Load Balancing Routing Algorithm of Low-Orbit Communication Satellite Network Traffic Based on Machine Learning

Satellite communication has become an important research trend in the field of communication technology. Low-orbit satellites have always been the focus of extensive attention by scholars due to their wide coverage, strong flexibility, and freedom from geographical constraints. This article introduces some technologies about low-orbit satellites and introduces a routing algorithm DDPG based on machine learning for simulation experiments. The performance of this algorithm is compared with the performance of three commonly used low-orbit satellite routing algorithms, and a conclusion is drawn. The routing algorithm based on machine learning has the smallest average delay, and the average value is 126 ms under different weights. Its packet loss rate is the smallest, with an average of 2.9%. Its throughput is the largest, with an average of 201.7 Mbps; its load distribution index is the smallest, with an average of 0.54. In summary, the performance of routing algorithms based on machine learning is better than general algorithms.

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DCAR: DTN Congestion Avoidance Routing Algorithm Based on Tokens in an Urban Environment

Journal of Sensors ◽

10.1155/2017/6523076 ◽

2017 ◽

Vol 2017 ◽

pp. 1-9

Author(s):

Hezhe Wang ◽

Hongwu Lv ◽

Huiqiang Wang ◽

Guangsheng Feng

Keyword(s):

Network Performance ◽

Routing Algorithm ◽

Relay Node ◽

Routing Algorithms ◽

Buffer Size ◽

Destination Node ◽

Delivery Ratio ◽

Movement Trajectory ◽

Average Delay ◽

Urban Scenario

When a delay/disruption tolerant network (DTN) is applied in an urban scenario, the network is mainly composed of mobile devices carried by pedestrians, cars, and other vehicles, and the node’s movement trajectory is closely related to its social relationships and regular life; thus, most existing DTN routing algorithms cannot show efficient network performance in urban scenarios. In this paper, we propose a routing algorithm, called DCRA, which divides the urban map into grids; fixed sink stations are established in specific grids such that the communication range of each fixed sink station can cover a specific number of grids; these grids are defined as a cluster and allocated a number of tokens in each cluster; the tokens in the cluster are controlled by the fixed sink station. A node will transmit messages to a relay node that has a larger remaining buffer size and encounters fixed sink stations or the destination node more frequently after it obtains a message transmit token. Simulation experiments are carried out to verify the performance of the DCAR under an urban scenario, and results show that the DCAR algorithm is superior to existing routing algorithms in terms of delivery ratio, average delay, and network overhead.

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Research on the WSN Node Localization Based on TOA

Journal of Applied Mathematics ◽

10.1155/2013/706064 ◽

2013 ◽

Vol 2013 ◽

pp. 1-6

Author(s):

Qing-hui Wang ◽

Ting-ting Lu ◽

Meng-long Liu ◽

Li-feng Wei

Keyword(s):

Large Scale ◽

Loss Rate ◽

Routing Algorithm ◽

Location Information ◽

Node Localization ◽

Average Delay ◽

Delay Reduction ◽

Node Location ◽

System Overhead ◽

Fixed Node

Regarding the tracking of moving target in the large-scale fixed scene, a new routing algorithm of LAODV in the principle of TOA localization is proposed. Then, the participation field of the fixed node based on the node location information is properly controlled, while the routing request area is reduced through combination of AODV and LAR during transmission of the location information. Simulation results show that the proposed algorithm renders satisfactory performance in terms of average delay reduction from end to end, packet loss rate, and routing overhead. As a result, the delay and system overhead during localization could be minimized.

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Enhanced Routing Algorithm Based on Reinforcement Machine Learning—A Case of VoIP Service

Sensors ◽

10.3390/s21020504 ◽

2021 ◽

Vol 21 (2) ◽

pp. 504

Author(s):

Davi Ribeiro Militani ◽

Hermes Pimenta de Moraes ◽

Renata Lopes Rosa ◽

Lunchakorn Wuttisittikulkij ◽

Miguel Arjona Ramírez ◽

...

Keyword(s):

Machine Learning ◽

Network Performance ◽

Routing Algorithm ◽

Variable Number ◽

Performance Comparison ◽

Routing Algorithms ◽

Network Data ◽

Dynamic Adjustment ◽

Network Parameters ◽

Control Message

The routing algorithm is one of the main factors that directly impact on network performance. However, conventional routing algorithms do not consider the network data history, for instances, overloaded paths or equipment faults. It is expected that routing algorithms based on machine learning present advantages using that network data. Nevertheless, in a routing algorithm based on reinforcement learning (RL) technique, additional control message headers could be required. In this context, this research presents an enhanced routing protocol based on RL, named e-RLRP, in which the overhead is reduced. Specifically, a dynamic adjustment in the Hello message interval is implemented to compensate the overhead generated by the use of RL. Different network scenarios with variable number of nodes, routes, traffic flows and degree of mobility are implemented, in which network parameters, such as packet loss, delay, throughput and overhead are obtained. Additionally, a Voice-over-IP (VoIP) communication scenario is implemented, in which the E-model algorithm is used to predict the communication quality. For performance comparison, the OLSR, BATMAN and RLRP protocols are used. Experimental results show that the e-RLRP reduces network overhead compared to RLRP, and overcomes in most cases all of these protocols, considering both network parameters and VoIP quality.

