scholarly journals Quantizing Radio Link Data Rates to Create Ever-changing Network Conditions in Tactical Networks

2020 ◽  
Author(s):  
Roberto Rigolin F. Lopes ◽  
Johannes Loevenich ◽  
Paulo H. Rettore ◽  
Sharath M. Eswarappa ◽  
Peter Sevenich
Keyword(s):  
Field Experiments ◽  
Time Window ◽  
Data Rate ◽  
Radio Link ◽  
Data Rates ◽  
Data Flows ◽  
Packet Latency ◽  
Tactical Networks ◽  
Link Data ◽  
And Control

Several sources of randomness can change the radio link data rate at the edge of tactical networks. Simulations and field experiments define these sources of randomness indirectly by choosing the mobility pattern, communication technology, number of nodes, terrain, obstacles and so on. Therefore, the distribution of change in the network conditions is unknown until the experiment is executed. We start with the hypothesis that a model can quantize the network conditions, using a set of states updated within a time window, to define and control the distribution of change in the link data rate before the experiment is executed. The goal is to quantify how much variation in the link data rate a tactical system can handle and how long it takes to resume IP data-flows after link disconnections. Our model includes functions to combine patterns of change together, transforming one pattern into another, jumping between patterns, and creating loops among different patterns of change. We use exemplary patterns to show how the change in the data rate impacts other link metrics, such as latency and jitter. Our hypothesis is verified with experiments using VHF radios over different patterns of change created by our model. We compute the inter-packet latency of three types of IP data-flows (broadcast, unicast and overlay) to highlight the time to resume data-flows after long link disconnections. The experimental results also support the discussion on the advantages and limitations of our model, which was designed to test tactical systems using military radios.

scholarly journals Quantizing Radio Link Data Rates to Create Ever-changing Network Conditions in Tactical Networks

2020 ◽  
Author(s):  
Roberto Rigolin F. Lopes ◽  
Johannes Loevenich ◽  
Paulo H. Rettore ◽  
Sharath M. Eswarappa ◽  
Peter Sevenich
Keyword(s):  
Field Experiments ◽  
Time Window ◽  
Data Rate ◽  
Radio Link ◽  
Data Rates ◽  
Data Flows ◽  
Packet Latency ◽  
Tactical Networks ◽  
Link Data ◽  
And Control

Several sources of randomness can change the radio link data rate at the edge of tactical networks. Simulations and field experiments define these sources of randomness indirectly by choosing the mobility pattern, communication technology, number of nodes, terrain, obstacles and so on. Therefore, the distribution of change in the network conditions is unknown until the experiment is executed. We start with the hypothesis that a model can quantize the network conditions, using a set of states updated within a time window, to define and control the distribution of change in the link data rate before the experiment is executed. The goal is to quantify how much variation in the link data rate a tactical system can handle and how long it takes to resume IP data-flows after link disconnections. Our model includes functions to combine patterns of change together, transforming one pattern into another, jumping between patterns, and creating loops among different patterns of change. We use exemplary patterns to show how the change in the data rate impacts other link metrics, such as latency and jitter. Our hypothesis is verified with experiments using VHF radios over different patterns of change created by our model. We compute the inter-packet latency of three types of IP data-flows (broadcast, unicast and overlay) to highlight the time to resume data-flows after long link disconnections. The experimental results also support the discussion on the advantages and limitations of our model, which was designed to test tactical systems using military radios.

scholarly journals Quantizing Radio Link Data Rates to Create Ever-Changing Network Conditions in Tactical Networks

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 188015-188035
Author(s):  
Roberto Rigolin F. Lopes ◽  
Johannes Loevenich ◽  
Paulo H. L. Rettore ◽  
Sharath Maligera Eswarappa ◽  
Peter Sevenich
Keyword(s):  
Radio Link ◽  
Data Rates ◽  
Tactical Networks ◽  
Link Data

scholarly journals Queuing over Ever-changing Communication Scenarios in Tactical Networks

2020 ◽  
Author(s):  
Roberto Rigolin F. Lopes ◽  
Pooja Hanavadi Balaraju ◽  
Paulo H. Rettore ◽  
Peter Sevenich
Keyword(s):  
Control Systems ◽  
Information Exchange ◽  
Data Rate ◽  
Cross Layer ◽  
Buffer Occupancy ◽  
Data Flows ◽  
Tactical Networks ◽  
Quantitative Results ◽  
Using Data ◽  
And Control

