This PDF file contains the front matter associated with SPIE Proceedings Volume 8742, including the Title Page, Copyright information, Table of Contents, Introduction, and Conference Committee listing.

In a coalition context, data fusion involves combining of soft (e.g., field reports, intelligence reports) and hard (e.g., acoustic, imagery) sensory data such that the resulting output is better than what it would have been if the data are taken individually. However, due to the lack of explicit semantics attached with such data, it is difficult to automatically disseminate and put the right contextual data in the hands of the decision makers. In order to understand the data, explicit meaning needs to be added by means of categorizing and/or classifying the data in relationship to each other from base reference sources. In this paper, we present a semantic framework that provides automated mechanisms to expose real-time raw data effectively by presenting appropriate information needed for a given situation so that an informed decision could be made effectively. The system utilizes controlled natural language capabilities provided by the ITA (International Technology Alliance) Controlled English (CE) toolkit to provide a human-friendly semantic representation of messages so that the messages can be directly processed in human/machine hybrid environments. The Real-time Semantic Enrichment (RTSE) service adds relevant contextual information to raw data streams from domain knowledge bases using declarative rules. The rules define how the added semantics and context information are derived and stored in a semantic knowledge base. The software framework exposes contextual information from a variety of hard and soft data sources in a fast, reliable manner so that an informed decision can be made using semantic queries in intelligent software systems.

In this paper, we propose a system architecture for decision-making support on ISR (i.e., Intelligence, Surveil­lance, Reconnaissance) missions via optimizing resource allocation. We model a mission as a graph of tasks, each of which often requires exclusive access to some resources. Our system guides users through refinement of their needs through an interactive interface. To maximize the chances of executing new missions, the system searches for pre-existent information collected on the field that best fit the needs. If this search fails, a set of new requests representing users' requirements is considered to maximize the overall benefit constrained by limited resources. If an ISR request cannot be satisfied, feedback is generated to help the commander further refine or adjust their information requests in order to still provide support to the mission. In our work, we model both demands for resources and the importance of the information retrieved realistically in that they are not fully known at the time a mission is submitted and may change overtime during execution. The amount of resources consumed by a mission may not be deterministic; e.g., a mission may last slightly longer or shorter than expected, or more of a resource may be required to complete a task. Furthermore, the benefits received from the mission, which we call profits, may also be non-deterministic; e.g., successfully localizing a vehicle might be more important than expected for accomplishing the entire operation. Therefore, when satisfying ISR requirements we take into account both constraints on the underlying resources and uncertainty of demands and profits.

This paper reports on a feasibility study to explore the impact of advanced manufacturing on the production and maintenance of a 3D printed, unmanned aerial vehicle (UAV) in theatre. Specifically, this report focuses on fused deposition modeling (FDM), the selective deposition of a molten thermoplastic. FDM is already a forward deployed technology, primarily used for printing custom tools and replacement parts. The authors ask if it is feasible to expand the printers’ capacity to produce aerial platforms; the reduction in logistics and labor could significantly decrease costs per unit and enable far more platform customization and specialized deployment scenarios than are available in existing aircraft. The University of Virginia and The MITRE Corporation designed and built a prototype, 3D printed UAV for use as an aerial sensor platform. This report • Discusses the printed aerial platform, summarizes the design process, and compares printing methods • Describes the benefits and limitations to selecting FDM printers as the technology both for deployment as well as UAV design • Concludes with the current state and future expectations for FDM printing technologies relevant to UAV production. Our findings suggest that although 3D printing is not yet entirely field-ready, many of its advantages can already be realized.

The DIA, in conjunction with the Army Research Lab (ARL), wants to create an Unmanned Ground Sensor (UGS) controller that is (a) interoperable across all controller platforms, (b) capable of easily adding new sensors, radios, and processes and (c) backward compatible with existing UGS systems. To achieve this, a Terra Harvest controller was created that used Java JRE 1.6 and an Open Services Gateway initiative (OSGi) platform, named Terra Harvest Open Software Environment (THOSE). OSGi is an extensible framework that provides a modularized environment for deploying functionality in “bundles”. These bundles can publish, discover, and share services available from other external bundles or bundles provided by the controller core. With the addition of a web GUI used for interacting with THOSE, a natural step was then to create a common remote interface that allows 3rd party real-time interaction with the controller. This paper provides an overview of the THOSE system and its components as well as a description of the architectural structure of the remote interface, highlighting the interactions occurring between the controller and the remote interface and its role in providing a positive user experience for managing UGSS functions.

