequently, the novel EBbHR function is activated to facilitate the precise localization of all sensor hubs, thereby enhancing the overall data transmission. The success of the designed EBbHR is assessed by considering UWSN factors, and a comparison with current approaches is carried out to show the advancements made by the model.

Finite-horizon energy allocation scheme in energy harvesting-based linear wireless sensor network

Linear wireless sensor networks (LWSNs) are a specialized topology of wireless sensor networks (WSNs) widely used for environmental monitoring. Traditional WSNs rely on batteries for energy supply, limiting their performance due to battery capacity constraints, while renewable energy harvesting technology is an effective approach to alleviating the battery capacity bottleneck. However, the stochastic nature of renewable energy makes designing an efficient energy management scheme for network performance improvement a compelling research problem. In this paper, we investigate the problem of maximizing throughput over a finite-horizon time period for an energy harvesting-based linear wireless sensor network (EH-LWSN). The solution to the original problem is very complex, and this complexity mainly arises from two factors. First, the optimal energy allocation scheme has temporal coupling, i.e., the current optimal strategy relies on the energy harvested in the future. Second, the optimal energy allocation scheme has spatial coupling, i.e., the current optimal strategy of any node relies on the available energy of other nodes in the network. To address these challenges, we propose an iterative energy allocation algorithm for EH-LWSN. Firstly, we theoretically prove the optimality of the algorithm and analyze the time complexity of the algorithm. Next, we design the corresponding distributed version and consider the case of estimating the energy harvest. Finally, through experiments using a real-world renewable energy dataset, the results show that the proposed algorithm outperforms the other two heuristics energy allocation schemes in terms of network throughput.

Wireless network topology optimization algorithm based on discrete variables

In response to the missing discrete variables in current wireless network topology optimization, an improved particle swarm optimization algorithm based on fusion of discrete variables is proposed to obtain network discrete variables. A wireless network topology optimization model is constructed. The research results indicate that it has better anti-interference performance in complex situations, which contributes to improving network load balancing. The topology obtained by this method has independence and predictability. When optimizing network topology, it has high network node coverage. When the network nodes are 50, 100, 150, and 200, the connectivity is 99.85 %, 93.64 %, 91.25 %, and 90.18 %, respectively. The testing time is 19s, 34s, 54s, and 64s respectively, which has the best optimization performance. The method can effectively improve the missing discrete variables in wireless network topology optimization, which has good performance.

Models for the optimization of radio signaling capabilities in 5G systems

Optimizing radio signaling capabilities in 5G systems involves various modeling techniques that can be systematized as follows - channel modeling, signal processing, optimization algorithms, queuing theory, machine learning, information theory, radio resource management, interference management, network topology and routing. The purpose of writing the article is to analyze mathematically the methods for optimizing radio signaling capabilities in 5G systems and to propose different models representing the behavior of the radio channel in variable environments and conditions. To consider algorithms and mathematical models for processing and analysis of radio signals received by gNB and user devices. To discuss mathematical models for analyzing and optimizing radio system performance in terms of delay, packet loss and throughput. Research and analysis of radio signaling capabilities in 5G systems, presentation of models related to the behavior of the radio channel in different environments and conditions, analysis of algorithms and mathematical models for processing and analysis of radio signals received from 5G base stations and end user devices can be noted as contributions to the development of the article.

Symmetric Collaborative Fault-Tolerant Control of Multi-Intelligence under Long-Range Transmission in Air–Ground Integrated Wireless High-Mobility Self-Organizing Networks

With the continuous development and progress of wireless self-organizing network communication technology, how to carry out long-distance cooperative control of multiple intelligences under the framework of an air–ground integrated wireless high-mobility self-organizing network has become a hot and difficult topic that needs to be solved urgently. This paper takes the air–ground integrated wireless high-mobility self-organizing network system as the basic framework and focuses on solving the long-distance cooperative fault-tolerant control of multi-intelligent bodies and the topological stability of a wireless mobile self-organizing network. To solve the above problems, a direct neural network with a robust adaptive fault-tolerant controller is designed in this paper. By constructing a symmetric population neural network model and combining it with the Lyapunov stabilization criterion, the system feedback matrix K has the ability of autonomous adaptive learning, and symmetrically distorts, rotates, or scales the training data to instantly adjust the system’s fault-tolerant corrections and adaptive adjusting factors to resist the unknown disturbances and faults, to achieve the goals of multi-intelligent body stable control and the stable operation of a wireless high-mobility self-organizing network topology. Simulation results show that with the feedback adjustment of the multi-system under the designed controller, the multi-system as a whole has good fault-tolerant performance and autonomous learning approximation performance, and the tracking error asymptotically converges to zero. The experimental results show that the multi-flight subsystems fly stably, the air–ground integrated wireless high-mobility self-organizing network topology has good stability performance, and the maximum relative improvement of the topology stability performance is 50%.

