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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.

We introduce an approach to representing intelligence, surveillance, and reconnaissance (ISR) tasks at a relatively high level in controlled natural language. We demonstrate that this facilitates both human interpretation and machine processing of tasks. More specically, it allows the automatic assignment of sensing assets to tasks, and the informed sharing of tasks between collaborating users in a coalition environment. To enable automatic matching of sensor types to tasks, we created a machine-processable knowledge representation based on the Military Missions and Means Framework (MMF), and implemented a semantic reasoner to match task types to sensor types. We combined this mechanism with a sensor-task assignment procedure based on a well-known distributed protocol for resource allocation. In this paper, we re-formulate the MMF ontology in Controlled English (CE), a type of controlled natural language designed to be readable by a native English speaker whilst representing information in a structured, unambiguous form to facilitate machine processing. We show how CE can be used to describe both ISR tasks (for example, detection, localization, or identication of particular kinds of object) and sensing assets (for example, acoustic, visual, or seismic sensors, mounted on motes or unmanned vehicles). We show how these representations enable an automatic sensor-task assignment process. Where a group of users are cooperating in a coalition, we show how CE task summaries give users in the eld a high-level picture of ISR coverage of an area of interest. This allows them to make ecient use of sensing resources by sharing tasks.

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.