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

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.

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

10.36227/techrxiv.12894785 ◽

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.

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

10.36227/techrxiv.12894785.v1 ◽

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.

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Virtualized Fog Network with Load Balancing for IoT based Fog-to-Cloud

JOIV International Journal on Informatics Visualization ◽

10.30630/joiv.4.3.374 ◽

2020 ◽

Vol 4 (3) ◽

Author(s):

Istabraq M. Al-Joboury ◽

Emad H. Al-Hemiary

Keyword(s):

Load Balancing ◽

Packet Loss ◽

Fog Computing ◽

Transport Protocol ◽

Network Simulator ◽

Huge Number ◽

Message Queue ◽

Limited Bandwidth ◽

Cloud Architecture ◽

Fog Network

Fog Computing is a new concept made by Cisco to provide same functionalities of Cloud Computing but near to Things to enhance performance such as reduce delay and response time. Packet loss may occur on single Fog server over a huge number of messages from Things because of several factors like limited bandwidth and capacity of queues in server. In this paper, Internet of Things based Fog-to-Cloud architecture is proposed to solve the problem of packet loss on Fog server using Load Balancing and virtualization. The architecture consists of 5 layers, namely: Things, gateway, Fog, Cloud, and application. Fog layer is virtualized to specified number of Fog servers using Graphical Network Simulator-3 and VirtualBox on local physical server. Server Load Balancing router is configured to distribute the huge traffic in Weighted Round Robin technique using Message Queue Telemetry Transport protocol. Then, maximum message from Fog layer are selected and sent to Cloud layer and the rest of messages are deleted within 1 hour using our proposed Data-in-Motion technique for storage, processing, and monitoring of messages. Thus, improving the performance of the Fog layer for storage and processing of messages, as well as reducing the packet loss to half and increasing throughput to 4 times than using single Fog server.

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

IEEE Networking Letters ◽

10.1109/lnet.2021.3066536 ◽

2021 ◽

pp. 1-1

Author(s):

Johannes F. Loevenich ◽

Roberto Rigolin F. Lopes ◽

Paulo H. Rettore ◽

Sharath M. Eswarappa ◽

Peter Sevenich

Keyword(s):

Message Delivery ◽

Tactical Networks

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QoS based Radio Resource Allocation and Scheduling with Different User Data Rate Requirements for OFDMA Systems

2007 IEEE 18th International Symposium on Personal, Indoor and Mobile Radio Communications ◽

10.1109/pimrc.2007.4394699 ◽

2007 ◽

Cited By ~ 10

Author(s):

Ricardo B. Santos ◽

Francisco Rafael M. Lima ◽

Walter C. Freitas ◽

Francisco Rodrigo P. Cavalcanti

Keyword(s):

Resource Allocation ◽

Data Rate ◽

Radio Resource Allocation ◽

Radio Resource ◽

User Data

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Wireless Body Area Network (WBAN)-Based Telemedicine for Emergency Care

Sensors ◽

10.3390/s20072153 ◽

2020 ◽

Vol 20 (7) ◽

pp. 2153 ◽

Cited By ~ 1

Author(s):

Latha R ◽

Vetrivelan P

Keyword(s):

Packet Loss ◽

Loss Rate ◽

Bayes Theorem ◽

Data Rate ◽

Packet Loss Rate ◽

Medical Decision ◽

Area Network ◽

Body Area ◽

Delay Sensitive ◽

Emergency Monitoring

This paper is a collection of telemedicine techniques used by wireless body area networks (WBANs) for emergency conditions. Furthermore, Bayes’ theorem is proposed for predicting emergency conditions. With prior knowledge, the posterior probability can be found along with the observed evidence. The probability of sending emergency messages can be determined using Bayes’ theorem with the likelihood evidence. It can be viewed as medical decision-making, since diagnosis conditions such as emergency monitoring, delay-sensitive monitoring, and general monitoring are analyzed with its network characteristics, including data rate, cost, packet loss rate, latency, and jitter. This paper explains the network model with 16 variables, with one describing immediate consultation, as well as another three describing emergency monitoring, delay-sensitive monitoring, and general monitoring. The remaining 12 variables are observations related to latency, cost, packet loss rate, data rate, and jitter.

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Priority Scheduling Algorithm for Traffic in a LTE-based Defence Mesh Network Incorporating Centralised Scheduling Architecture

Defence Science Journal ◽

10.14429/dsj.67.10593 ◽

2017 ◽

Vol 67 (5) ◽

pp. 581

Author(s):

Sidharth Shukla ◽

Vimal Bhatia

Keyword(s):

Packet Loss ◽

Mesh Networks ◽

Scheduling Algorithm ◽

Data Rate ◽

Mesh Network ◽

High Data Rate ◽

Priority Scheduling ◽

High Data ◽

Wireless Mesh

<p>Wireless mesh networks (WMN) are the networks of future and can operate on multiple protocols ranging from WiFi, WiMax to long term evolution (LTE). As a recent trend defence networks are incorporating off-the-shelf, state of the art commercial protocols to enhance the capability of their networks. LTE is one such commercially available protocol which is easy to deploy and provide high data rate which can be ideally implemented in WMN for defence networks. To enable these high data rate services LTE-based defence mesh networks (DMN) are the requirement of the day and future. However, LTE-based DMN are prone to congestion at times of active operations or full-fledged war. The congestion scenarios may lead to LTE packet loss. Hence, it is pertinent that these networks amalgamate information grooming algorithms to alleviate the throughput of the network in peak hour conditions. An efficient priority scheduling algorithm based on class of service prioritisation, data rate consumption and location of origin of traffic in the DMN is proposed. The simulations demonstrate that by incorporating the proposed priority scheduling algorithm, the overall packet loss of priority packets in the DMN reduces substantially.</p>

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