The DIA wants to create an UGS controller that is interoperable across all controller platforms; capable of easily adding new sensors, radios, and processes; as well as having backward compatibility with existing UGS systems. To achieve this, a Terra Harvest controller was created that used Java JRE 1.6 and an OSGi platform, name Terra Harvest Open Software Environment (THOSE). OSGi is an extensible framework that provides a modularized environment that allows functionality to be deployed in “bundles”. These bundles can publish, discover, and share services available from other bundles or bundles provided by the controller core. The next step the project was to create a GUI interface that interacts with the THOSE. This paper dicusses the architecture of the THOSE GUI and demonstrates how to use the THOSE GUI to deploy a bundle for an asset, communications device, or algorithm; plan missions, configure assets and communication devices real-time; command and control assets, and view operations of the various assets, etc.

The Army Research Lab (ARL), in collaboration with the Defense Intelligence Agency (DIA) and representatives from industry, recently validated the feasibility of the Terra Harvest architecture by successfully integrating dozens of Intelligence, Surveillance, and Reconnaissance (ISR) assets at the Trident Spectre 12 (TS12) exercise in Fort Story, VA. Based on the exercise, it is evident that Terra Harvest will greatly simplify the process of integrating disparate ISR systems. By reducing this complexity, Terra Harvest will increase the variety of devices U.S. soldiers have at their disposal giving them a greater technological advantage over their adversaries than ever before. This paper describes McQ's effort to develop Terra Harvest compliant plug-ins for its UGS along with lessons learned from their demonstration at TS12.

The UK Land Open System Architecture (LOSA) is an open, service-based architecture for systems integration and interoperability in the land environment. It is being developed in order to deliver coherent and agile force elements at readiness to operations. LOSA affects planning, delivery and force generation, and supports Future Force 2020. This paper will review the objectives of LOSA and the progress made to date; before focusing on an approach to achieve plug-and-play interoperability of ISR assets. This approach has been proposed to the US DoD Coalition Warfare Program Office as a programme to develop a technology solution to achieve the goal of ISR interoperability. The approach leverages the efforts of the UK Land Open System Architecture (LOSA) and the US Terra Harvest (TH) programs. An open architecture approach is used to enable rapid integration and for disparate assets to autonomously operate collaboratively and coherently; assets share situational awareness and cue other assets when a prescribed set of operational conditions are met. The objective of the interoperability programme being to develop a common lexicon and coherent approach to collaborative operation and information release.

In October 2012 the UK MoD sponsored a multi-vendor field integration trial in support of its Land Open Systems Architecture (LOSA), an open, service based architecture for systems integration and interoperability which builds on the progress made with the Generic Vehicle Architecture (GVA, DefStan 23-09), Generic Base Architecture (GBA, DefStan 23-13) and the Generic Soldier Architecture (DefStan 23-12) programs. The aim of this trial was to experiment with a common data and power interoperability across and in support of the Soldier, Vehicles and Bases domains. This paper presents an overview of the field trial and discusses how the ITA Information Fabric, technology originated in the US and UK International Technology Alliance program, was extended to support the control of the data interoperability layer across various data bearers. This included: (a) interoperability and information sharing across multiple stove piped and legacy solutions; (b) command and control and bandwidth optimization of streamed data (e.g. video) over a peer-to-peer ad-hoc network across multiple domains- integration of disparate sensor systems; (c) integration with DDS based C2 systems.