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An Adaptive Routing Algorithm Based on Relation Tree in DTN

Sensors ◽

10.3390/s21237847 ◽

2021 ◽

Vol 21 (23) ◽

pp. 7847

Author(s):

Diyue Chen ◽

Hongyan Cui ◽

Roy E. Welsch

Keyword(s):

Negative Feedback ◽

Routing Algorithm ◽

Adaptive Routing ◽

Routing Algorithms ◽

Source Node ◽

Message Delivery ◽

Average Delay ◽

Hop Count ◽

Simulation Results ◽

Delay Tolerant

It is found that nodes in Delay Tolerant Networks (DTN) exhibit stable social attributes similar to those of people. In this paper, an adaptive routing algorithm based on Relation Tree (AR-RT) for DTN is proposed. Each node constructs its own Relation Tree based on the historical encounter frequency, and will adopt different forwarding strategies based on the Relation Tree in the forwarding phase, so as to achieve more targeted forwarding. To further improve the scalability of the algorithm, the source node dynamically controls the initial maximum number of message copies according to its own cache occupancy, which enables the node to make negative feedback to network environment changes. Simulation results show that the AR-RT algorithm proposed in this paper has significant advantages over existing routing algorithms in terms of average delay, average hop count, and message delivery rate.

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A STRATEGY OF DESIGNING ROUTING ALGORITHMS BASED ON IDEAL ROUTINGS

International Journal of Modern Physics C ◽

10.1142/s012918319900005x ◽

1999 ◽

Vol 10 (01) ◽

pp. 63-94 ◽

Cited By ~ 12

Author(s):

K. SHINJO ◽

S. SHIMOGAWA ◽

J. YAMADA ◽

K. OIDA

Keyword(s):

Routing Algorithm ◽

Simple Algorithm ◽

Packet Delay ◽

Routing Algorithms ◽

Performance Limits ◽

Internet Routing ◽

Average Delay ◽

Switched Networks ◽

Design Elements ◽

The Ideal

This paper proposes a strategy of designing routing algorithms for connectionless packet-switched networks. This strategy consists of three design elements as follows: [A] the notion of ideal routings is introduced to provide the upper performance limits attained by improving routing algorithm and it serves as a standard to measure the performance of other algorithms; [B] a method of constructing simple algorithms is presented under implementation conditions from ideal routings; [C] a method is described to enhance the performance limits of [A]. By using these elements, simple algorithms with a maximum degree of performance attainment are realized. By "degree of performance attainment", we mean that we can see how much room is left for the improvement of algorithms. We develop [A] and [B] with the performance measures of throughput and average packet delay and the M/M/1 queuing network. We decide ideal static routings and their performance limits from [A]. We obtain a new simple algorithm from [B] based on the notion of the ideal routings in implementation conditions. The designed algorithm improves the throughput and the average delay, which are comparable to those from ideal static routings. This improvement is contrasted to the adaptive and distributed OSPF (Open Shortest Path First), a standard Internet routing protocol.

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Implementasi Voip Server Berbasis IPV6 Dengan Raspberry PI

Manutech : Jurnal Teknologi Manufaktur ◽

10.33504/manutech.v9i01.32 ◽

2019 ◽

Vol 9 (01) ◽

pp. 47-54

Author(s):

Rabbai San Arif ◽

Yuli Fitrisia ◽

Agus Urip Ari Wibowo

Keyword(s):

Internet Protocol ◽

Wireless Transmission ◽

Raspberry Pi ◽

Test Results ◽

Average Delay ◽

Ipv6 Network ◽

Ip Network ◽

Selection Of ◽

Better Than

Voice over Internet Protocol (VoIP) is a telecommunications technology that is able to pass the communication service in Internet Protocol networks so as to allow communicating between users in an IP network. However VoIP technology still has weakness in the Quality of Service (QoS). VOPI weaknesses is affected by the selection of the physical servers used. In this research, VoIP is configured on Linux operating system with Asterisk as VoIP application server and integrated on a Raspberry Pi by using wired and wireless network as the transmission medium. Because of depletion of IPv4 capacity that can be used on the network, it needs to be applied to VoIP system using the IPv6 network protocol with supports devices. The test results by using a wired transmission medium that has obtained are the average delay is 117.851 ms, jitter is 5.796 ms, packet loss is 0.38%, throughput is 962.861 kbps, 8.33% of CPU usage and 59.33% of memory usage. The analysis shows that the wired transmission media is better than the wireless transmission media and wireless-wired.