Design and analysis of wireless data center network topology HCDCN based on VLC

With the rapid development of the Internet of Things, big data, cloud computing and other industries, the data center network, as the core infrastructure of the computing industry, is becoming increasingly important. Wireless communication technology is developing rapidly. This paper uses visible light wireless communication technology to build a wireless data center network, solving the problems of complex wiring and expansion difficulties in the existing data center network.Aiming at the problems of insufficient spectrum, low bandwidth utilization and large energy consumption in the existing wireless data center, this paper adopts the visible light wireless communication technology based on LED, and proposes a new wireless data center network structure HCDCN. Firstly, work have been done to analyze the feasibility of introducing data centers in this technology, and transform server racks in combination with the characteristics of the technology. Secondly, the construction process of the structure is introduced and a new coding rule is proposed. In order to increase the routing efficiency and fault tolerance, the rack top link routing path is added, and an efficient routing algorithm is designed. Finally, the aggregate throughput of HCDCN under different scales is obtained through experiments, and the network performance of HCDCN structure is compared with that of OWCells and Mesh structure. Experiments have proved that HCDCN's network performance is better than the other two structures under the same conditions. The new wireless data center network proposed in this paper has largely alleviated the bandwidth shortage of hotspot servers.

Recent Advancements in IoT Implementation for Environmental, Safety, and Production Monitoring in Underground Mines

Internet of Things (IoT) technology has been widely used for real-time monitoring of the environment, safety and production in underground mines. This paper presents the basic structure of a Mine Internet of Things (MIoT) system based on a widely used three-layer IoT architecture, classifies types of sensors commonly used in underground mines by specific application, and introduces available wired and wireless communication technologies and network topologies that can be applied in underground mines. This paper provides a comprehensive review of recent developments in IoT applications in underground mines to monitor various environmental parameters, including mine gas and dust concentrations, temperature, humidity and airflow, groundwater, ground support and seismic activity. MIoT applications for fire and hazard detection, personnel and equipment positioning, and production safety management have also been investigated. This paper highlights key challenges for the broad application of IoT technology in underground mines such as operation disruption, additional investment, limited battery life, poor quality of underground communication, and difficulty in data management. Further research on novel advanced techniques, such as self-powered sensors, MIoT standardization and underground wireless communication technologies, is essential to improve the applicability and effectiveness of IoT applications in underground mines.

An optimal design method for communication topology of wireless sensor networks to implement fully distributed optimal control in IoT-enabled smart buildings

In smart buildings enabled by IoT technologies, wireless sensor networks (WSNs) are promising platforms to implement novel fully distributed optimal control approaches according to the edge computing paradigm. This requires knowledge from wireless communication and distributed computation fields where communication topologies are both critical. Communication topologies are designed considering network energy consumption and stability in wireless communication field, while considering optimization convergence speed in distributed computation field. But there is no inter-disciplinary design method considering these issues simultaneously. This study therefore proposes an optimal design method for communication topology of WSNs to implement fully distributed optimal control approaches. System control performance, network energy consumption and network stability are integrated into the objective function for the design. For a WSN consisting of n sensors, an integer programming problem with n(n − 1)/2 design variables, i.e., elements in Laplacian matrix representing the existence of communication links, is formulated and solved by the genetic algorithm (GA). The optimal topology of a WSN, on which a fully distributed optimal control approach is implemented for optimally controlling a multi-zone dedicated outdoor air system (DOAS), is designed by the proposed method. A co-simulation testbed is constructed to test and validate the proposed method by comparing the optimal topology with different topologies. The optimal topology provides satisfactory system control performance (CO2Ave = 784 ppm, CO2Max = 916 ppm, CO2 unmet hour = 1.82 h and EDOAS = 122.50 kWh), low network energy consumption (2564.12 J/Day) and high network stability (53.90 days). The proposed method facilitates the development and applications of IoT technologies in smart buildings.

