Building New Reliable Wireless Network by Using Distributed Control

2011 ◽  
Vol 84-85 ◽  
pp. 160-166
Author(s):  
Xin Ying Wang
Keyword(s):  
Wireless Networks ◽  
Wireless Network ◽  
Distributed Control ◽  
Network Architecture ◽  
Large Scale ◽  
Complex Function ◽  
Packet Delay ◽  
Control Performance ◽  
Wireless Data ◽  
Packet Losses

With the development of the pioneering deployments in multi-hop wireless networks, although the relative research have not proven successful. The performance of routing and transport is often unstable due to contention induced packet losses, especially when the network is large and the offered load is high. A reliable wireless network architecture by using distributed control for large-scale multi-hop wireless networks has been present in this paper, The design objective is to optimize the control performance. This control performance is a complex function of the network parameters, such as throughput, packet delay and packet loss probabilities. The goal of optimizing the control performance imposes implicit tradeoffs on the wireless network design as opposed to the explicit tradeoffs typical in wireless data and voice applications. Our analysis suggests that our approach will deliver improved service to users while greatly reducing support effort and cost.

Large Scale Cognitive Wireless Networks

2013 ◽  
pp. 26-52
Author(s):  
Liang Song ◽  
Petros Spachos ◽  
Dimitrios Hatzinakos
Keyword(s):  
Energy Efficiency ◽  
Wireless Networks ◽  
Cognitive Radio ◽  
Data Aggregation ◽  
Network Architecture ◽  
Large Scale ◽  
Vehicular Networks ◽  
Network Resource ◽  
Cognitive Wireless Networks ◽  
Definition Of

Cognitive radio has been proposed to have spectrum agility (or opportunistic spectrum access). In this chapter, the authors introduce the extended network architecture of cognitive radio network, which accesses not only spectrum resource but also wireless stations (networking nodes) and high-level application data opportunistically: the large-scale cognitive wireless networks. The developed network architecture is based upon a re-definition of wireless linkage: as functional abstraction of proximity communications among wireless stations. The operation spectrum and participating stations of such abstract wireless links are opportunistically decided based on their instantaneous availability. It is able to maximize wireless network resource utilization and achieve much higher performance in large-scale wireless networks, where the networking environment can change fast (usually in millisecond level) in terms of spectrum and wireless station availability. The authors further introduce opportunistic routing and opportunistic data aggregation under the developed network architecture, which results in an implementation of cognitive unicast and cognitive data-aggregation wireless-link modules. In both works, it is shown that network performance and energy efficiency can improve with network scale (such as including station density). The applications of large-scale cognitive wireless networks are further discussed in new (and smart) beyond-3G wireless infrastructures, including for example real-time wireless sensor networks, indoor/underground wireless tracking networks, broadband wireless networks, smart grid and utility networks, smart vehicular networks, and emergency networks. In all such applications, the cognitive wireless networks can provide the most cost-effective wireless bandwidth and the best energy efficiency.

scholarly journals A Survey on Data Analysis on Large-Scale Wireless Networks: Online Stream Processing, Trends, and Challenges

2020 ◽  
Author(s):  
Dianne Scherly Varela de Medeiros ◽  
Helio do Nascimento Cunha Neto ◽  
Martin Andreoni Lopez ◽  
Luiz Claudio Schara Magalhães ◽  
Natalia Castro Fernandes ◽  
...  
Keyword(s):  
Machine Learning ◽  
Wireless Networks ◽  
Big Data ◽  
Wireless Network ◽  
Data Stream ◽  
Large Scale ◽  
Stream Processing ◽  
Knowledge Extraction ◽  
Machine Learning Algorithms ◽  
Data Stream Processing

Abstract In this paper we focus on knowledge extraction from large-scale wireless networks through stream processing. We present the primary methods for sampling, data collection, and monitoring of wireless networks and we characterize knowledge extraction as a machine learning problem on big data stream processing. We show the main trends in big data stream processing frameworks. Additionally, we explore the data preprocessing, feature engineering, and the machine learning algorithms applied to the scenario of wireless network analytics. We address challenges and present research projects in wireless network monitoring and stream processing. Finally, future perspectives, such as deep learning and reinforcement learning in stream processing, are anticipated.

scholarly journals Stochastic Geometry and Performance Analysis of Large Scale Wireless Networks

2021 ◽  
Vol 3 (3) ◽  
pp. 161-174
Author(s):  
Joy Iong-Zong Chen ◽  
Kong-Long Lai
Keyword(s):  
Wireless Network ◽  
Network Architecture ◽  
Stochastic Geometry ◽  
Large Scale ◽  
Ad Hoc ◽  
Scheduling Algorithm ◽  
Analytical Framework ◽  
Massive Growth ◽  
Monte Carlo Simulator ◽  
And Performance

