AAAI'90 Proceedings of the eighth National conference on Artificial intelligence - Volume 1
Sensor deployment and target localization in distributed sensor networks
ACM Transactions on Embedded Computing Systems (TECS)
Uncertainty-aware and coverage-oriented deployment for sensor networks
Journal of Parallel and Distributed Computing
A self-organising algorithm for sensor placement in wireless mobile microsensor networks
International Journal of Wireless and Mobile Computing
Optimal placement of distributed sensors against moving targets
ACM Transactions on Sensor Networks (TOSN)
Coverage and Reliability of Randomly Distributed Sensor Systems with Heterogeneous Detection Range
International Journal of Distributed Sensor Networks
Computational environmental models aid sensor placement optimization
MILCOM'06 Proceedings of the 2006 IEEE conference on Military communications
A cellular learning automata-based deployment strategy for mobile wireless sensor networks
Journal of Parallel and Distributed Computing
Optimal multiobjective placement of distributed sensors against moving targets
ACM Transactions on Sensor Networks (TOSN)
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Problems such as searching for enemy units on a battlefield, detecting smugglers as they cross an international border, and skip tracing involve generating plans to employ limited collection resources to search for moving targets that are trying to avoid detection. This paper presents an automated approach for generating plans to search for ''elusive agents''. We also describe an implementation for the military problem of searching for mobile enemy ground units. The approach consists of three steps. First, it uses automated mobility and terrain analysis to hypothesize a set of possible movement plans for the targets. These plans are weighted with user-specified and heuristic probability estimates. Next, models of the available sensor resources are applied to identify observation ''windows''. These windows are regions in space and time where the target agents may be detected if they are following one of the hypothesized plans. Third, we generate a search plan for the available sensor assets (which can be any combination of mobile and fixed sensors) by heuristically searching through alternative subsets of the observation windows. Each search plan, defined as a temporally-ordered set of observation windows, is evaluated by exercising an automaticallyconstructed Bayesian network that summarizes the results of the terrain, route planning, and sensor coverage analysis. An empirical evaluation of this system was performed with results supporting its utility.