Interoperation of the Wireless Emulation Laboratory 

(WEL) and the System-of-Systems Survivability 

Simulation(S4): 2011 Year-End Status of the 

Director’s Strategic Initiative and Results

by Jeffrey A. Smith, Brian Rivera, and Rommie L. Hardy

ARL-TR-5867 January 2012

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Army Research Laboratory

White Sands Missile Range, NM 88005

ARL-TR-5867 January 2012

Interoperation of the Wireless Emulation Laboratory 

(WEL) and the System-of-Systems Survivability 

Simulation(S4): 2011 Year-End Status of the 

Director’s Strategic Initiative and Results

Jeffrey A. Smith

Survivability/Lethality Analysis Directorate, ARL

Brian Rivera and Rommie L. Hardy

Computational and Information Sciences Directorate, ARL

Approved for public release; distribution unlimited. 
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Interoperation of the Wireless Emulation Laboratory (WEL) and the System-ofSystems Survivability Simulation (S4): 2011 Year-End Status of the Director’s 

Strategic Initiative and Results

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14. ABSTRACT

The U.S. Department of Defense (DoD) is pursuing an end-to-end, seamless network-centric enterprise communications 

infrastructure. Evaluating this network is essential to guiding requirements, design, and procurement activities. However, the DoD 

currently lacks the capability to analyze the complex networked interactions between Soldiers and equipment. The U.S. Army 

Research Laboratory (ARL) has made significant investments developing a variety of modeling and analysis capabilities to analyze 

the impact of technology solutions on Soldiers. ARL researchers need a suite of tools allowing the System-of-Systems Analysis 

(SoSA) of technology impact on mission performance.

ARL’s Computational and Information Sciences Directorate (CISD) has developed the Wireless Emulation Laboratory (WEL) to 

provide a controlled, repeatable emulation environment for the research, development, and evaluation of networking and information 

assurance algorithms for tactical wireless networks. 

The System-of-Systems Survivability Simulation (S4), developed by ARL’s Survivability/Lethality Analysis Directorate (SLAD),

allows analysts to assess how threat effects, such as ballistics, computer network operations, and electronic warfare, impact a smallscale force in a mission context. 

This Director’s Strategic Initiative (DSI), a collaborative research effort between CISD and SLAD to interoperate the WEL and S4, 

enhances the analysis ability of both directorates. The WEL enhances the communications realism of the S4 suite and supports 

increased fidelity in the modeling of communications effects. The S4 enhances the domain fidelity with an embedded military 

domain–driven decision-making environment to better measure network technology interactions between Soldiers and equipment. 

Success will significantly improve the Army’s ability to conduct SoSA and assess the impact of new equipment and technology on 

Soldiers and warfighters in a mission context.

15. SUBJECT TERMS

S4, WEL, DSI, network, MANET, mission, survivability, vulnerability, SLVA

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Jeffrey A. Smith

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iii

Contents

List of Figures iv

List of Tables iv

1. Introduction/Objective 1

1.1 Background .....................................................................................................................1

1.2 Objective .........................................................................................................................1

2. Approach 2

2.1 The System-of-Systems Survivability Simulation (S4) ..................................................2

2.2 The Wireless Emulation Laboratory ...............................................................................3

2.3 Approach .........................................................................................................................5

2.3.1 Phase 1 Effort ......................................................................................................6

2.3.2 Phase 2 Effort ......................................................................................................7

2.3.3 Phase 3 and Beyond Effort..................................................................................7

3. Results 7

3.1 Chronology of Key Events..............................................................................................7

3.2 Phase 1 Progress..............................................................................................................8

4. Conclusions 8

4.1 Transitions.......................................................................................................................8

4.2 Future Research...............................................................................................................9

5. References 10

Appendix. Phase 1 Python Conversion Scripts 11

List of Symbols, Abbreviations, and Acronyms 13

Distribution List 15
iv

List of Figures

Figure 1. EMANE platform server hosting four NEMs. ................................................................4

Figure 2. Three-phase approach to interoperability........................................................................5

