Supervisory control of mobile sensor networks: math formulation, simulation, and implementation

Comparing the performance of expert user heuristics and an integer linear program in aircraft carrier deck operations

Planning operations across a number of domains can be considered as resource allocation problems with timing constraints. An unexplored instance of such a problem domain is the aircraft carrier flight deck, where, in current operations, replanning is done without the aid of any computerized decision support. Rather, veteran operators employ a set of experience-based heuristics to quickly generate new operating schedules. These expert user heuristics are neither codified nor evaluated by the United States Navy; they have grown solely from the convergent experiences of supervisory staff. As unmanned aerial vehicles (UAVs) are introduced in the aircraft carrier domain, these heuristics may require alterations due to differing capabilities. The inclusion of UAVs also allows for new opportunities for on-line planning and control, providing an alternative to the current heuristic-based replanning methodology. To investigate these issues formally, we have developed a decision support system for flight deck operations that utilizes a conventional integer linear program-based planning algorithm. In this system, a human operator sets both the goals and constraints for the algorithm, which then returns a proposed schedule for operator approval. As a part of validating this system, the performance of this collaborative human-automation planner was compared with that of the expert user heuristics over a set of test scenarios. The resulting analysis shows that human heuristics often outperform the plans produced by an optimization algorithm, but are also often more conservative.

Probabilistic track coverage in cooperative sensor networks

The quality of service of a network performing cooperative track detection is represented by the probability of obtaining multiple elementary detections over time along a target track. Recently, two different lines of research, namely, distributed-search theory and geometric transversals, have been used in the literature for deriving the probability of track detection as a function of random and deterministic sensors' positions, respectively. In this paper, we prove that these two approaches are equivalent under the same problem formulation. Also, we present a new performance function that is derived by extending the geometric-transversal approach to the case of random sensors' positions using Poisson flats. As a result, a unified approach for addressing track detection in both deterministic and probabilistic sensor networks is obtained. The new performance function is validated through numerical simulations and is shown to bring about considerable computational savings for both deterministic and probabilistic sensor networks.

Deadlock Avoidance Policy in Mobile Wireless Sensor Networks with Free Choice Resource Routing

Efficient control schemes are required for effective cooperation of robot teams in a mobile wireless sensor network. If the robots (resources) are also in charge of executing multiple simultaneous missions, then risks of deadlocks due to the presence of shared resources among different missions increase and have to be tackled. Discrete event control with deadlock avoidance has been used in the past for robot team coordination for the case of multi reentrant flowline models with shared resources. In this paper we present an analysis of deadlock avoidance for a generalized case of multi reentrant flow line systems (MRF) called the Free Choice Multi Reentrant Flow Line systems (FMRF). In FMRF, some tasks have multiple resource choices; hence routing decisions have to be made and current results in deadlock avoidance for MRF do not hold. This analysis is based on the so-called Circular Waits (CW) of the resources in the system. For FMRF, the well known notions of Critical Siphons and Critical Subsystems must be generalized and we redefine these objects for such systems. Our second contribution provides a matrix formulation that efficiently computes the objects required for deadlock avoidance in FMRF systems. A MAXWIP dispatching policy is formulated for deadlock avoidance in FMRF systems. According to this policy, deadlock in FMRF is avoided by limiting the work in progress (WIP) in the critical subsystems of each CW. Implemented results of the proposed scheme in a WSN test-bed is presented in the paper.

Patch models and their applications to multivehicle command and control

We introduce patch models, a computational modeling formalism for multivehicle combat domains, based on spatiotemporal abstraction methods developed in the computer science community. The framework yields models that are expressive enough to accommodate nontrivial controlled vehicle dynamics while being within the representational capabilities of common artificial intelligence techniques used in the construction of autonomous systems. The framework allows several key design requirements of next-generation network-centric command and control systems, such as maintenance of shared situation awareness, to be achieved. Major features include support for multiple situation models at each decision node and rapid mission plan adaptation. We describe the formal specification of patch models and our prototype implementation, i.e., Patchworks. The capabilities of patch models are validated through a combat mission simulation in Patchworks, which involves two defending teams protecting a camp from an enemy attacking team.