The FICAPS Project has been established as a Project of the European Defence Agency based on an initiative of Germany and France. Goal of this Project was to derive Guidelines, which by a proper implementation in future developments improve Camp Protection Systems (CPS) by enabling and improving interoperability between Camp Protection Systems and its Equipments of different Nations involved in multinational missions. These Guidelines shall allow for: • Real-time information exchange between equipments and systems of different suppliers and nations (even via SatCom), • Quick and easy replacement of equipments (even of different Nations) at run-time in the field by means of plug and play capability, thus lowering the operational and logistic costs and making the system highly available, • Enhancement of system capabilities (open and modular systems) by adding new equipment with new capabilities (just plug-in, automatic adjustment of the HMI Human Machine Interface) without costly and time consuming validation and test on system level (validation and test can be done on Equipment level), Four scenarios have been identified to summarize the interoperability requirements from an operational viewpoint. To prove the definitions given in the Guideline Document, a French and a German Demonstration System, based on existing national assets, were realized. Demonstrations, showing the capabilities given by the defined interoperability requirements with respect to the operational scenarios, were performed. Demonstrations included remote control of a CPS by another CPS, remote sensor control (Electro-Optic/InfraRed EO/IR) and remote effector control. This capability can be applied to extend the protection area or to protect distant infrastructural assets Demonstrations have been performed. The required interoperability functionality was shown successfully. Even if the focus of the FICAPS project was on camp protection, the solution found is also appropriate for other force protection and ISR (Intelligence Surveillance Reconnaissance) tasks not only due to its flexibility but also due to the chosen interfacing.

In 2006, the US Army Research Laboratory (ARL) and the UK Ministry of Defence (MoD) established a collaborative research alliance with academia and industry, called the International Technology Alliance (ITA) to address fundamental issues concerning Network and Information Sciences. Under the ITA research program, a US-UK transition project on "ITA Policy Controlled Information Query and Dissemination" was funded in 2011 by OSD's Coalition Warfare Program (CWP). The goal of this CWP project is to develop an extensible capability of performing distributed federated query and information dissemination across a coalition network of distributed disparate data/information sources with access­ controlled policies. The CWP project is lead by US Army Research Laboratory (ARL) and UK Defence Science Technology Laboratory (Dstl) with software development by IBM UK and IBM US. The CWP project exploits two key technology components developed within the ITA, namely the Gaian Database and integrated Access Policy Decision and Enforcement mechanisms. The Gaian Database (GaianDB) is a Dynamic Distributed Federated Database (DDFD) that addresses a need to share information among coalition members by providing a means for policy-controlled access to data across a network of heterogeneous data sources. GaianDB implements a SQL-compliant Store-Locally-Query-Anywhere (SLQA) approach providing software applications with global access to data from any node in the database network via standard SQL queries. Security policy is stored locally and enforced at the database node level, reducing potential for unauthorized data access and waste of network bandwidth. A key metric of success for a CWP project is the transition of coalition-related technology from TRL-3 or 4 to TRL-6 or higher. Thus, the end goal of this CWP project was to demonstrate the GaianDB and policy technology within an operational environment at the NATO Intelligence Fusion Centre (NIFC) at Molesworth RAF. An initial demonstration of this technology in a 'stand alone' environment was undertaken at the NIFC in November 2011 using a data set comprised of 140,000 documents. Recently the system has been modified to include a secure authentication mechanism based on a Kerberos ticketing framework and this has now been integrated onto the NIFC Battlefield Information, Collection, and Exploitation System (BICES) network. In summary, the paper discusses the CWP project; the two key technologies (i.e., Gaian Database and integrated Access Policy Decision and Enforcement mechanisms) developed within the US UK ITA research program; how these have been integrated into the NIFC BICES; and future plans for the program.

Existing configuration of boundary protection devices, which validate the content and context of information crossing between security domains, uses a set of accreditor-agreed steps individually agreed for every situation. This has traditionally been a slow and exacting process of negotiation between integrators and accreditors. The Decentralized Operation Procedure (DOP) technique allows interoperability definitions of system interactions to be created as XML files and deployed across the battlefield environment. By extending the security information definitions within the DOP technique, it is intended to provide sufficient incorporated information to allow boundary protection devices to also immediately load and utilize a DOP XML file and then apply established standards of security. This allows boundary devices to be updated with the same dynamism as the deployment of new DOPs and DOP interoperability definitions to also exploit coalitional capabilities having crossed security boundaries. The proposal describes an open and published boundary definition to support the aims of the MOD 23-13 Generic Base Architecture Defense Standard when working with coalition partners. The research aims are; a) to identify each element within a DOP that requires security characteristics to be described; b) create a means to define security characteristics using XML; c) determine whether external validation of an approved DOP requires additional authentication; d) determine the actions that end users will have to perform on boundary protection devices in support of these aims. The paper will present the XML security extensions and the results of a practical implementation achieved through the modification of an existing accredited barrier device.