This paper introduces a hierarchy of queues complementing each other to handle ever-changing communication scenarios in tactical networks. The first queue stores the QoS-constrained messages from command and control systems. These messages are fragmented into IP packets, which are stored in a queue of packets (second) to be sent to the radio buffer (third), which is a queue with limited space therefore, open to overflow. We start with the hypothesis that these three queues can handle ever-changing user(s) data flows (problem A) through ever-changing network conditions (problem B) using cross-layer information exchange, such as buffer occupancy, data rate, queue size and latency (problem A|B). We introduce two stochastic models to create sequences of QoS-constrained messages (A) and to create ever-changing network conditions (B). In sequence, we sketch a control loop to shape A to B to test our hypothesis using model A|B, which defines enforcement points at the incoming/outgoing chains of the system together with a control plane. Then, we discuss experimental results in a network with VHF radios using data flows that overflows the radio buffer over ever-changing data rate patterns. We discuss quantitative results showing the performance and limitations of our solutions for problems A, B and A|B.

scholarly journals Queuing over Ever-changing Communication Scenarios in Tactical Networks

2020 ◽  
Author(s):  
Roberto Rigolin F. Lopes ◽  
Pooja Hanavadi Balaraju ◽  
Paulo H. Rettore ◽  
Peter Sevenich
Keyword(s):  
Control Systems ◽  
Information Exchange ◽  
Data Rate ◽  
Cross Layer ◽  
Buffer Occupancy ◽  
Data Flows ◽  
Tactical Networks ◽  
Quantitative Results ◽  
Using Data ◽  
And Control

This paper introduces a hierarchy of queues complementing each other to handle ever-changing communication scenarios in tactical networks. The first queue stores the QoS-constrained messages from command and control systems. These messages are fragmented into IP packets, which are stored in a queue of packets (second) to be sent to the radio buffer (third), which is a queue with limited space therefore, open to overflow. We start with the hypothesis that these three queues can handle ever-changing user(s) data flows (problem A) through ever-changing network conditions (problem B) using cross-layer information exchange, such as buffer occupancy, data rate, queue size and latency (problem A|B). We introduce two stochastic models to create sequences of QoS-constrained messages (A) and to create ever-changing network conditions (B). In sequence, we sketch a control loop to shape A to B to test our hypothesis using model A|B, which defines enforcement points at the incoming/outgoing chains of the system together with a control plane. Then, we discuss experimental results in a network with VHF radios using data flows that overflows the radio buffer over ever-changing data rate patterns. We discuss quantitative results showing the performance and limitations of our solutions for problems A, B and A|B.

scholarly journals Maximizing the Probability of Message Delivery over Ever-changing Communication Scenarios in Tactical Networks

2020 ◽  
Author(s):  
Johannes Loevenich ◽  
Roberto Rigolin F. Lopes ◽  
Paulo H. Rettore ◽  
Sharath M. Eswarappa ◽  
Peter Sevenich
Keyword(s):  
Packet Loss ◽  
Data Rate ◽  
Transport Protocol ◽  
Message Delivery ◽  
Optimal Parameters ◽  
User Data ◽  
Tactical Networks ◽  
Link Data ◽  
Store And Forward ◽  
Ip Packets

This letter introduces a stochastic model to maximize the probability of message delivery over ever-changing communication scenarios in tactical networks. Our model improves modern tactical systems implementing store-and-forward mechanisms organized in a hierarchy of layers for messages, IP packets and radios. The goal is to compute close to optimal parameters for a transport protocol by computing the optimum redundancy for the user data-flow to overcome packet loss during changes in the link data rate, including disconnections. Experiments in a VHF network illustrate the numerical results from our model using messages with different sizes over two patterns of data rate change.

scholarly journals Maximizing the Probability of Message Delivery over Ever-changing Communication Scenarios in Tactical Networks

2020 ◽  
Author(s):  
Johannes Loevenich ◽  
Roberto Rigolin F. Lopes ◽  
Paulo H. Rettore ◽  
Sharath M. Eswarappa ◽  
Peter Sevenich
Keyword(s):  
Packet Loss ◽  
Data Rate ◽  
Transport Protocol ◽  
Message Delivery ◽  
Optimal Parameters ◽  
User Data ◽  
Tactical Networks ◽  
Link Data ◽  
Store And Forward ◽  
Ip Packets

This letter introduces a stochastic model to maximize the probability of message delivery over ever-changing communication scenarios in tactical networks. Our model improves modern tactical systems implementing store-and-forward mechanisms organized in a hierarchy of layers for messages, IP packets and radios. The goal is to compute close to optimal parameters for a transport protocol by computing the optimum redundancy for the user data-flow to overcome packet loss during changes in the link data rate, including disconnections. Experiments in a VHF network illustrate the numerical results from our model using messages with different sizes over two patterns of data rate change.