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.

scholarly journals A Survey on Data Analysis on Large-Scale Wireless Networks: Online Stream Processing, Trends, and Challenges

2020 ◽  
Author(s):  
Dianne Scherly Varela de Medeiros ◽  
Helio do Nascimento Cunha Neto ◽  
Martin Andreoni Lopez ◽  
Luiz Claudio Schara Magalhães ◽  
Natalia Castro Fernandes ◽  
...  
Keyword(s):  
Machine Learning ◽  
Wireless Networks ◽  
Big Data ◽  
Wireless Network ◽  
Data Stream ◽  
Large Scale ◽  
Stream Processing ◽  
Knowledge Extraction ◽  
Machine Learning Algorithms ◽  
Data Stream Processing

Abstract In this paper we focus on knowledge extraction from large-scale wireless networks through stream processing. We present the primary methods for sampling, data collection, and monitoring of wireless networks and we characterize knowledge extraction as a machine learning problem on big data stream processing. We show the main trends in big data stream processing frameworks. Additionally, we explore the data preprocessing, feature engineering, and the machine learning algorithms applied to the scenario of wireless network analytics. We address challenges and present research projects in wireless network monitoring and stream processing. Finally, future perspectives, such as deep learning and reinforcement learning in stream processing, are anticipated.

scholarly journals A Theoretical Framework for Quality-Aware Cross-Layer Optimized Wireless Multimedia Communications

2008 ◽  
Vol 2008 ◽  
pp. 1-10 ◽  
Author(s):  
Song Ci ◽  
Haohong Wang ◽  
Dalei Wu
Keyword(s):  
Dynamic Programming ◽  
Wireless Networks ◽  
Network Architecture ◽  
Large Scale ◽  
Multimedia Systems ◽  
Multimedia Communications ◽  
Cross Layer ◽  
Cross Layer Design ◽  
Wireless Multimedia ◽  
Cross Layer Optimization

Although cross-layer has been thought as one of the most effective and efficient ways for multimedia communications over wireless networks and a plethora of research has been done in this area, there is still lacking of a rigorous mathematical model to gain in-depth understanding of cross-layer design tradeoffs, spanning from application layer to physical layer. As a result, many existing cross-layer designs enhance the performance of certain layers at the price of either introducing side effects to the overall system performance or violating the syntax and semantics of the layered network architecture. Therefore, lacking of a rigorous theoretical study makes existing cross-layer designs rely on heuristic approaches which are unable to guarantee sound results efficiently and consistently. In this paper, we attempt to fill this gap and develop a new methodological foundation for cross-layer design in wireless multimedia communications. We first introduce a delay-distortion-driven cross-layer optimization framework which can be solved as a large-scale dynamic programming problem. Then, we present new approximate dynamic programming based on significance measure and sensitivity analysis for high-dimensional nonlinear cross-layer optimization in support of real-time multimedia applications. The major contribution of this paper is to present the first rigorous theoretical modeling for integrated cross-layer control and optimization in wireless multimedia communications, providing design insights into multimedia communications over current wireless networks and throwing light on design optimization of the next-generation wireless multimedia systems and networks.

scholarly journals Video Transmission In Wireless Multihop Networks

2019 ◽  
Vol 8 (6) ◽  
pp. 3562-3565
Keyword(s):  
Wireless Networks ◽  
Wireless Network ◽  
Video Streaming ◽  
Video Transmission ◽  
Packet Scheduling ◽  
Video Quality ◽  
Video Communications ◽  
Wireless Data ◽  
Wireless Data Transmission ◽  
Wide Range

Multi hop wireless networks are being deployed in many video streaming applications because they have several potential features for next generation wireless communications. Though optimal encoding techniques offers significant quality retention in video transmission still it is insufficient to overcome the challenges ahead over wireless network transmission. In order to support wide range video communications in an efficient way certain Quality of service has to be retained in multi hop wireless network. To address this issue, this paper investigates several encoding and routing protocols video delivery over multi hop wireless networks. Specifically, we first investigate several encoding framework for videos and wireless data transmission over WMNs through individual paths; we then investigate the challenges ahead to formulate resistant routing model for least possible video quality dictions which incorporate channel status as well as the encoder properties over video characteristics. In this framework, routing techniques which can maximally used to achieve good video traffic with improved system performance. However, video streaming also have very stringent delay requirements, which makes it difficult to find optimal routes with the least possible distortions. To address this problem, we investigate several enhanced version of packet scheduling techniques for video communications over multi path multi hob multi user wireless network environment.

scholarly journals The Application of Wireless Network in the Process of Remote Data Collection

