Proc. SPIE. 10190, Ground/Air Multisensor Interoperability, Integration, and Networking for Persistent ISR VIII
KEYWORDS: Defense and security, Analytics, Visual process modeling, Data modeling, Data processing, Directed self assembly, Intelligence systems, Lanthanum, Current controlled current source, Data fusion
In order to address the above challenge, in this paper, we highlight our research vision and sketch some initial solutions into the problem domain. Specifically, we propose means to (1) automatically infer formal task requirements from mission specifications; (2) discover data, services, and their features automatically to satisfy the identified requirements; (3) create and augment shared domain models automatically; (4) efficiently offload services to the network edge and across coalition boundaries adhering to their computational properties and costs; and (5) optimally allocate and adjust services while respecting the constraints of operating environment and service fit. We envision that the research will result in a framework which enables self-description, discover, and assemble capabilities to both data and services in support of coalition mission goals.
Proc. SPIE. 10190, Ground/Air Multisensor Interoperability, Integration, and Networking for Persistent ISR VIII
KEYWORDS: Defense and security, Internet, Cameras, Sensors, Data storage, Surveillance, Sensing systems, Reconnaissance, Intelligence systems, Data communications, Tactical intelligence, Information security, Intelligent sensors, Instrument modeling, RGB color model
In the wake of rapid maturing of Internet of Things (IoT) approaches and technologies in the commercial sector, the IoT is increasingly seen as a key ‘disruptive’ technology in military environments. Future operational environments are expected to be characterized by a lower proportion of human participants and a higher proportion of autonomous and semi-autonomous devices. This view is reflected in both US ‘third offset’ and UK ‘information age’ thinking and is likely to have a profound effect on how multinational coalition operations are conducted in the future. Much of the initial consideration of IoT adoption in the military domain has rightly focused on security concerns, reflecting similar cautions in the early era of electronic commerce. As IoT approaches mature, this initial technical focus is likely to shift to considerations of interactivity and policy. In this paper, rather than considering the broader range of IoT applications in the military context, we focus on roles for IoT concepts and devices in future intelligence, surveillance and reconnaissance (ISR) tasks, drawing on experience in sensor-mission resourcing and human-computer collaboration (HCC) for ISR. We highlight the importance of low training overheads in the adoption of IoT approaches, and the need to balance proactivity and interactivity (push vs pull modes). As with sensing systems over the last decade, we emphasize that, to be valuable in ISR tasks, IoT devices will need a degree of mission-awareness in addition to an ability to self-manage their limited resources (power, memory, bandwidth, computation, etc). In coalition operations, the management and potential sharing of IoT devices and systems among partners (e.g., in cross-coalition tactical-edge ISR teams) becomes a key issue due heterogeneous factors such as language, policy, procedure and doctrine. Finally, we briefly outline a platform that we have developed in order to experiment with human-IoT teaming on ISR tasks, in both physical and virtual settings.
In a military scenario, commanders need to determine what kinds of information will help them execute missions.
The amount of information available to support each mission is constrained by the availability of information
assets. For example, there may be limits on the numbers of sensors that can be deployed to cover a certain
area, and limits on the bandwidth available to collect data from those sensors for processing. Therefore, options
for satisfying information requirements should take into consideration constraints on the underlying information
assets, which in certain cases could simultaneously support multiple missions. In this paper, we propose a
system architecture for modeling missions and allocating information assets among them. We model a mission
as a graph of tasks with temporal and probabilistic relations. Each task requires some information provided by the
information assets. Our system suggests which information assets should be allocated among missions. Missions
are compatible with each other if their needs do not exceed the limits of the information assets; otherwise,
feedback is sent to the commander indicating information requirements need to be adjusted. The decision loop
will eventually converge and the utilization of the resources is maximized.
Tactical networks should be optimized to deliver the maximum amount of useful information from which decisions may
be made. This requires that both the quality and amount of information be considered. The quality of information may be
judged by both intrinsic and contextual attributes. We define the operational information content capacity (OICC) as the
measure of the amount of useful information a network can deliver. In this paper we discuss several ways to quantify
OICC and determine the residual information content capacity in a network based on a set of information requests.
We first define functions which relate specific metrics to the quality of a piece of information to be used for a certain
purpose. From this we determine the amount of data required to deliver the information to its recipient and the resultant
"information bits" which can be derived. We then provide two illustrative examples highlighting the use of OICC.