List of Tables

Table 1. Chief technical challenges to interoperability. .................................................................6
1

1. Introduction/Objective 

1.1 Background

The Department of Defense (DoD) aims to provide warfighters with an end-to-end, seamless 

network-centric enterprise communications network. Evaluating the achievable performance of 

such a network in operational environments is essential to guiding requirements, design, and 

procurement activities. However, the DoD currently lacks the capability to analyze the complex 

networked interactions between warfighters and equipment in that operational environment. The 

U.S. Army Research Laboratory (ARL) has made significant investments developing a variety of 

modeling and analysis capabilities to analyze the impact of technology solutions on warfighters. 

Yet, there is no effective means to link high-fidelity communication modeling with System-ofSystems Analysis (SoSA) tools to provide a mission-based performance analysis tool set for 

evaluating this end-to-end enterprise infrastructure.

ARL’s Computational and Information Sciences Directorate (CISD) has developed the Wireless 

Emulation Laboratory (WEL). The WEL provides a controlled, repeatable emulation 

environment for the research, development, and evaluation of networking and information 

assurance algorithms for tactical wireless networks. ARL currently uses the WEL to conduct 

basic and applied research in wireless networking and security.

ARL’s Survivability/Lethality Analysis Directorate (SLAD) has developed the System-ofSystems Survivability Simulation (S4). S4 is a simulation engine and a set of software tools that 

allow analysts to assess how threat effects, such as ballistics, computer network operations

(CNO), and electronic warfare, impact a small-scale force in a mission context. ARL currently 

uses S4 to conduct SoSA of battalion or smaller-sized forces.

1.2 Objective

At the corporate level, the long-term goal of this Director’s Strategic Initiative (DSI) research 

effort is to develop an interoperable suite of tools that support SoSA regarding the impact of 

network technology on mission performance. However, the intent of this DSI is not to create a 

robust set of tools but to assess the feasibility of integrating two existing tools and accomplishing 

some level of integration. If this DSI succeeds, it will provide a seed capability for the long-term

corporate goal.

At the directorate level, CISD’s objective is to improve the modeling of military decision making 

in the WEL and to increase the realism of the military scenarios used in WEL emulations. 

SLAD’s objective is to increase the fidelity of its communication modeling in S4 by adding 

engineering-level Mobile Ad hoc Network (MANET) emulation capabilities, thereby enhancing

the fidelity of its system-of-systems analysis activities.
2

2. Approach 

2.1 The System-of-Systems Survivability Simulation (S4)

SLAD’s mission is to provide survivability, lethality, and vulnerability analyses (SLVA) and 

expert consultation to its customers. Important customers include the Army’s independent 

evaluator the Army Test and Evaluation Command, program managers, and Army decision 

makers. Traditionally, this activity focused on single-thread analyses; such analyses characterize 

the interaction between a single item of equipment and one or more threats, as if that interaction 

took place in isolation from all else. Although the SLVA of individual items remains important, 

it is no longer sufficient to address the technical and business concerns of many SLAD 

customers. The newer concerns are inherently at the SoSA level. Army and defense leadership 

is intent on fielding a network-enabled force and acquiring complex packages of military 

capabilities that will support the full range of Force Operating Capabilities (1). A comprehensive 

analysis of these packages requires us to portray the results from subtle engineering interactions 

among different systems in the capability packages. We must consider the whole system of 

systems (2).

SLAD is using and further developing S4 (3) to approach these broader survivability issues (4). 

Because S4 provides the ability to analyze capability packages in a mission context, SLAD 

analysts are no longer limited to tools that work only for single-threat analysis. We use S4 to 

illuminate higher-level complexities and interactions in the context of explicit operational 

missions. By assessing survivability issues in the context of relevant operational missions, 

analysts can now provide metrics that address broader and more subtle analytical questions that 

have been beyond the reach of single-threat analysis. The results are also more relevant to the 

warfighter because we develop them in an operational rather than a merely technical context.