Consider a future where joint, unmanned operations are the norm. A fleet of autonomous airborne systems conducts overwatch and surveillance for their land and sea brethren, accurately reporting adversary position and aptly guiding the group of autonomous land and sea warriors into position to conduct a successful takedown. Sounds a bit like science fiction, but reality is just around the corner. The DoD ISR Enterprise has evolved significantly over the past decade and has learned many a harsh lesson along the way. Autonomous system operations supporting the warfighter have also evolved, arguably to a point where integration into the ISR Enterprise is a must, in order to reap the benefits that these highly capable systems possess. Achieving meaningful integration, however, is not without its challenges. The ISR Enterprise, for example, is still plagued with “stovepipe” efforts – sufficiently filling a niche for an immediate customer need, but doing little to service the needs of the greater enterprise. This paper will examine the science of autonomy, the challenges and potential benefits that it brings to the ISR Enterprise and recommendations that will facilitate smooth integration of emerging autonomous systems with the mature suite of traditional manned and unmanned ISR platforms.

This paper briefly describes the set-up of the sensors and the instrumentation deployed by the French-German Research Institute of Saint-Louis (ISL) during the last NATO/ACG3/SG2 HFI Threat Data Collection (Trial PROTEUS which has been conducted during the summer 2012 in Slovenia). The main purpose of this trial was the measurements of weapon and ammunition signatures for threat warning and hostile fire indicator (HFI) system development. The used weapons vary from small caliber rifles to anti-tank rockets in ground-to-ground shooting configurations. For the ISL team, the objectives consisted in measuring the acoustic signals for detection and localization of weapon firing events. Experimental results of sound localization obtained by using ground based sensors are presented and analyzed under various conditions.

Acoustic sensors are being employed on airborne platforms, such as Persistent Threat Detection System (PTDS) and Persistent Ground Surveillance System (PGSS), for source localization. Under certain atmospheric conditions, airborne sensors offer a distinct advantage over ground sensors. Among other factors, the performance of airborne sensors is affected by refraction of sound signals due to vertical gradients in temperature and wind velocity. A comprehensive experiment in source localization with an aerostat-mounted acoustic system was conducted in summer of 2010 at Yuma Proving Ground (YPG). Acoustic sources on the ground consisted of one-pound TNT denotations and small arms firings. The height of the aerostat was approximately 1 km above the ground. In this paper, horizontal, azimuthal, and elevation errors in source localization and their statistics are studied in detail. Initially, straight-line propagation is assumed; then refraction corrections are introduced to improve source localization and decrease the errors. The corrections are based on a recently developed theory [Ostashev, et. al, JASA 2008] which accounts for sound refraction due to vertical profiles of temperature and wind velocity. During the 2010 YPG field test, the vertical profiles were measured only up to a height of approximately 100 m. Therefore, the European Center for Medium-range Weather Forecasts (ECMWF) is used to generate the profiles for July of 2010.

The rivers are in some circumstances part of the ground battlefield. Microseisms induced at the riverbed or ground at the river surrounding might be consequence of military activities (military ground transports, explosions, troop’s activities, etc). Vibrations of those fluid-solid structures are modeled in terms of solid displacement and change of fluid pressure. This time varying fluid pressure in river, which originates from ground microseisms, is possible to detect with hydrophones. Therefore, hydroacoustic measurements in rivers enables detecting, identification and localization various types of military noisy activities at the ground as and those, which origin is in the river water (hydrodynamics of water flow, wind, waves, river vessels, etc). In this paper are presented river ambient noise measurements of the three great rivers: the Danube, the Sava and the Tisa, which flows in north part of Serbia in purpose to establish limits in detection of the ground vibrations in relatively wide frequency range from zero to 20 kHz. To confirm statement that the river is a part of ground battlefield, and that hydroacoustic noise is possible to use in detecting and analyzing ground microseisms induced by civil or military activities, some previous collected data of hydroacoustic noise measurement in the rivers are used. The data of the river ambient noise include noise induced by civil engineering activities, that ordinary take place in large cities, noise that produced ships and ambient noise of the river when human activities are significantly reduced. The poly spectral method was used in analysis such events.