2021 ◽  
Vol 2074 (1) ◽  
pp. 012031
Author(s):  
Hong Lv ◽  
Weina Huang
Keyword(s):  
Wireless Networks ◽  
Data Collection ◽  
Wireless Network ◽  
Data Transmission ◽  
Transmission Channel ◽  
Wireless Data ◽  
Server Side ◽  
Wireless Data Transmission ◽  
Network Transmission ◽  
Remote Data

Abstract The use of wireless network for remote data collection can provide a fast and reliable wireless data transmission channel for those monitoring points involving a wide area and scattered equipment layout with the help of its large coverage and high communication quality. This article analyzes the characteristics and advantages of wireless networks, and then discusses the networking scheme for remote data collection using wireless networks, and analyzes the reliability of network transmission. Finally, some program fragments on the server side are given.

scholarly journals Scalable Internet of Things Network Design Using Multi-hop IEEE 802.11ah

2021 ◽  
Author(s):  
Nurzaman Ahmed ◽  
Md. Iftekahr Hussain
Keyword(s):  
Internet Of Things ◽  
Traffic Congestion ◽  
Network Architecture ◽  
Large Scale ◽  
Single Channel ◽  
Packet Delay ◽  
Access Point ◽  
Network Capacity ◽  
Delay Performance ◽  
Ieee 802.11Ah

Abstract The emerging IEEE 802.11ah is a promising communication standard for large-scale networks particularly the Internet of Things (IoT). The single-channel-based centralized channel access mechanism employed in 802.11ah does not scale well in such networks and leads to poor data reception quality. In this paper, we propose a multi-band sectorization and dynamic load balancing scheme for improving scalability. These features facilitate multi-hop communication more efficiently and enhance network capacity. Traffic congestion issues prevailing around the access point node due to the large volume of uplink traffic is mitigated by allowing simultaneous transmission using multiple orthogonal channels and sectors. Simulation and analytical results establish the essence of the novel protocol by showing significant improvements in terms of throughput and average packet delay over the existing schemes. The proposed network architecture improves throughput and delay performance up to 150% and 100% respectively compared to the relevant schemes.

scholarly journals A survey on data analysis on large-Scale wireless networks: online stream processing, trends, and challenges

2020 ◽  
Vol 11 (1) ◽  
Author(s):  
Dianne S. V. Medeiros ◽  
Helio N. Cunha Neto ◽  
Martin Andreoni Lopez ◽  
Luiz Claudio S. Magalhães ◽  
Natalia C. Fernandes ◽  
...  
Keyword(s):  
Machine Learning ◽  
Wireless Networks ◽  
Big Data ◽  
Wireless Network ◽  
Data Stream ◽  
Large Scale ◽  
Stream Processing ◽  
Knowledge Extraction ◽  
Machine Learning Algorithms ◽  
Data Stream Processing

Abstract In this paper we focus on knowledge extraction from large-scale wireless networks through stream processing. We present the primary methods for sampling, data collection, and monitoring of wireless networks and we characterize knowledge extraction as a machine learning problem on big data stream processing. We show the main trends in big data stream processing frameworks. Additionally, we explore the data preprocessing, feature engineering, and the machine learning algorithms applied to the scenario of wireless network analytics. We address challenges and present research projects in wireless network monitoring and stream processing. Finally, future perspectives, such as deep learning and reinforcement learning in stream processing, are anticipated.

Non-Blind Image Deconvolution Based on “Ringing” Removal Using Convolutional Neural Network

2020 ◽  
Vol 2020 (10) ◽  
pp. 181-1-181-7
Author(s):  
Takahiro Kudo ◽  
Takanori Fujisawa ◽  
Takuro Yamaguchi ◽  
Masaaki Ikehara
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Network Architecture ◽  
Large Scale ◽  
Blind Deconvolution ◽  
Training Dataset ◽  
Image Deconvolution ◽  
Classic Problem ◽  
Key Points ◽  
Blind Image

Image deconvolution has been an important issue recently. It has two kinds of approaches: non-blind and blind. Non-blind deconvolution is a classic problem of image deblurring, which assumes that the PSF is known and does not change universally in space. Recently, Convolutional Neural Network (CNN) has been used for non-blind deconvolution. Though CNNs can deal with complex changes for unknown images, some CNN-based conventional methods can only handle small PSFs and does not consider the use of large PSFs in the real world. In this paper we propose a non-blind deconvolution framework based on a CNN that can remove large scale ringing in a deblurred image. Our method has three key points. The first is that our network architecture is able to preserve both large and small features in the image. The second is that the training dataset is created to preserve the details. The third is that we extend the images to minimize the effects of large ringing on the image borders. In our experiments, we used three kinds of large PSFs and were able to observe high-precision results from our method both quantitatively and qualitatively.

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