S4 is a constructive simulation and a set of software tools. At its core, S4 is a Java 

implementation of an agent-based modeling paradigm (e.g., see Wooldridge [5, 6]); however, 

unlike other agent-based models, in S4 each agent carries with it explicit representations of the 

military or tactical decision-making processes carried out by battalion, company, or platoon 

leaders in the future force. While the original impetus for S4 was the study of information flow 

and tactical decision making, as the need arises, S4 incorporates models of particular effects 

from subject matter experts in areas of ballistics, CNO, electronic warfare, mobility, etc. Each 

agent in S4 must respond to information about itself, its superiors, peers, and subordinates, as 

well as its adversaries in a manner consistent with the military decision-making process and 

Army doctrine. However, threat effects, such as ballistic events, electronic warfare attacks, and 

CNO, can perturb information flow and thus agent decisions. Consequently, with S4, analysts 

can assess the system-of-systems impact that these perturbations—for example, a loss of a road 

wheel or the presence of a threat jammer—will have on current and future force mission 
3

execution. To support these assessments, S4 has developed a comprehensive set of user 

interface–based analytical tools, methodologies, and software interfaces to capture warfighter 

knowledge in an easy and domain-relevant manner.

2.2 The Wireless Emulation Laboratory

The WEL at ARL was developed so that researchers could evaluate the actual software being 

used at the network layer and above. The WEL was fashioned to produce a controlled,

repeatable experimentation environment for the research, development, and evaluation of 

communication and security algorithms for tactical wireless mobile ad-hoc networks. We use 

emulation as a means to efficiently and realistically study MANET as opposed to simulation and 

experimentation. Emulation provides a middle ground between the two; whereas the systems 

and applications are real, only the lower layers, Media Access Control/Physical (MAC/PHY), of 

the network stack are simulated. 

The WEL originated as several laptops connected together in the emulation environment using a 

suite of tools originally developed by the Naval Research Laboratory called Mobile Ad-hoc 

Network Emulator. This suite of tools allowed for only homogeneous types of networks to be 

modeled and was limited in its ability to fully model the MAC and PHY layers. Currently in the 

WEL, the study of MANET is realized by conducting and analyzing real-time emulation 

experiments driven by the Extensible Mobile Ad-hoc Network Emulator (EMANE) and a suite 

of software tools used for experiment/scenario design, visualization, and analysis. EMANE 

allows for the creation of heterogeneous network emulation by using a pluggable MAC and PHY 

layer architecture. EMANE bases this pluggable architecture on the use of Extensible Markup 

Language (XML) to decouple the network emulation software components. The EMANE 

software is based on three main components (modules): the Network Emulation Module (NEM), 

Transport Module, and Event Module. A depiction of the models is given in figure 1.
4

Figure 1. EMANE platform server hosting four NEMs.

The NEM is the heart of EMANE’s emulation ability. It listens to “over-the-air” (OTA) packets 

that are transmitted in the emulation environment by connecting to a multicast channel. A NEM 

encapsulates all of the MAC and PHY layer implementation for a defined radio type. Currently,

the following three different MAC implementations are implemented within the EMANE suite of 

tools: RF_pipe, 802.11a/b/g, and Soldier Radio Waveform (SRW). Each model utilizes the 

universal PHY layer implementation. 

The RF_pipe’s MAC model is a generic radio model that provides simple jitter and delay effects 

and the ability to enable and disable listening in promiscuous mode. In promiscuous mode, all 

OTA packets are sent up from the PHY layer to the network layer. If promiscuous mode is not 

enabled, only multicast/broadcast and unicast packets bound for that local node are sent up to the 

network layer. The 802.11a/b/g model emulates the IEEE 802.11 MAC layer’s distributed 

coordination function channel access scheme on top of the IEEE 802.11 direct spread spectrum 

sequence and orthogonal frequency division multiplexing signals in space. The 802.11 a/b/g 

model has an additional feature that can incorporate flow control when used with the EMANE 

Virtual Transport. Flow control provides a mechanism to adjust the modulation scheme used to 

conform to the IEEE 802.11 standard. The third model that is incorporated into the WEL is the 

SRW model. This model is based on early implementations of the Joint Tactical Radio System 

(JTRS) tactical military communications waveform designed to network radios on the battlefield.