Air-to-Ground Situation Assessment (SA) requires gathering information on the entities evolving on the ground (e.g., people, vehicles), and inferring the relations among them and their final intent. Several airborne sensor data might concur in the compilation of such high-level picture, which is aimed at identifying threats and promptly raising alarms. However, this process is intrinsically prone to errors: as the evidence - provided to the SA algorithm - originates from noisy sensor observations, the final outcome is also affected by uncertainty. High-level inferred variables, such as "Situation" and "Threat Level", can be seen as error-affected, incomplete estimates of the ground truth, hence they are subject to improvement by properly steering the data acquisition process. In this paper we address the problem of optimizing the air route of the sensing platform, in order to reduce the number of false declarations or the delay in threat declaration in the SA stage. Specifically, we consider the problem of detecting a hostile behavior between pairs of ground targets by exploiting track data generated from airborne bearings-only measurements. We model the optimization problem with a sequential Markov Decision Process (MDP): the platform sequentially selects the best maneuver (i.e., its acceleration vector) in order to maximize the total reward over an infinite horizon. We define the potential contribution of an action as a function of the expected environmental conditions (e.g., obscurations of the line-of-sight) and the improvement of the localization accuracy achievable for the tracked objects. We demonstrate that following the optimized trajectory the delay in the declaration of a hostile behavior between two targets is reduced at the same erroneous declaration rate.

Multisensor data fusion combines data from multiple sensors to overcome interferences that may not be possible from a single sensor or source alone. In military application data fusion can be used to integrate the individual sensor data into common operational picture of the battlefield. However, there is still possibility to improve quality of the individual sensor. Improving of accuracy in estimation of spatial location is investigated in this paper. Some novel methods and algorithms for estimation of spatial location are compared such as Discrete Probability Density (DPD) method, fusion of multiple bearing lines and mean-square distance algorithm. These methods for estimation of spatial location use two-step positioning technique (indirect technique) based on estimation of a specified parameter such as angle of arrival (AOA). In the network where is possible to provide multiple spatial locations from the spatially close sources, clustering of estimated spatial location is very important. The estimated spatial locations that correspond to a source are spatially close to one another will have a larger likelihood than those estimated spatial locations that are not correspond to the source. In this paper methods and algorithms for estimation of spatial location are compared where it is multiple spatial locations, for the same sources spatially close. Clustering has been performed based on estimated spatial locations and appropriates the covariance matrix.

Low cost, power and bandwidth UGS can be used to fill the growing need for surveillance in remote environments. In particular, linear and 2D thermal sensor systems can run for up to months at a time and their deployment can be scaled to suit the size of the mission. Thermal silhouette profilers like Brimrose's SPOT system reduce power and bandwidth requirements by performing elementary classification and only transmitting binary data using optimized compression methods. These systems satisfy the demands for an increasing number of surveillance operations where reduced bandwidth and power consumption are mission critical.

This paper presents a novel key management scheme for a tiered self-healing wireless sensor network. A tiered wireless sensor network with self-healing features draws extensive attention because of its scalability, robustness and reliability. A tiered key management scheme to support the cluster membership and the self-healing features is proposed with emphasis on the control of a node's participation and re-keying when joining a new cluster. The security and overhead analysis demonstrates the proposed scheme is effective and efficient with respect to communication, computation, and memory overhead for operation in a hostile environment.

An algorithm for the autonomous identification of and tasking to collect additional data required to complete a goal is presented. This assertion-form goal is decomposed autonomously into an initial set of data collection tasks. Once these are completed, information gaps may exist or new information collection requirements may be identified. A utilitymaximization, cost-minimization metric is applied to ascertain what data collection tasks craft should be assigned. This decision making process is performed at each level of the hierarchy, decomposing large-scale needs into progressively smaller assignments. The utility of this control approach is assessed for persistent surveillance and planetary science applications.