Broadcast scheduling has been extensively studied in wireless environments, where a base station broadcasts
data to multiple users. Due to the sole wireless channel's limited bandwidth, only a subset of the needs may be
satisfiable, and so maximizing total (weighted) throughput is a popular objective. In many realistic applications,
however, data are dependent or correlated in the sense that the joint utility of a set of items is not simply the
sum of their individual utilities. On the one hand, substitute data may provide overlapping information, so one
piece of data item may have lower value if a second data item has already been delivered; on the other hand,
complementary data are more valuable than the sum of their parts, if, for example, one data item is only useful
in the presence of a second data item.
In this paper, we define a data bundle to be a set of data items with possibly nonadditive joint utility, and we
study a resulting broadcast scheduling optimization problem whose objective is to maximize the utility provided
by the data delivered.
Utility-based cross-layer optimization is a valuable tool for resource management in mission-oriented wireless
sensor networks (WSN). The benefits of this technique include the ability to take application- or mission-level
utilities into account and to dynamically adapt to the highly variable environment of tactical WSNs. Recently,
we developed a family of distributed protocols which adapts the bandwidth and energy usage in mission-oriented
WSN in order to optimally allocate resources among multiple missions, that may have specific demands depending
on their priority, and also variable schedules, entering and leaving the network at different times.<sup>9-12</sup> In this
paper, we illustrate the practical applicability of this family of protocols in tactical networks by implementing one
of the protocols, which ensures optimal rate adaptation for congestion control in mission-oriented networks,9 on a
real-time 802.11b network using the ITA Sensor Fabric.<sup>13</sup> The ITA Sensor Fabric is a middleware infrastructure,
developed as part of the International Technology Alliance (ITA) in Network and Information Science,<sup>14</sup> to
address the challenges in the areas of sensor identification, classification, interoperability and sensor data sharing,
dissemination and consumability, commonly present in tactical WSNs.<sup>15</sup> Through this implementation, we (i)
study the practical challenges arising from the implementation and (ii) provide a proof of concept regarding
the applicability of this family of protocols for efficient resource management in tactical WSNs amidst the
heterogeneous and dynamic sets of sensors, missions and middle-ware.
Timely dissemination of information to mobile users is vital in many applications. In a critical situation, no
network infrastructure may be available for use in dissemination, over and above the on-board storage capability
of the mobile users themselves. We consider the following specialized content distribution application: a group
of users equipped with wireless devices build an ad hoc network in order cooperatively to retrieve information
from certain regions (the mission sites). Each user requires access to some set of information items originating
from sources lying within a region. Each user desires low-latency access to its desired data items, upon request
(i.e., when pulled). In order to minimize average response time, we allow users to pull data either directly from
sources or, when possible, from other nearby users who have already pulled, and continue to carry, the desired
data items. That is, we allow for data to be pushed to one user and then pulled by one or more additional users.
The total latency experienced by a user vis-vis a certain data item is then in general a combination of the push
delay and the pull delay. We assume each delay time is a function of the hop distance between the pair of points
Our goal in this paper is to assign data to mobile users, in order to minimize the total cost and the average
latency experienced by all the users. In a static setting, we solve this problem in two different schemes, one of
which is easy to solve but wasteful, one of which relates to NP-hard problems but is less so. Then in a dynamic
setting, we adapt the algorithm for the static setting and develop a new algorithm with respect to users' gradual
arrival. In the end we show a trade-off can be made between minimizing the cost and latency.
In this paper we propose system architecture for providing direction and dissemination in military environments.
We start with a description of the problem of direction and dissemination. We then present our high level architecture
and describe the functions of the main system components on which we focus. This includes the types of information
and means by which they may be delivered, the filtering and fusion engines employed to focus and limit the information
sent to each personnel, and the schedulers used to determine the order of delivery. We consider a structure that includes
sending information directly to personnel, or depending on bandwidth and delay constraints, sending meta-information
to personnel to assist in self-retrieval of information from a peer-to-peer network of sensors and other personnel. We
illustrate the operation of the architecture using a specific military scenario.