The Transport Module creates and manages the connection to each respective NEM and serves 

as the entry/exit point of the NEM stack. This connection is the bridge linking the node to the 
5

NEM that is responsible for the transmission of packets across the emulation environment. 

When an application sends out a packet, it is pushed through the NEM stack using the transport 

module where it is placed on the OTA channel to emulate a transmitted packet. Likewise, when 

a node receives a packet, it is picked up off of the OTA channel and processed by the PHY and 

MAC layer to determine if the packet should be passed up to the transport module. If the packet 

is pushed up the NEM stack to the transport module, it is injected into the kernel IP stack and 

used by the corresponding application. 

The Event Module is the general framework that provides for the creation and management of 

generators and agents of events. This framework resembles a classic client/server method, where 

the agents register to receive events from the generators that correspond to the given NEM. An 

example of this exists in the way that the NEM is updated with position or path-loss information. 

The interval in which events can be transmitted may vary, but in general events occur every 

second. When the agent receives the event, it passes the event to any application that is listening 

on the NEM for an event-driven process. One of the most common examples of this is the use of 

a global positioning system (GPS). The event generator sends out the GPS coordinates of each 

of the NEMs, and each NEM listens for its respective GPS coordinates and uses that information 

to determine connectivity within the given scenario. 

2.3 Approach

Our approach consists of three phases. Each phase seeks to build upon the knowledge gained in 

the previous step to enable additional increasing interoperability between the two tool suites. 

Figure 2 shows the process of adding interoperability between the tool sets.

Figure 2. Three-phase approach to interoperability.
6

2.3.1 Phase 1 Effort

The primary objective of phase 1 is determining what interoperation means in the context of the 

S4 and the WEL as well as identifying the required resources. In phase 1, we will exchange tools 

and demonstrate the use of S4 outputs as inputs to the WEL. We will limit interoperation to 

sequentially using the scenario and output files/results from one tool as an input to the other tool.

In particular, we will examine the technical challenges (table 1) and develop techniques that will 

overcome these challenges or identify the limits of achievable interoperability. At a high level, 

S4 runs as a single-threaded application with all of the decision-making processes running on a 

single machine. In contrast, the WEL runs in a distributed manner, with each user/radio running 

on a separate machine (actual hardware or virtual machine). Therefore, techniques to link the 

centralized decision-making processes within S4 with the distributed processes in the WEL need 

to be developed. In addition, the team must address the issue of time management before the 

tools can interoperate. S4 is a constructive simulation with discrete time steps (0.5 s) for 

decision-making reasoning, and depending on the level of decision-making actions being 

computed, it may run faster or slower than real time. The WEL, in contrast, is continually time 

based for real-time protocol operation.

Table 1. Chief technical challenges to interoperability.

Technical Challenges S4 WEL

Structure Centralized, single-threaded constructive 

simulation

Distributed, multithreaded, 

emulation

Execution May be faster or slower than real time Real time 

Simulation time Discrete Continuous 

Repeatability Repeatable for a given random number 

seed

Not necessarily deterministic 

because nodes run applications and 

protocols

Message content Only traffic of interest is modeled, and 

content has meaning

Bits and bytes modeled but content 

often abstracted 

Entity state Can be altered by many means—ballistics, 

electronic warfare, CNO, etc. Primarily network state 

In addition, it is important to note that S4 is deterministic for a given random-number seed. WEL

is not necessarily deterministic (nodes run applications/protocols). The impact on this difference 

on SoSA will need careful consideration. Of lesser concern but still important are issues 

associated with managing and exchanging entity state information between the tools and 

managing message content.
7

2.3.2 Phase 2 Effort

In phase 1, the team identified possible approaches to overcoming the technical barriers 

associated with interoperation. In phase 2, the team will apply this knowledge to create a partial 

link between S4 and the WEL. Overall, the level of interoperation will be sequential; however, at 

the completion of phase 2, platform position data from S4 will dynamically drive the network 

node locations in the WEL. The WEL will still pass communications data that underwent a 

software conversion process prior to operation. This enables the team to demonstrate S4 driving 

mobility in the WEL at runtime while at the same time allowing the team time to identify 

approaches to the far more complex problem of handling communications between S4 and the 

WEL in real time.