Data acquisition and data fusion systems are becoming increasingly complex, being in fact systems of systems, where every component may be a system with varying levels of autonomy by themselves. Possible changes in system configuration by entities joining or being removed from the system make the system complex. As synchronous operation cannot be expected in such a system configuration, the temporal and spatial correctness of data must be achieved via other means. This paper presents the concept of mediated interactions as a method for ensuring correctness of computation in a distributed system. The mediator associated with each computing entity is responsible for online checking of the data both before it is sent out at the sender side and before it is received at the receiver side, ensuring that only data satisfying the validity constraints of the receiver-side data processing algorithm is used in computation. This assumes that each data item is augmented with metadata, which enables online data validation. The validity and quality dimensions in use depend on the system requirements defined by a specific problem and situational context; they may be temporal, spatial and involve various data quality dimensions, such as accuracy, confidence, relevance, credibility, and reliability. Among other capabilities, the mediator is able to cope with the unknowns in the temporal dimension that occur at runtime and are not predictable, such as channel delay, jitter of clocks and processing delays. This capability becomes an especially relevant factor in multi-tasking systems and in configurations in which a computing entity may have to process a variable number of parallel streams of data. Both the architecture and a simulation case study of a distributed data fusion scenario are presented in the paper.

Traditional tracking frameworks are challenged by low video frame rate scenarios, because the appearances and locations of the target may change considerably in consecutive frames. Our paper presents a saliency-based temporal association dependency (STAD) framework to deal with such a low frame rate scenario and demonstrate good results in our robot testbed. We first use median filter to create a background of the scene, then apply background subtraction to every new frame to decide the rough position of the target. With the help of the markers on the robots, we use a gradient voting algorithm to detect the high responses of the directions of the robots. Finally, a template matching with branch pruning is used to obtain the finer estimation of the pose of the robots. To make the tracking-by-detection framework stable, we further introduce the temporal constraints using a previously detected result as well as an association technique. Our experiments show that our method can achieve a very stable tracking result and outperforms some state-of-the-art trackers such as Meanshift, Online-AdaBoosting, Mulitple-Instance-Learning, Tracking-Learning-Detection etc. Also. we demonstrate that our algorithm provides near real-time solutions given the low frame rate requirement.

Classifying acoustic signals detected by distributed sensor networks is a difficult problem due to the wide variations that can occur in the transmission of terrestrial, subterranean, seismic and aerial events. An acoustic event classifier was developed that uses particle swarm optimization to perform a flexible time correlation of a sensed acoustic signature to reference data. In order to mitigate the effects from interference such as multipath, the classifier fuses signatures from multiple sensors to form a composite sensed acoustic signature and then automatically matches the composite signature with reference data. The approach can classify all types of acoustic events but is particularly well suited to explosive events such as gun shots, mortar blasts and improvised explosive devices that produce an acoustic signature having a shock wave component that is aperiodic and non-linear. The classifier was applied to field data and yielded excellent results in terms of reconstructing degraded acoustic signatures from multiple sensors and in classifying disparate acoustic events.

As the number of devices collecting and sending data in the world are increasing, finding ways to visualize and understand that data is becoming more and more of a problem. This has often been coined as the problem of “Big Data.” The Virtual Geoint Center (VGC) aims to aid in solving that problem by providing a way to combine the use of the virtual world with outside tools. Using open-source software such as OpenSim and Blender, the VGC uses a visually stunning 3D environment to display the data sent to it. The VGC is broken up into two major components: The Kinect Minimap, and the Geoint Map. The Kinect Minimap uses the Microsoft Kinect and its open-source software to make a miniature display of people the Kinect detects in front of it. The Geoint Map collect smartphone sensor information from online databases and displays them in real time onto a map generated by Google Maps. By combining outside tools and the virtual world, the VGC can help a user “visualize” data, and provide additional tools to “understand” the data.