Fault-Tolerant Routing for Wireless Sensor Networks using a Gaussian Network

Abstract: Wireless Sensor Networks (WSNs) are utilised for sensing various physical and environmental variables like temperature, pressure, motion, and pollution. A component of a network to continue operating even when some sensor nodes fail is regarded as fault tolerance in wireless sensor networks. Due to battery life deadlines and the challenges associated with charging or replacing failing nodes, the deployment of sensor nodes in the target area is done in a dense manner to maximise coverage and connection. This paper implements a hybrid fault-tolerant routing methodology to address the problem of fault tolerance in hierarchical topology for wireless sensor networks (WSNs). The network location is partitioned into small square grids, with a Gaussian integer serving as each grid's cluster head. A Gaussian network is created through communicating these cluster heads together. Discovering the Gaussian network's shortest path and multi-path routing, the project implements a hybrid Fault-tolerant Clustering routing protocol based on Gaussian network for Wireless Sensor Network (FCGW). The purpose of FCGW is to improve fault tolerance and reduce energy consumption for Wireless Sensor Networks. Experimental results using MATLAB shows that FCGW protocol has high data reliability. In addition, the FCGW protocol consumes lesser energy in the network compared to other protocols namely FT-LEACH, HEED and PSO-UFC

Network Topology Inference Based on Timing Meta-Data

A set of low-cost sensors is deployed to infer the network topology of a self-organizing wireless network. The sensors operate in a non-invasive fashion, extracting only the timings of data packets and acknowledgment (ACK) packets from all nodes in a network. The meta-data also reports the source node of each packet, but not the destination nodes or the contents of the packets. A central processor collects the meta-data from the sensors, and the goal is for the processor to infer the network topology based solely on such information. Prior work leveraged causality metrics to identify which links are active. If the data timings and ACK timings of two nodes – say node 1 and node 2, respectively – are causally related, this may be taken as evidence that node 1 is communicating to node 2 (which sends back ACK packets to node 1). This paper starts with the observation that packet losses can weaken the causality relationship between data and ACK timing streams. To obviate this problem, a new Expectation Maximization (EM)-based algorithm is introduced – EM-causality discovery algorithm (EM-CDA) – which treats packet losses as latent variables. EM-CDA iterates between the estimation of packet losses and the evaluation of causality metrics. The method is validated through extensive experiments in wireless sensor networks on the NS-3 simulation platform.

A Novel Topology Optimization Protocol Based on an Improved Crow Search Algorithm for the Perception Layer of the Internet of Things

In wireless sensor networks, each sensor node has a finite amount of energy to expend. The clustering method is an efficient way to deal with the imbalance in node energy consumption. A topology optimization technique for wireless sensor networks based on the Cauchy variation optimization crow search algorithm (CM-CSA) is suggested to address the issues of rapid energy consumption, short life cycles, and unstable topology in wireless sensor networks. At the same time, a clustering approach for wireless sensor networks based on the enhanced Cauchy mutation crow search algorithm is developed to address the issue of the crow algorithm's sluggish convergence speed and ease of falling into the local optimum. It utilizes the Cauchy mutation to improve the population's variety and prevent settling for the local optimum, as well as to broaden the range of variation and the capacity to carry out global searches. When the leader realizes he is being followed, the discriminative probability is introduced to improve the current person's location update approach. According to the simulation findings, the suggested CM-CSA algorithm decreases the network's average energy consumption by 66.7%, 50%, and 33.3% and enhances its connectivity performance by 52.9%, 37.6%, and 23.5% when compared to the PSO algorithm, AFSA method, and basic CSA algorithm.