We will develop common data exchange tools to support using scenario data and experimental 

results from the different tools. We will also identify and document methods for solving more 

“complex” dynamic interactions, such as exchanging entity-state data and sharing 

communication events at run time.

2.3.3 Phase 3 and Beyond Effort

In phase 3, and based upon our experiences with phase 2 interoperation, the team will adapt our 

shared toolbox to support more effective analysis of system of systems. We will extend 

interoperation to more complex dynamic interactions between WEL and S4, and enhance the 

realism and accuracy of our joint analysis capability.

3. Results 

3.1 Chronology of Key Events

While this DSI received its initial funding in April 2011, in two occasions the team presented its 

proposal to external audiences as an initial peer review. In January 2011, the audience was a 

select group of the National Research Council (NRC) Technical Advisory Board (TAB) referred 

to as the mini-TAB. ARL convened the mini-TAB to focus on specific issues related to SLAD’s 

SoSA program. The proposal received favorable reviews to the extent that the team determined 

that we were on a technically sound approach. In April 2011, the team presented the proposal to 

the NRC Cross-Cutting TAB, with similar results. In August 2011, the team presented the 

proposal to the full NRC TAB at the review of SLAD’s program. At this review, the TAB panel 

members again gave a positive review of the efforts to date and suggested several possible uses 

for the S4\WEL when we attained full interoperability. In all these cases, the TAB panelist 

agreed with the assertion that the functionality intended for this DSI targeted the right problems.
8

3.2 Phase 1 Progress

The WEL/S4 integration is based upon a three-phase approach where in the first phase, outputs 

from S4 are to be implemented in the WEL. The initial outputs that are being implemented are 

the mobility and communication behaviors of the nodes in the S4 simulation. The mobility is 

needed to feed GPS coordinates into EMANE in order to generate the events needed to 

determine connectivity. During this first phase, ARL CISD began to understand how the S4 

software was implemented, where its output files were located, and what those files represented. 

Based off of trial runs of the S4 software, it was determined that the file “platform_moves.csv” 

contained the GPS coordinates (in terms of meters) of each node used in the simulation. In order 

for this to be understood by EMANE’s event generator, this file needed to be read and converted 

into an XML file of corresponding node’s latitudinal and longitudinal coordinates. This was 

accomplished by using information in the “TerrainMetadata.txt” file, which gave the latitude and 

longitude coordinates (in meters) of the lower-left position of the map used in the S4 simulation. 

Once this information about the map used in the S4 scenario is determined, the GPS coordinates 

can be calculated using the function in the appendix. 

The output that is being implemented within this phase is the communication behavior. This 

relates to the flow of information within the S4 simulation. Certain nodes send information at 

particular times during the simulation, and those interactions need to be reflected in the 

emulation environment. As an initial implementation, we are using the 802.11a/b/g radio model 

to determine network connectivity. This is different from what S4 is using as a radio model, but 

the intent is to recreate the flow of information through the network while developing a model 

that reflects the radios being used in S4. The flow of information initially will be done using a 

toll called Real-Time Application Representative (RAPR). This tool uses message generation 

software that sends and responds to data traffic patterns. If the traffic patterns in S4 can be 

determined from its output, then RAPR can be used to model that flow between the nodes. The 

work in this portion of phase 1 is still ongoing.