The U.S Army Research Laboratory (ARL) has built a “Wireless Emulation Lab” to support research in wireless mobile networks. In our current experimentation environment, our researchers need the capability to run clusters of heterogeneous nodes to model emulated wireless tactical networks where each node could contain a different operating system, application set, and physical hardware. To complicate matters, most experiments require the researcher to have root privileges. Our previous solution of using a single shared cluster of statically deployed virtual machines did not sufficiently separate each user’s experiment due to undesirable network crosstalk, thus only one experiment could be run at a time. In addition, the cluster did not make efficient use of our servers and physical networks. To address these concerns, we created the Dynamically Allocated Virtual Clustering management system (DAVC). This system leverages existing open-source software to create private clusters of nodes that are either virtual or physical machines. These clusters can be utilized for software development, experimentation, and integration with existing hardware and software. The system uses the Grid Engine job scheduler to efficiently allocate virtual machines to idle systems and networks. The system deploys stateless nodes via network booting. The system uses 802.1Q Virtual LANs (VLANs) to prevent experimentation crosstalk and to allow for complex, private networks eliminating the need to map each virtual machine to a specific switch port. The system monitors the health of the clusters and the underlying physical servers and it maintains cluster usage statistics for historical trends. Users can start private clusters of heterogeneous nodes with root privileges for the duration of the experiment. Users also control when to shutdown their clusters.

In 2006, the US Army Research Laboratory (ARL) and the UK Ministry of Defence (MoD) established a collab­ orative research alliance with academia and industry, named the International Technology Alliance in Network and Information Science (ITA) 1 to address fundamental issues concerning Network and Information Sciences. Research performed under the ITA was extended through a collaboration between ARL and IBM UK to char­ acterize and define a software stack and tooling that will become the reference framework for network science experimentation. A key element to the success and validation of ITA theoretical research is experimentation in a controlled environment that can, as best as possible, emulate the real world conditions and context. Ex­ perimental validation in a network emulation environment contributes to the validation of theoretical concepts and algorithms, the investigation of more complex scenarios that span multiple research areas, exposing gaps in the theory that may need special attention, identifying additional areas where the research might focus and develop, and reproducible experimentation, which enables and facilitates the comparison of results from multiple executions of the same experiment. To accomplish these experimentation goals, the framework needs to foster the collaboration across multiple disciplines and facilitate the sharing of existing and new assets in a common emulation environment. The framework also needs to be extensible for the integration of new features, emula­ tion models, monitoring, and integration with real, external assets that can be linked to any given experiment. This paper discusses the work resulting from the ARL/ IBM UK collaboration to build a framework to support experimentations and foster collaboration within and across different research groups.

In domains such as emergency response and military operations the sharing of Intelligence, Surveillance and Reconnaissance (ISR) assets among different coalition partners is regulated through policies. Traditionally, poli­ cies are created at the center of a coalitions network by high-level decision makers and expressed in low-level policy languages (e.g. Common Information Model SPL) by technical personnel, which makes them difficult to be understood by non-technical users at the edge of the network. Moreover, policies must often be modified by negotiation among coalition partners, typically in rapid response to the changing operational situation. Com­ monly, the users who must cope first with situational changes are those on the edge, so it would be very effective if they were able to create and negotiate policies themselves. We investigate the use of Controlled English (CE) as a means to define a policy representation that is both human-friendly and machine processable. We show how a CE model can capture a variety of policy types, including those based on a traditional asset ownership model, and those defining team-based asset sharing across a coalition. The use of CE is intended to benefit coalition networks by bridging the gap between technical and non-technical users in terms of policy creation and negoti­ ation, while at the same time being directly processable by a policy-checking system without transformation to any other technical representation.

In this paper, we consider a problem related to service management and deployment in tactical military networks. Tactical networks are typically hybrid wireless networks in which there are both static and mobile nodes with several wireless interfaces, such as 802.11, 3G, satellite, etc. In tactical networks, performance degradation in services could prove fatal, so it must be diagnosed quickly. This degradation could be due to mobility or bottlenecks in capacity at network layer. We provide a cross-layer framework to detect and diagnose these causes of performance degradation as part of service management; it includes a monitoring model of services and a network model for hybrid wireless networks. In addition, we give a working example in tactical military networks to illustrate our framework. We provide an experimental setup to simulate our hybrid wireless tactical network scenario along with preliminary results.

