Queuing Over Ever-Changing Communication Scenarios in Tactical Networks

Abstract

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.

TNT: A Tactical Network Test Platform to Evaluate Military Systems Over Ever-Changing Scenarios

“…However, most of them have limited discussion about (i) how to adapt user data flows to the network conditions; (ii) military systems are evaluated mostly under stable or nonstochastic network and user data flow changes; (iii) they are not reproducible due to the lack of detailed methods description and resources used, or even (iii) the results are not quantified to be compared. In [28], [33], the authors proposed to distinguish the methodology of testing military systems in three problems A the user data flow, B the network changes and A|B the military system that must handle both changes independently. For example, [32] solves problem A by introducing a model to generate ever-changing user data flows using a set of QoS-constrained messages.…”

“…Taking into account mobility metrics, the authors in [35] analyzed a list of available models that meet the requirements of tactical scenarios also identifying models that can be extended to meet such requirements. Regarding ever-changing communication scenarios, the present investigation advances our previous investigations [28], [31], [32] by introducing mobility models to create network scenarios that can be characterized by mobility metrics; modeling the communication area in order to convert any sequence of network states (link data rate) to a mobility trace and viceversa; and wrapping these methods together with a monitoring and data analysis approach, providing a software platform to automate performance tests in tactical networks.…”

“…In order to introduce the element of chance at the application layer, TNT proposes a model to create user data flows that can simulate different applications through the generation of different traffic flows. For this purpose, we extend the user data flow model defined in our previous investigation [28], Model A (A, λ, f.m <metric> ), where A defines the distribution of message type, the arrival time is defined by λ A together with a distribution function f for all message metrics f.m <metric> such as {size, reliability, time of expire} to model M A .…”

Maximizing the Probability of Message Delivery Over Ever-Changing Communication Scenarios in Tactical Networks

“…1. This figure shows the end-to-end communication scenario with the sender and the receiver connected through a radio link, composing of an ever-changing communication scenario [1], [2], [6]. Each node has a control plane (c) and two chains: one for incoming (i) data-flows and another for outgoing (o) data-flows, both sitting in at least four layers, namely radio (0), packet (1), message (2) and proxy/broker (3).…”

“…C OMMUNICATION scenarios at the edge of tactical networks are exposed to several sources of randomness that can change the radio link data rate [1]. Therefore, tactical systems might have to deliver messages over ever-changing scenarios with both user data-flows and network conditions changing independently [2]. Given the wide range of military operations, it is challenging to design tactical systems that can thrive in arbitrary communication scenarios, also including link disconnections.…”