towards modeling the behavior of physical intruders in a region monitored by a wireless sensor network
A priority task for homeland security is the coverage of large spans of open border that cannot be continuously physically monitored for intrusion. Low-cost monitoring solutions based on wireless sensor networks have been identified as an effective means to perform perimeter monitoring. An ad-hoc wireless sensor network scattered near a border could be used to perform surveillance over a large area with relatively little human intervention. Determining the effectiveness of such an autonomous network in detecting and thwarting an intelligent intruder is a difficult task. We propose a model for an intelligent attacker that attempts to find a detection-free path in a region with sparse sensing coverage. In particular, we apply reinforcement learning (RL) - a machine learning approach - for our model. RL algorithms are well suited for scenarios in which specifying and finding an optimal solution is difficult. By using RL, our attacker can easily adapt to new scenarios by translating constraints into rewards. We compare our RL-based technique to a reasonable heuristic in simulation. Our results suggest that our RL-based attacker model is significantly more effective, and therefore more realistic, than the heuristic approach.
wireless sensor network reinforcement learning border monitoring