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Exploring the Use of Machine Learning to Automate the Qualitative Coding of Church-related Tweets

Fieldwork in Religion ◽

10.1558/firn.40610 ◽

2020 ◽

Vol 14 (2) ◽

pp. 140-159

Author(s):

Anthony-Paul Cooper ◽

Emmanuel Awuni Kolog ◽

Erkki Sutinen

Keyword(s):

Machine Learning ◽

Online Community ◽

High Volume ◽

Machine Learning Algorithms ◽

Supervised Machine Learning ◽

Social Media Data ◽

Twitter Data ◽

Resource Intensity ◽

Media Data ◽

Better Than

This article builds on previous research around the exploration of the content of church-related tweets. It does so by exploring whether the qualitative thematic coding of such tweets can, in part, be automated by the use of machine learning. It compares three supervised machine learning algorithms to understand how useful each algorithm is at a classification task, based on a dataset of human-coded church-related tweets. The study finds that one such algorithm, Naïve-Bayes, performs better than the other algorithms considered, returning Precision, Recall and F-measure values which each exceed an acceptable threshold of 70%. This has far-reaching consequences at a time where the high volume of social media data, in this case, Twitter data, means that the resource-intensity of manual coding approaches can act as a barrier to understanding how the online community interacts with, and talks about, church. The findings presented in this article offer a way forward for scholars of digital theology to better understand the content of online church discourse.

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Identification of Anti-cancer Peptides Based on Multi-classifier System

Combinatorial Chemistry & High Throughput Screening ◽

10.2174/1386207322666191203141102 ◽

2020 ◽

Vol 22 (10) ◽

pp. 694-704 ◽

Cited By ~ 2

Author(s):

Wanben Zhong ◽

Bineng Zhong ◽

Hongbo Zhang ◽

Ziyi Chen ◽

Yan Chen

Keyword(s):

Machine Learning ◽

Side Effect ◽

Learning Models ◽

Normal Cells ◽

Classifier System ◽

Prediction Rate ◽

Anti Cancer ◽

Feature Information ◽

Machine Learning Models ◽

Better Than

Aim and Objective: Cancer is one of the deadliest diseases, taking the lives of millions every year. Traditional methods of treating cancer are expensive and toxic to normal cells. Fortunately, anti-cancer peptides (ACPs) can eliminate this side effect. However, the identification and development of new anti Materials and Methods: In our study, a multi-classifier system was used, combined with multiple machine learning models, to predict anti-cancer peptides. These individual learners are composed of different feature information and algorithms, and form a multi-classifier system by voting. Results and Conclusion: The experiments show that the overall prediction rate of each individual learner is above 80% and the overall accuracy of multi-classifier system for anti-cancer peptides prediction can reach 95.93%, which is better than the existing prediction model.

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Computing Possible Futures

10.1093/oso/9780198846420.001.0001 ◽

2019 ◽

Cited By ~ 2

Author(s):

William B. Rouse

Keyword(s):

Artificial Intelligence ◽

Machine Learning ◽

Computational Modeling ◽

Mental Model ◽

Data Analytics ◽

Computational Models ◽

Senior Managers ◽

Interactive Visualizations ◽

Use Of Models ◽

Better Than

This book discusses the use of models and interactive visualizations to explore designs of systems and policies in determining whether such designs would be effective. Executives and senior managers are very interested in what “data analytics” can do for them and, quite recently, what the prospects are for artificial intelligence and machine learning. They want to understand and then invest wisely. They are reasonably skeptical, having experienced overselling and under-delivery. They ask about reasonable and realistic expectations. Their concern is with the futurity of decisions they are currently entertaining. They cannot fully address this concern empirically. Thus, they need some way to make predictions. The problem is that one rarely can predict exactly what will happen, only what might happen. To overcome this limitation, executives can be provided predictions of possible futures and the conditions under which each scenario is likely to emerge. Models can help them to understand these possible futures. Most executives find such candor refreshing, perhaps even liberating. Their job becomes one of imagining and designing a portfolio of possible futures, assisted by interactive computational models. Understanding and managing uncertainty is central to their job. Indeed, doing this better than competitors is a hallmark of success. This book is intended to help them understand what fundamentally needs to be done, why it needs to be done, and how to do it. The hope is that readers will discuss this book and develop a “shared mental model” of computational modeling in the process, which will greatly enhance their chances of success.

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NLOS Multipath Classification of GNSS Signal Correlation Output Using Machine Learning

Sensors ◽

10.3390/s21072503 ◽

2021 ◽

Vol 21 (7) ◽

pp. 2503

Author(s):

Taro Suzuki ◽

Yoshiharu Amano

Keyword(s):

Machine Learning ◽

Satellite System ◽

Training Data ◽

Support Vector ◽

Positioning Errors ◽

Automated Method ◽

Global Navigation Satellite ◽

Better Than ◽

Signal Correlation

This paper proposes a method for detecting non-line-of-sight (NLOS) multipath, which causes large positioning errors in a global navigation satellite system (GNSS). We use GNSS signal correlation output, which is the most primitive GNSS signal processing output, to detect NLOS multipath based on machine learning. The shape of the multi-correlator outputs is distorted due to the NLOS multipath. The features of the shape of the multi-correlator are used to discriminate the NLOS multipath. We implement two supervised learning methods, a support vector machine (SVM) and a neural network (NN), and compare their performance. In addition, we also propose an automated method of collecting training data for LOS and NLOS signals of machine learning. The evaluation of the proposed NLOS detection method in an urban environment confirmed that NN was better than SVM, and 97.7% of NLOS signals were correctly discriminated.

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