An evaluation model of 2019-nCoV prevention effectiveness based on immigration data in China (Preprint)

2020 ◽  
Author(s):  
Qiangsheng Huang
Keyword(s):  
Prevention And Control ◽  
Time Window ◽  
Evaluation Model ◽  
Control Measures ◽  
Related Data ◽  
The Face ◽  
Prevention And Control Measures ◽  
And Control ◽  
Best Fit

BACKGROUND As of the end of February 2020, 2019-nCoV is currently well controlled in China. However, the virus is now spreading globally. OBJECTIVE This study aimed to evaluate the effectiveness of outbreak prevention and control measures in a region. METHODS A model is built for find the best fit for two sets of data (the number of daily new diagnosed, and the risk value of incoming immigration population). The parameters (offset and time window) in the model can be used as the evaluation of effectiveness of outbreak prevention and control. RESULTS Through study, it is found that the parameter offset and time window in the model can accurately reflect the prevention effectiveness. Some related data and public news confirm this result. And this method has advantages over the method using R0 in two aspects. CONCLUSIONS If the epidemic situation is well controlled, the virus is not terrible. Now the daily new diagnosed patients in most regions of China is quickly reduced to zero or close to zero. Chinese can do a good job in the face of huge epidemic pressure. Therefore, if other countries can do well in prevention and control, the epidemic in those places can also pass quickly.

The Effect of Trifluralin Applied Preplant on Grass Weed Control and Establishment and Yield of Barley (Hordeum vulgare)

1997 ◽  
Vol 11 (3) ◽  
pp. 515-519 ◽  
Author(s):  
Julio A. Scursoni ◽  
Emilio H. Satorre
Keyword(s):  
Weed Control ◽  
Field Experiments ◽  
Herbicide Application ◽  
Planting Pattern ◽  
Weed Density ◽  
Time Of Application ◽  
Density And Biomass ◽  
And Control ◽  
Crop Planting ◽  
Grass Weed

The objective of this paper was to evaluate the effect of preplant applications of trifluralin on barley stand and yield, and control of grass weeds in field experiments during 1992 and 1993. Factors examined were: (1) crop planting patterns (conventional drill with rows 15 cm apart and deep-seeder drill with rows 25 cm apart), (2) herbicide application times (22 d before sowing and immediately before sowing), and (3) herbicide application. During 1993, hand-weeded plots also were established. Trifluralin applied preplant at 528 g ai/ha reduced weed density and biomass. Weed control was higher under conventional planting than under the deep planting pattern, and there was no effect of the time of application on herbicide efficacy. There was no herbicide injury to the crop, and grain yield was higher in treated than in untreated plots due to successful weed control.

scholarly journals Metalearners for estimating heterogeneous treatment effects using machine learning

2019 ◽  
Vol 116 (10) ◽  
pp. 4156-4165 ◽  
Author(s):  
Sören R. Künzel ◽  
Jasjeet S. Sekhon ◽  
Peter J. Bickel ◽  
Bin Yu
Keyword(s):  
Machine Learning ◽  
Treatment Effects ◽  
Field Experiments ◽  
Average Treatment Effect ◽  
Regularity Conditions ◽  
Heterogeneous Treatment Effects ◽  
Lipschitz Continuous ◽  
Additive Regression ◽  
Underlying Mechanisms ◽  
And Control

There is growing interest in estimating and analyzing heterogeneous treatment effects in experimental and observational studies. We describe a number of metaalgorithms that can take advantage of any supervised learning or regression method in machine learning and statistics to estimate the conditional average treatment effect (CATE) function. Metaalgorithms build on base algorithms—such as random forests (RFs), Bayesian additive regression trees (BARTs), or neural networks—to estimate the CATE, a function that the base algorithms are not designed to estimate directly. We introduce a metaalgorithm, the X-learner, that is provably efficient when the number of units in one treatment group is much larger than in the other and can exploit structural properties of the CATE function. For example, if the CATE function is linear and the response functions in treatment and control are Lipschitz-continuous, the X-learner can still achieve the parametric rate under regularity conditions. We then introduce versions of the X-learner that use RF and BART as base learners. In extensive simulation studies, the X-learner performs favorably, although none of the metalearners is uniformly the best. In two persuasion field experiments from political science, we demonstrate how our X-learner can be used to target treatment regimes and to shed light on underlying mechanisms. A software package is provided that implements our methods.

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