In recent years, not only United States Armed Services but other Law-enforcement agencies have shown increasing interest in employing drones for various surveillance and reconnaissance purposes. Further, recent advancements in autonomous drone control and navigation technology have tremendously increased the geographic extent of dronebased missions beyond the conventional line-of-sight coverage. Without any sophisticated requirement on data links to control them remotely (human-in-loop), drones are proving to be a reliable and effective means of securing personnel and soldiers operating in hostile environments. However, this autonomous breed of drones can potentially prove to be a significant threat when acquired by antisocial groups who wish to target property and life in urban settlements. To further escalate the issue, the standard detection techniques like RADARs, RF data link signature scanners, etc..., prove futile as the drones are smaller in size to evade successful detection by a RADAR based system in urban environment and being autonomous, have the capability of operating without a traceable active data link (RF). Hence, towards investigating possible practical solutions for the issue, the research team at AFRL’s Tec^Edge Labs under SATE and YATE programs has developed a highly scalable, geographically distributable and easily deployable smartphone-based collaborative platform that can aid in detecting and tracking unidentified hostile drones. In its current state, this collaborative platform built on the paradigm of “Human-as-Sensors”, consists primarily of an intelligent Smartphone application that leverages appropriate sensors on the device to capture a drone’s attributes (flight direction, orientation, shape, color, etc..,) with real-time collaboration capabilities through a highly composable sensor cloud and an intelligent processing module (based on a Probabilistic model) that can estimate and predict the possible flight path of a hostile drone based on multiple (geographically distributed) observation data points. This developed collaborative sensing platform has been field tested and proven to be effective in providing real-time alerting mechanism for the personnel in the field to avert or subdue the potential damages caused by the detected hostile drones.

Active and Agile Environmental and Biological sensing are becoming obligatory to generate prompt warnings for the troops and law enforcements conducting missions in hostile environments. The traditional static sensing mesh networks which provide a coarse-grained (far-field) measurement of the environmental conditions like air quality, radiation , CO₂, etc … would not serve the dynamic and localized changes in the environment, which requires a fine-grained (near-field) sensing solutions. Further, sensing the biological conditions of (healthy and injured) personnel in a contaminated environment and providing a personalized analysis of the life-threatening conditions in real-time would greatly aid the success of the mission. In this vein, under SATE and YATE programs, the research team at AFRL Tec^Edge Discovery labs had demonstrated the feasibility of developing Smartphone applications , that employ a suite of external environmental and biological sensors, which provide fine-grained and customized sensing in real-time fashion. In its current state, these smartphone applications leverage a custom designed modular standalone embedded platform (with external sensors) that can be integrated seamlessly with Smartphones for sensing and further provides connectivity to a back-end data architecture for archiving, analysis and dissemination of real-time alerts. Additionally, the developed smartphone applications have been successfully tested in the field with varied environmental sensors to sense humidity, CO₂/CO, wind, etc…, ; and with varied biological sensors to sense body temperature and pulse with apt real-time analysis

The rise of social networking platforms like Twitter, Facebook, etc…, have provided seamless sharing of information (as chat, video and other media) among its user community on a global scale. Further, the proliferation of the smartphones and their connectivity networks has powered the ordinary individuals to share and acquire information regarding the events happening in his/her immediate vicinity in a real-time fashion. This human-centric sensed data being generated in “human-as-sensor” approach is tremendously valuable as it delivered mostly with apt annotations and ground truth that would be missing in traditional machine-centric sensors, besides high redundancy factor (same data thru multiple users). Further, when appropriately employed this real-time data can support in detecting localized events like fire, accidents, shooting, etc…, as they unfold and pin-point individuals being affected by those events. This spatiotemporal information, when made available for first responders in the event vicinity (or approaching it) can greatly assist them to make effective decisions to protect property and life in a timely fashion. In this vein, under SATE and YATE programs, the research team at AFRL Tec^Edge Discovery labs had demonstrated the feasibility of developing Smartphone applications, that can provide a augmented reality view of the appropriate detected events in a given geographical location (localized) and also provide an event search capability over a large geographic extent. In its current state, the application thru its backend connectivity utilizes a data (Text & Image) processing framework, which deals with data challenges like; identifying and aggregating important events, analyzing and correlating the events temporally and spatially and building a search enabled event database. Further, the smartphone application with its backend data processing workflow has been successfully field tested with live user generated feeds.