Robust multi-sensor scheduling for multi-site surveillance

IEEE Transactions on Parallel and Distributed Systems, 2000

We address a new and general maximal lifetime problem in sensor-target surveillance. We assume that each sensor can watch at most k targets (k ! 1) and each target should be watched by h sensors (h ! 1) at any time. The problem is to schedule sensors to watch targets and forward the sensed data to a base station such that the lifetime of the surveillance network is maximized. This general problem includes the existing ones as its special cases (k ¼ 1 and h ¼ 1 in [12] and k ¼ 1 and h ! 2 in [13]). It is also important in practice because some sensors can monitor multiple or all targets within their surveillance ranges and multisensor fusion (i.e., watching a target by multiple sensors) gives better surveillance results. The problem involves several subproblems and one of them is a new matching problem called ðk; hÞ-matching. The ðk; hÞ-matching problem is a generalized version of the classic bipartite matching problem (when k ¼ h ¼ 1, ðk; hÞ-matching becomes bipartite matching). We design an efficient (k, h)-matching algorithm to solve the ðk; hÞ-matching problem and then solve the general maximal lifetime problem. As a byproduct of this study, the ðk; hÞ-matching problem and the proposed ðk; hÞ-matching algorithm can potentially be applied to other problems in computer science and operations research.

Intrusion detection surveillance applications with wireless video sensor networks are those applications which require low energy consumption to increase network lifetime while at the time require a high level of quality of service. This paper show by simulation how a dynamic risk management scheme based on Bezier curves that takes into account the application's criticality can provide fast event detection for mission-critical surveillance applications while increasing at the same the network lifetime. We will also present some preliminary results on how to further increase network lifetime when some video sensor nodes have mobility feature. The motivation behind node's mobility is that video applications are characterized by their large amount of data so that a extra mobility cost can be justified as the video duration increases.

GLOBECOM 2009 - 2009 IEEE Global Telecommunications Conference, 2009

We address a new and general maximal lifetime problem in sensor-target surveillance. We assume that each sensor can watch at most k targets (k ! 1) and each target should be watched by h sensors (h ! 1) at any time. The problem is to schedule sensors to watch targets and forward the sensed data to a base station such that the lifetime of the surveillance network is maximized. This general problem includes the existing ones as its special cases (k ¼ 1 and h ¼ 1 in [12] and k ¼ 1 and h ! 2 in [13]). It is also important in practice because some sensors can monitor multiple or all targets within their surveillance ranges and multisensor fusion (i.e., watching a target by multiple sensors) gives better surveillance results. The problem involves several subproblems and one of them is a new matching problem called ðk; hÞ-matching. The ðk; hÞ-matching problem is a generalized version of the classic bipartite matching problem (when k ¼ h ¼ 1, ðk; hÞ-matching becomes bipartite matching). We design an efficient (k, h)-matching algorithm to solve the ðk; hÞ-matching problem and then solve the general maximal lifetime problem. As a byproduct of this study, the ðk; hÞ-matching problem and the proposed ðk; hÞ-matching algorithm can potentially be applied to other problems in computer science and operations research.

Proc. Int. Conf. …, 2008

IEEE Transactions on …, 2010

2008

2019

A supervisory mission is considered in which a team of unmanned vehicles visits a set of targets and collects sensory data to be analyzed in real-time by a remotely-located human operator. A framework is proposed to simultaneously construct the operator’s task-processing schedule and each vehicle’s target visitation route, with the dual goal of moderating the operator’s task load and preventing unnecessary vehicle loitering. The joint scheduling/routing problem is posed as a mixed-integer (non-linear) program which can be equivalently represented as a mixed-integer linear program through expansion of the solution space. In single vehicle missions, it is shown that an alternative linearization exists that does not increase the problem size. Next, a dynamic solution strategy is introduced that incrementally constructs suboptimal schedules and routes by solving a comparatively small, mixed-integer linear program whenever the operator finishes a task. Using a scenario-based extension, t...

Automatica, 2006

In this note we consider the following problem. Suppose a set of sensors is jointly trying to estimate a process. One sensor takes a measurement at every time step and the measurements are then exchanged among all the sensors. What is the sensor schedule that results in the minimum error covariance? We describe a stochastic sensor selection strategy that is easy to implement and is computationally tractable. The problem described above comes up in many domains out of which we discuss two. In the sensor selection problem, there are multiple sensors that cannot operate simultaneously (e.g., sonars in the same frequency band). Thus measurements need to be scheduled. In the sensor coverage problem, a geographical area needs to be covered by mobile sensors each with limited range. Thus from every position, the sensors obtain a different view-point of the area and the sensors need to optimize their trajectories. The algorithm is applied to these problems and illustrated through simple examples. ᭧

2016

Remote sensing systems have firmly established a role in providing immense value to commercial industry, scientific exploration, and national security. Continued maturation of sensing technology has reduced the cost of deploying highly-capable sensors while at the same time increased reliance on the information these sensors can provide. The demand for time on these sensors is unlikely to diminish. Coordination of next-generation sensor systems, larger constellations of satellites, unmanned aerial vehicles, ground telescopes, etc. is prohibitively complex for existing heuristicsbased scheduling techniques. The project was a two-year collaboration spanning multiple Sandia centers and included a partnership with Texas A&M University. We have developed algorithms and software for collection scheduling, remote sensor field-of-view pointing models, and bandwidthconstrained prioritization of sensor data. Our approach followed best practices from the operations research and computational geometry communities. These models provide several advantages over state of the art techniques. In particular, our approach is more flexible compared to heuristics that tightly couple models and solution techniques. First, our mixed-integer linear models afford a rigorous analysis so that sensor planners can quantitatively describe a schedule relative to the best possible. Optimal or near-optimal schedules can be produced with commercial solvers in operational run-times. These models can be modified and extended to incorporate different scheduling and resource constraints and objective function definitions. Further, we have extended these models to proactively schedule sensors under weather and ad hoc collection uncertainty. This approach stands in contrast to existing deterministic schedulers which assume a single future weather or ad hoc collection scenario. The field-of-view pointing algorithm produces a mosaic with the fewest number of images required to fully cover a region of interest. The bandwidth-constrained algorithms find the highest priority information that can be transmitted. All of these are based on mixed-integer linear programs so that, in the future, collection scheduling, field-of-view, and bandwidth prioritization can be combined into a single problem. Experiments conducted using the developed models, commercial solvers, and benchmark datasets have demonstrated that proactively scheduling against uncertainty regularly and significantly outperforms deterministic schedulers.

International Journal of Distributed Sensor Networks, 2013

The use of a spatially distributed set of sensors has become a cost-effective approach to achieve surveillance coverage against moving targets. As more sensors are utilized in a collaborative manner, the optimal placement of sensors becomes critical to achieve the most efficient coverage. In this paper, we develop a numerical optimization approach to place distributed sets of sensors to perform surveillance against moving targets over extended areas. In particular, we develop a genetic algorithm solution to find spatial sensor density functions that maximize effectiveness against moving targets, where the surveillance performance of individual sensors is dependent on their absolute position in the region as well as their relative position to both the expected target(s) and any asset that is being protected. The density function representation of optimal sensor locations is shown to provide a computationally efficient method for determining sensor asset location planning. We illustrate the effective performance of this method on numerical examples based on problems of general area surveillance and risk-based surveillance in protection of an asset.