Channel Estimation for High-Speed Railway Wireless Communications: A Generative Adversarial Network Approach

In high-speed railways, the wireless channel and network topology change rapidly due to the high-speed movement of trains and the constant change of the location of communication equipment. The topology is affected by channel noise, making accurate channel estimation more difficult. Therefore, the way to obtain accurate channel state information (CSI) is the greatest challenge. In this paper, a two-stage channel-estimation method based on generative adversarial networks (cGAN) is proposed for MIMO-OFDM systems in high-mobility scenarios. The complex channel matrix is treated as an image, and the cGAN is trained against it to generate a more realistic channel image. In addition, the noise2noise (N2N) algorithm is used to denoise the pilot signal received by the base station to improve the estimation quality. Simulation experiments have shown the proposed N2N-cGAN algorithm has better robustness. In particular, the N2N-cGAN algorithm can be adapted to the case of fewer pilot sequences.

Coordinated Sum-Rate Maximization in Multicell MU-MIMO With Deep Unrolling

Coordinated weighted sum-rate maximization in multicell MIMO networks with intra- and intercell interference and local channel state at the base stations is recognized as an important yet difficult problem. A classical, locally optimal solution is obtained by the weighted minimum mean squared error (WMMSE) algorithm which facilitates a distributed implementation in multicell networks. However, it often suffers from slow convergence and therefore large communication overhead. To obtain more practical solutions, the unrolling/unfolding of traditional iterative algorithms gained significant attention. In this work, we demonstrate a complete unfolding of the WMMSE algorithm for transceiver design in multicell MU-MIMO interference channels with local channel state information. The resulting architecture termed GCN-WMMSE applies ideas from graph signal processing and is agnostic to different wireless network topologies, while exhibiting a low number of trainable parameters and high efficiency w.r.t. training data. It significantly reduces the number of required iterations while achieving performance similar to the WMMSE algorithm, alleviating the overhead in a distributed deployment. Additionally, we review previous architectures based on unrolling the WMMSE algorithm and compare them to GCN-WMMSE in their specific applicable domains.

무선통신으로 연결되어 연쇄적으로 동작하는 다수의 키네틱 모듈로 구성된 대형 공간을 위한 인스톨레이션 작품 연구

A tendency in large-scale new media installation arts is to build media façades or immersive experience environments using overwhelming amounts and sizes of light displays covering the whole area of an architectural structure, involving the intensive use of capital, materials, and technology. In this research, a different approach was taken with regard to kinetic light art installations, in which lights are used sparingly and are sparsely distributed in the space. In this appropriate installation approach, a minimalistic attitude is taken where the installation is built with simple materials and technology, and modestly takes only the appropriate amount of space at the installation site. In this approach, an installation does not intend to conquer and change the site into something new but only to signify and amplify the identity of the site space. To achieve this goal, the installation takes the form of computational media, generatively adding life to the space in real time and interactively responding to the people living in the space. An installation prototype, Waves, composed of eleven kinetically waving light wands about the size of a hand, was developed. They are distributed in a large, long corridor space and form a large moving wave of light along the space. The light wave has two modes: generative small tides and interactive large waves. To achieve the choreographic functionality, a daisy chain network topology of small wireless networks among three modules in series is used. This type of network is feasible for large-scale installations because it is self-contained and independent of the internet infrastructure, while also allowing for the easy addition of new modules. This research is unique in its approach to overlapping areas of kinetics, light, and network art. The result of this research may provide a platform for future installation works in this area, including the use of other forms of media, other sizes of modules, other methods of interaction with users, and other ways to arrange such modules in space, just to list a few.

Mechanical Vibration Test Based on the Wireless Vibration Monitoring System

In order to apply wireless sensor networks to mechanical vibration monitoring, the author proposes a wireless network topology with multiple data collection points for mechanical vibration monitoring. This structure reduces the transmission load of the data collection point, increases the data transmission rate of the network, balances the energy dissipation in the network, and utilizes the general wireless sensor network hardware platform. The network transmission protocol and related auxiliary mechanisms are designed and implemented, and a wireless vibration monitoring test platform is constructed. The transmission performance of the network structure with multiple data collection points is evaluated through the actual test. The experimental results show that by using the wireless sensor network topology with multiple data collection points, it can meet the requirements of continuous transmission of vibration data obtained by 1 kHz sampling. Conclusion. The system performance of the wireless sensor network based on this network structure has been improved under the condition of general hardware, and the network structure of multiple data collection points shows good performance in the process of high-speed data transmission.