A sensor network in the field is usually required to support multiple sensing tasks or missions to be accomplished simultaneously. Since missions might compete for the exclusive usage of the same sensing resource we need to assign individual sensors to missions. Missions are usually characterized by an uncertain demand for sensing resource capabilities. In this paper we model this assignment problem by introducing the Sensor Utility Maximization (SUM) model, where each sensor-mission pair is associated with a utility offer. Moreover each mission is associated with a priority and with an uncertain utility demand. We also define the benefit or profit that a sensor can bring to a mission as the fraction of mission's demand that the sensor is able to satisfy, scaled by the priority of the mission. The goal is to find a sensor assignment that maximizes the total profit, while ensuring that the total utility cumulated by each mission does not exceed its uncertain demand. SUM is NP-Complete and is a special case of the well known Generalized Assignment Problem (GAP), which groups many knapsack-style problems. We compare four algorithms: two previous algorithms for problems related to SUM, an improved implementation of a state-of-the-art pre-existing approximation algorithm for GAP, and a new greedy algorithm. Simulation results show that our greedy algorithm appears to offer the best trade-off between quality of solution and computation cost.
Making decisions on how best to utilise limited intelligence, surveillance and reconnaisance (ISR) resources is a
key issue in mission planning. This requires judgements about which kinds of available sensors are more or less
appropriate for specific ISR tasks in a mission. A methodological approach to addressing this kind of decision
problem in the military context is the Missions and Means Framework (MMF), which provides a structured way
to analyse a mission in terms of tasks, and assess the effectiveness of various means for accomplishing those
tasks. Moreover, the problem can be defined as knowledge-based matchmaking: matching the ISR requirements
of tasks to the ISR-providing capabilities of available sensors. In this paper we show how the MMF can be
represented formally as an ontology (that is, a specification of a conceptualisation); we also represent knowledge
about ISR requirements and sensors, and then use automated reasoning to solve the matchmaking problem. We
adopt the Semantic Web approach and the Web Ontology Language (OWL), allowing us to import elements of
existing sensor knowledge bases. Our core ontologies use the description logic subset of OWL, providing efficient
reasoning. We describe a prototype tool as a proof-of-concept for our approach. We discuss the various kinds
of possible sensor-mission matches, both exact and inexact, and how the tool helps mission planners consider
alternative choices of sensors.
This paper examines the practical challenges in the application of the distributed network utility maximization
(NUM) framework to the problem of resource allocation and sensor device adaptation in a mission-centric wireless
sensor network (WSN) environment. By providing rich (multi-modal), real-time information about a variety of
(often inaccessible or hostile) operating environments, sensors such as video, acoustic and short-aperture radar
enhance the situational awareness of many battlefield missions. Prior work on the applicability of the NUM
framework to mission-centric WSNs has focused on tackling the challenges introduced by i) the definition of
an individual mission's utility as a collective function of multiple sensor flows and ii) the dissemination of an
individual sensor's data via a multicast tree to multiple consuming missions. However, the practical application
and performance of this framework is influenced by several parameters internal to the framework and also by
implementation-specific decisions. This is made further complex due to mobile nodes. In this paper, we use
discrete-event simulations to study the effects of these parameters on the performance of the protocol in terms
of speed of convergence, packet loss, and signaling overhead thereby addressing the challenges posed by wireless
interference and node mobility in ad-hoc battlefield scenarios. This study provides better understanding of the
issues involved in the practical adaptation of the NUM framework. It also helps identify potential avenues of
improvement within the framework and protocol.
Ad-hoc sensor networks need to create their own network after deployment. Various schemes have been suggested
for sensors to create a better coverage pattern than if they are randomly deployed. A better coverage pattern
translates into a geometry of having disks cover an area completely and even redundantly. In this paper, we
present two coverage arrangements which turn out to be equivalent to grid lattice arrangements and analyze
One of the main goals of sensor networks is to provide accurate information about a sensing field for an extended
period of time. This requires collecting measurements from as many sensors as possible to have a better view
of the sensor surroundings. However, due to energy limitations and to prolong the network lifetime, the number
of active sensors should be kept to a minimum. To resolve this conflict of interest, sensor selection schemes
are used. In this paper, we survey different schemes that are used to select sensors. Based on the purpose of
selection, we classify the schemes into (1) coverage schemes, (2) target tracking and localization schemes, (3)
single mission assignment schemes and (4) multiple missions assignment schemes. We also look at solutions to
relevant problems from other areas and consider their applicability to sensor networks. Finally, we take a look
at the open research problems in this field.