4. Conclusions 

4.1 Transitions

As currently envisioned, and given the inherent complexity in the integration, we expect this DSI 

to transition to internal customers in the form of SLAD and CISD. Among the interests 

expressed, SLAD expects to use the interoperation of the WEL and S4 to “tune” its Brigade and 

Below, Propagation and Protocol (B2P2) model. B2P2 is the existing communication model in 

S4 that allows the passing of messages between agents; however, it is a simulation of what the 

WEL emulates and as such will never have the fidelity of the WEL. In using the S4/WEL to 

tune B2P2, SLAD expects a more realistic representation of network effects (latencies, losses, 

etc.) when it runs S4 constructively in its SoSA activities. In the long term, SLAD expects to use 
9

its SoSA capabilities to assess the survivability gains and the vulnerability impacts of 

developmental radio and networking technologies such as JTRS and the Warfighter’s 

Information Network-Tactical (WIN-T). Ideally, the intent is to identify survivability gains from 

the networks to “self-form,” “adapt,” and “heal.” Additionally, SLAD will use its SoSA 

capabilities to determine if these self-forming and adaptive networks introduce vulnerabilities 

and, if so, evaluate the mitigations we can deploy and assess their impacts.

From CISD’s perspective, the internal transition of this project would enhance the ability to 

evaluate other ongoing research efforts for mission effectiveness. In particular, the research 

being performed in the Quality of Information project would be able to leverage this tool to 

determine a level of effectiveness when certain weights are placed on the information passed 

throughout the network. The effects of placing importance on some data over others can be 

determined when used in a scenario that requires mission urgency. This tool provides a platform 

for evaluating how effective and efficient the “quality” placed on information becomes in 

obtaining the mission objective. The same is evident in the research being performed on 

distributed dynamic federated databases. CISD is researching Gaian Databases to distributively 

store information that can be dynamically retrieved. This information can come from many 

different sources and can be used by any subscriber to the stream of information. This tool can 

determine whether this database system is effective in accomplishing the mission. Additionally, 

the tool provides an electronic warfare capability that is not present in the WEL. The transition 

of this capability will help to enhance the development of effective tools and protocols used in a 

MANET environment. 

4.2 Future Research

Since this DSI has only been active for approximately 6 months, it is perhaps premature to plan 

follow-on research efforts. However, there are several near-term obstacles that we will need to 

address to accomplish this DSI. These research areas target several of the key barriers identified 

in Table —namely, the ability to manage simulation time, repeatability, closed loop 

communications, and entity state. Our expectations at this point are that these target areas are in 

decreasing priority. Since S4 is a constructive simulation, and the WEL is at its core an

emulator, developing an approach to simulation time management is essential to creating an 

interoperable simulation. We expect to tackle this effort first; however, we do not expect a 

workable solution until year 2 to early year 3 in the DSI. A second major effort is to ensure that 

once we join S4 and the WEL, we can interoperate them in a repeatable manner, and by 

repeatable we mean that the simulation outputs are identical for a given random number seed. 

Our intent here is to ensure that we are able to reproduce results as needed to support the analysis 

mission of SLAD and the algorithm development mission of CISD. Here we also do not expect 

results until year 2 or 3 of the DSI. Finally, the two remaining thrust areas, that of closed loop 

communications and the exchange of entity states, we expect to address in the course of 

addressing our first two priorities; that is, we expect to identify approaches to manage these 

requirements late in the first year of execution or in the second year of this DSI.
10

5. References

1. Headquarters, Department of the Army. Force Operating Capabilities; TRADOC Pamphlet 

525-66; Training and Doctrine Command: Ft. Monroe, VA, March 7, 2008.

2. Starks, M. W.; Flores, R. New Foundations for Survivability/Lethality/Vulnerability Analysis 

(SLVA); ARL-TN-216; U.S. Army Research Laboratory: White Sands Missile Range, NM, 

June 2004.

3. Bernstein, R., Jr.; Flores, R.; Starks, M. W. Objectives and Capabilities of the System of 

Systems Survivability Simulation (S4); ARL-TN-260; U.S. Army Research Laboratory: 

White Sands Missile Range, NM, July 2006.

4. Smith, J. A.; Starks, M. W. Toward a Survivability/Lethality Analysis Directorate (SLAD) 

Methodology for Conducting System of Systems Analysis (SoSA); ARL-TR-5555; U.S. Army 

Research Laboratory: White Sands Missile Range, NM, June 2011.