Design of Antennas Using Wireless Mesh Network

With the increasing demand for QoS and the expansion in deployment scenarios, the issue of designing wireless networks to suitably handle traffic loads has become important. While such issues have been handled in wired networks for backhaul traffic, the problem of wireless network topology design and provisioning still remains in its infancy. The issues discussed in this paper considered together as the problem of wireless network design. Any wireless network to be deployed has to address one or more of these issues.

Multi-Hop Routing Protocols for Oil Pipeline Leak Detection Systems

In recent years, various applications have emerged requiring linear topologies of wireless sensor networks (WSN). Such topologies are used in pipeline (water/oil/gas) monitoring systems. The linear structure has a significant impact on network performance in terms of delay, throughput, and power consumption. Regarding communication efficiency, routing protocols play a critical role, considering the special requirements of linear topology and energy resources. Therefore, the challenge is to design effective routing protocols that can address the diverse requirements of the monitoring system. In this paper, we present various wireless communication technologies and existing leak detection systems. We review different routing protocols focusing on multi-hop hierarchical protocols, highlighting the limitations and design issues related to packet routing in linear pipeline leak detection networks. Additionally, we present a LoRa multi-hop model for monitoring aboveground oil pipelines. A set of model parameters are identified such as the distance between sensors. In addition, the paper determines some calculations to estimate traffic congestion and energy consumption. Several alternative model designs are investigated. The model is evaluated using different multi-hop communication scenarios, and we compare the data rate and energy to provide an energy-efficient and low-cost leak detection system.

Security-Enabled Retransmission and Energy Conservation Architecture With Cluster-Based Multipath Routing in Heterogeneous Wireless Networks

The primary requirements of a heterogeneous wireless network topology, adaptive and smart resource allocation to users, protocols for routing and lifetime enhancement, access to the network with security and appropriate network selections. Routing algorithms deliberate on the performance of the network to evenly distribute load and thus enhance the lifespan of individual nodes, clustering algorithm decides on allowing the right nodes into the network for enhanced security feature, and finally the ability to analyse, predict the context of individual nodes/sensors in the network. Architecture of the proposed network includes the parameters such as decision making ability to sustain the clusters, decision on members of the clusters until the communication process is completed, local network abilities and disabilities, price, preferences of individuals, terminal and access points of the service providers. Network lifetime of the entire network is observed to be enhanced up to 91% with triple layer architecture.

An Energy-Efficient Intelligent Framework of UAV-Enhanced Vehicular Networks: Open Problems and a Case Study

As the heterogeneous service requirements in vehicular networks (VNs) increase, caching, computing, and communication (3C) resources should be dynamically allocated. Unlike a traditional cellular wireless network with a fixed topology among base stations (BSs), unmanned aerial vehicles (UAVs) can constitute an energy-efficient rearrangeable wireless network serving VNs. The 3C resource allocation and UAV-based wireless network topology can be adaptively adjusted according to the service requirements of VNs by appropriate artificial intelligence (AI) algorithms. Although AI algorithms have been well studied in wireless networks, an energy-efficient intelligent frame for UAV service and arrangement should be further investigated.

Energy-Efficient Data Transmission in Mobility-Aware Wireless Networks

Data transmission scheme is an effective mode to improve the energy-efficiency in packet delivery. In this paper, we investigate the energy-efficient data transmission over reliable links and unreliable links in mobility-aware wireless networks. The network topologies in mobility-aware wireless networks are changed from one time slot to another, and they could be described by a sequence of static graphs. We first model these network topologies in a time period as a virtual space-time graph. On the virtual space-time graph, energy-efficient data transmission problems over reliable links and unreliable links are defined. The aim of the two data transmission problems is to find a spatial-temporal path with the minimum energy cost. Next, we propose an Energy-Efficient Data Transmission algorithm over Reliable Links (EEDT-RL) to find the optimal space-time path. Based on EEDT-RL, we also develop a heuristic data transmission protocol over unreliable links (named EEDT-UL), in which the path reliability is taken into consideration. Simulation results show that our proposed algorithms perform well in terms of energy cost and transmission count compared with some existing algorithms.