5. Wooldridge, M. J. Reasoning About Rational Agents; MIT Press: Cambridge, MA, 2000.

6. Wooldridge, M. J. An Introduction to Multiagent Systems; J. Wiley: New York, 2002.
11

Appendix. Phase 1 Python Conversion Scripts

Python code to convert meters to latitude and longitude:

#############################################################################

########### 

 # __meters2GPS

 #

 # parameters: lat_n_meters, lon_n_meters

 #

 # description:

 # This routine will change the meter value of the lat, lon, alt format

 # of the coordinates into the GPS (lat, lon, alt) format needed for

 # EMANE. 

 

#############################################################################

###########

 def compute_lat_lon(self, lat_n_meters, lon_n_meters):

 R = 6367*1000 # Circumference of the earth @ equator

 # LL_LAT comes from terrainFile

 # This is equation to find latitude

 

 latitude = ((float(lat_n_meters) * 180)/(R * math.pi))+ self.LL_LAT 

 # This is equation to find longitude

 # LL_LON comes from terrainFile

 

longitude = ((float(lon_n_meters) *180)/(R* math.pi *

math.cos(self.LL_LAT)))+ self.LL_LON 

 return latitude, longitude
12

INTENTIONALLY LEFT BLANK.
13

List of Symbols, Abbreviations, and Acronyms

ARL U.S. Army Research Laboratory

B2P2 Brigade and Below, Propagation and Protocol

CISD Computational and Information Sciences Directorate

CNO Computer Network Operation

DOD Department of Defense

DSI Director’s Strategic Initiative

EMANE Extensible Mobile Ad-hoc Network Emulator

GPS global positioning system

IEEE Institute of Electrical and Electronics Engineers

IP Internet protocol

JTRS Joint Tactical Radio System

MAC Media Access Control

MANET Mobile Ad-hoc Network

NEM Network Emulation Module

NRC National Research Council

OTA over the air

PHY Physical

RAPR Real-Time Application Representative

S4 System-of-Systems Survivability Simulation

SLAD Survivability/Lethality Analysis Directorate

SLVA survivability, lethality, and vulnerability analyses

SoSA System-of-Systems Analysis

SRW Soldier Radio Waveform

TAB Technical Advisory Board

WEL Wireless Emulation Laboratory
14

WIN-T Warfighters Information Network – Tactical

XML Extensible Markup Language
15

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ARMAMENT RSRCH DEV & ENGRG CTR

ARMAMENT ENGRG & TECHNLGY CTR

ATTN AMSRD AAR AEF T J MATTS

BLDG 305

ABERDEEN PROVING GROUND MD 21005-5001

1 CD US ARMY RSRCH LAB

1 WORD MELE ASSOCIATES

ATTN RDRL SLE E D NEVAREZ

BLDG 1622 RM 216

WHITE SANDS MISSILE RANGE NM 88002-5501

1 US ARMY INFO SYS ENGRG CMND

ATTN AMSEL IE TD A RIVERA

FT HUACHUCA AZ 85613-5300

1 COMMANDER

US ARMY RDECOM

ATTN AMSRD AMR W C MCCORKLE

5400 FOWLER RD

REDSTONE ARSENAL AL 35898-5000

1 US ARMY RSRCH LAB

ELEC ATTN RDRL SLE J A SMITH

BLDG 1624

WHITE SANDS MISSILE RANGE NM 88002

1 US GOVERNMENT PRINT OFF

DEPOSITORY RECEIVING SECTION

ATTN MAIL STOP IDAD J TATE

732 NORTH CAPITOL ST NW

WASHINGTON DC 20402

3 HC US ARMY RSRCH LAB

2 ELEC ATTN IMNE ALC HRR MAIL & RECORDS MGMT

ATTN RDRL CIN T B RIVERIA

ATTN RDRL CIN T R HARDY 

ATTN RDRL CIO LL TECHL LIB 

ATTN RDRL CIO MT TECHL PUB

ADELPHI MD 20783-1197
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